V Gorenjak

D R Vance

A M Petrelis

M G Stathopoulou

S Dadé

S El

T Xie

E Marouli

C Masson

H Murray

L Doherty

J Lamont

P Fitzgerald

S Visvikis

Stathopoulou

S Visvikis-Siest

D Ruggiero

J Chatelin

M Rancier

G Weryha

M J Kurth

A A Aldasoro Arguinano

G Dagher

G Dedoussis

P Deloukas

J Marc

M Simmaco

R Schaik

F Innocenti

J L Merlin

J Schneider

B Z Alizadeh

M Ciullo

S Seshadri

B Hiegel

S Kumar

R Barouki

E Boerwinkle

Telomere length determinants

Keywords:

to Dr Sophie Visvikis-Siest, for your encouragement, when you proposed this three-year journey, and motivation all the way through. For all the exceptional opportunities I had! They helped to develop my expertise, meet remarkable scientists and gave me unforgettable work experiences. You trusted in my abilities

A special thanks goes to the LORRAINE Region and RHU, who financed my PhD studies.

Without their support, this work would not be possible. Also, I would like to gratefully acknowledge GEENAGE and FEDER projects, who financially supported our research work.

analysed through detailed bioinformatics analysis. Methylation of genes, such as TPX2 and HAS, could affect their activity and would in turn cause increased VEGF-A concentrations.

VEGF-A was further investigated for its relation with 11 cytokines, important mediators of common physiological pathways of the majority of chronic diseases. VEGF-A protein levels were associated with IL-4, MCP1 and EGF. Specific VEGF-A mRNA isoforms were also investigated for their association with cytokines; VEGF 165 showed significant associations with MCP1 and IL-1α and VEGF 189 with IL-4 and IL-6. Together with another important biomarker, TL, we studied the role of VEGF-A in atherosclerosis, and we identified one VEGF-A related genetic variant significantly associated with telomere attrition (calculated as a ratio between leukocyte TL and muscular TL). This genetic variant could present a common denominator of chronic diseases.

In conclusion, the employment of diverse methodologies for the investigation of common chronic diseases risk factors and pathways provided new diagnostic markers and generated results, which could help to improve the diseases risk prediction based on the individual genetic "make-up". New insights into associations between different biomarkers might help in understanding the (patho) physiological pathways common between CVDs and other chronic diseases. Finally, we hope that the results of this thesis through close collaboration with industry will facilitate the implementation of personalised healthcare.

RÉSUMÉ

La lutte contre les maladies chroniques courantes, qui sont caractérisées par des mécanismes complexes de régulation moléculaire, nécessite aujourd"hui la mise en oeuvre de nouvelles stratégies de prédiction du risque et de prévention. La médecine personnalisée représente une approche sophistiquée pour réussir la prise en charge des morbidités de populations vieillissantes. Dans le cadre de ces travaux de thèse inspirés par les principes de la médecine personnalisée, nous décrivons une approche intégrative qui associe plusieurs méthodologies « -omiques ». Nous avons utilisé un modèle de « dénominateur commun » pour les maladies cardiovasculaires et d"autres maladies chroniques afin d'identifier des biomarqueurs associés aux facteurs de risque et aux voies biologiques de maladies courantes.

Les résultats de ces travaux sont présentés dans 5 publications originales, 3 articles de revue et 1 publication originale en cours de redaction. Ils sont tous destinés à renforcer et faciliter la mise en oeuvre de la médecine personnalisée. Trois principaux biomarqueurs ont été étudiés, TREM2, VEGF-A et la longueur des télomères (LT), et ce dans la population d"individus en bonne santé apparente de l"étude familiale STANISLAS (SFS). Par l"étude de variants génétiques localisés dans la région comportant le gène TREM2, nous avons identifié une association entre le SNP rs6918289 et à la fois de taux élevés de TNF-α et une augmentation de l"épaisseur intima-média de l"artère fémorale (IMT-F). L"inflammation et l"IMT-F étant des indicateurs importants de la formation de plaque d"athérosclérose, l"allèle mineur (T) de rs6918289 pourrait être un allèle à risque pour les maladies inflammatoires et l"athérosclérose.

Grâce à des études d"association panépigénomique (EWAS), nous avons identifié de nouveaux marqueurs épigénétiques liés à des facteurs de risque de maladies courantes, à l"obésité abdominale et à des taux élevés de lipides. Un site CpG de méthylation (cg16170243) était associé à une augmentation du tour de taille, contribuant à expliquer la régulation épigénétique de l"obésité abdominale par une résistance accrue à l'insuline. De plus, une étude EWAS des taux de triglycérides a permis d"identifier deux sites CpG significatifs. L"un de ces deux sites a pu être confirmé dans le tissu adipeux (cg04580029) et a permis de mieux comprendre la régulation épigénétique de l"augmentation du taux de triglycérides. Les résultats de ces deux dernières études ont donné lieu à de nouveaux biomarqueurs épigénétiques qui pourraient être utilisés dans l"avenir dans des applications En conclusion, l"utilisation de diverses méthodologies pour étudier les facteurs de risque et les voies impliquées dans des maladies chroniques courantes a permis d"identifier de nouveaux marqueurs diagnostiques et de générer des résultats qui pourraient améliorer la prédiction du risque de maladie basée sur le profil génétique de chaque individu. Les nouvelles données apportées sur les associations entre les différents biomarqueurs pourraient aider à mieux comprendre les voies physiopathologiques communes entre les maladies cardiovasculaires et d"autres maladies chroniques. Enfin, nous espérons que, grâce à une collaboration étroite avec des partenaires privés, les résultats de ces travaux de thèse faciliteront la mise en oeuvre de la médecine personnalisée. 5. Novel associations between DNA methylation and VEGF-A PBMCs levels. ..................... Table 6. Significant associations between VEGF-A protein levels and inflammatory molecules. .... Table 7. Significant associations between the expression of VEGF-A mRNA isoforms ................... Table 8. Association of the SNPs in TREM2 region with TNF-α levels in children of the SFS. ....... Table 9. Cela a marqué une nouvelle ère en médecine mais nécessitait d"énormes efforts en termes de développement de nouvelles technologies, méthodologies, bases de données (big data) et de cohortes à populations importantes afin de générer les données nécessaires pour expliquer les principes de la régulation génétique de maladies complexes. L"épidémiologie génétique a émergé en tant que nouvelle discipline à part entière consacrée à l"étude du rôle de facteurs génétiques et leurs interactions avec l"environnement. Les études d"association pangénomique (GWAS) à grande échelle qui analysent plusieurs milliers de polymorphismes mononucléotidiques (SNP) chez plusieurs milliers d"individus ont permis d"identifier de nombreux variants génétiques. Ces variants contribuent tous dans une certaine mesure à la survenue des maladies complexes polygéniques et multifactorielles. Pourtant, cela n"a pas suffi pour comprendre pleinement la régulation génétique des maladies chroniques. Le chaînon manquant entre l"exposition environnementale, la génétique et de nombreuses maladies courantes que les études d"association pangénomique n"arrivaient pas à expliquer a été attribué aux modifications épigénétiques de la séquence d"ADN. L"étude des modifications de la méthylation du génome est devenue ces derniers temps l"outil épigénomique le plus souvent utilisé pour étudier l"impact de l"environnement sur la régulation génique.

LIST OF FIGURES

Mais de quelle façon toutes ces avancées ont-elles contribuées à changer la médecine ?

Nous nous trouvons encore aujourd"hui dans la période de transition : des tests génétiques sont maintenant utilisés pour dépister des maladies, les premières thérapies géniques sont en voie d"approbation et de plus en plus d"outils de médecine personnalisée pénètrent le marché, mais les références en milieu hospitalier restent toujours les approches médicales classiques.

Les laboratoires de recherche et l"industrie pharmaceutique ont la ferme intention d"améliorer le système actuel et de proposer de nouvelles stratégies basées sur le profil génétique de chaque individu.

Des liens doivent maintenant être établis entre les vastes quantités de données utiles générées au cours de la dernière décennie afin d"apporter de nouvelles connaissances en biologie et physiopathologie humaine. De tels efforts pourraient améliorer les stratégies actuelles de prévention, de prédiction et de traitement de maladies chroniques fréquentes et permettre de remplacer les approches existantes par des outils de médecine personnalisée. Cela implique le développement de biomarqueurs qui serviront à déterminer la susceptibilité par rapport à une maladie, le diagnostic, le pronostic et le traitement, et permettront de proposer de nouvelles cibles thérapeutiques basées sur la connaissance des voies biologiques et les liens entre les molécules impliquées.

L"unité de recherche UMR INSERM U1122 Interactions Gène-Environnement en Physiopathologie Cardiovasculaire (IGE-PCV) a déjà adopté les nouvelles méthodologies de la médecine personnalisée et s"attache à explorer les composants génétiques et environnementaux impliqués dans des maladies courantes liées à l"âge. Notre grand Centre de

Ressources Biologiques (CRB IGE-PCV), constitué de populations saines et pathologiques permettant d"étudier les maladies cardiovasculaires, est au coeur des travaux de l"unité. Cette volonté de créer une grande base de données pour des études d"épidémiologie génétique a mené au lancement de l"étude familiale STANISLAS. Comprenant les données longitudinales de plus de 1000 familles, cette cohorte représente une des principales populations de la biobanque et a fourni une vaste quantité de données génétiques, épigénétiques et cliniques pour l"étude de la variabilité de phénotypes pathologiques intermédiaires et de déterminants environnementaux.

La disponibilité de données issues de notre biobanque et nos collaborations étroites avec d"autres cohortes nous ont permis d"étudier la régulation génétique et épigénétique de phénotypes intermédiaires impliqués dans plusieurs maladies liées à l"âge afin d"identifier des biomarqueurs communs.

Pour ce faire, nous avons mis en oeuvre diverses approches, notamment les études d"association panépigénomique (EWAS) pour analyser les mécanismes de régulation épigénétique de différentes molécules, et les études d"association entre des variants génétiques et différents phénotypes intermédiaires pour décrire l"architecture génétique de traits complexes.

À ce moment-là, nous concentrions tous nos efforts sur la connaissance des phénotypes intermédiaires impliqués dans les mécanismes physiopathologiques et des facteurs de risque présentant un intérêt spécial et potentiellement utilisables en pratique clinique. Comprendre les caractéristiques génétiques complexes de ces phénotypes permettrait de mieux -identifier les patients en prenant en compte leur profil génétique, et connaître les associations entre différentes molécules, ce qui permettrait de proposer des traitements complémentaires. Enfin, de nouveaux biomarqueurs de susceptibilité pourraient être identifiés grâce à la compréhension de la régulation épigénétique de facteurs de risque intermédiaires, ce qui faciliterait le pronostic précoce et la prévention de la survenue de maladies chroniques.

Hypothèses et objectifs

Des biomarqueurs génétiques et moléculaires impliqués dans plusieurs voies biologiques pourraient jouer un rôle important dans le développement de maladies chroniques.

L"identification de voies biologiques communes et d"interactions partagées pourraient donc améliorer notre compréhension de la physiopathologie des maladies et nous aider à découvrir de nouveaux biomarqueurs, des « dénominateurs communs » pour les maladies et les comorbidités liées à l"âge.

Les études d"association pangénomique ont permis d"identifier un grand nombre de variants liés à des phénotypes spécifiques. Nous formulons l"hypothèse suivante : l"association des variants génétiques d"un biomarqueur d"intérêt à d"autres molécules impliquées dans la même maladie pourrait aider à identifier des voies moléculaires partagées entre les biomarqueurs et de nouveaux biomarqueurs génétiques qui serviraient à évaluer le risque de maladies apparentées.

Ces dernières années ont vu émerger les études épigénétiques qui augmentent grandement le potentiel d"approches de prédiction et de prévention personnalisées. Les études d"association panépigénomique (EWAS) permettent d"identifier des sites CpG méthylés qui jouent un rôle important dans la régulation des phénotypes intermédiaires. En présence de conditions environnementales défavorables, la méthylation représente une des modifications précoces de l"ADN, les sites CpG liés à des facteurs de risque peuvent donc servir de biomarqueurs de susceptibilité pour toutes les maladies associées. En revanche, si le site CpG intervient dans la régulation d'un gène impliqué dans une maladie, cette méthylation pourrait éventuellement constituer une cible thérapeutique.

Lors de notre étude de biomarqueurs moléculaires, nous nous sommes concentrés sur différentes molécules impliquées dans des processus physiopathologiques courants, comme l'inflammation et l"angiogenèse. En plus des cytokines inflammatoires les plus courants, nous avons porté un intérêt particulier à TREM 2, au VEGF-A et à la longueur des télomères (LT).

VEGF-A et LT sont des exemples parfaits de biomarqueurs impliqués dans différents mécanismes physiopathologiques. Nous avons donc supposé qu"analysés ensemble, ils pourraient constituer un outil puissant pour les applications de médecine personnalisée.

En outre, les collaborations entre les laboratoires de recherche et l"industrie représentent une part importante de la mise en oeuvre progressive de la médecine personnalisée, la manière la plus rapide de transférer les connaissances fondamentales vers une application clinique. À cette fin, il est indispensable de participer activement aux projets des industriels cherchant à améliorer le diagnostic et le traitement de maladies chroniques courantes par des approches de médecine personnalisée.

Pour résumer, l"objectif de cette thèse était d"identifier des biomarqueurs communs à plusieurs maladies chroniques par l"étude des interactions entre des variants génétiques et des phénotypes intermédiaires au moyen de méthodologies « -omiques », afin de proposer un ensemble de biomarqueurs qui, lorsqu"analysés conjointement, pourraient améliorer la prédiction du risque de maladie et le traitement dans une stratégie globale de médecine personnalisée.

Les objectifs spécifiques établis pour le développement d"une approche de médecine personnalisée à plusieurs biomarqueurs sont :

 Résumer les avancées les plus marquantes dans le domaine de la médecine personnalisée.

 Étudier l"association entre les sites CpG de méthylation de l"ADN et les phénotypes intermédiaires tels que les taux de lipides sanguins et le tour de taille dans une population d"individus sains, afin d"identifier les mécanismes sous-jacents qui relient la méthylation de l"ADN au métabolisme des lipides et l"obésité abdominale.

 Étudier l"association entre les sites CpG de méthylation de l"ADN et le taux de VEGF-A dans une population d"individus sains, afin d"identifier les mécanismes sous-jacents qui relient la méthylation de l"ADN à l"angiogenèse.

 Identifier des voies physiologiques communes entre le VEGF-A, TREM-2 et LT, et d"autres cytokines inflammatoires, au moyen de l"étude d"association.

 Évaluer le rôle de LT dans la médecine personnalisée, et son utilité sur les plans diagnostique, pronostique et thérapeutique.

 Se familiariser avec le milieu professionnel et la collaboration avec des partenaires de l"industrie du diagnostic clinique intéressés dans le transfert de résultats de recherche vers des outils de médecine personnalisée.

Discussion

Dans ce travail, nous avons exploré la méthylation de l"ADN et utilisé différentes méthodologies d"étude d"association afin d"identifier de nouveaux biomarqueurs et des voies moléculaires communes ayant un effet sur les phénotypes intermédiaires et les facteurs de risque de maladies chroniques. Nos résultats sont destinés à servir d"outils de prédiction pour contribuer à la découverte de nouvelles options thérapeutiques et à accroître les connaissances actuelles de la régulation moléculaire de processus biologiques communs. En cherchant à atteindre les objectifs définis, nous avons établi :

1. L"état des connaissances de la médecine personnalisée et la contribution importante apportée au système de santé actuel, ainsi que l"étendue de l"investissement scientifique nécessaire pour accompagner son développement et améliorer les programmes individualisés de soins.

2. Une association entre une augmentation du taux de TG (triglycérides) et les sites CpG cg08897188 et cg04580029. Le taux de TG est un facteur de risque intermédiaire important dans plusieurs maladies chroniques. Un site CpG lié au taux de TG pourrait être utilisé comme biomarqueur d"évaluation du risque ou intervenir dans l"élaboration de nouvelles stratégies thérapeutiques.

3. Une association entre un tour de taille élevé et le site CpG cg16170243, qui a permis de mieux comprendre la régulation complexe de l"obésité mais qui nécessite d"être étudiée davantage. Ce résultat pourrait se révéler utile lors de la recherche ultérieure de nouveaux facteurs de risque de l"obésité, ainsi que pour d"éventuelles applications thérapeutiques.

4. Une association entre le taux de VEGF-A et 20 sites CpG significatifs, ce qui souligne les mécanismes épigénétiques impliqués dans la régulation physiologique de ce biomarqueur angiogénique. 6. Une association entre rs6918289, un variant génétique lié à TREM2, et des valeurs élevées du taux de TNF-α et d"IMT-F, ce qui suggère que l"allèle mineur T de rs6918289 pourrait être considéré comme un facteur de risque de l"inflammation et de l"athérosclérose.

7. Une association entre rs6993770, un variant génétique lié au VEGF-A, et l"attrition des télomères (défini par LTL/LTM) dans une étude cas-contrôle sur l"athérosclérose, ce qui suggère une implication combinée de VEGF-A et LT dans l"étiologie des maladies cardiovasculaires.

8. L"état des connaissances actuelles portant sur l"utilité future des mesures de LT dans des applications cliniques de médecine personnalisée et le besoin d"études longitudinales débutant dès l"enfance. De telles études aideraient à comprendre la biologie des télomères au cours des premières années de vie, et permettraient d"évaluer l"impact de facteurs environnementaux pendant l"enfance sur LT chez les adultes et la susceptibilité par rapport à des maladies chroniques courantes.

Les maladies chroniques courantes, telles que les maladies cardiovasculaires, le cancer et le diabète de type 2, constituent la cause la plus fréquente de mortalité prématurée chez l"adulte (1). Elles ne se déclarent pas rapidement, mais surviennent petit à petit à force d"accumuler divers facteurs de risque environnementaux chez un individu au profil génétique défavorable (2). Nombreux de ces facteurs de risque pourraient être prévenus, et l"identification précoce des patients à risque, associée à des mesures de promotion de la santé appropriées, pourrait réduire la prévalence des maladies chroniques sans recours à des interventions thérapeutiques (3). La prévention des maladies chroniques ne doit pas débuter à la survenue de la maladie mais bien en amont en gérant et en luttant contre les expositions à effet délétère connu, d"où le besoin de nouveaux biomarqueurs de prédiction du risque (4).

Les phénotypes intermédiaires ou les traits quantitatifs correspondent aux manifestations causées par les mécanismes des maladies complexes et peuvent être utilisés lors d"études moléculaires et génétiques (5). Leur variation phénotypique est distribuée de manière continue dans les populations naturelles, en raison de la ségrégation des allèles au niveau de plusieurs loci et celle-ci est influencée par des effets génétiques et environnementaux complexes. La variation génétique quantitative peut expliquer la susceptibilité à des maladies complexes courantes et les variants génétiques impliqués peuvent être considérés comme des facteurs de risque potentiels (6,[START_REF] Mackay | Q&A: Genetic analysis of quantitative traits[END_REF]. De manière similaire, la variation épigénétique quantitative peut expliquer une partie de la variation des traits physiologiques et peut servir de biomarqueur de la susceptibilité (8).

Les phénotypes intermédiaires (appelés aussi facteurs de risque intermédiaires) sont l"expression de facteurs de risque modifiables, par exemple des taux élevés de cholestérol LDL et de TG pour l"évaluation d"une lipidémie anormale, un IMC et un tour de taille élevé pour l"évaluation de l"obésité, etc. Ces phénotypes sont d"un intérêt particulier pour la recherche dans le domaine de la médecine personnalisée car ils sont impliqués dans de nombreuses maladies chroniques et peuvent être évités en mettant en oeuvre des mesures préventives appropriées (9,10).

En médecine personnalisée, les biomarqueurs permettent d"évaluer les processus biologiques normaux, de détecter des processus anormaux et d"établir le pronostic d'une maladie (11,12).

À présent, l"identification de nouveaux biomarqueurs pour la médecine personnalisée nécessite non seulement une bonne compréhension de la maladie, mais aussi l"utilisation combinée d"approches génomiques et moléculaires afin de détecter des variants ayant une valeur clinique prédictive. De nombreuses maladies partagent les mêmes mécanismes pathologiques, notamment l"inflammation et l"angiogenèse. Il est donc particulièrement important d"étudier ces processus. Les biomarqueurs identifiés dans de telles conditions sont susceptibles d"être impliqués dans plusieurs maladies et donc d"être plus performants pour le développement de stratégies thérapeutiques efficaces contre plusieurs comorbidités (13).

Dans ce travail de thèse, nous avons étudié les facteurs de risque intermédiaires et les voies biologiques impliquées dans un éventail de maladies chroniques. Nous avons mis en oeuvre différentes méthodologies pour évaluer leur régulation et pour établir le lien entre les facteurs et la médecine personnalisée.

•

Il est aujourd'hui établi que l"exposition à des facteurs environnementaux indésirables peut induire des modifications de la méthylation de l"ADN de nombreuses années avant la survenue d'une maladie (14). Un biomarqueur de la méthylation de l"ADN démontré comme étant associé à un taux élevé de TG ou un tour de taille élevé pourrait, seul ou combiné à d"autres biomarqueurs, aider à identifier les personnes les plus susceptibles de présenter un risque accru de maladie chronique. Chez ces individus à risque, des mesures préventives précoces de gestion du mode de vie pourraient aider à limiter l"apparition de ces phénotypes intermédiaires (15).

De plus, contrairement à la séquence d"ADN, les modifications épigénétiques de l"ADN sont un des mécanismes clés de modulation de la transcription de l"ARNm et sont sensibles à des interventions environnementales et pharmacologiques. L"environnement peut modifier les profils épigénétiques qui jouent un rôle important dans la santé. Ces informations peuvent être transmises aux cellules filles lors de la réplication cellulaire (16). Des nombreuses substances thérapeutiques sont capables de modifier les profils de méthylation et de cibler des sites associés à un pronostic défavorable (17). Par conséquent, cette méthodologie ouvre la voie pour la découverte de nouvelles options thérapeutiques (18,19).

Grâce à la méthodologie EWAS, les travaux menés au cours de cette thèse sur la population d"individus sains de la cohorte SFS ont permis d"identifier 2 nouveaux sites de méthylation associés au taux de TG, 1 nouveau site de méthylation associé au tour de taille, et La dérégulation de la lipidémie est un des facteurs de risque les plus importants pour les maladies cardiovasculaires (20). Les taux de lipides sont influencés par la génétique mais aussi par les expositions environnementales, qui peuvent modifier le profil de méthylation de l"ADN. La connaissance de ces profils épigénétiques pourrait expliquer la variabilité interindividu des phénotypes lipidiques ainsi que les mécanismes de régulation sous-jacents (21).

Différentes études ont été menées sur la régulation épigénétique des lipides chez des sujets présentant une maladie cardiovasculaire. La régulation de traits lipidiques peut varier de manière significative dans la population en bonne santé et il est vraisemblable que de nouveaux sites CpG impliqués dans la régulation des gènes pourront être identifiés et aideront à mieux comprendre les mécanismes complexes intervenant dans le métabolisme des lipides.

Au moyen d"une approche sur l"ensemble de l"épigénome, nous avons identifié deux sites CpG associés de façon significative au taux de TG : cg08897188 (chromosome 7q36.1 ; β = -2,80 ; P = 1,39 × 10 -8 ) et cg04580029 (chromosome 16p13.3 ; β = 3,09 ; P = 5,75 × 10 -9 ).

Les modifications épigénétiques de l"ADN ont un effet régulateur important sur la différenciation cellulaire et il a été démontré que la méthylation de l"ADN pouvait être tissuspécifique (22,23). La méthylation de différents types de cellules pouvant varier de manière importante, cette étude a été préparée en prenant en considération le type de cellules utilisées, c"est-à-dire les cellules du sang, et d"autres ajustements techniques nécessaires (effets fixes, puce de méthylation).

Lors de la recherche de nouveaux biomarqueurs, la disponibilité du tissu est un point essentiel à prendre en considération. L"utilisation d'un tissu dont le prélèvement nécessite une intervention chirurgicale compliquerait la mise en place de l"approche sur le plan clinique. Il nous a donc semblé que les sites méthylés devaient être détectables dans l"ADN du sang et que l"étude EWAS de recherche de nouveaux biomarqueurs génétiques devait être effectuée sur l"ADN extrait du sang de la cohorte de découverte (24). Toutefois, pour mieux comprendre la régulation épigénétique d"un phénotype particulier, comme le taux de TG par exemple, il a été convenu qu"un tissu lié au phénotype serait à utiliser également, si disponible (25), car cela pourrait apporter des données supplémentaires confirmant les mécanismes génétiques qui associent le site de méthylation au phénotype.

Le tissu adipeux étant étroitement lié à la biologie des TG (26), il a donc sélectionné comme source pour les analyses de méthylation de l"ADN dans la cohorte de réplication. En tout, des échantillons d"ADN provenant de tissu adipeux de 662 participants à l"étude MuTHER ont été testés pour l"association avec cg08897188 et cg04580029. L"association du site CpG 04580029 et du taux de TG a été confirmée, ce qui implique que la relation identifiée entre le site CpG et le taux de TG est également biologiquement plausible. En outre, la réplication dans le tissu adipeux du résultat démontré initialement dans le sang montre que l"étude de la méthylation de l"ADN dans le sang total est une approche pertinente pour l"identification de modifications épigénomiques, et confirme les conclusions d"études antérieures (27,28).

Deux gènes ont été identifiés à proximité de la sonde cg08897188. PRKAG2, qui code pour la sous-unité fixant l"AMP/ATP de la protéine kinase activée par l"AMP (AMPK), et un transcrit non codant du gène AC093583.1, LOC644090. Le site Cg04580029 est localisé dans le promoteur du gène KREMEN2 qui code pour dickkopf1 (Dkk1), un antagoniste sécrété de la voie de signalisation Wnt/β-caténine. Ces deux sites CpG associés au taux de TG étaient localisés dans un environnement génétique fortement régulé. L"identification de sites de méthylation significative à proximité d"une région de modification importante des histones est particulièrement intéressante car les modifications des histones sont un des composants clé de la régulation transcriptionnelle (29). Ces modifications sont à l"origine de l"organisation du génome en régions actives (l"ADN devient accessible à la transcription) et inactives (l"ADN est compact est moins accessible à la transcription ; elles participent donc à la régulation de l"expression génique (30). La méthylation à proximité d"une histone est donc susceptible d"être impliquée de manière active dans la régulation transcriptionnelle de la région.

Pour conclure, plusieurs études épidémiologiques, cliniques et génétiques ont démontré que des taux élevés de TG constituent un biomarqueur du risque de maladie cardiovasculaire (31).

Les triglycérides contribuent au durcissement des artères et à l"épaississement de la paroi artérielle et donc augmentent le risque de coronaropathie (32), d"accident vasculaire cérébral (AVC), d"infarctus et de cardiopathie (33). De plus, des concentrations élevées en TG sont également impliquées dans d"autres maladies comme le diabète, l"insuffisance rénale chronique et le syndrome métabolique (34). Nos travaux de recherche de sites CpG associés de manière significative au taux de TG a permis d"identifier deux biomarqueurs potentiels qui pourraient faciliter l"évaluation précoce du risque de maladie cardiovasculaire, ou qui pourrait servir de cible pour de nouvelles stratégies thérapeutiques basées sur la méthylation qui seront probablement largement développées dans l"avenir.

Une approche similaire a été employée pour étudier les profils de méthylation associés au tour (35). De nombreux effets néfastes sur la santé ont été associés à un excès de tissu adipeux autour de la partie centrale du corps, appelé obésité abdominale, et estimé habituellement en mesurant le tour de taille [START_REF] Després | Abdominal obesity and metabolic syndrome[END_REF]. L"obésité est un trait complexe dont la régulation n"est toujours pas pleinement établie. Malgré de nombreux travaux pour élucider les mécanismes de régulation épigénétique de l"obésité, de nouvelles cohortes doivent être constituées pour une entière compréhension de ce phénomène.

L"utilisation de populations d"individus sains peut aider à éviter les divergences générées par les cas extrêmes d"obésité et de comorbidités dans les cohortes de populations spécifiques [START_REF] Dhana | An Epigenome-Wide Association Study (EWAS) of Obesity-Related Traits[END_REF]. Cette étude a permis d"identifier une association positive significative, jusqu"à-là inconnue, entre le tour de taille ajusté pour l"IMC et le site de méthylation cg16170243 (chromosome 18q21.1 ; β = 2,32 ; P adj = 0,048).

Deux mécanismes peuvent expliquer l"association entre cg16170243 et le tour de taille.

Premièrement, des facteurs environnementaux non spécifiques pourraient induire la méthylation du site cg16170243, ce qui entraînerait à son tour une augmentation du tour de taille et une susceptibilité accrue vis-à-vis de l"obésité abdominale. Deuxièmement, un environnement obésogène pourrait être à l'origine de la méthylation du site cg16170243, ce qui se traduirait par l"association de ce site de méthylation avec le tour de taille. Les deux mécanismes sont tout aussi possibles et sont discutés dans ce manuscrit, bien que des hypothèses plus détaillées aient été développées dans l"article pour expliquer le premier mécanisme.

Brièvement, le gène ST8SIA5 dans lequel est localisée la sonde cg16170243 code pour une enzyme sialyltransférase impliquée dans la synthèse de gangliosides. Les gangliosides interagissent avec des molécules des voies de transduction du signal, telles que les récepteurs à activité tyrosine kinase (RTK). Une modification de la composition en gangliosides induit la dissociation des RTK des microdomaines enrichis en glycolipides, ce qui pourrait aboutir à une phosphorylation réduite des récepteurs et une résistance à l"insuline. Ainsi, la méthylation de ce site CpG pourrait modifier le niveau d"expression du gène ST8SIA5, ce qui aurait une répercussion sur la synthèse des gangliosides, entraînant à son tour une résistance accrue à l"insuline qui est un facteur de risque connu pour le développement de l"obésité [START_REF] Kahn | Obesity and insulin resistance[END_REF].

Le deuxième mécanisme suppose que l"adiposité aurait un effet sur la méthylation de l"ADN, comme discuté en détail par Wahl et al. (39). Dans leur étude, ces auteurs ont démontré que l"adiposité entraînait une modification de la méthylation de la plupart des sites CpG identifiés comme étant associés à l"IMC. La méthode employée pour évaluer la causalité de cette relation reposait sur un score de risque génétique pondéré qui combinait les effets connus pour influencer l"IMC sur l"ensemble des SNP. Dans notre étude, l"association entre les polymorphismes associés au tour de taille et la méthylation de cg16170243 a été testée, mais aucune association significative n"a été constatée. Toutefois, nous ne pouvons pas exclure l"hypothèse selon laquelle la méthylation du site cg16170243 soit due à une adiposité accrue.

Des travaux supplémentaires sont nécessaires pour déterminer la nature du lien entre le site CpG identifié et le tour de taille.

Le rôle de la méthylation de l"ADN en tant que médiateur du lien de causalité entre des expositions variables et le risque de maladie peut être évalué par randomisation mendélienne à deux échantillons à l"aide des données statistiques résumées de l"EWAS pour le phénotype observé. Dans une première étape, l"effet causal du phénotype observé sur les marqueurs épigénétiques est établi. Lors d"une deuxième étape, la nature causale des marqueurs épigénétiques sur un état de santé est évaluée [START_REF] Relton | Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease[END_REF]. Une telle approche pourrait permettre d"identifier les loci pertinents pour des études fonctionnelles (24).

Le troisième volet de ces travaux analysant la méthylation pangénomique s"est attaché à étudier la régulation épigénétique du VEGF-A dans une population de 211 adultes et enfants sains de la cohorte SFS. Les résultats ont été publiés dans l"article intitulé « Epigenome-wide association study in healthy individuals identifies significant associations with DNA methylation and PBMCs extracts VEGF-A concentration ». Le VEGF-A est un médiateur important de l"angiogenèse et de l"inflammation, ainsi qu"une cible biologique clé pour le traitement de plusieurs maladies où ces phénomènes sont impliqués dans le processus pathologique [START_REF] Zittermann | Endothelial growth factors VEGF and bFGF differentially enhance monocyte and neutrophil recruitment to inflammation[END_REF][START_REF] Pandey | Mechanisms of VEGF (Vascular Endothelial Growth Factor) inhibitor-associated hypertension and vascular disease[END_REF][START_REF] Carmeliet | Angiogenesis in cancer and other diseases[END_REF]. Le VEGF-A présente une héritabilité élevée (> 60 %, telle que déterminée lors de travaux antérieurs sur la cohorte SFS ( 44)), qui peut être expliquée en partie par 10 SNP associés de manière significative au VEGF-A [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF][START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. Bien que sa régulation génétique ait déjà été étudiée au moyen d"études d"association pangénomique (GWAS), les résultats de la présente étude sont à notre connaissance les premiers à mettre en évidence la régulation épigénétique du VEGF-A par EWAS.

Nous avons identifié 20 sites CpG associés de manière significative au taux de VEGF-A dans des extraits de cellules mononuclées du sang périphérique [START_REF] Tomii | TPX2 expression is associated with poor survival in gastric cancer[END_REF]. Des études ont montré que l"inhibition de l"expression de TPX2 peut empêcher la prolifération et l"invasion des cellules de cancer de côlon, et que ceci est peut-être lié à une baisse de l"expression du gène VEGF-A [START_REF] Jian | TPX2 gene-silencing inhibits the proliferation and invasion of human colon cancer SW480 cells[END_REF]. Le gène HAS1, quant à lui, code pour la synthèse d"acide hyaluronique qui joue un rôle essentiel dans le développement tissulaire et l"homéostasie des protéines de la membrane plasmique. Il est également impliqué dans des arthropathies inflammatoires et dégénératives. Il a été démontré que des ARNsi ciblant le gène HAS1 réduisent l"expression de VEGF-A [START_REF] Wang | HNF1AAS1 promotes growth and metastasis of esophageal squamous cell carcinoma by sponging miR214 to upregulate the expression of SOX-4[END_REF].

En plus des deux gènes présentés ci-dessus, des liens avec le VEGF-A ont été trouvés pour 6 autres gènes à proximité des sites CpG identifiés. La méthylation pourrait expliquer certaines modifications de l"expression du VEGF-A observées dans des études antérieures mais à ce jour ells sont inexpliquées. Dans l"avenir, l"exploration de la méthylation de l"ADN associée au VEGF-A devrait cependant être axée sur des populations spécifiques, tels que les patients atteints de cancer ou de maladies cardiovasculaires. Chez les patients atteints de cancer, la production de VEGF-A est augmentée afin de déclencher le processus d"angiogenèse et permet de vasculariser le tumeur en développement [START_REF] Costache | VEGF Expression in Pancreatic Cancer and Other Malignancies: A Review of the Literature[END_REF]. L"étude du rôle de la méthylation de l"ADN dans la régulation de la surexpression du VEGF-A pourrait offrir des perspectives intéressantes. Associées aux résultats EWAS obtenus pour une population en bonne santé, les données d"une telle étude pourraient permettre d"identifier des sites de méthylation différentielle et peut-être contribuer au développement de stratégies alternatives aux traitements anti-angiogéniques actuels.

Dans cette étude, les taux de VEGF-A ont été mesurés dans des extraits cellulaires de PBMCs, et non dans le sérum ou le plasma, qui sont les sources biologiques les plus souvent utilisées pour la détermination du VEGF-A. Les PBMCs sont une source de VEGF-A et de nombreuses autres molécules de signalisation. Leur rôle important dans plusieurs voies biologiques a été établi dans de nombreuses études [START_REF] Visvikis-Siest | Peripheral blood mononuclear cells (PBMCs): a possible model for studying cardiovascular biology systems[END_REF]. La production du VEGF-A dans les cellules mononuclées est donc en lien direct avec les processus biologiques dans lesquels il est impliqué (angiogenèse et inflammation).

Comme dans toutes les discussions concernant les résultats d"études EWAS, il faut garder à l"esprit que la méthylation pourrait être la cause mais aussi la conséquence de taux élevés de VEGF-A. Cela pourrait être établi par randomisation mendélienne. Des avancées supplémentaires en épigénétique sont nécessaires pour le développement de nouvelles méthodologies permettant de déterminer de façon plus simple la causalité entre les sites CpG détectés et le phénotype. Cela améliorerait considérablement notre compréhension du rôle des marqueurs épigénétiques et accélérerait la mise en application des résultats sur le plan diagnostique et thérapeutique.

•

En raison de l"implication important du VEGF-A dans la physiopathologie des maladies au moyen de deux processus pathologiques majeurs (et interdépendants), l"angiogenèse et l"inflammation [START_REF] Shaik-Dasthagirisaheb | Vascular Endothelial Growth Factor (VEGF), Mast Cells and Inflammation[END_REF], l"association entre les taux de VEGF-A mesurés dans les extraits de PBMCs et 11 cytokines, des indicateurs importants de l"inflammation, a été étudiée. Les relations entre les deux processus physiologiques ont été décrites dans l"article intitulé « Peripheral blood mononuclear cell extract VEGF protein levels and VEGF mRNA.

Associations with inflammatory molecules in a healthy population ». L"objectif premier de ces travaux était d"identifier les molécules inflammatoires qui pourraient être utilisées conjointement avec le taux de VEGF-A dans des applications prédictives ou thérapeutiques de médecine personnalisée.

Comme indiqué dans l"article suscité, une des originalités de l"étude était d"utiliser les extraits de PBMCs comme source pour toutes les mesures des molécules analysées et des isoformes du VEGF. Les PBMCs sont des cellules qui répondent de façon sélective à l"activation du système immunitaire et jouent un rôle majeur dans l"immunité chez l"homme en synthétisant et en libérant des cytokines inflammatoires et des facteurs de croissance [START_REF] Jager | Simultaneous detection of 15 human cytokines in a single sample of stimulated peripheral blood mononuclear cells[END_REF][START_REF] Turner | Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease[END_REF]. La production simultanée de différents médiateurs par un même groupe de cellules semble suggérer une activité conjointe dans le même mécanisme physiopathologique.

L"association entre à la fois la concentration protéique de Pour résumer, IL-4, une cytokine plurifonctionnelle ayant des activités anti-inflammatoires et anti-tumorales, était associée au taux de la protéine VEGF-A et à l"expression de l"ARNm de l"isoforme VEGF 189 . Il a déjà été démontré que l"IL-4 est liée au VEGF-A en tant que facteur important de recrutement des macrophages associés aux tumeurs, connus pour promouvoir l"angiogenèse [START_REF] Linde | Vascular endothelial growth factor-induced skin carcinogenesis depends on recruitment and alternative activation of macrophages[END_REF]. En outre, une expression accrue de l"ARNm du gène VEGF-A a été mise en évidence dans des cellules musculaires lisses des voies respiratoires humaines lorsque stimulées par l"IL-4 en culture cellulaire [START_REF] Faffe | Interleukin-13 and Interleukin-4 Induce Vascular Endothelial Growth Factor Release from Airway Smooth Muscle Cells: Role of Vascular Endothelial Growth Factor Genotype[END_REF]. Dans cette étude, nous avons identifié l"isoforme VEGF 189 comme l"isoforme principale responsable de l"association entre le VEGF-A et l"IL-4. Néanmoins, ce résultat doit être confirmé par d"autres études.

MCP-1, une chimiokine angiogénique impliquée dans la régulation, la migration et l'infiltration des monocytes et des macrophages, était associée aux niveaux d"expression de la protéine VEGF-A et de l"ARNm de l"isoforme VEGF 165 . Ces résultats vont dans le même sens que ceux d"études antérieures qui ont montré que MCP-1 augmentait l"expression du gène HIF1A dans des cellules endothéliales d"aorte humaine, ce qui augmentait aussi l"expression du VEGF 165 dans la paroi aortique [START_REF] Hong | Monocyte chemoattractant protein-1-induced angiogenesis is mediated by vascular endothelial growth factor-A[END_REF].

Une autre molécule inflammatoire, l"EGF, était associée au taux de protéine VEGF-A dans notre population. L"EGF est un médiateur important de la croissance, de la prolifération et de la différenciation de nombreux types cellulaires. Il a été démontré dans différents types de cellules que ces deux facteurs de croissance, EGF et VEGF, jouent des rôles majeurs dans la tumorigénèse ainsi plusieurs mécanismes d"action conjointe ont été proposés. Le ciblage combiné des voies de signalisation du VEGF et de l"EGF a été étudié dans le but de développer de nouvelles approches thérapeutiques de lutte contre le cancer avec des résultats encourageants pour le développement de nouveaux médicaments [START_REF] Ciardiello | Interaction between the epidermal growth factor receptor (EGFR) and the vascular endothelial growth factor (VEGF) pathways: a rational approach for multi-target anticancer therapy[END_REF][START_REF] Tortora | Combined targeting of EGFR-dependent and VEGFdependent pathways: rationale, preclinical studies and clinical applications[END_REF][START_REF] Tabernero | The role of VEGF and EGFR inhibition: implications for combining anti-VEGF and anti-EGFR agents[END_REF][START_REF] Winder | Vascular endothelial growth factor and epidermal growth factor signaling pathways as therapeutic targets for colorectal cancer[END_REF].

Enfin, IL- pour le SNP rs6918289, qui était associé à des taux plasmatiques de TNF-α élevés (β découverte = 0,33 ; P découverte = 0,00147).

Pour augmenter la taille de la population de découverte, le SNP rs6918289 a été génotypé de novo chez 393 adultes et 277 enfants. Lorsque analysé dans ces deux sous-populations de la cohorte SFS, nous avons démontré une association significative entre l"allèle T du SNP rs6918289 et des taux plasmatiques élevés de TNF-α (β SFS = 0,49 ; P SFS = 0,0017). Ces résultats ont été confirmés dans une cohorte indépendante de 916 individus de nationalité française (β = 0,202 ; P = 0,023).

Une étude d"association a également été menée avec IMT-F dans un échantillon de 350 adultes issus de la cohorte SFS pour qui des mesures IMT-F étaient disponibles. Les résultats de l"étude ont montré une association significative avec l"allèle mineur (T) du SNP rs6918289 (β = 0,024 ; P = 0,026).

Bien que les premières études sur la protéine TREM2 portaient essentiellement sur les réponses inflammatoires médiées par les macrophages et les neutrophiles dans des maladies neurodégénératives comme la maladie d"Alzheimer, la sclérose latérale amyotrophique (maladie de Charcot) et la maladie de Parkinson [START_REF] Jiang | TREM2 in Alzheimer's disease[END_REF], des résultats récents ont également démontré un rôle important pour la réponse des macrophages induite par TREM2 dans d"autres maladies [START_REF] Fernández-Ruiz | Redefining leukocytes in atherosclerosis[END_REF]. L"article suscité a apporté les premiers éléments éclaircissant l"association génétique de TREM2 avec TNF-α et IMT-F, des indicateurs importants de la formation des plaques d"athérosclérose.

IMT-F est un marqueur précoce et non invasif de l"athérosclérose et aussi un facteur de prédiction indépendant des maladies cardiovasculaires qui pourrait faciliter la détection précoce de plaques d"athérosclérose par échographie (par mesure de la distance entre l"interface média-adventice et l"interface intima-lumière) [START_REF] Polak | Carotid-wall intima-media thickness and cardiovascular events[END_REF][START_REF] Giannoukas | Common femoral artery intima-media thickness as marker for cardiovascular disease in asymptomatic adults[END_REF][START_REF] Touboul | Mannheim Carotid Intima-Media Thickness Consensus (2004-2006)[END_REF]. Le TNF-α intervient dans l"athérogenèse à travers différents mécanismes au moyen desquels cette cytokine inflammatoire agit sur l"endothélium [START_REF] Urschel | TNF-α in the cardiovascular system: from physiology to therapy[END_REF]. Le TNF-α peut augmenter la perméabilité vasculaire pour permettre l"infiltration de cellules inflammatoires [START_REF] Petrache | The role of the microtubules in tumor necrosis factor-alpha-induced endothelial cell permeability[END_REF] et promeut la migration de cellules immunitaires dans le tissu pathologique ou endommagé afin de le protéger de pathogènes potentiellement nocifs. Il peut inhiber la vasodilatation dépendante de l"endothélium en réduisant la formation et la biodisponibilité du NO, stimuler la production de l"anion superoxyde au niveau vasculaire et interférer avec les voies de signalisation de l"insuline dans les cellules endothéliales pour induire une résistance à l"insuline [START_REF] Steyers | Endothelial dysfunction in chronic inflammatory diseases[END_REF].

Compte-tenu de ce qui précède, les résultats de cette étude suggèrent que l"allèle mineur (T) de rs6918289, qui est associé à un taux de TNF-α et une valeur IMT-F élevés, pourrait constituer un allèle à risque pour les maladies inflammatoires et l"athérosclérose. Le mécanisme permettant d"expliquer ceci est encore inconnu. Toutefois, l"analyse bioinformatique de la région génétique a permis de formuler trois hypothèses concernant l"association entre rs6918289 et un taux de TNF-α élevé.

Premièrement, rs6918289 est impliqué dans le régulation post-transcriptionnelle et est associé à la protéine de liaison à l"ARN PABPC1 [START_REF] Guo | rSNPBase: a database for curated regulatory SNPs[END_REF], qui lie la région 3" poly(A) des ARNm.

L"allèle mineur (T) de rs6918289 pourrait avoir un effet sur la protéine PABPC1 qui à son tour affecterait la stabilité de l"ARNm de TREM2. Des études antérieures ont démontré que le niveau d"expression de TREM2 peut avoir un effet sur les concentrations de TNF-α [START_REF] Turnbull | Cutting edge: TREM-2 attenuates macrophage activation[END_REF][START_REF] Liu | Impact of TREM-2 gene silencing on inflammatory response of endotoxin-induced acute lung injury in mice[END_REF][START_REF] Gao | Silencing of triggering receptor expressed on myeloid cells-2 enhances the inflammatory responses of alveolar macrophages to lipopolysaccharide[END_REF].

L"effet de l"allèle mineur (T) conduirait à une production moindre de TREM2, ce qui entraînerait une augmentation du taux de TNF-α chez le même individu.

La deuxième hypothèse est en lien avec la localisation de rs6918289 entre deux séquences génomiques de liaison de CTCF (protéine à 11 motifs doigts de zinc) qui peuvent se lier ensemble et former une boucle de chromatine. Cette section de chromatine devient alors un domaine d"association topologique (TAD) qui peut avoir un effet sur l"expression génique et d"autres fonctions cellulaires. Nous formulons donc l"hypothèse que l"allèle mineur (T) du SNP rs6918289 promeut la fixation de CTCF et par conséquent déclenche une action insulatrice du gène TREM2. pourrait également être un médiateur de l"effet de rs6918289 sur la concentration de TNF-α.

Le remodelage d"une artère est étroitement dépendant de l"état inflammatoire. L"association entre rs6918289 et IMT-F est donc très probablement due à un taux élevé de TNF-α qui découle de l"effet de l"allèle mineur (T) du SNP rs6918289, ce qui suggère un rôle indirect de rs6918289 sur IMT-F. [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF][START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. LTL représente le longueur des télomères dans des cellules hématopoïétiques à prolifération rapide, alors que LTM correspond à la longueur dans un tissu musculaire squelettique à faible taux de réplication, et donc avec une LT comparable à la valeur initiale [START_REF] Benetos | Short leukocyte telomere length precedes clinical expression of atherosclerosis: the blood-and-muscle model[END_REF].

Le raccourcissement de la LT a été associé à l"athérosclérose et est considéré comme un facteur de risque possible pour les maladies cardiovasculaires [START_REF] Khan | Telomeres and atherosclerosis[END_REF]. De manière similaire, il a été démontré que le VEGF-A potentialisait le mécanisme physiopathologique de formation et de déstabilisation des plaques d"athérosclérose [START_REF] Camaré | Angiogenesis in the atherosclerotic plaque[END_REF]. Outre leur rôle important dans l"athérogenèse, ces deux biomarqueurs sont impliqués dans d"autres maladies chroniques courantes. L"étude TELARTA, actuellement toujours en cours, repose sur la création d'une cohorte cas-contrôle pour explorer les rôles du VEGF-A et de LT dans l"athérosclérose.

Cependant, les résultats de cette étude pourront être extrapolés et appliqués à d"autres maladies où ces deux biomarqueurs sont impliqués, notamment le cancer.

Ces travaux ont permis d"identifier des liens entre l"allèle mineur (T) du SNP rs6993770 et le rapport LTL/LTM (P = 0,001143). Cela suggère l"existence d"un mécanisme de régulation génétique commune pour le VEGF-A et l"attrition des télomères, possiblement au moyen d'un processus qui agit sur les deux biomarqueurs. Le SNP rs6993770 est l"un des variants les plus significatifs associés à un taux élevé de VEGF-A circulant. Ensemble avec trois autres SNP (rs6921438, rs4416670 et rs10738760), ces associations permettent d"expliquer 48 % de l"héritabilité du taux sérique de VEGF [START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. Il a également été établi que le SNP rs6993770 est associé à une variation du taux de cholestérol HDL [START_REF] Stathopoulou | A common variant highly associated with plasma VEGFA levels also contributes to the variation of both LDL-C and HDL-C[END_REF], au nombre d"érythrocytes, de plaquettes, et à la concentration en IL-12 (85).

Dans le sang, le VEGF est présent dans le plasma, les plaquettes et les leucocytes [START_REF] Kut | Where is VEGF in the body? A meta-analysis of VEGF distribution in cancer[END_REF].

Plusieurs études ont signalé une corrélation entre la concentration en VEGF-A et les plaquettes. Ces dernières sont particulièrement importantes dans la cicatrisation et pourraient jouer un rôle de stimulation de la croissance tumorale dépendante de l"angiogenèse par l"intermédiaire de leur fonction de transporteurs du VEGF-A [START_REF] George | Correlation of plasma and serum vascular endothelial growth factor levels with platelet count in colorectal cancer: clinical evidence of platelet scavenging?[END_REF][START_REF] Benoy | Serum interleukin 6, plasma VEGF, serum VEGF, and VEGF platelet load in breast cancer patients[END_REF].

Le SNP rs6993770 est localisé dans l'intron du gène ZFPM2-201 (protéine à motifs doigt de zinc, membre 2 de la famille FOG). Les protéines FOG modulent l"action des protéines de la famille GATA qui sont des régulateurs importants de l'hématopoïèse et de la cardiogenèse [START_REF] Genecards | The human gene database[END_REF]. Le variant génétique rs6993770 pourrait avoir un effet sur l"activité du gène ZFPM2-201 qui à son tour modifierait la régulation de l"hématopoïèse par une action sur les protéines GATA. Une augmentation de l"hématopoïèse pourrait entraîner une hausse du taux de renouvellement des cellules et donc à une accélération de l"attrition des télomères [START_REF] Rufer | Telomere fluorescence measurements in granulocytes and T lymphocyte subsets point to a high turnover of hematopoietic stem cells and memory T cells in early childhood[END_REF]. De plus, une augmentation de l"hématopoïèse conduirait à une production accrue de plaquettes, ce qui expliquerait le taux plus élevé de VEGF-A. Ces hypothèses semblent vraisemblables car une accélération de l"attrition des télomères était le seul phénotype associé de manière significative à ce variant génétique, alors qu"aucune association significative n"était détectée avec LTL et LTM.

La grande variabilité inter-individu de la longueur des télomères est connue, et est observable dès la naissance [START_REF] Youngren | Synchrony in telomere length of the human fetus[END_REF]. Plus tard dans la vie de nombreux facteurs peuvent accélérer l"attrition (taux de renouvellement cellulaire élevé causé par l"inflammation, le stress oxydatif, etc.

( Bien que cette hypothèse semble plausible, aucune association n"a été détectée entre la distribution de l"allèle à risque (T) de rs6993770 et le degré d"athérosclérose dans notre population d"étude. Nous pourrions donc supposer que rs6993770 représente un facteur de risque pour une attrition accélérée des télomères, qui à son tour entraîne l"athérosclérose.

Toutefois, son rôle de facteur de prédiction des maladies cardiovasculaires doit être examiné plus en détail. L"association de rs6993770 à l"attrition des télomères dans des études cascontrôles d"autres maladies liées aux télomères, comme le cancer, doit également être évaluée.

•

La longueur des télomères est en effet un biomarqueur précieux lié à un grand nombre de maladies, allant de maladies monogéniques rares aux maladies chroniques courantes (97)[START_REF] Sanders | Telomere length in epidemiology: a biomarker of aging, age-related disease, both, or neither?[END_REF][START_REF] Falandry | Telomere length is a prognostic biomarker in elderly advanced ovarian cancer patients: a multicenter GINECO study[END_REF].

Le raccourcissement continu de la longueur des télomères tout au long de la vie a lié LT à la longévité et a intrigué les scientifiques à la recherche d"une horloge biologique permettant de prédire l"espérance de vie (100 To do so, we employed a variety of approaches, including epigenome-wide association studies (EWAS) for investigation of epigenetic regulatory mechanisms of different molecules, and studies of associations between genetic variants and various intermediate phenotypes to describe the genetic architecture of complex traits.

We were then continuously focused on building the knowledge around intermediate

Chronic diseases

Chronic diseases are conditions that persist over a long period of time and can hinder independence and the quality of life of the individual. Generally, they cannot be prevented by vaccines or cured by medication. In the last decades, chronic diseases became a major cause of death and the main public health problem of occidental countries (102). Among them, most common types are cardiovascular diseases (CVD), cancer, chronic respiratory diseases and diabetes. Disease rates are increasing globally, currently accounting for almost 70% of all deaths worldwide (1).

Therefore, countries are investing in public health to make significant improvements in chronic disease prevention, control and management. Most chronic diseases emerge in middle age after a long exposure to an unhealthy lifestyle, caused primarily by four major risk factors: tobacco use, physical inactivity, harmful use of alcohol and unhealthy diets (Figure 1). As those factors may be modifiable, fighting against them is one of the essential strategies of chronic disease prevention. A proper understanding of their influence on health outcomes is the key to promote health and well-being (1). In reality, most common diseases are multi-factorial, caused by variants in numerous genes that interact with each other and with a wide range of environmental exposures. For this reason, public health genomics has evolved to integrate advancements in genomics into the fields of personalised medicine and public health (103).

Because of the combination of multiple genes and environmental factors involved in the aetiology of common chronic diseases, they are often referred to as "complex diseases". Their management is often further aggravated with the concurrence of multiple chronic conditions (comorbidities) within the same individual (Figure 2). In occidental countries, about one in four adults have at least two chronic conditions and more than half of older adults suffer from three or more (104). Comorbidities are deteriorating the health outcomes, patient"s functional status and are increasing the medication intake. The current health-care system is largely configured for individual diseases rather than multimorbidity, which is also raising the risk of medical error. Complementary strategies are therefore needed to support clinicians and provide personalised and comprehensive patient care (3).

Cardiovascular diseases

CVDs are the most common cause of death worldwide, causing 31% of all global deaths.

Heart attacks and strokes are the two prevailing cardiovascular events, representing together 85% of CVD mortality (105). There are different types of CVD, which often overlap by treatment, symptoms, and prevention strategies. They include coronary heart disease, cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease and pulmonary embolism (Figure 3). Atherosclerosis is an arterial disease, which is believed to be a major cause of CVD (106). It is characterised with chronic inflammation, caused by interactions between modified lipoproteins, monocyte-derived macrophages, T cells and normal cellular elements of the arterial wall (Figure 4). Continuous inflammation can result in the development of complex lesions called atherosclerotic plaques, that decrease the arterial lumen (107).

The early-stage lesions of atherosclerosis consist of sub-endothelial accumulation of cholesterol-engorged macrophages termed "foam cells". Atherosclerosis develops gradually and does not cause any symptoms until the stiffed artery fails to supply an adequate quantity of blood to organs and tissue. The first appearances, named "fatty streaks", are not clinically significant, but are the precursors of more advanced lesions, which appear with the accumulation of lipid-rich necrotic debris and smooth muscle cells (68).

With time, a combination of calcification and ulceration at the luminal surface and haemorrhage from small vessels that grow into the lesion from the media of the blood vessel wall form increasingly complex plaques. These advanced lesions can disturb the blood flow and cause symptoms, which can finally result in serious conditions, i.e. coronary artery disease (CAD), cerebrovascular disease or peripheral arterial disease. However, the most important clinical complication is an acute occlusion of the vessel due to the formation of a thrombus or blood clot, resulting in myocardial infarction or stroke (68). The most important risk factor for the development of atherosclerosis is elevated plasma cholesterol level, followed by hypertension, diabetes, smoking, male gender and inflammatory markers (108). Though some genetic risk factors have been identified, elevated cholesterol is so far the only identified factor, which is sufficient to drive the development of the disease (107). The overall genetic impact on disease susceptibility is still unknown and remains to be fully explored (108).

Diabetes

Diabetes is a group of metabolic diseases that impair the body's ability to process blood glucose, caused by defects in insulin secretion, insulin action, or both. The chronic hyperglycaemia can result in long-term damage, dysfunction and failure of different organs, especially the eyes, kidneys, nerves, heart and blood vessels. Therefore, diabetes is often related to cardiovascular pathologies, which are the most prevalent cause of morbidity and mortality in diabetic patients (109,110). According to the World Health Organisation (WHO), there are currently around 422 million people worldwide suffering from diabetes, which is equal to 1 out of 11 adults (111). There exist different types, which differ in pathogenic processes involved in the development of a disease. Most common conditions are type 1, type 2 and gestational diabetes.

Type 1 diabetes is autoimmune destruction of insulin-producing pancreatic β cells.

Chronically inflamed pancreatic cell islets lose the function of insulin secretion, which results

in deregulated blood glucose. Both, complex genetics of type 1 diabetes and the lack of knowledge regarding the immune response implicated in disease are hindering the progress in understanding and treatment of this disorder (112).

Type 2 diabetes is characterised by insulin resistance, a condition in which cells fail to respond to insulin properly. Insulin resistance demonstrates as decreased peripheral glucose uptake combined with increased endogenous glucose production, which can be further impaired with augmented lipolysis, elevated free fatty acid levels, along with the accumulation of intermediary lipid metabolites (113). Type 2 diabetes is a multifactorial disease with an important interaction between genetics and environmental causes (114).

Gestational diabetes is defined as any degree of glucose intolerance with onset or first recognition during pregnancy (115). It develops as a combination of insulin resistance, which is alone one of many physiological changes occurring during pregnancy, accompanied by pancreatic β-cell insufficiency. The genetic studies of gestational diabetes showed that susceptibility for a disorder is likely to be common to type 2 diabetes; however, it remains largely unknown (116).

Cancer

Cancer is the second largest cause of death globally and is estimated to account for almost 10 million deaths in 2018. It is a generic term for a group of diseases characterised by the growth of transformed cells that proliferate beyond their usual boundaries and tend to invade neighbouring tissues and spread to other organs. Among the most common types of cancers there are lung, female breast, colorectal and prostate cancer (117,118).

The transformed cancer cells form tumours (Figure 5), which may be either benign or malignant. Only malignant tumours are properly referred to as cancers and differ from benign tumours in their capacity to invade surrounding normal tissue and spread throughout the body via the circulatory or lymphatic systems. Development of malignancy is a complex multistep process and diverse factors may affect the likelihood of cancer development (119). Transformations of cancer cells are driven by mutations that may be inherited, induced by environmental factors, or result from DNA replication errors (120). Therefore, many cancers are related to potentially modifiable risk factors. Cigarette smoking was associated with the most cancer deaths than any other risk factor, accounting for nearly 30% of deaths, followed by excess body weight (121). It has been estimated that between 30% and 50% of cancer deaths could be prevented by modifying or avoiding key risk factors, from where the need for early diagnosis and screening (117).

Identification of carriers of gene mutations, susceptible to common cancers has been shown as the most impactful method for cancer control. With screening, prevention and early detection, national population-testing programs to identify carriers of mutation will soon be integrated into healthcare systems. To fully deliver a precision prevention program, extended mutation studies are still required (122).

Epigenetic marks can also be considered as potential markers of cancer development and progression as they can define distinct cellular identities and specific cell-cell interactions which are driving tumorigenesis. The reversibility of these alterations is bringing hope for future epigenetic therapies that could be used for clinical prevention and treatment (123).

The major chronic disease risk factors

The risk factors for chronic diseases are most often classified into two groups: modifiable and non-modifiable. Some of the most important modifiable risk factors are obesity, dyslipidaemia, physical inactivity and tobacco use, while age, sex and heritability are referred to as non-modifiable risk factors.

Obesity

Obesity is defined as abnormal or excessive fat accumulation that presents a risk for a number of comorbidities, including type 2 diabetes, CVD and cancer (35). A general population measure of obesity is the body mass index (BMI), i.e. a person"s weight (kg) divided by the square of his or her height (m 2 ). A BMI ≥ 25 kg/m 2 is considered as overweight and BMI ≥ 30 kg/m 2 is defined as obesity. Many negative effects of obesity are linked to excessive fat tissue around the stomach, known as abdominal or central obesity. As tall people with abdominal obesity can have a normal BMI, waist circumference (WC) has been recommended as a measurement to assess this risk factor, rather than BMI [START_REF] Després | Abdominal obesity and metabolic syndrome[END_REF].

Some countries have recognised obesity as a chronic disease, where foods with high energy density are the primary agent that adversely affects a host, alongside with other environmental factors, such as low physical activity (124). Other health professionals describe it rather as a behavioural problem that leads to disease (125). Nonetheless, obesity is a growing health issue (Figure 6), with nearly a third of the world's population classified as overweight or obese in 2019 ( 126). Understanding the underlying mechanisms leading to obesity and linking obesity to chronic diseases is necessary for the development of the proper therapeutic strategies against it (9).

Obesity and CVD are linked through high levels of fat mass, which worsen most of CVD risk factors, such as plasma lipids, blood pressure, glucose/insulin resistance and inflammation.

Moreover, the body adaptation of carrying an extra load in daily activities has been shown to place an extra burden on the heart, resulting in left and right ventricular alterations that ultimately lead to ventricular hypertrophy and enlargement, predisposing to heart failure (127). cholesterol, qualitative changes in low-density lipoprotein (LDL) and raised triglycerides (TGs). In 46% to 60% of cases, elevated or decreased levels of these lipoproteins may be related to genetic alterations (129). Normally, however, dyslipidaemia reflects the interaction between both genetic influences and environmental factors, such as unhealthy diet and a sedentary lifestyle (130).

Regulation of these perturbations is essential in the prevention of coronary heart disease and other atherosclerotic and chronic diseases. Treatment of dyslipidaemia is normally straightforward; however, some of the disorders are complex and thus resistant to simple treatment strategies (20). The knowledge of the molecular basis of dyslipidaemias allows their Atherosclerosis is the major human disease associated with cholesterol and lipid metabolism.

The accumulation of plasma lipoproteins in the subendothelium is the earliest detectable event in atherogenesis (132). Serum levels of LDL play a key role in this process, whereas HDL has Besides atherosclerosis, increased cholesterol levels were associated with several other CVDs (stroke, coronary heart disease, heart attacks), as well as cancer cell malignancies (134) and Alzheimer's disease (AD) (135).

Hypertriglyceridaemia

Hypertriglyceridaemia is defined as fasting plasma TG concentration ≥150 mg/dL (>1.7 mmol/l). It results from the interaction of genetic and non-genetic factors, specially poor glycaemic control and obesity (136). Plasma TG levels partially reflect the concentration of the triglyceride-rich lipoproteins (TRLs): VLDL, chylomicrons and their remnants.

Hypertriglyceridaemia often causes the reduction of HDL levels and an increase in atherogenic small dense LDL levels and is an important risk factor for CVD, type 2 diabetes, chronic kidney disease and acute pancreatitis (137).

Macrophages incorporate cholesterol issued from TRLs, resulting in the formation of foam cells, which accumulate and contribute to the formation of atherosclerotic plaque (Figure 8).

TRLs activate a number of proinflammatory, procoagulant and proapoptotic signalling pathways that play a fundamental role in the pathogenesis of atherosclerosis (137). Though LDL was considered as a primary risk factor for atherosclerosis, studies showed that elevated TG levels are independently associated with increased incidence of CV events, suggesting that high TG levels should be individually recognised as an important target for therapy (137-139). The effect of low-grade inflammation can persist even 10 to 20 years after quitting smoking.

In addition, cigarette smoke contains many carcerogenic substances, which are activated by metabolic processes and converted by cytochrome P450 enzymes to electrophilic entities.

Electrophilic entities then covalently bind to DNA and form DNA adducts, which can initiate carcinogenesis (144-146).

1.1.4.5 Age and sex

In the occidental countries, age is the main independent risk factor for the development of most common chronic disorders, including cancer, CVD and neurodegenerative disorders, which are often referred to as "ageing diseases" (147). With advanced age, long-term accumulation of all kinds of risk factors causes alterations, altogether leading to a decline in function and an increase in the chance of disease or death (2). Age-related phenotypes are largely variable between individuals. They are regulated with complex genetic mechanisms that contribute to a large number of independent processes of damage accumulation with little or no common causality (148). Mechanisms implicated in ageing are related either to the maintenance of metabolic and energy homeostasis, or to damage repair, and show complex implication in aetiology of ageing diseases (2).

However, the above-discussed processes do not occur at the same time in men and women.

Globally, under the age of 85 years, the number of deaths is greater for men than for women in all cause-of-mortality categories (149). Sex is, therefore, another important non-modifiable risk factor. Because it affects a wide range of physiological functions, it has an impact on susceptibility, age of onset and the progression of a variety of diseases. Sex differences are particularly well-investigated in CVDs because of strong epidemiological evidence that men and women face different risks and confront different outcomes (150). Often, women develop chronic pathologies later and are less likely to die from intentional and unintentional injuries (Figure 9). The majority of the sex differences is caused by differences in endocrine and immune systems (151). Sex-specific diseases occur only in people of one sex, whereas sex-related diseases are more common to one sex. A classic example of the sex-specific difference is the occurrence of ovarian cancer only in women and prostate cancer only in men. The example of the sexrelated difference, on the other hand, is multiple sclerosis, where women have a greater susceptibility to a disease, while men have worse disease progression (152). Finally, it is important to keep in mind that besides the impact of the immune and endocrine system, there are genetic influences on sex-specific differences in the epidemiology of common diseases, which are studied by gene-environment interactions (153).

Heritability

Family history of a disease is a piece of important information for understanding the risks of developing a chronic disease. It comes from common genetics, environment, lifestyle and habits shared among family members, which can significantly contribute to susceptibility for disease. The main characteristics of inherited diseases are the following: they can occur earlier than expected (before the age of 55), are identified in more than one close relative, are not sex-specific (can appear in a sex which is normally less likely to have this disease, such as breast cancer for men) and can appear in specific combination among relatives (154). Unlike monogenic diseases, common chronic pathologies involve many genes with varied and complex inheritance patterns. The calculation of risk for such conditions is thus much more complicated. Therefore, observations of data collected from large families affected by a disease are compared to a family medical history as they can help to estimate the probability of developing a disease (155).

Common disease pathways

Complex diseases are mediated by different pathogenesis pathways, which can increase the incidence of comorbidities, suggesting that various human diseases could be biologically connected ( 156). The good understanding of such common pathways and interaction between them (Figure 10) is particularly interesting, as it might result in the development of therapeutic strategies effective across multiple diseases (13). The acute inflammatory process is terminated when the inducers are removed, and the tissue is repaired. Inflammation is beneficial as long as it is well-controlled but can become harmful for the tissue if deregulated. In some cases, the process persists for a longer period, and a chronic inflammation appears. Normally, chronic inflammation is caused by continuous stimuli of allergens, unrepaired tissue damage or chronic infection. In some cases, the initiating trigger does not seem to be associated with any classical inducer (159, 161). Such chronic inflammatory response has been observed in a wide variety of conditions, including type 2 diabetes, cardiovascular and neurodegenerative diseases, obesity, cancer, asthma, chronic inflammatory rheumatic disorders and ageing (162). Inflammation and pathologic processes are often caught in a reciprocal circle of continuous positive feedback stimulation (159). The initiation of chronic inflammation is still not well understood, but it is believed to be related to the homeostatic imbalance caused by the malfunction of the tissue (159, 161).

Risk factors linked to chronic diseases have been shown to increase the inflammatory response. Therefore, downregulation of inflammation-associated risk factors could prevent or delay the onset of inflammation-induced diseases (163). A pathological inflammatory response can be controlled at multiple levels, especially through the production of inflammatory mediators. However, there are still substantial knowledge gaps in understanding of regulatory principles, due to the complexity of the inflammatory response and the multitude of components involved (159).

Cytokines

Cytokines are a category of small proteins (~5-20 kDa), important in cell signalling, and are common inflammation mediators (Figure 12). Most important cytokines and their function in inflammation are presented in Table 1.

Table 1. Molecules from the class of cytokines and their role in the inflammation process. Cytokines modulate the inflammation through a complex network of interactions, by which they regulate the activation of the resident cells and newly recruited inflammatory cells. More specifically, they are the major determinants of the make-up of the cellular infiltrate, state of cellular activation, and systemic responses to inflammation. Their functioning is often deregulated during chronic inflammation, which causes excessive inflammatory cytokine production that can lead to tissue damage, organ failure, and ultimately, death. A better knowledge of cytokine interaction in inflammatory response could help in the effort to regulate pathological processes with identification of the therapeutic agents, which could be used in the treatment of chronic diseases (160, 165).

TREM Family proteins

The triggering receptors expressed on myeloid cells (TREM) are a family of cell-surface molecules, responsible for the regulation of innate resistance, inflammation, bone homeostasis, neurological development and blood coagulation (166). At least one member of TREM family is expressed on the surface of each monocyte, macrophage, myeloid dendritic cell, plasmacytoid dendritic cell, neutrophil, microglia, osteoclast and platelet. They are essential for the defence against pathogens and participate in fine-tuning of immune reactivity by amplification or reducing of toll-like receptor (TLR)-induced signals, which activate innate immune cells to clear the pathogen. The process of fine-tuning is essential in the prevention of excessive inflammation that could result in tissue damage (167).

 TREM1

TREM1 is an amplifier of the systemic inflammatory response, commonly associated with sepsis. During infection, receptor expression is modulated, and soluble TREM-1 is released. This leads to a cascade of intracellular events that result in inflammatory effects, such as cytokine production, degranulation of neutrophils, and phagocytosis (168). TREM1

tunes the septic response to facilitate the efficient clearance of the pathogen without damaging the host (167).

 TREM2

TREM2 is an important negative regulator of autoimmunity. It is expressed on newly differentiated and alternatively activated macrophages, where it acts to restrain macrophage activation [START_REF] Turnbull | Cutting edge: TREM-2 attenuates macrophage activation[END_REF]. TREM2 can suppress macrophage TNF-α and IL-6 production and promote an anti-inflammatory state. It was first detected on human monocyte-derived dendritic cells where it is also required for the differentiation of myelinating oligodendrocytes. Later, it was also found on other cell types, including endothelial cells, but its function in this cell type is not yet fully characterised (166). Single nucleotide polymorphisms (SNPs) in the gene encoding TREM2 have been associated with AD, implying on a key role of TREM2 molecule in the pathology of disease ( 169). Moreover, model animal studies have shown its importance in other inflammatory diseases, such as chronic obstructive pulmonary disease (COPD), multiple sclerosis, and stroke (170). Its physiological role and mechanism in CVD remain to be fully investigated.

Angiogenesis

A proper vascularisation that enables a continuous flow of oxygen and nutrients is crucial for the normal functioning of every tissue. The construction of the vascular system starts in embryotic development with vasculogenesis, a process that allows the formation of the primary vascular plexus. Vasculogenesis is followed by angiogenesis, which enables a formation of new blood vessels from existing vasculature through the interaction of the endothelial and smooth muscle cells (171,172).

Two different types of the angiogenic process occur in utero and in adults. Firstly, sprouting angiogenesis, which is characterised by the growth of sprouts from the pre-existing vascular network toward an angiogenic stimulus, such as VEGF-A (173). It is the most common and best-understood mechanism of angiogenesis (Figure 14). Angiogenesis is a physiological mechanism, which mostly occurs in embryogenesis. In adult tissues, it is regulated through tightly controlled processes and appears during the female reproductive cycle and in wound healing (176). However, unregulated angiogenesis is linked to a great number of pathologies, caused either by exaggerated or inadequate angiogenic activity (Figure 15). Stimulation and repression of these mechanisms have therefore been studied for application in many pathological conditions. Ischemic heart disease, peripheral arterial disease, and wound healing could benefit from induced neovascularisation. On the other hand, decreasing or inhibiting angiogenesis is already used in the treatment of several types of cancer and ophthalmic conditions, and could be potentially used as therapeutic also in rheumatoid arthritis and other diseases [START_REF] Carmeliet | Angiogenesis in cancer and other diseases[END_REF]174,177).

VEGF-A signalling pathway is a principal target of anti-angiogenic drugs as it is increasingly expressed in most human cancers (178). The importance of VEGF-A in chronic diseases will be discussed in the next chapter.

Besides growth factors, cancer cells secrete a variety of cytokines that can also stimulate classical angiogenic signalling pathways, or they induce an inflammatory response that initiates angiogenesis and consequent vascularisation of the tumour (179). The common regulation of both processes could result in new agents, which could be used in the treatment of related disorders.

Biomarkers

A biomarker has been defined as "characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" (180). Generally speaking, biomarkers indicate a change in expression or state of measured characteristics that correlates with the risk or progression of a disease. They allow the examination of normal biological processes, diagnosis of pathogenic processes and prognosis of a disease. Moreover, they play a critical role in improving the drug development process as surrogate endpoints (11,12).

Biomarkers have been classified in different categories based on their use in clinical practice (181):

 Susceptibility/risk biomarkers are used for indication of the potential for developing a disease or medical condition in an individual, who does not currently have a clinically apparent disease or medical condition.

 Diagnostic biomarkers are used for detection or confirmation of the presence of a disease or specific condition, or for identification of a subtype of the disease.

 Monitoring biomarkers are used for serial assessment of presence, status or extent of a disease or medical condition, or to provide evidence of exposure to (or an effect of) a medical product or environmental agent.

 Prognostic biomarkers are used to identify the likelihood of a clinical event, disease recurrence, or disease progression in patients with a disease or medical condition of interest.

 Predictive biomarkers are used to predict the likelihood to experience a favourable or unfavourable effect from the exposure to a medical product or environmental agent.

 Pharmacodynamic/response biomarkers are used for detection of changes in response to exposure to a medical product or an environmental agent.

 Safety biomarkers are used before or after an exposure to medical intervention or environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse event.

There is a special need for the use of biomarkers in the field of personalised medicine, which is directed towards precise diagnostics of disease and stratification of patients regarding their condition, in order to generate the optimal treatment and decrease the risk for side effects, caused by inappropriate therapy. The development of specific diagnostic biomarkers that would allow the identification of individuals with a disease and classification of a disease subtype are therefore particularly important (11). The need for the new biomarkers A good understanding of a disease, a combination of genomic and molecular features and identification of common pathological pathways in comorbidities are necessary for the design of biomarker research. Two important biomarkers, frequently involved in the pathology of chronic diseases are VEGF-A and telomere length.

VEGF-A

VEGF-A is a multifunctional signal protein, which works as a key regulator of both physiological and pathological angiogenesis, is involved in inflammation ( 41) and has been related to a variety of pathologies, such as cancer, CVDs and neurodegenerative disorders (185). It is known also as vascular permeability factor, because of its key role in inducing of vascular leakage, which is especially important in the process of inflammation (Figure 15). The action of VEGF-A signalling is exercised through binding to the tyrosine kinases Flt-1

(VEGFR-1) and Flk-1/KDR (VEGFR-2), high-affinity VEGF receptors (Figure 16), found on a wide variety of cell types (187). On ligand binding, VEGFR-2 dimerises and activates its receptor tyrosine kinase activity, which results in autophosphorylation of the intracellular domains. This can then lead to the activation of a variety of signalling pathways [START_REF] Pandey | Mechanisms of VEGF (Vascular Endothelial Growth Factor) inhibitor-associated hypertension and vascular disease[END_REF]. VEGF-A has become a prospective target for the design of anti-cancer treatment and bevacizumab was the first VEGF-A inhibitor approved for the treatment of cancer (191).

Besides bevacizumab, few other anti-VEGF medications have already entered the market for the treatment of certain types of cancers. However, the trade-off for such therapy is often the occurrence of cardiovascular side effects (192,193).

In normal physiological conditions in adults, anti-angiogenic isoforms are expressed in nonangiogenic tissues and represent more than 50% of the total amount of VEGF-A protein (194). In the occurrence of cancer, a reversal of the situation is observed, and pro-angiogenic isoforms are expressed in place of the anti-angiogenic isoforms, leading to neovascularisation and development of tumour (188,190). It has been suggested that the expression profile of isoforms of different types of tumours could affect the response of anti-VEGF therapies. The interesting fact is that alternative splicing, leading to the expression of different isoforms, may depend on environmental stimuli. It is expected that a better understanding of this mechanism could enable us to charge other molecules, not just VEGF-A, for tumour suppression activities (195). Because VEGF-A is a mediator of both, angiogenesis and inflammation, which are closely involved in many pathologies, inflammatory cells are likely to be the next potential targets for anti-angiogenic therapies [START_REF] Shaik-Dasthagirisaheb | Vascular Endothelial Growth Factor (VEGF), Mast Cells and Inflammation[END_REF].

Telomere length

A telomere is a repetitive sequence TTAGGG on the ends of the eukaryotic chromosomes Other biomarkers include VEGF-A, EGF ( 224), E-selectin, L-selectin, P-selectin, IL-1β, IL-2, IL-6, IL 223) and show quite inconsistent results, pointing towards the complexity of these molecular interactions, which still need to be elucidated.

Methodologies of personalised medicine

Personalised medicine, commonly known as precision medicine (Figure 19), refers to a "medical model using characterisation of individuals" phenotypes and genotypes (e.g. In other words, personalised medicine changed the traditional medical approaches by adding a wide variety of data to the common clinical information of a patient, using a combined knowledge to enable better risk prediction, diagnosis, prognosis and treatment (226).

In the aspect of therapeutic development, personalised medicine can be divided into three different categories (227):

 Individualised therapies, where a single medicine is created for a single patient.

 Precision medicines, which are medicines accompanied by specific test to determine the overall likelihood of a beneficial effect.

 Targeted medicines, which are medicines specific to a single molecular target and can be robustly tested to identify patients who can receive the medicine.

The fast evolution of molecular diagnostics based on a more detailed understanding of a disease and its mechanisms has resulted in therapeutic alternatives in different clinical areas, in particular oncology. Today, there are more than 90 medicines on the market that require testing to determine accurate dosing (227).

Applications of personalised medicine are based on the fundamental discoveries, acquired from the use of the advanced technologies that enabled the insight into genetic code. At the beginning of the 21 st century, the gold standard became the GWAS. Afterwards, highthroughput "-omics" technologies developed to understand genetic causality of complex traits of human diseases through parallel analysis of genes, transcripts, and proteins and to detect qualitative as well as quantitative levels of multiple genes, transcripts, and proteins simultaneously. They have been playing a key role in the identification of effective models that predict phenotypic traits and outcomes, in the discovery of biomarkers, and generation of important insights into the genetic foundations of the heritability of complex traits. "Omics"

technologies helped to accelerate the medical-genetics analyses and contributed to the advances in molecular diagnosis and personalised medical treatment (228-230).

Genetics

The Human Genome Project (HGP), which was accomplished in 2003, provided a complete sequence of human DNA, which served as a foundation to gene catalogues, understanding of structure and regulation of genes, determination of the variants in different populations and linking the variants to specific diseases. Understanding the genetic basis of disease was expected to lead to better targeted therapies. Therefore, the genetic diagnosis became an objective of medical scientists who were seeking an earlier identification of the affected patients and the improvement of patient"s treatments (231).

The sequencing of amplified fragments corresponding to the particular gene regions is the conventional way of searching for mutations in a gene. It is mostly used for discoveries of genetic variants in genes related to simple monogenic diseases. Sophisticated molecular methods coupled with computational methods allowed the development of new analytical techniques which were designed to unravel the genetic basis of complex diseases (232). The innovation of genetic methodologies resulted in the production of large amounts of data that need the support of statistical and computational methods to be correctly processed. The main statistical and computational methods in genetic studies are presented below.

Association studies

Association studies use the genetic information to find a link between a complex disease or a particular condition and genetic or epigenetic variant. The genetic variants associated with a complex disease are often common polymorphisms that individually have little impact on the phenotype, but could be considered a cause of disease in combination with other risk variants (233).

The identification of variants associated with a specific disease gave the idea of a polygenic risk score calculationa score reflecting the sum of all known risk alleles, weighted by how risky each variant is known to be to assess the genetic susceptibility for a particular disease (234). The polygenic risk score for determination of some complex cardiovascular pathologies and some types of cancers has already proved useful in the detection of individuals at risk. Such information can serve clinicians to take the same preventive measures as they do for patients meeting other high-risk criteria (235). It is expected that artificial intelligence-based algorithms for identification of patients with disease risk factors, patients with the need for a guideline-based screening or need for the application of pharmacogenetic guidelines to assist with drug selection and administration will soon be developed (236).

SNPs are the most common traits tested in genetic association studies. Simple sequence repeats (microsatellite markers), insertion/deletions, variable-number tandem repeats (VNTRs), and copy-number variants (CNVs) are other structural variants used for identification of genomic alterations involved in complex diseases (233).

Association studies are most often designed as a case-control study or quantitative trait approach.

 Case-control study is based on stratification of the population in two samples: "cases" and "controls", which are compared to detect variants with a different allelic frequency among two studied groups. It is probably one of the most common approaches used to study the genetic association (237). A Pearson test (2 df) and a Fisher exact test are the most common statistical methods to analyse the associations of a single SNP in casecontrol populations (238).

 Quantitative trait approach uses disease-associated quantitative traits (also known as intermediate phenotypes) as proxy disease phenotypes in molecular genetic investigations of complex diseases. Intermediate phenotypes are traits with normally distributed phenotypic variation in natural populations. Normally, they describe either morphology (height, weight), physiology (blood pressure), behaviour (aggression), or molecular phenotypes (TL, TNF-α levels, gene expression levels) (6,[START_REF] Mackay | Q&A: Genetic analysis of quantitative traits[END_REF]. Quantitative trait locus (QTL) analysis is a statistical method that links phenotypic data (trait measurements) and genotypic data (usually molecular markers) in an attempt to explain the genetic basis of variation in complex traits (239). The natural statistical tools for quantitative traits are linear regression and analysis of variance (ANOVA). ANOVA compares the null hypothesis of no association with a general alternative, whereas linear regression achieves a reduction in degrees of freedom from 2 to 1 by assuming a linear relationship between the mean value of the trait and genotype (238).

Most common types of association studies include candidate polymorphisms, candidate gene, fine mapping and genome-wide studies (238). Candidate polymorphism study focuses on one individual SNP that is suspected to be related to a disease. Linkage and candidate gene studies were mainly used to study the genetic basis of diseases before the mapping of SNPs across the human genome and the realisation that a subset of these SNPs can capture ("tag") common genetic variation via linkage disequilibrium, which led to the development GWAS (233).

In GWAS, millions of commonly varying polymorphisms are genotyped to detect variants specific for the particular phenotype (such as a molecule or a disease). The variation of the entire genome is investigated to identify genetic variants that are related to the phenotype of interest. It is thus considered as a non-candidate gene driven approach (233). GWAS became a powerful tool for the investigation of common complex diseases in a population (Figure 20).

In the years 2005-2018 there were 3639 GWAS reported, which investigated altogether 3508 traits (240). The classical approach for identification of the genetic alteration of hereditary disease is the sequence of causative genes. The functional characterisation of the protein carrying the variant is the only direct criterion to demonstrate the pathogenicity of a variant (232).

Nevertheless, bioinformatics analysis with the help of numerous databases (p.ex. Ensembl, dbSNP) can help to retrieve information about a variant and provide a list of previous studies where the same variant was reported, if existing. Moreover, it can assist in the identification of the mutation type of the variant and in the understanding of its causative effect (232).

Variants commonly identified variations include: MR is based on three key assumptions:


1. The genetic variant is reliably associated with the biomarker (exposure).

2. The genetic variant is not associated with any confounding factor (U).

3. The genetic variant only influences the risk of disease through the biomarker of the interest.

Epigenetics

Epigenetics is the study of heritable changes, which can modify the outcome of a locus or As mentioned previously, epigenetic changes, especially DNAm, can be interesting also for therapeutic applications. DNAm inhibitors have become the mainstay for the treatment of certain haematological malignancies and show great potential for other diseases that have been caused by impaired epigenetic systems (18,19). Currently, there are still no DNAmbased diagnostic biomarkers or treatments for CVDs, and further researches are warranted to EWAS studies present few challenges in the interpretation of the results, issued from the facts that DNAm patterns can be tissue-specific and are not static, but can change over time.

Furthermore, EWAS associations can be causal as well as consequential for the phenotype of interest. Determination of causality in EWAS is critical as it helps elucidate the functional role of the phenotype-associated methylation site and potential utility in terms of diagnostics or therapeutics (264).

Gene expression studiestranscriptomics

Gene expression is a conversion of genetic information from DNA into a functional gene product (RNA or protein). It occurs in a strongly regulated two-step process: 1) transcription of DNA to RNA, catalysed by RNA polymerase enzymes, followed by processing of resulted mRNA molecule (Figure 24), and 2) translation of RNA, followed by subsequent posttranslational processing of the protein molecule to provide a functional protein (266).

Gene regulation is a cellular process that controls the rate and manner of gene expression. It comprises a complex set of interactions between genes, RNA molecules, proteins (including transcription factors) and other components of the expression system. More particularly, it determines the rate of translation, regulates the processing of RNA molecules, including alternative splicing to produce more than one product from a single gene, regulates the stability of mRNA molecules and the rate of translation. Constitutive gene expression is present in all cell types to enable the basic cellular function provided by the housekeeping genes. Other genes are expressed only in a certain cell type, which permits a cell to specialise and perform a particular function (267). Most common techniques used to measure and detect transcription activity are the following:

 RT-PCR: a sensitive in vitro method used for detecting and comparing the levels of mRNA and the surface proteins. In RT-PCR, the RNA molecule is first converted into a complementary DNA (cDNA) using reverse transcriptase. The cDNA is then used as a template for exponential amplification using PCR. It can be performed as real-time PCR for relative or absolute quantification, or as end-point PCR (270).

 Northern blot: a widely used procedure for analysing the molecular size and abundance of mRNA among a mixture of RNA. It can allow the analysis of a sample of RNA from a particular tissue or cell type to measure the RNA expression of particular genes. The isolated RNA is denaturated and loaded on an agarose gel and separated by electrophoresis (271).

 RNA-Seq: a combination of next-generation sequencing techniques with computational methods to sequence whole transcriptomes. It allows the measurement of relative abundance of RNA, as well as the detection of additional variations such as fusion genes, post-transcriptional edits and novel splice sites (272).

 DNA microarrays: a technique used for gene expression profiling, which consists of short nucleotide oligomers, known as "probes," that are arrayed on a solid substrate.

Transcript abundance is determined by hybridisation of fluorescently labelled transcripts to these probes (272). The technique is based on the incorporation of a stem-loop structure into a gene, which then incorporates the structure also into newly synthesised RNA. The stem-loops can be detected using a fusion of GFP and the MS2 coat protein (275).

Challenges of personalised medicine

To summarise, personalised medicine is using complex approaches to form a medical model that combines characterisation of individuals' phenotypes and genotypes to provide a healthcare system which will better respond to patient needs.

To do so, it has to act in different time steps to provide appropriate biomarkers: to assess risk or clinical progression of a disease, to predict diagnose and support the therapeutic decision (Figure 25). Moreover, it assists in development of medicaments tailored by individual profile to provide appropriate medical intervention. For the successful integration of new scientific results into public health, personalised medicine has to overcome five main challenges to which it is continuously confronted (277):

 Developing awareness and empowerment  Integrating big data and ICT solutions.

 Translating basic to clinical research and beyond.

 Bringing innovation to the market.

 Shaping sustainable healthcare.

Chapter II. HYPOTHESIS AND OBJECTIVES __________________________________________________________________________________ Common chronic diseases are complex traits, affected by the effects of multiple genes in combination with lifestyle and environmental risk factors. Thanks to the development of new technologies, personalised medicine enabled to improve their preventive, predictive and curative strategies. New scientific discoveries are now needed for further identification and characterisation of biomarkers, elucidation of molecular regulatory mechanisms and proposition of therapeutic targets that will serve in the application of personalised healthcare.

Genetic and molecular biomarkers involved in multiple pathways play a key role in the development of chronic diseases. The identification of common pathways and interactions could thus ameliorate our understanding of diseases" pathophysiology and would assist in discovering novel biomarkers, "common denominators" for ageing diseases and comorbidities.

GWAS studies identified a large number of variants related to specific phenotypes. We have hypothesised that a genetic variant associated with a biomarker of interest, which would show association with another biomarker involved in the same pathology may help in the identification of shared biological pathways between molecules. Moreover, such genetic variants could be considered as novel genetic biomarkers, useful in the risk assessment of comorbidities.

Studies of the epigenetics are emerging in the last few years, and are bringing a big potential for predictive and preventive personalised therapies. EWAS allow detection of methylated CpG sites that play an important role in the regulation of intermediate phenotypes. Because methylation can represent an early modifiable change of DNA, caused by unfavourable environmental conditions, CpG sites associated with a particular risk factor can be used as susceptibility biomarkers for all related pathologies. On the other hand, if the CpG site is involved in the regulation of gene involved in the aetiology of a disease, such methylation mark represents a potential therapeutic target.

In the investigation of molecular biomarkers, we primarily focused on various molecules implicated in common pathophysiological processes, such as inflammation and angiogenesis.

Besides common inflammatory cytokines, we were particularly interested in TREM2, VEGF-A and TL. VEGF-A and TL are the perfect examples of biomarkers, involved in multiple pathophysiological mechanisms. We thus assumed that, combined, they could present a powerful tool for personalised medicine applications.

Furthermore, a collaboration between research and industry presents a key element for the progress in the implementation of personalised medicine, the fastest way to transfer the knowledge from a research laboratory to a clinical application. For this reason, we believe that active participation in industrial projects aiming to improve diagnostics and treatment of common chronic diseases, and serving to the field of personalised medicine is indispensable.

To summarise, the goal of this thesis was to identify biomarkers, common for different chronic diseases through investigation of association between genetic variants and intermediate phenotypes and application of "-omics" methodologies, to finally propose a combination of biomarkers, which would improve the disease risk prediction and treatment in a useful way for personalised medicine.

Three main objectives were pursued in this thesis work:

 To highlight new variants associated with the risk factors for common chronic diseases.

 To find molecular and genetic associations between biomarkers involved in shared biological pathways.

 To summarise the existing knowledge from our research field and offer it to the next generation of researchers.

The specific objectives set to develop a multiple-biomarker approach in personalised medicine are:

1. To summarise the greatest historical achievements in personalised medicine.

Hypertensive population

Hypertensive population available at the BRC IGE-PCV consists of hypertensive individuals (n=945) without any present diagnostics of CVD or other chronic diseases, and not treated with any antihypertensive therapy. This population has been enrolled using similar standardised protocols and methods for the collection of samples, data and biological measurements as those applied in the SFS cohort.

The MuTHER study

The "MuTHER" ("Multiple tissue human expression resource") study is a project under the Wellcome Trust Foundation, aiming to develop a resource of detailed genetic (genome-wide association, resequence) and genomic (expression, methylation) data from a range of tissues collected from UK twins, aiming to support efforts to understand mechanisms involved in common traits susceptibility (280). Lymphocytes, subcutaneous fat, muscle and skin biopsies have been obtained from 856 twins of European ancestry, from the well-characterised TwinsUK BioResource. All samples and information were collected with written and signed informed consent. The project has been approved by the local ethics committees of all institutions involved (280).

DNA methylation assessment and genotyping

Blood samples of all individuals from the SFS were taken after overnight fasting and were collected in EDTA vacuum tubes. Whole blood DNA was extracted by the Miller technique ( 281) and was stored on -80 °C in BRC IGE-PCV.

Punch biopsies of subcutaneous adipose tissue from a photo-protected area of the stomach adjacent and inferior to the umbilicus were obtained from 856 healthy female twins who participated in the MuTHER cohort. Skin from the punch biopsy was then dissected to separate it from adipose tissue, and both samples were weighed and immediately frozen with liquid nitrogen (282).

DNA methylation assessment

Blood DNA methylation assessment was performed by Infinium HumanMethylation450

BeadChip (Illumina, San Diego, CA, USA). Illumina is using Infinium I and II arrays with probes for detection of methylated and unmethylated CpG sites. Methylation ratio referred to as beta value by Ilumina"s software, is the proportion methylated / (methylated + unmethylated) for each CpG in the population of cells from which DNA was extracted (265,283). A detection p-value was generated for every CpG with minfi R package.

Methylation levels were also estimated in subcutaneous adipose tissue-derived from the female twins enrolled in the MuTHER study. Methylation profiling was performed with

Illumina"s Infinium HumanMethylation450 BeadChip. Arrays were scanned with the IlluminaHiScan SQ scanner, and raw data were imported to the GenomeStudio v.2010.3 software with the methylation module 1.8.2 for the extraction of the image intensities.

Genotyping

Whole-genome genotyping of the SFS was performed using Illumina ® human CNV370-Duo array following the manufacturer"s protocols. Genotypes were called using Illumina ® protocol for BeadStation genotyping solutions, based on the GenCall ® software application to automatically cluster, call genotypes, and assign confidence scores using the GenCall ® clustering algorithm GenTrain.

Genotyping of SNPs in the SFS, hypertensive population and TELARTA study was performed by the Laboratory of the Government Chemist (LGC Ltd., Teddington, UK), using a PCR-based KASP assay, which enables bi-allelic scoring of SNPs and insertions and deletions (Indels) at specific loci (284).

Gene expression analysis

PBMC isolation

Peripheral blood mononuclear cells (PBMCs) of individuals of the SFS were isolated using the density gradient centrifugation method, first developed by Bøyum in 1968 (285). Briefly, whole blood was collected in tubes containing sodium heparin. Hanks" Balanced Salt Solution (SIGMA Aldrich, reference: H6648) was added into 15 mL tubes with blood and poured gently into a 15 mL tube with Ficoll paque TM plus solution (Sigma Aldrich, reference: 17-1440-02). Ficoll paque medium is denser than lymphocytes, monocytes, and platelets (meaning these will remain above it), but less dense than granulocytes and erythrocytes, which will drop below it. The contents of the tube were centrifuged for 30 min at 300 g at room temperature. A PBMCs ring was retrieved and collected into a 15 mL tube, filled with

Hanks" Balanced Salt Solution and centrifuged for 10 min at 1000 g at room temperature. The supernatant was aspirated and 2 mL of Hanks" Balanced Salt Solution was added. The solution was well suspended, filled up to 15 mL with Hanks" Balanced Salt Solution and centrifuged for 10 min at 1000 g at room temperature. The PBMC ring was collected into Eppendorf tube with 1 mL of Hanks" Balanced Salt Solution. PBMCs populations were evaluated by microscopic observation after May-Grunwald-Giemsa staining, and the PBMCs concentration was normalised to 10 6 cells/mL in Hank"s Buffer. After final centrifugation for 5 min at 1000 g at room temperature, the supernatant was aspirated, and the pellet of PBMCs was processed immediately or stored at -80 °C.

mRNA quantification

Total mRNA was extracted and quantified from isolated PBMCs using the MagNA Pure LC

Protein measurement

Total protein extraction from PBMCs

The lysis solution composed of cell lysis buffer (CelLyticTM-M, SIGMA Aldrich, reference: C2978) and protease inhibitor (0.5 %, Protease Inhibitor Cocktail, SIGMA Aldrich, reference:

P8215) was added to the PBMC pellet, as recommended by the manufacturer (SIGMA Aldrich). It was stirred for 15 min at room temperature and centrifuged for 15 min at 12000 g at 4 °C. The supernatant was collected and was immediately used for further analysis or stored at -80 °C.

Protein quantification

Quantification of inflammatory molecules (IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, MCP-

Lipid measurement

For the SFS cohort, total serum cholesterol (TC) and serum TGs were measured using standard enzymatic methods (Merck, Darmstadt, Germany), automated on AU5021 (Olympus; Merck). Serum HDL was measured by phosphotungstate precipitation on a Cobas-Mira (Roche, Basel, Switzerland), while LDL levels were calculated using the Friedewald formula (288).

Telomere length measurement

TL in the samples of TELARTA study was measured in DNA extracted by the phenol/chloroform method from peripheral blood leukocytes and skeletal muscle in the surgical field. Measurements were performed in duplicate by Southern blots of the terminal restriction fragments, as previously described [START_REF] Benetos | Short leukocyte telomere length precedes clinical expression of atherosclerosis: the blood-and-muscle model[END_REF]. The analysis was performed in the laboratory UMR INSERM U1116 DCAC.

Statistical analysis

Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Therefore, values of studied phenotypes, such as lipid levels, waist circumference and inflammatory molecule levels were tested for normality. Log-transformation of the dependent variable was applied to satisfy the normality assumption if values were not normally distributed.

Methylation analysis

A linear mixed-effects model was used to analyse the association between methylation levels and phenotypes (lipid levels, waist circumference and VEGF-A levels) in the SFS population, accounting for relatedness between individuals. The model used was adjusted for gender, age, BMI, family structure, and other relevant covariates. Bonferroni correction and false discovery rate (FDR) methods (<0.05) were used for the correction of the results for multiple testing.

Correlation and association analyses

Linear regression models were applied to study associations between genes and intermediate phenotypes, using different inheritance models (additive, dominant and recessive model).

Mixed models adjusted for the family structure were applied to the SFS population, to correct for possible issues of stratification due to familial resemblance. The models were adjusted for age and sex. Analyses were performed using the R packages GWAF and PLINK. Nonparametric correlation analyses (Spearman correlation) were performed to study links between VEGF-A and different inflammatory molecules.

Characterisation: expression of recombinant antibody fragments

Recombinant antibody fragments acquired using phage display technology were characterised for their specificity and stability, using a standardised protocol of Randox ltd., in Crumlin, UK. Briefly, extracted plasmid DNA (miniprep) of antibody fragment was mixed with WK6 cells (E. coli) and heat-shocked at 42 °C to allow the transfer of DNA into a bacterial cell.

Transformed bacteria were spread over the agar plate containing ampicillin and streptomycin and incubated overnight. The contents of the grown agar plate were re-suspended into the 10 mL culture of Terrific broth (TB) medium heated on 37 °C. When the optimal growth was achieved, the culture was raised into 100 mL and 1 L, respectively, and induced with isopropyl β-D-1-thiogalactopyranoside (IPTG) for the overnight incubation. Cells were collected the next day and lysed to extract the synthesised protein. A sample of cell lysate was serially diluted and added on enzyme-linked immunosorbent assay (ELISA) plates coated with the antigen of interest and control antigens, to test the specificity of the isolated antibody.

The rest of the collected protein was purified using Talon resin on gravity columns. The final concentration of the protein in the sample was measured using UV spectrophotometer.

Samples of purified protein were stored on three different temperatures: 37 °C, 4 °C and -20 °C. Proteins that showed good specificity for the antigen were tested for stability after a week of storage. Samples from three different temperatures were prepared in 8 concentrations and were added on ELISA plates coated with the antigen of interest. Dilution curves of three temperatures were compared to assess the stability of the antibody.

Chapter IV. PUBLICATIONS -RESULTS

__________________________________________________________________________________ Publication Nº 1

Personalised Medicine: The Odyssey from Hope to Practice. The review article "Personalised Medicine: The Odyssey from Hope to Practice' was designed as an introduction to the most significant achievements in the field of personalised medicine, which lead the way to today"s innovative and perspective healthcare. The review is an original mixture of the ancient Homer"s poem "The Odyssey' together with the scientific facts, which are enrolling throughout the travelling of the poem"s main hero Odysseus.

The article starts with the first and the most important achievement that enabled the expansion of genetic studiessequencing of the whole genome within the HGP. Further on, it presents the technological and methodological development, discusses the practical application of the acquired knowledge in clinics and pharmacological studies, and summarises the ambitious international projects, which made a step forward in the understanding and use of genomics in everyday clinical and research practice. The article is addressed to the wide audience interested in learning about breakthrough discoveries, as well as to the health experts who seek an entertaining and educative reading of a general medical topic.

The review article was introduced during a biannual international conference on personalised medicine: "Systems Medicine Personalised Health and Therapy -The Odyssey from Hope to

Practice", which took place in Santorini, Greece, on 30 th September -3 rd October 2018 and was organised by the Unit UMR INSERM U1122; IGE-PCV.

The review article Personalised Medicine: The Odyssey from Hope to Practice is a suitable introduction to the results of the thesis, as it shows the importance of investing to the research work in the emerging field of personalised medicine.

Chapter IV. PUBLICATIONS -RESULTS

Epigenetics mechanisms are implicated in the regulation of genes involved in metabolism and regulation of blood lipid levels (290). Studying the epigenetic regulation of blood lipids in an individual could enable the insight into the susceptibility for common chronic diseases even before the alert values of a particular phenotype (e.g. increased TG levels) are expressed (Figure 26).

Objective

The aim of the study was to perform an EWAS on blood lipid levels in a healthy population, in order to unravel the mechanisms of epigenetic regulation of blood lipids with the detection of CpG methylation sites associated with the levels of TG, TC, LDL and HDL.

Populations and analysis

Discovery cohort consisted of 211 healthy adults and children (75 families) from the first recruitment of the SFS. Blood DNA methylation assessment was performed by Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA). EWAS was carried out using the R packages minfi and CpGassoc in a linear mixed-effect model, accounting for age, gender, BMI, smoking and individual blood cell counts as fixed effect, and methylation chip and family structure as random effect.

Replication of the significant results from the discovery cohort was performed in the adipose tissue of 662 participants from the MuTHER study. Methylation profiling was performed with Illumina"s Infinium HumanMethylation450 BeadChip. Raw data was imported to the GenomeStudio v.2010.3 software whereas the association analysis of the two significant probes (detected in the discovery cohort) with TG levels was performed by the CpGassoc package in R, in a linear mixed-effect model, accounted for age, gender, BMI and smoking as fixed effect, and methylation chip as random effect.

Genotyping was performed by the Infinium CoreExome Illumina assay. SNPs related to the phenotype of interest and located in the same chromosome as the significant methylation sites were selected from the NHGRI-EBI GWAS catalogue.

Results

From the four blood lipid phenotypes assessed in this study, significant results were observed only with TG levels (FDR < 0.05). More specifically, two probes demonstrated statistically significant associations (Table 2), namely cg08897188 located in the PRKAG2 gene on chromosome 7 (7q36.1) and cg04580029 located in the KREMEN2 gene on chromosome 16 (16p13.3). In the replication cohort, the two significant probes cg08897188 and cg04580029 were determined in DNA acquired from the adipose tissue and were tested for the association with TG levels. An association at a nominal significance level was identified for cg04580029 (Table 3). Bioinformatics analysis showed that the replicated probe cg04580029 is located in the promoter region of the KREMEN2 gene, three base pairs away of the transcription start site of the KREMEN2 gene, overlapping the DNase hypersensitivity peak and histone peak. Probe cg08897188, on the other hand, is located between the PRKAG2 gene and AC093583.1 gene, in the proximity of transcription factor binding sites and histone peaks.

SNPs previously associated with TG levels in GWAS studies and located in the same chromosomes as the identified methylation sites were tested for their associations with the two methylation sites because DNA sequence variance can impact on methylation levels.

Altogether, 16 SNPs located on chromosomes 7 and 16 were identified. None of the polymorphisms showed the association with the identified CpG sites.

Conclusion

The

Under submission

Obesity is a medical condition characterised by an excess amount of body fat, associated with a higher risk for a number of chronic diseases, including diabetes, CVD and cancer (291). BMI is the most widely used method for the definition of obesity, whereas indices of abdominal obesity are believed to be better discriminators of obesity-related health risks (292,293). In most cases, obesity is a result of increased intake of hypercaloric food in combination with limited sports activity. Besides these environmental risk factors, an important genetic variability in susceptibility for obesity has been demonstrated in genetic studies, which identified a large number of common variants related to adiposity levels (294). (34).

In addition, an important role in the development and monitoring of obesity has been assigned to epigenetic mechanisms that are modulating the genetic expression and can thus affect a particular phenotype (Figure 27). Epigenetic markers that increase susceptibility for obesity can be inherited, as well as they can be modifiable and appear during the life course as a result of obesogenic environmental conditions (295).

Objective

The goal of this study was to perform an EWAS on waist circumference (WC) in a healthy population, to get an insight into epigenetic regulation of central obesity with detection of CpG methylation sites associated with WC. Genotyping was performed by the Infinium CoreExome Illumina assay. SNPs located in the same chromosome as the significant methylation site were selected from the NHGRI-EBI GWAS catalogue.

Populations and analysis

Healthy related individuals of

Results

The results of the EWAS (Table 4) showed a novel significant positive association of the probe cg16170243 with WC, adjusted for BMI (β=2.32, SE=0.41; P adj =0.048). SNPs previously related to WC levels were tested for their associations with methylation.

Altogether, three SNPs located on chromosome 18 were identified. No association was identified between these three polymorphisms and methylation at cg16170243, indicating that the association between CpG and WC does not originate from the relation between SNPs and methylated CpG site.

Conclusion

The study presented in the publication "Epigenome-Wide Association Study Detects a Novel

Loci Associated with Central Obesity in Healthy Subjects' identified a novel CpG site associated with increased WC and gave new insights into epigenetic regulation of obesity.

These findings improved the knowledge of epigenetic regulation of obesity and might be useful in a future determination of obesity risk factors.

A limitation of our study is combined population, small sample size and the lack of tissuespecific replication. Further analysis is warranted to overcome these issues.

Publication Nº 4

Epigenome-wide association study in healthy individuals identifies significant associations with DNA methylation and PBMCs extracts VEGF-A concentration. VEGF-A (Figure 29) is a mitogen that acts on endothelial cells and has various effects, including mediating increased vascular permeability, angiogenesis and endothelial cell growth, promoting cell migration, and inhibiting apoptosis (296). VEGF-A is involved in the pathogenesis of CVD ( 185), as well as other chronic diseases, such as cancer, type 2 diabetes, osteoporosis, osteoarthritis and COPD. It has been previously shown that blood levels of VEGF-A are highly heritable, with 60.5 % of variance attributable to genetic factors in the SFS [START_REF] Berrahmoune | Heritability for plasma VEGF concentration in the Stanislas family study[END_REF]. Ten polymorphisms were identified that explained more than 50% of the heritability of VEGF-A [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF][START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]

Objective

The goal of this study was to perform an EWAS on VEGF-A levels in a healthy population, in order to get an insight into epigenetic regulation of VEGF-A levels from the PBMCs extracts with detection of CpG methylation sites associated with VEGF-A levels. VEGF-A was measured using the Randox high sensitivity multiplex cytokine and growth factor array (Evidence Investigator Analyzer, Randox Laboratories Ltd., Crumlin, United Kingdom). Measurements were performed in the protein extracts from PBMCs, isolated from the whole blood.

Populations and analysis

VEGF

Bioinformatics analysis of results was performed using the Ensembl browser. Common biological pathways of annotated genes with VEGF-A were analysed using GeneMANIA app.

Results

EWAS identified 20 CpG sites significant after Holm-Bonferroni adjustment (<1.6x10 -7 ).

Results are presented with the Manhattan plot (Figure 28), displaying adjusted P-values of the association between methylation probes and VEGF-A levels in PBMCs extracts. The annotation program did not identify the nearest gene for all significant CpGs. Therefore, we have conducted an in silico analysis to define the genetic environment in the vicinity of those CpG sites and detect the nearest regulatory features on which these methylation sites could act (nearest gene defined after bioinformatics analysis for each CpG site is listed in the legend of Table 5).

For all 20 CpG sites, we have performed detailed bioinformatics and bibliographical analysis to reveal possible genetic pathways common between VEGF-A and concerned genes. We have identified CpG sites, which have been previously implicated in VEGF-related biological processes, such as cell proliferation, cell growth, angiogenesis and related diseases. Such relations were found for TPX2 gene, significantly associated with cg21838233 (P= 4.01 x 10 - 12 ) and HAS1 gene found in the vicinity of cg06785213 (P= 1.06 x 10 -08 ), as well as with ARL4A, ETV1, CTNNB1, TBL1XR1 and TSHZ1.

Conclusion

The study presented in the publication "Epigenome-wide association study in healthy individuals identifies significant associations with DNA methylation and PBMCs extracts VEGF-A concentration' detected 20 CpG sites associated with VEGF-A levels and gave new insights into epigenetic regulation of this important biomarker. This knowledge could be further useful in the understanding of genes that contribute to the regulation of VEGF-A levels via methylation-caused gene silencing.

A new study with a higher sample size is warranted to detect CpG sites related to the physiological regulation of VEGF-A. On the other hand, patient-populations are necessary to detect CpG sites involved in pathological regulation of VEGF-A level.

normalise the distribution of data. Data was tested for outliers, which were removed before correlation and linear regression analysis.

Associations of specific VEGF-A mRNA isoforms and inflammatory molecules were assessed using identical statistical procedures.

Mixed models adjusted for the family structure were also applied to correct for possible issues of stratification due to familial resemblance.

Results

A correlation study has been performed between VEGF-A protein levels and inflammatory molecules. A significant trend was observed between VEGF-A and EGF, IL-1β, IL-8, MCP1

and TNF-α. Moreover, VEGF-A protein levels were associated with IL-4, MCP-1 and EGF levels in linear regression models adjusted for age and gender (Table 6). The linear regression analyses for VEGF-A protein and mRNA levels were repeated using mixed models adjusted for family structure. The mixed model gave the same significant results as the linear regression model.

Altogether, five inflammatory molecules showed association with VEGF-A (protein or mRNA) levels (Figure 31). Detailed bibliographical research has been done to explain the relations of identified cytokines with VEGF-A.

Conclusion

The manuscript entitled "Peripheral blood mononuclear cells extracts VEGF protein levels and VEGF mRNA: Associations with inflammatory molecules in a healthy population' gave new insights in the association of inflammatory cytokines with VEGF-A, which could contribute to the understanding of the angiogenesis process and could ultimately offer novel targeted therapies for VEGF-A-related pathologies.

Publication Nº 6

The polymorphism rs6918289 located in the downstream region of the TREM2 gene is associated with TNF-alpha levels and IMT-F. TREM2 is a cell surface receptor of the immunoglobulin superfamily, primarily expressed on myeloid cells and macrophages, which exposes important anti-inflammatory characteristics via inhibition of the cytokine production by macrophages [START_REF] Turnbull | Cutting edge: TREM-2 attenuates macrophage activation[END_REF]. It is particularly known for its protective role in neurodegenerative diseases, where it prevents neuronal damages, caused by an inflammatory response via repression of microglia-mediated cytokine production [START_REF] Jiang | TREM2 in Alzheimer's disease[END_REF]. Its association with increased risk for AD was demonstrated in large GWAS, which identified heterozygous rare genetic variants of TREM2 significantly associated with AD (302, 303).

Figure 32:

Cytokine and macrophage infiltration in atherosclerotic lesions: TREM2 high macrophages were detected in advanced atherosclerosis and TREM2 expression was found in human atherosclerotic lesions ( 69).

(T) of rs6918289 with the phenotypes of interest. These models are grouping the alleles of the gene of interest in three groups: TT, GG (homozygote genotypes) and TG (heterozygote genotype). As T is the "risk" allele, the genetic models can be presented as follows (306):

• Dominant: "TT + TG" versus "GG",

• Recessive: "TT" versus "TG + GG",

• Additive: "TT" versus "TG" versus "GG"

Statistical analysis was performed in the combined SFS population, comprising 809 individuals (Table 10). Additive and recessive models showed a significant association between rs6918289 and TNF-α (p=0.0026 and p=0.0017, respectively).

Association analysis was repeated in the independent French population (n=916), where rs6918289 showed a positive association with TNF-α in the recessive model of the analysis (p=0.023) (Table 9). The meta-analysis of both cohorts showed a marginal association of rs6918289 with TNF-α using the additive model (p=0.072) and significant association using the recessive model (p=0.0003).

The minor allele (T) of rs6918289 was significantly associated with increased IMT-F in a population of adults from the SFS. Association was significant using the additive (p=0.026) and dominant (p=0.026) genetic model (Table 10).

Conclusion

The study presented in the publication "The polymorphism rs6918289 located in the downstream region of the TREM2 gene' is associated with TNF-α and IMT-F' highlighted a new potential risk allele for inflammation and atherosclerosis, which affects the blood levels of TNF-α and is associated with increased IMT-F. These findings support the results of the previous studies linking variants within chromosomal 6p21.1 loci to atherosclerosis and are raising the awareness to consider genomic region 6p21.1 as a candidate susceptibility loci for atherosclerosis.

The five investigated genetic variants were chosen based on their proximity to the TREM2 gene and were not based on the previous association identified through GWAS studies. The reason for such a decision is that not all SNPs were available in our genotyped population. We have chosen the genetic variants based on the predisposition that they are involved in the regulation of the TREM2 gene. A new study, where genome-wide identified TREM2 variants would be used for the association studies could be useful for the further exploration of the relations between TREM2, TNF-α and IMT-F. Nevertheless, a study with bigger sample size and replication of IMT-F results are needed to confirm our results and consider their further clinical utility.

Publication Nº 7

The future of telomere length in personalised medicine.

Vesna Gorenjak, Samina Akbar, Maria G. Stathopoulou and Sophie Visvikis-Siest Front Biosci (Landmark Ed), March 2018, 1;23:1628-1654 TL is recently being advertised by private companies as a biomarker of ageing and age-related conditions, yet, biochemical laboratories still haven"t applied TL in routine clinical assessment (307). The abundance of information is making it difficult to understand the current role of TL in diagnostics and predict its future role in the field of personalised medicine.

In the review article entitled "The future of telomere length in personalized medicine' we gathered the most pertinent information to present the advances, the challenges and the perspectives of TL as a biomarker of ageing and a biomarker of diverse monogenic and chronic diseases. We discussed the role of TL in the setup of these diseases, as well as in treatment. Because we are aware of the complexity of pathological pathways and the importance of understanding and including multiple biomarkers approach in the next generation of TL research, we also discussed the association of TL with other biomarkers of inflammation, angiogenesis and oxidative stress. Finally, the heritability of TL was described and a large review of all GWAS studies of TL was included, with a systematic presentation of most significant genetic variants related to TL.

The review pointed out the perspective of novel techniques for TL measurement using wholegenome sequencing (WGS). WGS could permit a computational assessment of TL and allow large association studies using the existing whole-genome data (308). This would generate new information on genetic association of TL with a wide variety of diseases and phenotypes.

Because of the significant association of TL with many different pathologies (309) it is not likely that TL could ever be a unique biomarker for a specific age-related chronic disease.

Alone or in combination with other molecules, TL could rather present a biomarker of the general health of an individual, where an accelerated rate of the telomere base-pair loss would exhibit an alarming signal, indicating an increased inflammation or cell senescence. Chapter IV. PUBLICATIONS -RESULTS

Publication Nº 8

Telomere length determinants in childhood. The review article entitled "Telomere length determinants in childhood' was a product of the preparation for a project in collaboration with the university hospital in Nancy, aiming to perform TL research in newborns. In order to understand the state-of-the-art of the subject, a detailed analysis of the literature, investigating the telomere attrition in early childhood and the determinants of newborn TL, was conducted.

The childhood seems particularly important period for the onset of the baseline TL [START_REF] Entringer | The fetal programming of telomere biology hypothesis: an update[END_REF], which also impacts on adulthood TL and eventually influences the pathological processes leading to common chronic diseases. Various environmental influences on newborn TL, especially during pregnancy, showed a significant impact and demonstrated the importance of a healthy pregnancy on the overall health of an individual (310-313).

Telomere dynamics has been described in several studies and despite the substantial limitations (low sample size, absence of longitudinal studies), we were able to draw general conclusions for telomere regulation (314-316). The main knowledge gap in the field is the understanding of genetic variants associated with childhood TL, as only three medium-sized genetic studies were conducted up to date. All of them agreed that genetic variants identified in adult studies do not reflect the TL in the children population. However, the studies did not replicate variants that could be significant for childhood TL (317,318). Our review article pointed out the importance of new GWAS, performed in newborn and childhood populations, to obtain further information on genetic regulation of TL during childhood.

Unfortunately, the design of such a study would require a long preparation to acquire the appropriate population of sufficient sample size and all necessary ethical permissions. It would thus be impossible to finalise it during the time of this thesis. However, the article "Telomere length determinants in childhood' made a step further in the research of TL and offered a general insight into childhood TL, which will be useful for anyone planning to study this field further.

Finally, we concluded that TL is a prospective biomarker that could be used in the future as an indicator of general health, especially in longitudinal patient follow-up during their lifetime, which was in accordance with our previous publication. Regular measurement of TL starting in the childhood could offer an insight into the rate of individual cellular turnover and could alert the physicians about the physiological changes in case of the accelerated telomere attrition.

Introduction

A telomere is a repeated sequence of TTAGGG nucleotides on the ends of eukaryotic chromosomes, serving in the maintenance of the chromosomal integrity during cell replication (319). As it is gradually shortening with each mitotic division, it has been considered as an indicator of accelerated cellular ageing in the pathogenesis of atherosclerosis [START_REF] Samani | Telomere shortening in atherosclerosis[END_REF]. This process is characterised by increased inflammation, mediated by a production of cytokines mainly from blood-borne inflammatory and immune cells, but also from vascular endothelial and smooth muscle cells (320).

VEGF-A is a key regulator of physiological and pathological angiogenesis and a mediator of vascular permeability in inflammatory disorders (185). It plays a critical role in cancer, where several VEGF-A signalling inhibitors have already been approved by the Food and Drug Administration (FDA) for oncological treatment (321). VEGF-A also seems to be important in CVDs (322), including atherosclerosis, where it promotes neoangiogenesis in the early stages of the disease as well as in the advanced atherosclerotic plaques [START_REF] Camaré | Angiogenesis in the atherosclerotic plaque[END_REF].

VEGF-A and TL are promising biomarkers for detection and monitoring of chronic pathologies, as they are both a) involved in disease pathways, such as inflammation and angiogenesis [START_REF] Zittermann | Endothelial growth factors VEGF and bFGF differentially enhance monocyte and neutrophil recruitment to inflammation[END_REF]323), b) highly heritable [START_REF] Berrahmoune | Heritability for plasma VEGF concentration in the Stanislas family study[END_REF]324) andc) have important inter-individual variability (325,326). In combination, these two biomarkers could become a new tool for calculation of risk factors in personalised diagnostics of chronic diseases. The association of plasma concentrations of VEGF-A with leukocyte telomere length (LTL) has been previously studied (327). To expand the knowledge of the common pathways of both biomarkers, the current study aimed to investigate the genetic associations between VEGF-A and TL.

Accordingly, ten VEGF-A genome-wide significant variants that together explain up to 52% of the VEGF-A phenotypic variance [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF] were investigated for association with LTL and muscle TL (MTL), as well as telomere attrition (the ratio between LTL and MTL, calculated as LTL/MTL) in a case-control study of atherosclerotic patients. The study is using LTL as an approximation of highly proliferative tissue (hematopoietic system), MTL as an approximation of a minimally proliferative tissue (skeletal muscle) and their ratio LTL/MTL as an approximation of telomere attrition throughout life.

Methods

Population

The population of TELARTA study was genotyped for ten VEGF-A related genetic variants (402 individuals with available measurements of LTL and MTL as previously described ( 81)). The VEGF-A plasma levels were measured in 324 French individuals from the TELARTA study.

Telomere length measurement

TL was measured in skeletal muscle and blood leukocytes. Muscle biopsies were obtained by surgeons and cardiologists from six different sites in different patients undergoing surgery or pacemaker/defibrillator implantation, as previously described in [START_REF] Benetos | Short leukocyte telomere length precedes clinical expression of atherosclerosis: the blood-and-muscle model[END_REF]328). Skeletal muscle biopsy (100 to 200 mg) was collected from each individual and stored on -80 °C. Blood samples from each patient were collected in EDTA tubes prior to surgery, and stored on -80 °C until further use.

TL measurement was performed by the Southern blot analysis of terminal restriction fragments, as previously described (329). Briefly, DNA was extracted from the muscle tissue and leukocyte cells by the phenol/chloroform method, and treated overnight with restriction enzymes HinfI and RsaI (Roche Diagnostics GmbH, Germany). Digested DNA samples and DNA ladder were resolved on 0.5% (wt/vol) agarose gels for 23 hours. After the depurination, denaturation and neutralisation of agarose gel, DNA was transferred on a positively charged nylon membrane (Roche) using a vacuum blotter (Biorad, Hercules, CA). Membranes were hybridised at 42 °C with the digoxigenin-labelled telomeric probe to enable the luminescent detection and readings using the charge-coupled device camera (Las 4000, Fuji).

Measurements were performed in duplicate on separate gels. The measurement repeatability, as determined by the intraclass correlation coefficient, was 0.99 (95% confidence interval, 0.817-1.0) and 0.98 (95% confidence interval, 0.81-1.0) for LTL and MTL, respectively. The repeatability of the means of two duplicates, known as the extrapolated repeatability, was 0.995 and 0.991 for LTL and MTL, respectively.

VEGF-A protein measurement

VEGF-A protein was measured in plasma samples, using Cytokine Array I on Randox semiautomated benchtop immunoanalyser (Evidence Investigator Analyzer, Randox Laboratories Ltd., Crumlin, United Kingdom). Cytokine Array I is a high sensitivity multiplex cytokine and growth factor array, which enables simultaneous detection of 12 cytokines and growth factors in a single sample.

Genotyping

Ten VEGF-A related genetic variants (rs10761741, rs10738760, rs6921438, rs7043199, rs6993770, rs4416670, rs114694170, rs34528081, rs4782371 and rs2639990), previously identified in GWAS, were genotyped in blood cell DNA samples using a PCR-based KASP assay (284). Genotyping was performed by the Laboratory of the Government Chemist (LGC Ltd., Teddington, UK). The SNP rs10761741 did not follow the HWE and was excluded from further analyses. HWE investigates the inheritance of genetic polymorphisms tested in the population-genetic studies.

If the measured genotype frequencies of SNP deviate from the expected one, the results should be treated cautiously because the observed genotype distribution in tested population does not represent genotype distribution in the overall population (330).

Nine SNPs altogether were tested for direct effects with LTL, MTL and LTL/MTL in the additive genetic model. LTL-values were log-transformed to follow the normal distribution.

None of the SNPs was significantly associated with LTL (Table 12) or MTL (Table 13). A significant association was identified between minor allele (T) of rs6993770 and LTL/MTL (P=0.001143, β=0.0148, SE=0.004516) (Table 14). Bioinformatics analysis showed that rs6993770 is located on the intron region (Figure 33) of the ZFPM2 (zinc finger protein, FOG family member 2) gene.

There was no significant epistatic interaction identified for three tested phenotypes (LTL, MTL and LTL/MTL) according to the selected threshold (p<0.00046).

Association between VEGF-A plasma concentrations and TL

VEGF-A plasma concentrations were tested for association with LTL, MTL and LTL/MTL using multiple regression analysis (Table 15). LTL and MTL values were adjusted for age and sex, whereas LTL/MTL values were adjusted only for age. There was no significant relation of VEGF-A plasma concentrations with investigated TL phenotypes.

Association between VEGF-A plasma concentrations and ASCVD

VEGF-A has been previously associated with atherosclerosis progression and lesion. To further explore its role in atherosclerosis, VEGF-A plasma concentrations of ASCVD patients (N =153) and controls (n=171) were compared. Results of the analysis showed that plasma concentrations of VEGF-A were significantly higher in the control group compared to the group of ASCVD patients (p<0.0001). The difference remained significant after adjustment for age and sex.

Because treatment with lipid-lowering medication can significantly impact on VEGF-A plasma concentration, statin-treated individuals were excluded from the second analysis. The difference remained significant in VEGF-A plasma concentrations of the ASCVD patients (n=51) compared to controls (n=147) after the exclusion of the statin-treated individuals (p=0.03).

Discussion

The present study investigated genetic associations between VEGF-A and telomere length measured in leukocytes, skeletal muscle cells and their ratio (LTL/MTL). It identified a direct association between minor allele (T) of rs6993770 and LTL/MTL (P=0.001143). This result suggests a common genetic regulation between VEGF-A and telomere attrition, possibly through a molecular process that affects both biomarkers.

Rs6993770 is one of the most significant variants associated with circulating VEGF-A levels.

The minor allele (T) of SNP has been previously related to decreased VEGF-A levels.

Together with three other SNPs (rs6921438, rs4416670 and rs10738760), rs6993770 explained 48% of the heritability of serum VEGF-A levels [START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. Besides VEGF-A, it has been related to variation in HDL cholesterol [START_REF] Stathopoulou | A common variant highly associated with plasma VEGFA levels also contributes to the variation of both LDL-C and HDL-C[END_REF], erythrocyte count, IL-12 levels, and platelets (85).

In the blood, VEGF-A can be found in plasma, platelets and leukocytes [START_REF] Kut | Where is VEGF in the body? A meta-analysis of VEGF distribution in cancer[END_REF]. Several studies reported the correlation between the concentration of VEGF-A and platelets, which are particularly important in wound healing and may have a stimulating role in angiogenesisdependent tumour growth through their function as transporters of VEGF-A [START_REF] George | Correlation of plasma and serum vascular endothelial growth factor levels with platelet count in colorectal cancer: clinical evidence of platelet scavenging?[END_REF][START_REF] Benoy | Serum interleukin 6, plasma VEGF, serum VEGF, and VEGF platelet load in breast cancer patients[END_REF].

In the present study, minor allele (T) of rs6993770 was associated with increased LTL/MTL ratio, indicating the protective role of allele (T) in telomere attrition. Rs6993770 is located in the intron of the ZFPM2 gene, coding for a FOG family member protein. The FOG (Friend of GATA) proteins can both activate and down-regulate expression of GATA-target genes, resulting in modulation of GATA family proteins activity. GATA proteins are crucial regulators of haematopoiesis and cardiogenesis via the control of haemoglobin synthesis [START_REF] Genecards | The human gene database[END_REF].

The genetic variant rs6993770 could likely impact on the activity of the ZFPM2 gene, which would, in turn, affect GATA protein regulation of haematopoiesis. The risk allele (A) of the identified SNP could lead to increased haematopoiesis, which may result in high cellular turnover and thus, faster telomere attrition [START_REF] Rufer | Telomere fluorescence measurements in granulocytes and T lymphocyte subsets point to a high turnover of hematopoietic stem cells and memory T cells in early childhood[END_REF]. Moreover, increased haematopoiesis could lead to bigger production of platelets, which would explain the higher levels of VEGF-A in subjects with this risk variant and the previously identified association of rs6993770 with platelets (331). Such hypothesis seems plausible since telomere attrition was the only phenotype that was significantly related to the genetic variant, whereas LTL and MTL did not show significant association with rs6993770.

TL is known for its high interindividual variation, which is evident already at birth [START_REF] Youngren | Synchrony in telomere length of the human fetus[END_REF].

Afterwards, numerous factors can accelerate the attrition, e.g. increased cellular turnover caused by increased inflammation, oxidative stress [START_REF] Houben | Telomere length assessment: biomarker of chronic oxidative stress? Free[END_REF], or can have a protective effect on TL, e.g. regular sport activity, healthy diet [START_REF] Ludlow | Relationship between physical activity level, telomere length, and telomerase activity[END_REF]. However, the effects of these factors on TL are rather small comparing to the large variations in the baseline TL among individuals [START_REF] Aviv | Reflections on telomere dynamics and ageing-related diseases in humans[END_REF].

Therefore, the use of LTL as a biomarker could be limiting without comparison to the baseline TL. Determination of the LTL is not as informative as it would be in combination with telomere attrition over time. Accelerated attrition within an individual"s TL is supposed to be a more accurate biomarker for describing pathological processes in the organism [START_REF] Entringer | The fetal programming of telomere biology hypothesis: an update[END_REF].

As muscular cells present a minimally proliferative tissue, MTL can be considered as baseline TL, and overall LTL attrition can be calculated as LTL/MTL.

Shorter LTL in atherosclerotic disease has been previously explained as a consequence of the increased cellular turnover due to the chronic systemic inflammation [START_REF] Samani | Telomere shortening in atherosclerosis[END_REF]. A recent study further developed this hypothesis and suggested that accelerated telomere attrition has been related to atherosclerosis, rather than shorter LTL. As the study demonstrated that differences in telomere attrition between atherosclerosis patients and controls were consistent across the age range of the studied population, it has been suggested that increased attrition in the early life is more likely be a major cause of shorter LTL in atherosclerosis patients [START_REF] Benetos | Short leukocyte telomere length precedes clinical expression of atherosclerosis: the blood-and-muscle model[END_REF]. The finding of our study could be in agreement with this result. The risk allele (A) of SNP rs6993770 may cause increased haematopoiesis and thus increased leukocyte telomere attrition, especially in childhood, when cellular turnover is the highest. Shorter telomeres are linked to chromosomal instability, which causes apoptosis. Continuous apoptosis can lead to early replicative senescence of vascular tissue and thus the start of the early formation of atherosclerotic plaque.

Even though this hypothesis seems likely, no association was found between the distribution of the risk allele (A) of rs6993770 and atherosclerosis status in the studied population. We might, however, still suppose that allele (A) of rs6993770 presents a risk factor for faster telomere attrition, which can result in atherosclerosis. Nevertheless, its predictive role for the risk of the CVDs should be further investigated. Telomere attrition in the case-control studies of other telomere related pathologies, such as cancer, should also be investigated for an association with rs6993770.

Epistatic interactions between nine investigated genetic variants and three phenotypes have been analysed to detect a combined effect of genetic variants on investigated phenotypes. No significant epistatic interactions were observed at the established threshold level.

Besides the genetic association between TL and VEGF-A, this study also investigated the associations of VEGF-A plasma levels of with LTL, MTL and telomere attrition, and the differences in VEGF-A plasma levels of ASCVD patients and controls.

Linear regression analysis did not identify any association between VEGF-A plasma levels and LTL, MTL or telomere attrition. The previous studies which investigated the association between LTL and VEGF-A levels reported inconsistent findings. No statistically significant association was reported between LTL and plasma concentrations of VEGF-A in the longitudinal study population consisting of 87 subjects (332). On the other hand, a study of patients with knee osteoarthritis identified a negative correlation between VEGF-A plasma levels and LTL (327). Further studies are warranted to fully explore the association of both biomarkers.

Similarly, studies investigating the plasma levels of VEGF-A in groups of atherosclerotic patients and controls gave discordant results. VEGF-A levels were increased in patients with uncomplicated hyperlipidemia free of major underlying atherosclerosis and patients with hyperlipidemia plus established atherosclerosis (333), as well as in patients with peripheral artery disease (PAD) and CAD, compared to the controls (334). On the other hand, VEGF-A plasma concentration appeared to be similar in CAD patients and patients without diseased coronaries (335) in another study. Nevertheless, in all cases, statin-treated atherosclerosis patients had lower VEGF-A concentrations compared to untreated patients (333,335).

In our study, ASCVD patients had significantly lower VEGF-A plasma levels compared to the controls before excluding the statin-treated individuals. When the analysis was repeated only with individuals without prescribed lipid-lowering medication, this difference was smaller but remained significant.

To conclude, this is the first study investigating the association of VEGF-A related genetic variants with LTL, MTL and telomere attrition. The study identified a significant association between rs6993770 and telomere attrition, possibly through the modification of the expression of GATA proteins, which results in a direct impact on hematopoiesis and production of platelets. No association was observed between VEGF-A plasma levels and LTL, MTL and telomere attrition. The levels of VEGF-A did not significantly differ in groups or atherosclerosis patients and controls.

Chapter V. GENERAL DISCUSSION __________________________________________________________________________________ In the present work, we explored DNA methylation and diverse association studies methodologies for the identification of novel biomarkers and common molecular pathways that affect intermediate phenotypes and risk factors for chronic diseases. Our findings aimed to serve as prediction tools, to contribute to new therapeutic possibilities and to expand the current knowledge of molecular regulation in common biological processes. Following the objectives pursued, we have demonstrated:

1. The state of the art of the large contribution that personalised medicine gave to the current health system, and the importance of further scientific investment to accompany it for the improvement of individualised patient care.

2. An association between increased TG levels and CpG sites cg08897188 and cg04580029. TG levels are an important intermediate risk factor for various chronic diseases. The identification of a CpG site associated with TG levels could serve as a risk assessment biomarker or could contribute to new treatment strategies.

3. An association between increased WC and CpG site cg16170243, which gave an insight into the complex regulation of obesity that merit further investigation. This discovery might be useful in the future determination of obesity risk factors, as well as in potential therapeutic applications.

4. An association between VEGF-A levels and 20 significant CpG sites, underlying the epigenetic mechanisms that contribute to the physiological regulation of this angiogenic biomarker.

5. Associations of VEGF-A with IL-4, MCP-1 and EGF, associations of VEGF 165 isoform with MCP-1 and IL-1α, and associations of VEGF 189 isoform with IL-4 and IL-6, which gave new insights into the links of inflammatory cytokines with VEGF-A and thus contributed to the understanding of the physiological interactions between angiogenesis and inflammation.

6. An association between TREM2-related genetic variant rs6918289 and increased TNFα levels and IMT-F, suggesting that minor allele T of rs6918289 could be considered a risk factor for inflammation and atherosclerosis. Common chronic diseases, such as CVD, cancer or type 2 diabetes, are the most common cause of premature death in adults (1). They do not occur at once but appear after a long-term accumulation of various environmental risk factors combined with an unfavourable genetic profile (2). Many of these factors are preventable, and early identification of patients at risk, combined with appropriate health promotion management could decrease the prevalence of chronic diseases even, in some cases, without the need of therapeutic interventions (3).

Prevention of chronic diseases therefore does not start with the appearance of a disease itself, but with managing and fighting against the exposures known for deleterious impact on health, from where the need for new risk prediction biomarkers (4).

Intermediate phenotypes or quantitative traits are reflecting mechanism-related manifestations of complex diseases and can be used for molecular and genetic investigations (5). They have phenotypic variation continuously distributed in natural populations, which results from the segregation of alleles at multiple loci, and are influenced by complex genetic and environmental factors. Quantitative genetic variation can underlie susceptibility to common complex diseases and genetic variants involved can be considered as potential risk factors (6,[START_REF] Mackay | Q&A: Genetic analysis of quantitative traits[END_REF]. Similarly, quantitative epigenetic variation can explain a portion of the variation of physiological traits and can serve as susceptibility biomarker (8).

Modifiable risk factors are expressed through intermediate phenotypes (also named intermediate risk factors), which include increased LDL cholesterol and increased TG levels for assessment of abnormal blood lipids, increased BMI and increased WC for assessment of obesity, and others. They are particularly interesting for research in personalised medicine because they are involved in multiple chronic diseases and can be avoided with the right preventive health measures (9,10).

Biomarkers in personalised medicine allow the examination of normal biological processes, diagnosis of pathogenic processes and prognosis of the disease (11,12). Nowadays, the identification of new diagnostic tools for the personalised medicine requires not only a good understanding of the pathology but also the use of a combination of genomic and molecular features, to detect variants with predictive value in clinics. As many diseases are linked through common pathological pathways, in particular inflammation or angiogenesis, those pathways are specifically important for investigation. Biomarkers identified under such conditions are likely to be involved in multiple diseases and have better potential to participate in the development of therapeutic strategies effective across comorbidities (13).

In this thesis work, we investigated intermediate risk factors and biological pathways that are involved in a variety of chronic diseases. We used different methodologies to assess their regulation and link them to personalised medicine.

First, we focused on epigenetic studies to investigate two risk factors involved in common chronic diseases, mentioned above: lipid levels and obesity. As previous EWAS of obesity focused mainly on BMI, we decided to investigate central obesity phenotypes, WC and waistto-hip ratio, which we adjusted for BMI. These two studies identified altogether three risk variantsmethylation sites, associated with the investigated phenotypes. Because VEGF-A presents an important biomarker in common chronic diseases, we decided to use the same methodology to investigate the epigenetic regulation of VEGF-A levels. This was in our knowledge the first study using EWAS for identification of methylation sites associated with VEGF-A. A particularity of the study worth mentioning was that VEGF-A levels were measured in PBMCs extracts, instead of using more common methods of determination in plasma or serum samples. We identified many significant methylation sites which might help to explain VEGF-A epigenetic regulation, though none of them was identified on the gene which is directly involved in VEGF-A biology.

As one of the main hypothesis of the thesis was that a combination of biomarkers involved in the same pathology could better predict the susceptibility or outcomes of a disease, we continued our investigation of VEGF-A levels measured in PBMCs to unravel its links with cytokines and growth factors implicated in inflammation. We used VEGF-A protein levels, as well as mRNA levels of the four most abundant isoforms, which have different biological functions and should be systematically considered as separated biomarkers in biological studies. We identified interesting links with four cytokines and one growth factor, worthy of further attention.

TL is, as VEGF-A, involved in a variety of diseases and following the same hypothesis, we wished to unravel common links between both biomarkers to investigate their interplay in the pathogenesis of atherosclerosis. Relations between VEGF-A plasma concentrations and LTL have been previously studied. To expand the current knowledge we focused on their genetic links. We assessed direct associations between 10 GWAS identified SNPs of VEGF-A levels and TL measured in leukocytes, muscle cells and their ratio that reflects LTL attrition over time. We identified one genetic variant which was significantly associated with TL attrition and could present a risk factor for common chronic diseases, in which both, TL and VEGF-A, are involved. The study was performed on an atherosclerotic case-control population.

A similar approach for investigation of links between genetic variants and biomarkers was used in the research of associations between TREM2, TNF-α (biomarkers of inflammation)

and IMT-F (indicator of atherosclerosis lesions). TREM2 is an attractive molecule which has been neglected in the frames of cardiovascular research and was more often related to neurodegenerative diseases. We identified one SNP, located in the region of the TREM2 gene, which was significantly associated with TNF-α and IMT-F and could present a risk factor for atherosclerosis pathologies. This study was a part of the research project of our Unit, which dedicated itself to make the progress on TREM family molecule investigations.

The results of all publications form this thesis, which was dedicated to the discovery of new genetic and molecular biomarkers for identification of early risk factors for common chronic diseases, including lipids, obesity, inflammation, atherosclerosis and angiogenesis, are discussed below.

•

It is well-known that exposure to adverse environmental factors can induce DNA methylation changes long before disease appears (14). A DNA methylation biomarker that would show its relation with, e.g. increased TG levels or increased WC could alone, or in combination with other genetic biomarkers help in identification of individuals with high susceptibility for an increased risk for chronic diseases related to the observed phenotype. This could help in the early prevention of altered levels of intermediate risk factors with the right lifestyle management of individuals at risk (15).

Moreover, epigenetic modification of DNA is a key modulator of mRNA transcription, which is, unlike the DNA sequence, modifiable by environmental and pharmacological interventions. The environment can modify epigenetic patterns that play an important role in health. This information can be transmitted to daughter cells during cellular replication (16).

Many therapeutic agents can alter methylation patterns and target sites related to unfavourable health outcomes (17). Subsequently, this methodology opened the gates for the discoveries of new therapeutic possibilities (18,19).

Genome-wide DNA methylation studies are approaches for identification of novel biomarkers for risk assessment, diagnosis and prognosis, as well as for the potential therapeutic application. They are bringing new strategies for prevention and fight against diseases. Recent FDA approvals of DNA methylation-based diagnostic tools for colorectal cancer and DNA methylation-based therapeutics against haematological malignancies are the proof of the good direction and are giving the motivation for further exploration of the field (18,19,337).

Using the EWAS methodology, this thesis work reported two novel methylation sites linked to TG levels, one novel methylation site linked to WC, and 20 novel sites that explained epigenetic regulation of VEGF-A levels in physiological conditions, in the population of the healthy individuals from the SFS. Epigenetic DNA modifications are important regulators of tissue differentiation and it was previously demonstrated that DNA methylation could be tissue-specific (22,23). Because methylation among different cell types can vary distinctively, accounting for blood cell type was used in the planning of this study, together with other required technical adjustments (fixed effects, methylation chip).

In research of new potential biomarkers, the availability of the tissue is an important point to consider. The collection of a tissue that demands a chirurgical intervention is less likely to be used in everyday clinical practice. Therefore, we believe that the methylated site should be detectable in the blood DNA and that EWAS of the blood DNA should be used as a discovery cohort in studies looking for new genetic biomarkers (24). However, we agree that for understanding the epigenetic regulation of a particular phenotype, such as TG, a phenotyperelated tissue should also be considered for investigation, if available (25), as it can add an additional confirmation of the genetic mechanisms that are relating methylated site to the phenotype.

Adipose tissue is tightly linked to TG biology (26); therefore, it was used as a source for a DNA methylation assay in the replication cohort. Altogether, the DNA of the adipose tissue of 662 participants from the MuTHER study was tested for associations with cg08897188 and cg04580029. The association of CpG site cg04580029 and TG levels was replicated at a nominal significance level. The replication of the result in adipose tissue demonstrated that the whole blood DNA methylation is a relevant tissue for the identification of epigenomic changes, which is consistent with the conclusions of the previous studies (27,28).

With bioinformatics analysis, we identified two genes in the vicinity of cg08897188 probe. Both CpG sites related to TG levels were located in the regulatory rich genetic environment.

Identification of significant methylation sites in the proximity of histone peak is especially important since histone modifications are the key component of transcriptional regulation (29). They organise the genome into active regions (DNA gets accessible for transcription) or inactive regions (DNA is compact and less accessible for transcription) and thus regulate gene expression (30). Methylation in the proximity of histone is therefore likely to be actively involved in the transcriptional regulation of the region.

To conclude, epidemiological, clinical and genetic studies demonstrated that elevated TG levels are a biomarker for CVD risk (31). As they contribute to the hardening of the arteries and thickening of the artery walls, they can increase the risk for CAD (32), stroke, heart attack and heart disease (33). With the identification of CpG sites significantly associated with TG levels, we discovered two potential biomarkers that could help in the early assessment of the CVD risk or might be used in new methylation-based therapeutic strategies, which we can expect to be extensively developed in the future.

A similar approach was used for the investigation of the methylation patterns of WC and waist/hip ratio (adjusted for BMI) in research entitled "Epigenome-Wide Association Study Detects a Novel Loci Associated with Central Obesity in Healthy Subjects". The population consisted of 211 healthy adults and children from the SFS.

Obesity is an excessive fat accumulation that presents a risk for a number of comorbidities, including type 2 diabetes, CVD and cancer (35). Many negative effects are linked to the excessive fat tissue around the stomach, known as abdominal or central obesity, which is usually determined by WC [START_REF] Després | Abdominal obesity and metabolic syndrome[END_REF]. Obesity is a complex trait whose regulation is still not completely understood. Although big effort has been previously made to unravel the epigenetic regulatory mechanism, new cohorts are needed to contribute to the insights into epigenetic regulation of obesity phenotypes. Use of the populations of healthy individuals can help to avoid the discrepancies caused by extremes of obesity and comorbidity in populationspecific cohorts [START_REF] Dhana | An Epigenome-Wide Association Study (EWAS) of Obesity-Related Traits[END_REF]. This study identified a novel significant positive association of WC adjusted for BMI with methylation site cg16170243 (chromosome 18q21.1, β=2.32, P adj =0.048).

The association of cg16170243 and WC can be explained by two possible mechanisms.

Firstly, non-specific environmental factors might cause the methylation at cg16170243, which would, in turn, lead to increased WC and greater susceptibility for central obesity. Secondly, the obesogenic environment could cause the methylation at cg16170243, which would result in the association of this methylation site with WC. Both mechanisms are equally plausible and are discussed in the manuscript, although more detailed hypotheses were developed in the article to explain the first hypothesis.

Briefly, the gene ST8SIA5 overlapping the probe cg16170243 is coding for a sialyltransferase enzyme, involved in the synthesis of gangliosides. Gangliosides interact with molecules of signal transduction pathways, such as receptors tyrosine kinases (RTKs). Change in ganglioside composition induces the dissociation of RTKs from glycolipid-enriched microdomains, which may result in reduced phosphorylation of the receptors and cause insulin resistance. Thus, methylation of the CpG site could affect the expression level of the ST8SIA5 gene, which would lead to the affected synthesis of gangliosides, followed by increased insulin resistance, which is a known risk factor for the development of obesity [START_REF] Kahn | Obesity and insulin resistance[END_REF].

The second mechanism is suggesting that adiposity may influence DNA methylation, and this hypothesis was discussed in detail in the study of Wahl et al. (39). Their research demonstrated that adiposity caused the alterations in methylation at the majority of the identified CpG sites associated with BMI. The method used to investigate the causality of this relation was a GRS, which combines effects across SNPs known to influence BMI. In our study, the association between polymorphisms related to WC and methylation at cg16170243 has been tested, and no significant link was detected. However, we cannot exclude the possibility that increased adiposity determined the methylation at cg16170243. Further studies are warranted to determine the causal mechanism between the identified CpG site and WC.

The role of DNA methylation as a mediator on the causal pathway between modifiable exposures and disease risk can be assessed through two-sample Mendelian randomisation (MR), using EWAS summary statistics data of the observed phenotype. In the first step, the causal impact of the observed phenotype on epigenetic markers is established. In the second step, the casual nature of epigenetic markers on a health-related outcome is interrogated [START_REF] Relton | Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease[END_REF].

Such an approach can help to identify relevant loci for future functional studies (24).

The third study using genome-wide methylation assay focused on the determination of the epigenetic regulation of VEGF-A levels in a population of 211 healthy adults and children from the SFS, and is presented in the publication entitled "Epigenome-wide association study in healthy individuals identifies significant associations with DNA methylation and PBMCs extracts VEGF-A concentration".

VEGF-A is an important mediator of angiogenesis and inflammation, as well as a key biological target for the treatment of several diseases related to these pathological pathways [START_REF] Zittermann | Endothelial growth factors VEGF and bFGF differentially enhance monocyte and neutrophil recruitment to inflammation[END_REF][START_REF] Pandey | Mechanisms of VEGF (Vascular Endothelial Growth Factor) inhibitor-associated hypertension and vascular disease[END_REF][START_REF] Carmeliet | Angiogenesis in cancer and other diseases[END_REF]. The article identified 20 CpG sites (cg05739757, cg23333878, cg21838233, cg18815539, cg21968169, cg16333561, cg20547575, cg15014826, cg00117600, cg08759276, cg05275012, cg10517202, cg09614565, cg13689591, cg06934988, cg06785213, cg13332754, cg03551607, cg24364967, cg15057061) significantly associated with VEGF-A levels measured in PBMCs extracts, and discussed possible relations with genes, encoded in the vicinity of methylated sites.

Surprisingly, no identified CpG site was found in the regulatory region of a gene directly related to VEGF-A biology, such as VEGF-A (6p21.1), , KDR (4q12), HIF1A (4q23.2) etc. The reason for this might be the fact that increased levels of VEGF-A caused the methylation on unspecific sites. However, we identified methylation sites near genes that might be indirectly associated with VEGF-A regulation. Two most promising results were CpG cg21838233 (TPX2 gene) and cg06785213 (HAS1 gene). Firstly, TPX2 is a gene involved in spindle fibre development, commonly overexpressed in gastric cancer cells [START_REF] Tomii | TPX2 expression is associated with poor survival in gastric cancer[END_REF].

Studies showed that silencing of TPX2 could inhibit proliferation and invasion of colon cancer cells, which is possibly related to the down-regulation of the VEGF-A gene [START_REF] Jian | TPX2 gene-silencing inhibits the proliferation and invasion of human colon cancer SW480 cells[END_REF]. Secondly, HAS1 encodes hyaluronic acid synthase, which has an essential role in tissue development and homeostasis of plasma membrane proteins and has been implicated in inflammatory and degenerative arthropathies. It has been previously shown that HAS1-siRNA can restrain the expression of VEGF-A [START_REF] Wang | HNF1AAS1 promotes growth and metastasis of esophageal squamous cell carcinoma by sponging miR214 to upregulate the expression of SOX-4[END_REF].

Besides those two genes, links with VEGF-A related pathways were found for six other genes located in the vicinity of identified CpG sites. The methylation could explain the involvement in the regulation of VEGF-A expression that was previously identified (but not explained).

The future exploration of DNA methylation related to VEGF-A levels should be, however, focused on specific populations, such as cancer or CVD patients. In cancer patients, VEGF-A production is up-regulated to provide enough signal for the angiogenic process, which enables vascularisation of a growing tumour [START_REF] Costache | VEGF Expression in Pancreatic Cancer and Other Malignancies: A Review of the Literature[END_REF]. The role of DNA methylation in the regulation of VEGF-A overexpression could give interesting insights. In combination with the results of EWAS from the healthy population, such study could serve for the identification of differentially methylated sites and eventually contribute to the development of new alternatives to current antiangiogenic therapies.

VEGF-A levels in this study were measured in cellular extracts of PBMCs. PBMCs are a source of VEGF-A and many other signalling molecules. Their importance in different biological pathways has been previously reported [START_REF] Visvikis-Siest | Peripheral blood mononuclear cells (PBMCs): a possible model for studying cardiovascular biology systems[END_REF]. Determination of the VEGF-A production inside the mononuclear cells is therefore in straightforward link with related biological processes (angiogenesis and inflammation).

•

Because of the significant implication of VEGF-A in disease pathology via two major (and mutually dependent) pathological processesangiogenesis and inflammation [START_REF] Shaik-Dasthagirisaheb | Vascular Endothelial Growth Factor (VEGF), Mast Cells and Inflammation[END_REF], VEGF-A levels detected in PBMCs extracts were further investigated for their association with 11 cytokines, important indicators of inflammation. The association of both physiological pathways was demonstrated in the study entitled "Peripheral blood mononuclear cells extracts VEGF protein levels and VEGF mRNA: Associations with inflammatory molecules in a healthy population". The overall aim was to identify inflammatory molecules that could be used along with VEGF-A levels in predictive or therapeutic applications in personalised medicine.

As in the article above, particular originality of the study was the use of PBMCs extracts for measurement of all investigated molecules and determination of VEGF isoforms. PBMCs are cells that respond selectively to the immune system activation and are the major actors of the human body immunity, with an essential role in the synthesis and release of inflammatory cytokines and growth factors [START_REF] Jager | Simultaneous detection of 15 human cytokines in a single sample of stimulated peripheral blood mononuclear cells[END_REF][START_REF] Turner | Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease[END_REF]. Simultaneous production of different mediators by the same cell group is likely to suggest their involvement in the same pathophysiological mechanism.

Both, molecular levels of VEGF-A as well as mRNA levels of four most abundant VEGF-A Briefly, IL-4, a multi-functional cytokine with anti-inflammatory and anti-tumour activity was associated with VEGF-A protein levels and VEGF 189 mRNA levels. IL-4 has been previously related to VEGF-A as an important factor in the recruitment of tumour-associated macrophages, which are known to promote angiogenesis [START_REF] Linde | Vascular endothelial growth factor-induced skin carcinogenesis depends on recruitment and alternative activation of macrophages[END_REF]. Moreover, increased VEGF-A mRNA expression was identified in IL-4 induced cell cultures of human airway smooth muscle cells [START_REF] Faffe | Interleukin-13 and Interleukin-4 Induce Vascular Endothelial Growth Factor Release from Airway Smooth Muscle Cells: Role of Vascular Endothelial Growth Factor Genotype[END_REF]. In this work, we identified VEGF 189 as the main isoform responsible for the association of IL-4 with VEGF-A levels.

MCP-1, an angiogenic chemokine involved in the regulation, migration and infiltration of monocytes and macrophages, was associated with VEGF-A protein levels and VEGF 165 mRNA levels. These findings were in accordance with previous studies, which showed that MCP-1 up-regulated HIF1A gene expression in human aortic endothelial cells, which resulted in induced VEGF 165 expression in the aortic wall [START_REF] Hong | Monocyte chemoattractant protein-1-induced angiogenesis is mediated by vascular endothelial growth factor-A[END_REF].

Another inflammatory molecule, EGF, was associated with VEGF-A protein levels in our population. EGF is an important mediator in growth, proliferation and differentiation of numerous cell types. The major role of both associated growth factors in tumorigenesis has been demonstrated in various types of cancers, and different mechanisms of their common pathways have been proposed. Combination of VEGF/EGF signalling pathways was studied for the development of new therapeutic approaches against cancer and is already showing positive predictions for the future development of new medications [START_REF] Ciardiello | Interaction between the epidermal growth factor receptor (EGFR) and the vascular endothelial growth factor (VEGF) pathways: a rational approach for multi-target anticancer therapy[END_REF][START_REF] Tortora | Combined targeting of EGFR-dependent and VEGFdependent pathways: rationale, preclinical studies and clinical applications[END_REF][START_REF] Tabernero | The role of VEGF and EGFR inhibition: implications for combining anti-VEGF and anti-EGFR agents[END_REF][START_REF] Winder | Vascular endothelial growth factor and epidermal growth factor signaling pathways as therapeutic targets for colorectal cancer[END_REF].

Finally, IL-6 and IL-1α were associated with VEGF 189 and VEGF 165 , respectively. Previous studies on cell cultures detected induction of VEGF-A mRNA levels by both IL-6 and IL-1α.

IL-6 was shown to induce VEGF-A-mediated angiogenesis in cervical tumorigenesis, gastric carcinoma patients [START_REF] Huang | Interleukin-6 increases vascular endothelial growth factor and angiogenesis in gastric carcinoma[END_REF] and in malignant mesothelioma [START_REF] Adachi | Interleukin-6 induces both cell growth and VEGF production in malignant mesotheliomas[END_REF]. IL-1α stimulated the induction of VEGF-A mRNA synthesis in PBMCs in a dose-depended manner. Both IL-6 and IL-1α

were linked together in the regulation of VEGF-A in pancreatic cancer [START_REF] Tang | Interleukin-1alpha, 6 regulate the secretion of vascular endothelial growth factor A, C in pancreatic cancer[END_REF] and in adenoma cells [START_REF] Borg | Correlation of VEGF production with IL1 alpha and IL6 secretion by human pituitary adenoma cells[END_REF].

Physiological pathways in the human body are complex networks, regulated by various interacting molecules, which work together to build the physiological systems. Angiogenesis is the main process mediated by VEGF-A, which enables the growth of new blood vessels.

However, other mechanisms, including inflammation, are tightly involved in its regulation [START_REF] Loizzi | Biological Pathways Involved in Tumor Angiogenesis and Bevacizumab Based Anti-Angiogenic Therapy with Special References to Ovarian Cancer[END_REF][START_REF] Savant | The Role of Inflammation and Inflammatory Mediators in the Development, Progression, Metastasis, and Chemoresistance of Epithelial Ovarian Cancer[END_REF]. The interplay of both mechanisms can result in many common pathologies and should be considered as a potential target for prevention of related diseases.

With this study, we successfully demonstrated links between VEGF-A and inflammation molecules derived from PBMCs extracts. This discovery gave insights into shared pathways between investigated molecules, which could be useful for applications in personalised medicine.

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The third methodological approach used in this thesis was the study of genetic determinants for the investigation of the intermediate risk factors and biological pathways involved in different chronic diseases. The characteristics of the quantitative genetic variation were used for identification of genetic biomarkers, which could serve in risk prediction of atherosclerosis, a common pathological process in the majority of CVDs ( 68).

In the study entitled "The polymorphism rs6918289 located in the downstream region of the TREM2 gene is associated with TNF-alpha levels and IMT-F", we focused on two quantitative traits, TNF-α levels and IMT-F, both important indicators of atherosclerosis. As recent discoveries showed that TREM2 expression was found in human atherosclerotic lesions [START_REF] Fernández-Ruiz | Redefining leukocytes in atherosclerosis[END_REF], the objective of our research was to study the genetic variants located in the vicinity of TREM2 gene for an association with TNF-α levels and IMT-F.

First, we investigated five SNPs (rs7748777, rs6918289, rs7759295, rs9357347, rs6915083) located in the TREM2 gene region (50 kb upstream and downstream of the TREM2 gene, chromosome 6p21) for an association with TNF-α levels in 139 children from the SFS. We identified a new potential risk allele (T) of SNP rs6918289, which was associated with increased plasma levels of TNF-α (β discovery =0.33, P discovery =0.00147).

To increase the discovery population, SNP rs6918289 was de novo genotyped in 393 adults and 277 children. Analysis in combined SFS population reported a significant association between allele (T) of SNP rs6918289 and increased plasma levels of TNF-α (β SFS =0.49, P SFS =0.0017). These results were replicated in an independent cohort of 916 French individuals (β = 0.202, P = 0.023).

An association study was also performed with IMT-F in a sample of 350 adults from the SFS, where measurements of IMT-F were available. The results of the analysis showed significant association with minor allele (T) of the SNP rs6918289 (β = 0.024, P = 0.026).

Even though first researches related the TREM2 protein mostly with macrophage-and neutrophil-mediated inflammatory responses of neurodegenerative conditions, such as AD, amyotrophic lateral sclerosis, and Parkinson's disease [START_REF] Jiang | TREM2 in Alzheimer's disease[END_REF], recent discoveries demonstrated the importance of TREM2-mediated macrophage response also in cardiovascular pathologies [START_REF] Fernández-Ruiz | Redefining leukocytes in atherosclerosis[END_REF]. The following article provided the first insights into the possible genetic association of TREM2 with TNF-α and IMT-F.

IMT-F is a non-invasive, early marker of atherosclerosis and an independent predictor of CVD that can help to detect an early atherosclerotic plaque formation by using ultrasound to calculate media-adventitia interface to the intima-lumen interface [START_REF] Polak | Carotid-wall intima-media thickness and cardiovascular events[END_REF][START_REF] Giannoukas | Common femoral artery intima-media thickness as marker for cardiovascular disease in asymptomatic adults[END_REF][START_REF] Touboul | Mannheim Carotid Intima-Media Thickness Consensus (2004-2006)[END_REF]. The role of TNF-α in atherogenesis is explained through different mechanisms, by which this inflammatory cytokine acts on endothelium [START_REF] Urschel | TNF-α in the cardiovascular system: from physiology to therapy[END_REF]. TNF-α can increase the vascular permeability to allow the infiltration of inflammatory cells ( 75) and promote migration of immune cells into the diseased or injured tissues, in order to protect the tissues from harmful pathogens. It can impair endothelium-dependent vasodilation by decreasing NO formation and bioavailability, stimulate vascular superoxide production and can interfere with endothelial insulin signalling to induce insulin resistance [START_REF] Steyers | Endothelial dysfunction in chronic inflammatory diseases[END_REF].

In the view of the above, the results of this study suggest that the minor allele (T) of the rs6918289, which was associated with increased TNF-α and IMT-F, might be considered a risk allele for inflammatory diseases and atherosclerosis. The mechanism of this effect is so far unknown; however, bioinformatics analysis of the genetic region indicated three possible hypotheses that could explain the association of rs6918289 and increased TNF-α levels.

Firstly, rs6918289 is involved in the post-transcriptional regulation and is associated with RNA binding protein PABPC1 [START_REF] Guo | rSNPBase: a database for curated regulatory SNPs[END_REF], which binds to the 3" poly(A) region of the mRNAs. The minor allele (T) of rs6918289 could affect the PABPC1 protein, which would, in turn, disturb the stability of the TREM2 mRNA. Previous studies have shown that TREM2 levels can affect TNF-α concentrations [START_REF] Turnbull | Cutting edge: TREM-2 attenuates macrophage activation[END_REF][START_REF] Liu | Impact of TREM-2 gene silencing on inflammatory response of endotoxin-induced acute lung injury in mice[END_REF][START_REF] Gao | Silencing of triggering receptor expressed on myeloid cells-2 enhances the inflammatory responses of alveolar macrophages to lipopolysaccharide[END_REF]. The effect of the minor allele (T) would result in decreased TREM2 production, which would lead to elevated TNF-α levels in the same individual.

Secondly, rs6918289 is located between two CTCF (11-zinc finger protein) genomic sequences, which can bind together and form a chromatin loop, leading to a topologically associating domain (TAD) that can affect gene expression and other cellular functions. Thus, we hypothesise that the minor allele (T) of SNP rs6918289 could promote the binding of the CTCF and consequently trigger an insulation mechanism for the TREM2 gene.

Finally, the rs6918289 can be found in proximity to the Triggering Receptor Expressed On Myeloid Cells Like 1 (TREML1) gene. In silico analysis showed a significant correlation of rs6918289 with seven SNPs related to TREML1 gene. Therefore, TREML1 should also be considered as a possible mediator of the rs6918289 effect on TNF-α concentration.

Remodelling of the artery is highly dependent on the inflammatory state; thus the association of the rs6918289 with IMT-F is most likely due to the increased TNF-α levels, produced by the minor allele (T) of SNP rs6918289, suggesting the indirect role of rs6918289 on IMT-F.

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The study of the genetic variants was further utilised for investigation of the associations between two biomarkers, TL and VEGF-A. The aim of the research "Discovering the Relation between Telomere Length and VEGF-A" was to explore the association of LTL, MTL and telomere attrition (calculated as LTL/MTL) with 10 VEGF-A related genetic variants (rs10761741, rs10738760, rs6921438, rs7043199, rs6993770, rs4416670, rs114694170, rs34528081, rs4782371 and rs2639990) [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF][START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. LTL represented TL in the highly proliferative hematopoietic cells, whereas MTL represented a minimally replicative skeletal muscle tissue, which can be comparable to the baseline TL [START_REF] Benetos | Short leukocyte telomere length precedes clinical expression of atherosclerosis: the blood-and-muscle model[END_REF].

Shorter TL has been previously associated with atherosclerosis and was considered as a possible risk factor for CVDs [START_REF] Khan | Telomeres and atherosclerosis[END_REF]. Similarly, VEGF-A was demonstrated as a potential enhancer of the pathophysiologic mechanism of formation and destabilisation of atherosclerotic plaque [START_REF] Camaré | Angiogenesis in the atherosclerotic plaque[END_REF]. Besides their important role in atherogenesis, both biomarkers are implicated in other common chronic diseases. As the main population of this publication under redaction, the TELARTA study, is a case-control cohort of atherosclerosis patients, we investigated the role of VEGF-A and TL through this pathology. The results of the study, however, can be extrapolated to other diseases where both biomarkers play a distinct role, in particular cancer.

The present study identified links between the minor allele (T) of the SNP rs6993770 with LTL/MTL (P=0.001143). SNP rs6993770 is highly associated with VEGF-A plasma levels (P = 2.44x10 -60 ), as identified in a large GWAS (n>15,000) [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF]. We have conducted a bioinformatics analysis to investigate the genetic environment or the significant SNP and identify possible mechanisms that could help to explain the relation of VEGF-A and TL through significant genetic variant.

The bioinformatics analysis reviled that rs6993770, located on the ZFPM2 gene, could impact on VEGF-A levels and telomere attrition through modification of ZFPM2 activity. This would, in turn, affect the expression of GATA proteins, important regulators of haematopoiesis (346). Haematopoiesis is crucial for both investigated biomarkers. Increased cellular turnover causes faster telomere attrition, and also higher production of platelets, which are an important source of VEGF-A. The risk allele (A) of rs6993770 was associated with faster telomere attrition, platelet counts and higher concentrations of VEGF-A, which could all be a result of increased haematopoiesis.

Results of this study brought new insight into telomere regulation in diseases. The mechanism which relates shorter telomere to a variety of chronic diseases was investigated for a long time. One of the possible explanations for such an association was also faster LTL attrition, discussed in the recent studies [START_REF] Aviv | Reflections on telomere dynamics and ageing-related diseases in humans[END_REF]. Our results suggested that faster telomere attrition could be dependent on genetic predispositions and possibly contribute to the pathogenesis of chronic diseases.

An interesting association between VEGF-A and telomeres has been identified from studies investigated the effects of genes involved in telomere regulation on VEGF-A. hTERT (human telomerase reverse transcriptase) was identified as an activator of VEGF gene expression through interactions with the VEGF promoter and the transcription factor Sp1 (347).

All these findings are suggesting a complex common regulation mechanism of both biomarkers, which is worthy of further investigation.

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A telomere is indeed an important biomarker, related to a large variety of pathologies, starting from the rare monogenic disorders to common chronic diseases (97)[START_REF] Sanders | Telomere length in epidemiology: a biomarker of aging, age-related disease, both, or neither?[END_REF][START_REF] Falandry | Telomere length is a prognostic biomarker in elderly advanced ovarian cancer patients: a multicenter GINECO study[END_REF]. Its special characteristics of continuous shortening throughout life have linked TL to longevity and intrigued the scientists looking for a biological clock that could predict a lifespan (100). Its role in prediction, diagnosis and treatment of diseases has been studied during the last few decades, yet it did not bring any promising clinical applications (101).

Because we believe that there is still much to be discovered, we have dedicated two review articles to summarise the current knowledge about TL, to assess its potential and most of all, to point out the knowledge gaps, which are obstructing the way to profound understanding of telomere biology and could lead to successful use in personalised medicine. We have concluded that TL could be a perspective biomarker, systematically measured from birth.

Because of the high interindividual variability, a simple measurement of TL cannot be representative, whereas accelerated attrition could be extremely informative and could give the alert for the pathological changes in the individual. To assess this hypothesis, long-term longitudinal studies are warranted, with a special emphasis on the childhood period. TL is the second important biomarker highlighted in this thesis, also presenting a scientific target of our Unit. Our participation in the projects LUE: GEENAGE (Functional Genomic, Epigenomic and Environment interplay to impact the understanding, diagnosis and management of healthy and pathological Ageing) and TELARTA further encouraged our investment to explore telomere biology, which resulted in exciting collaborations. The investigation of the genetic associations with VEGF-A has been one of them.

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As for the next challenges on the way towards the application of telomeres as a biomarker in personalised medicine, we performed bibliographic research that allowed us to understand their genetic regulation, their important role in diseases and their association with other inflammatory molecules. Our particular interest in TL in children motivated us to design a study, which would unravel genetic and environmental determinants of childhood TL, to fill in the knowledge gaps arising from the lack of longitudinal studies and to finally test the hypotheses that are linking the fast TL attrition in the childhood to chronic diseases in adulthood. A review article that gathered the most pertinent information on childhood telomere was the first step of this process.

Finally, the TREM family proteins have been extensively studied by the researches of our Unit in recent years. The research on TREM2 presented in this thesis was a part of the work that has demonstrated an important role of TREM-related genetic variants in cardiovascular pathologies. TREM2, as a less known member of its protein family, has a big potential for novel discoveries that may be useful for application in personalised healthcare.

All the efforts of this thesis were dedicated to the improvements of personalised medicine:

understanding of biological pathways and genetic regulation of biomarkers, epigenetic mechanisms of risk factors and associations between molecules. The summary of the results acquired through the main research projects, showing the identified links between biomarkers, risk factors and molecular pathways investigated is schematically presented in Figure 34. the most important achievement from the field of the personalised medicine in a review article, which was named after the conference: The Odyssey from hope to practice. We hope that this article will be an encouragement for the further efforts that pave the way of implementation of personalised medicine in everyday clinical practice.

Perspectives of our results

All genetic variants identified in our studies need a functional characterisation (validation) to tests the hypothesis generated by genomic and statistical approaches in vitro or in vivo. This can be done using molecular assays (e.g. mRNA expression) or by biological assays (e.g.

human cell lines, animal models) (348).

More specifically, the perspectives of our results are the following:

VEGF-A

The studies of associations between VEGF-A and inflammatory molecules unravelled interesting links. However, systematic research of common molecular pathways of VEGF-A and inflammatory molecules needs further investigation. Firstly, the research should be repeated in a bigger sample of healthy individuals, and parallel measurement of the same biomarkers should be performed in plasma. This will enable a comparison of the cytokine and VEGF-A production by PBMCs to plasma concentrations released by all cytokine-producing cell types. Afterwards, cell cultures of PBMCs should be established and treated by angiogenic or inflammation stimuli to measure the production of molecules and mRNA isoforms under specific conditions. Associated molecules should then be measured in specific patients (cancer, cardiovascular pathologies) to consider their potential role as therapeutic targets.

The epigenomic research of VEGF-A reported in this thesis was the pilot study of VEGF-A methylation marks. As VEGF-A expression levels of different isoforms are also available for some of the individuals of the SFS, an EWAS could be performed to identify the epigenetic regulation of VEGF-A expression. Furthermore, the study should be repeated in a bigger sample, in a case-control population, where cancer patient without angiogenic therapy should be included. This could potentially result in the identification of differentially methylated genes involved in the regulation of angiogenesis. Such discovery would be extremely important for the novel generation of antiangiogenic treatments.

We have demonstrated an association of VEGF-A related genetic variant rs6993770 with telomere attrition, which could suggest common genetic regulatory mechanisms that impact both biomarkers and could thus be very interesting for application in personalised medicine.

The associations of VEGF-A related genetic variants have been previously investigated with other phenotypes and diseases, such as lipids, AD, type 2 diabetes, metabolic syndrome etc.

To systematically assess the role of VEGF-A in pathologies, intermediate risk factors and regulation of other inflammatory molecules, a Mendelian randomisation study should be performed, using the VEGF-A related genetic variants as instrumental variables for VEGF-A levels.

TG and WC

It would be necessary to replicate the identified CpG sites associated with TG and WC using bigger cohorts that would include obese populations. Because WC is related to increased deposit of adipose tissue on the waist, CpG site identified for its relation with WC should also be replicated using adipose DNA samples. Mendelian randomisation studies would be useful for identification of the causal role of identified CpGs in common chronic diseases.

Telomere length

VEGF-A related genetic variant rs6993770 that showed association with telomere attrition could explain interesting links between biomarkers and importance of TL in the aetiology of chronic diseases. LTL, MTL and telomere attrition should therefore be investigated for association with rs6993770 in populations of patients suffering from different pathologies previously associated with shorter LTL (numerous metabolic and inflammatory diseases, such as CVD, diabetes mellitus, ulcerative colitis, liver cirrhosis and systemic lupus erythematous).

This will help to determine whether the identified variant is associated with accelerated shortening in all diseases, or it is specific for atherosclerosis patients. SNP rs6993770 should be genotyped in bigger atherosclerosis case-control population, to investigate its potential for a susceptibility/risk biomarker in atherosclerotic diseases.

Longitudinal research that would follow individuals from birth to adulthood is essential in the efforts of implementation of TL as diagnostics/prognostic/susceptibility biomarker in personalised medicine. With the fast development of WGS technologies, TL could be measured using bioinformatics methods. Progress in this field is therefore necessary.

TREM-2

The genetic variant rs6918289 located in the TREM2 gene region should be investigated in

FOXL1

Regulation of metabolism, cell proliferation and gene expression during ontogenesis

Overexpression of FOXL1 has protective role in breast (17), gastric (18), bladder (19), renal

None

ABSTRACT

The fight against common chronic diseases, characterised by complex mechanisms of molecular regulation, requires the implementation of new risk prediction and prevention strategies. Personalised medicine offers sophisticated approaches for successful management of the morbidities of the ageing population. In this thesis, inspired by the principles of personalised medicine, we describe an integrative approach combining "-omics" methodologies. We use a model of a "common denominator" for cardiovascular disease (CVD) and other chronic diseases to identify biomarkers linked with common diseases risk factors and molecular pathways. The results of this work are presented in five original publications, three review articles, and one publication under redaction. All aim to strengthen and facilitate the implementation of personalised medicine in healthcare. The three main biomarkers of our interest are TREM2, VEGF-A and telomere length (TL) and are studied mainly in the supposed healthy population of The STANISLAS family study.

With the investigation of genetic variants, located in the region of the TREM2 gene, we identified the association of SNP rs6918289 with increased levels of TNF-α and intima-media thickness of the femoral artery (IMT-F). As inflammation and increased IMT-F are important indicators of the formation of the atherosclerotic plaque, the minor allele (T) of rs6918289 might be considered a risk allele for inflammatory diseases and atherosclerosis. With the use of epigenome-wide association studies (EWAS), we identified novel epigenetic biomarkers, related to common diseases risk factors: central obesity and increased lipid levels. One methylation site (CpG) was associated with increased waist circumference (cg16170243), which could explain the epigenetic regulation of central obesity via increased insulin resistance.

Moreover, an EWAS of the triglyceride (TG) levels identified two significant CpG sites, one of which was replicated in the adipose tissue (cg04580029), giving insights into epigenetic regulation of lipid levels. The results of these two last studies provided new epigenetic biomarkers and might contribute to future diagnostics and therapeutic interventions. An EWAS was also used to study the epigenetics of VEGF-A concentrations; 20 CpG sites were identified, and their relations with VEGF-A regulation were analysed through detailed bioinformatics analysis. Methylation of genes, such as TPX2 and HAS, could affect their activity and would in turn cause increased VEGF-A concentrations.

VEGF-A was further investigated for its relation with 11 cytokines, important mediators of common physiological pathways of the majority of chronic diseases. VEGF-A protein levels were associated with IL-4, MCP1 and EGF. Specific VEGF-A mRNA isoforms were also investigated for their association with cytokines; VEGF165 showed significant associations with MCP1 and IL-1α and VEGF189 with IL-4 and IL-6. Together with another important biomarker, TL, we studied the role of VEGF-A in atherosclerosis, and we identified one VEGF-A related genetic variant significantly associated with telomere attrition (calculated as a ratio between leukocyte TL and muscular TL). This genetic variant could present a common denominator of chronic diseases.

In conclusion, the employment of diverse methodologies for the investigation of common chronic diseases risk factors and pathways provided new diagnostic markers and generated results, which could help to improve the diseases risk prediction based on the individual genetic "make-up". New insights into associations between different biomarkers might help in understanding the (patho) physiological pathways common between CVDs and other chronic diseases. Finally, we hope that the results of this thesis through close collaboration with industry will facilitate the implementation of personalised healthcare.

Keywords: Biomarkers, VEGF-A, Telomeres, Personalised medicine, Epigenetics, Genetic associations

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RÉSUMÉ

La lutte contre les maladies chroniques courantes nécessite aujourd"hui la mise en oeuvre de nouvelles stratégies de prédiction du risque et de prévention. La médecine personnalisée représente une approche sophistiquée pour réussir la prise en charge des morbidités de populations vieillissantes. Dans le cadre de ces travaux de thèse inspirés par les principes de la médecine personnalisée, nous décrivons une approche intégrative qui associe plusieurs méthodologies « -omiques ». Nous avons utilisé un modèle de « dénominateur commun » pour les maladies cardiovasculaires et d"autres maladies chroniques afin d'identifier des biomarqueurs associés aux facteurs de risque et aux voies biologiques de maladies courantes.

Les résultats de ces travaux sont présentés dans 5 publications originales, 3 articles de revue et 1 publication originale en cours de redaction.

Trois principaux biomarqueurs ont été étudiés, TREM2, VEGF-A et la longueur des télomères (LT), et ce dans la population d"individus en bonne santé apparente de l"étude familiale STANISLAS (SFS). Par l"étude de variants génétiques localisés dans la région comportant le gène TREM2, nous avons identifié une association entre le SNP rs6918289 et à la fois de taux élevés de TNF-α et une augmentation de l"épaisseur intima-média de l"artère fémorale (IMT-F). L"inflammation et l"IMT-F étant des indicateurs importants de la formation de plaque d"athérosclérose, l"allèle mineur (T) de rs6918289 pourrait être un allèle à risque pour les maladies inflammatoires et l"athérosclérose. Grâce à des études d"association panépigénomique (EWAS), nous avons identifié de nouveaux marqueurs épigénétiques liés à des facteurs de risque de maladies courantes, à l"obésité abdominale et à des taux élevés de lipides. Un site CpG de méthylation (cg16170243) était associé à une augmentation du tour de taille, contribuant à expliquer la régulation épigénétique de l"obésité abdominale par une résistance accrue à l'insuline. De plus, une étude EWAS des taux de triglycérides a permis d"identifier deux sites CpG significatifs. L"un de ces deux sites a pu être confirmé dans le tissu adipeux (cg04580029) et a permis de mieux comprendre la régulation épigénétique de l"augmentation du taux de triglycérides. Les résultats de ces deux dernières études ont donné lieu à de nouveaux biomarqueurs épigénétiques qui pourraient être utilisés dans l"avenir dans des applications diagnostiques et thérapeutiques. Une étude EWAS a également été réalisée pour décrire la régulation épigénétique des concentrations de VEGF-A. Vingt sites CpG ont pu être identifiés ainsi et leurs relations avec la régulation du VEGF-A ont été examinées par analyse bioinformatique poussée. La méthylation de gènes tels que TPX2 ou HAS pourrait avoir un effet sur leur activité et donc conduire à une augmentation de la concentration de VEGF-A. Les liens entre le VEGF-A et 11 cytokines, des médiateurs importants des voies physiopathologiques courantes impliquées dans la plupart des maladies chroniques, ont également été étudiés. Le taux de protéine VEGF-A était associé à IL-4, MCP1 et EGF. Les associations entre les cytokines et des isoformes spécifiques de l"ARNm du VEGF-A ont également été évaluées : le VEGF165 était associé de manière significative à MCP1 et IL-1α, et le VEGF189 à IL-4 et IL-6. Nous avons étudié le rôle du VEGF-A et d"un autre biomarqueur important, LT, dans l"athérosclérose. Cela a permis d"identifier un variant génétique lié au VEGF-A associé de manière significative à l"attrition des télomères (calculé sous la forme d"un rapport de LT dans les leucocytes et de LT dans les cellules musculaires). Ce variant génétique pourrait constituer un dénominateur commun pour les maladies chroniques. En conclusion, l"utilisation de diverses méthodologies pour étudier les facteurs de risque et les voies impliquées dans des maladies chroniques courantes a permis d"identifier de nouveaux marqueurs diagnostiques et de générer des résultats qui pourraient améliorer la prédiction du risque de maladie basée sur le profil génétique de chaque individu. Les nouvelles données apportées sur les associations entre les différents biomarqueurs pourraient aider à mieux comprendre les voies physiopathologiques communes entre les maladies cardiovasculaires et d"autres maladies chroniques. Enfin, nous espérons que, grâce à une collaboration étroite avec des partenaires privés, les résultats de ces travaux de thèse faciliteront la mise en oeuvre de la médecine personnalisée.

Mots clés : Biomarqueurs, VEGF-A, Télomères, Médecine personnalisée, Epigénétique, Associations génétiques

  diagnostiques et thérapeutiques. Une étude EWAS a également été réalisée pour décrire la régulation épigénétique des concentrations de VEGF-A. Vingt sites CpG ont pu être identifiés ainsi et leurs relations avec la régulation du VEGF-A ont été examinées par analyse bioinformatique poussée. La méthylation de gènes tels que TPX2 ou HAS pourrait avoir un effet sur leur activité et donc conduire à une augmentation de la concentration de VEGF-A. Les liens entre le VEGF-A et 11 cytokines, des médiateurs importants des voies physiopathologiques courantes impliquées dans la plupart des maladies chroniques, ont également été étudiés. Le taux de protéine VEGF-A était associé à IL-4, MCP1 et EGF. Les associations entre les cytokines et des isoformes spécifiques de l"ARNm du VEGF-A ont également été évaluées : le VEGF 165 était associé de manière significative à MCP1 et IL-1α, et le VEGF 189 à IL-4 et IL-6. Nous avons étudié le rôle du VEGF-A et d"un autre biomarqueur important, LT, dans l"athérosclérose. Cela a permis d"identifier un variant génétique lié au VEGF-A associé de manière significative à l"attrition des télomères (calculé sous la forme d"un rapport de LT dans les leucocytes et de LT dans les cellules musculaires). Ce variant génétique pourrait constituer un dénominateur commun pour les maladies chroniques.

  BIBLIOGRAPHY ............................................................................................................................... OTHER PUBLICATIONS .................................................................................................................. ANNEX ...............................................................................................................................................

Figure 1 :

 1 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 2 :

 2 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 3 :

 3 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 4 :

 4 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 5 :

 5 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 6 :

 6 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 7 :

 7 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 8 :

 8 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 9 :

 9 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 10 :

 10 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 11 :

 11 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 12 :

 12 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 13 :

 13 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 14 :

 14 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 15 :

 15 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 16 :

 16 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 17 :

 17 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 18 :

 18 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 19 :

 19 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 20 :

 20 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 21 :

 21 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 22 :

 22 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 23 :

 23 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 24 :

 24 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 25 :

 25 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 26 :Figure 27 :Figure 28 :Figure 29 :

 26272829 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 30 :

 30 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 31 :

 31 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 32 :

 32 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 33 :

 33 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

Figure 34 : 1 . 2 . 3 . 4 .

 341234 Figure 1: Four major disease risk factors .............................................................................................Figure 2: Obesity-related comorbidities affect multiple organ systems. ..............................................Figure 3: Four main types of cardiovascular disease. ..........................................................................Figure 4: Cross-section of atherosclerotic plaque ................................................................................Figure 5: Cancer cells form a tumour ...................................................................................................Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI ......Figure 7: Obesity-induced inflammatory diseases ...............................................................................Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis ...................Figure 9: Sex difference in global mortality in the year 2017 ..............................................................Figure 10: Protein-protein interaction network ....................................................................................Figure 11: The inflammatory pathway .................................................................................................Figure 12: Production and action of cytokines .....................................................................................Figure 13: The cascade of events associated with sprouting angiogenesis ..........................................Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis ..........Figure 15: VEGF-A role in different (patho)physiological mechanisms .............................................Figure 16: Intracellular signalling pathways for VEGF-A ...................................................................Figure 17: The structure of telomere ....................................................................................................Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions ...........Figure 19: The strategy of personalised medicine ................................................................................Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 ...............Figure 21: A schematic presentation of MR ........................................................................................Figure 22: Major epigenetic mechanisms ............................................................................................Figure 23: DNA cytosine ring ..............................................................................................................Figure 24: Transcription of DNA as a part of gene expression ............................................................Figure 25: Tools needed for prediction and personalised care .............................................................Figure 26: Graphical abstract of the published article. .......................................................................Figure 27: Methylated DNA ..............................................................................................................Figure 28: Structure of the VEGF-A protein ......................................................................................Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels. ............................Figure 30: PBMCs are highly specialised immune cells ....................................................................Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels. .......Figure 32: Cytokine and macrophage infiltration in atherosclerotic lesions ......................................Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 ........................................Figure 34: Schematic presentation of applied methods ......................................................................

  Association of rs6918289 with TNF-α levels ....................................................................... Table 10. Association of rs6918289 with IMT-F ............................................................................... Table 11. Results of MAF and HWE analysis .................................................................................... Table 12. Association between VEGF-A related genetic variants and LTL. ..................................... Table 13. Association between VEGF-A related genetic variants and MTL. .................................... Table 14. Association between VEGF-A related genetic variants and LTL/MTL. ............................ Table 15: Multiple regression analysis of VEGF-A ........................................................................... PRÉSENTATION SYNTHÉTIQUE DE LA THÈSE __________________________________________________________________________________ Situation du sujet Au cours des dernières décennies, les systèmes de santé les plus avancés ont développé le concept de médecine personnalisée et ont adopté de nouvelles stratégies pour lutter contre les maladies chroniques courantes auxquelles sont confrontées les populations vieillissantes partout dans le monde. L"objectif est de formuler un diagnostic et un pronostic individualisés, et mettre en oeuvre des traitements sur mesure d"après les données personnelles du patient et pas uniquement d"après la maladie, qui a abouti au développement de nouveaux outils diagnostiques et thérapeutiques. Mais surtout, ces approches prennent en compte les informations génétiques de l"individu qui, associées à d"autres données cliniques, permettent de poser un diagnostic plus précis, de mettre en oeuvre un traitement plus efficace, et d"améliorer le pronostic et l"issue clinique.

5 .

 5 Des associations entre VEGF-A et IL-4, MCP-1 et EGF, entre l"isoforme VEGF 165 et MCP-1 et IL-1α, et entre l"isoforme VEGF 189 et IL-4 et IL-6. Ces associations fournissent un éclairage sur les liens entre les cytokines inflammatoires et le VEGF-A et permettent de mieux comprendre les interactions physiologiques entre l"angiogenèse et l"inflammation.

  20 nouveaux sites qui expliquent la régulation épigénétique de l"expression du VEGF-A dans des conditions physiologiques. Les résultats de ces trois études EWAS distinctes sont discutés ci-dessous. Tout d"abord, nous avons étudié les profils de méthylation associés aux taux de cholestérol total, LDL, HDL et TG dans une population de 211 adultes et enfants sains de la cohorte SFS. Les résultats ont été publiés dans l"article intitulé : « Epigenome-Wide Association Study (EWAS) of Blood Lipids in Healthy Population from STANISLAS Family Study (SFS) ».

  Enfin, rs6918289 se trouve à proximité du gène Triggering Receptor Expressed On Myeloid Cells Like 1 (TREML1). Des analyses in silico ont mis en évidence une corrélation significative entre rs6918289 et sept SNP associés au gène TREML1. De ce fait, TREML1

  Les résultats des recherches épigénomiques portant sur le VEGF-A décrits dans le cadre de cette thèse correspondent à une étude pilote des marques de méthylation du VEGF-A. Étant donné que des données d"expression des différentes isoformes du VEGF-A sont disponibles pour certains individus de la cohorte SFS, il était possible de réaliser une étude EWAS pour décrire la régulation épigénétique de l"expression du VEGF-A. Néanmoins, cette étude doit être répétée avec un échantillon d"étude plus grand provenant d"une population cas-contrôle comprenant des patients atteints de cancer qui ne reçoivent pas de traitement antiangiogénique. Cela pourrait permettre l"identification de gènes présentant une méthylation différentielle impliqués dans la régulation de l"angiogenèse. De telles découvertes représenteraient des avancées très importantes pour le développement d"une nouvelle génération de traitements anti-angiogéniques.Nous avons démontré une association entre le variant génétique rs6993770 lié au VEGF-A et l"attrition des télomères, ce qui suggère l"existence de mécanismes de régulation génétique communes agissant sur les deux biomarqueurs qui pourraient être intéressants en médecine personnalisée. Les associations entre des variants génétiques liés au VEGF-A et d"autres phénotypes et maladies, tels que les taux de lipides, la maladie d"Alzheimer, le diabète de type 2, le syndrome métabolique, ont déjà été étudiées. Une étude de randomisation mendélienne est nécessaire pour évaluer de manière systématique le rôle du VEGF-A dans différentes maladies, des facteurs de risque intermédiaires et la régulation d"autres molécules inflammatoires, en utilisant les variants génétiques liés au VEGF-A pour pallier au manque de données sur l"expression du VEGF-A.TG et tour de tailleIl est également nécessaire de confirmer les sites CpG identifiés comme étant associés au taux de TG et au tour de taille à l"aide de cohortes plus importantes comprenant des populations d"individus obèses. Parce que le trait « tour de taille » est lié à une augmentation de l"accumulation de tissu adipeux autour de la taille, le site CpG associé à ce trait doit aussi être confirmé dans des échantillons d"ADN extraits de tissu adipeux. Des études de randomisation mendélienne seraient utiles pour déterminer le rôle causal des sites CpG identifiés dans des maladies chroniques courantes.Longeur de télomèresLe variant génétique lié au VEGF-A, rs6993770, démontré comme étant associé à l"attrition des télomères pourrait expliquer les liens observés avec certains biomarqueurs et l"importance de LT dans l"étiologie des maladies chroniques. Une association entre LTL, LTM et l"attrition des télomères et le SNP rs6993770 est donc à rechercher dans des populations de patients atteints de différentes maladies associées à un raccourcissement de LTL (de nombreuses maladies métaboliques et inflammatoires, comme les maladies cardiovasculaires, le diabète, la rectocolite hémorragique, la cirrhose du foie et le lupus érythémateux systémique). Cela pourrait aider à déterminer si le variant identifié est associé à un raccourcissement accéléré dans toutes les maladies ou si celui-ci est spécifique à l"athérosclérose. Il conviendra de génotyper le SNP rs6993770 dans une population cas-contrôle plus importante de patients atteints d"athérosclérose afin d"évaluer son éventuelle utilité comme biomarqueur de la susceptibilité/du risque d"athérosclérose.La réalisation d"une étude longitudinale de suivi d"individus dès la naissance et jusqu"à l"âge adulte est essentielle pour la mise en application de LT comme biomarqueur diagnostique/pronostique/de susceptibilité en médecine personnalisée. Grâce au développement rapide de technologies de séquençage du génome entier, LT pourrait être mesurée à l"aide de méthodes bioinformatiques. Des progrès sont donc attendre dans ce domaine.TREM2Le variant génétique rs6918289 localisé dans la région du gène TREM2 doit être étudié par rapport aux taux de la protéine TREM2 afin de déterminer dans quelle mesure ce variant peut agir sur les concentrations plasmatiques de TREM2. Cela pourrait aider à comprendre les mécanismes qui relient rs6918289 à TNF-α et IMT-F. Des analyses transcriptomiques seront à réaliser ensuite pour expliquer la fonctionnalité du SNP et confirmer l"hypothèse proposée.Il conviendra également de tester ce variant génétique dans une population cas-contrôle plus importante de patients atteints d"athérosclérose afin d"évaluer son éventuelle utilité comme biomarqueur de la susceptibilité/du risque d"athérosclérose.FOREWORD__________________________________________________________________________________During the last few decades, modern healthcare systems developed the model of personalised medicine, which adopted new strategies for the fight against common chronic diseases that are affecting the lives of ageing populations around the globe. The wish to create diagnostics, treatment and prognostics tailored to the individual"s personal data, not only by disease, guided the development of new analytical tools and therapeutics. Most importantly, it included an individual"s genetic information in combination with other clinical data to achieve more precise diagnostics, more efficient treatment and better prognostics of pathology outcomes.The establishment of a whole new era in medicine demanded enormous efforts in the development of new technologies, methodologies, creating databases (big-data) and bigpopulation cohorts to generate information, which would explain the basis of the genetic regulation of complex diseases. Genetic epidemiology emerged as a new discipline to study the role of genetic factors and their interplay with the environment. Large-scale genome-wide association studies (GWAS) that genotype many thousands of single nucleotide polymorphisms (SNPs) in thousands of individuals have identified variants, each one contributing to some extent to a polygenic, multifactorial complex disorder. Yet, this was not sufficient for the complete understanding of genetic regulation of chronic diseases. The missing link between environmental exposures, genetics and many common diseases that GWAS couldn"t provide was partially attributed to epigenetic changes on DNA. The study of methylation changes on genome recently became the most frequently used epigenomic tool for studying the environmental impact on the regulation of genes. High-throughput technologies combined with computer science and informatics are now providing specialised tools which are used to view and analyse biological data from genomics, transcriptomics, proteomics and other related fields.But how all the above contribute to change the medicine? Today, we are still in the transition period, where genetic-based tests are being used for the screening of diseases, first gene therapies are being approved, and growing numbers of personalised medications are entering the market, whereas the classical medical approaches remain the gold standards in the hospitals. Research laboratories and the pharmaceutical industry are determined to improve the current system and offer new strategies based on the individual genetic "make-up".Large quantities of useful information that were generated in the last decade now need to be linked together to provide new knowledge on human biology and pathology. Such an effort could improve the current strategies of prevention, prediction and treatment of common chronic diseases and enrich them with tools provided by personalised medicine. This includes identification of biomarkers that will allow determination of susceptibility for disease, diagnosis, prognosis and treatment and proposition of new therapeutic targets based on the profound knowledge of biological pathways and links between participating molecules. The research Unit UMR INSERM U1122 Gene-Environment Interactions in Cardio-Vascular Physiopathology (IGE-PCV) has already embraced the new methodologies of personalised medicine and focused on the exploration of genetic and environmental components involved in common age-related diseases. The core of the Unit presents a large Biological Resources Centre (BRC) IGE-PCV, which consists of healthy and pathologic populations for the study of cardiovascular pathophysiology. The wish to create a big database that will serve for genetic epidemiology studies resulted in the initiation of the STANISLAS Family Study. With longitudinal data of more than 1000 families, it became one of the principal populations of the biobank and offered vast genetic, epigenetic and clinical data to study the variability of disease phenotypes and environmental determinants. Large availability of information from our biobank together with tight collaborations with other cohorts have made it possible for us to explore the genetic and epigenetic regulation of intermediate phenotypes that are involved in diverse age-related pathologies, in order to identify biomarkers common to different diseases pathways.

  phenotypes involved in disease pathways and related to risk factors, which presented a special interest and potential for clinical application. Understanding the complex genetic background of these phenotypes would help to provide better stratification of patients by taking into account their genetic profile. The knowledge of associations among different molecules, on the other hand, would offer complementary treatments. Finally, understanding of epigenetic regulation of intermediate risk factors would provide new susceptibility biomarkers, which could help in early prognostic and assist in the prevention of the onset of chronic pathologies.

Figure 1 :

 1 Figure 1: Four major disease risk factors: tobacco use, physical inactivity, unhealthy diet and harmful use of alcohol (1).

Figure 2 :

 2 Figure 2: Obesity-related comorbidities affect multiple organ systems. (Source: Rethink obesity).

  As for all chronic diseases, multiple factors are involved in the complex pathogenesis of CVD, from non-modifiable genetics, gender, age and ethnicity, to factors that can be modified by drugs or lifestyle change. The exposure to genetic and environmental risk factors can cause the manifestation of so-called "intermediate risk factors", which can be measured and can indicate an increased risk for cardiovascular complications. These are dyslipidaemia, hypertension, hyperglycaemia and obesity (105). Identification of intermediate risk factors can encourage lifestyle changes and initiate a drug treatment, which can normalise the condition and reduce the risk of CV events(4). Another level of complexity for CVD aetiology involves the interactions between risk factors, which are working in synergy with each other, and can considerably amplify each one"s impact on a disease(68).

Figure 3 :

 3 Figure 3: Four main types of cardiovascular disease.

Figure 4 :

 4 Figure 4: Cross-section of atherosclerotic plaque: disrupted endothelium, caused by increased inflammation in the arterial wall. (Source: Health Tap).

Figure 5 :

 5 Figure 5: Cancer cells form a tumour, which develops its own vascularisation to enable a continuous flow of oxygen and nutrients. (Source: Santé-Nutrition).

Figure 6 :

 6 Figure 6: Percentage of adults aged 18+ years old who are defined as obese based on their BMI (35).

Figure 7 :

 7 Figure 7: Obesity-induced inflammatory diseases: interaction between adipocytes and immune cells. (Source: Creative Diagnostics).

  correct diagnosis. Genetic diagnosis, on the other hand, can allow early detection, proper therapeutic guidance and as such contribute to the reduction of cardiovascular risk (129). Hypercholesterolaemia Hypercholesterolemia is the presence of high levels of cholesterol in the blood, resulting from an unhealthy diet, obesity, genetic factors or the presence of other diseases, such as type 2 diabetes. Most of the serum cholesterol is produced by the liver as triglyceride-rich particles in the form of very-low-density lipoprotein (VLDL), which transforms to LDL after removal of triglycerides. The formation of VLDL and LDL is increased in obesity and with a diet high in saturated fat. The role of LDL is to deliver cholesterol to tissues, as it is an essential component of cell membranes, hormones and vitamins. LDL is removed from the circulation by LDL receptors. Genetic defects in LDL receptor function are the usual cause of familial hypercholesterolaemia. HDL is involved in reverse cholesterol transport; it acquires excess cholesterol from the tissues and returns it to the liver. It maintains the balance of cholesterol levels in the arteries and is considered as an atheroprotective molecule (131).

  shown important protective effects by suppression of atherosclerosis progression (133). LDL can deliver huge amounts of cholesterol to macrophages and cause the formation of foamcells, which secrete inflammatory cytokines and contribute to smooth-muscle-cell migration and T-cell-mediated inflammatory and immune response. With time, cholesterol-loaded foam cells in combination with smooth muscle cells, extracellular matrix material and smoothmuscle-cell-derived scar tissue lead to slowly progressive lumen occlusion-a formation of advanced atherosclerotic plaque (132).

Figure 8 :

 8 Figure 8: Pathophysiology of TG-rich lipoproteins in the progression of atherosclerosis (137).

Figure 9 :

 9 Figure 9: Sex difference in global mortality in the year 2017, presented by the age group (149).

Figure 10 :

 10 Figure 10: Protein-protein interaction network. (Source: Kettenbach Lab).

Figure 11 :

 11 Figure 11: The inflammatory pathway consists of inducers, sensors, mediators, and target tissues (159).

Figure 12 :

 12 Figure 12: Production and action of cytokines. (Source: Shutterstock).

Figure 13 :

 13 Figure 13: The cascade of events associated with sprouting angiogenesis. (EC: endothelial cell, BM: basement membrane). (Source: The Angiogenesis Foundation).

Figure 14 :

 14 Figure 14: Two kinds of pathological situations caused by increased or reduced angiogenesis (175).

  encouraged the scientists for investigation which resulted in an abundance of publications reporting clinically useful molecular biomarkers (182). Despite the enormous expansion in literature, there is still only a limited number of biomarkers currently used in discovery, treatment, or monitoring of common chronic diseases (183).The problem stands in the complexity of chronic diseases, caused by the web of genetic variants and environmental risk factors that are each contributing to the development of pathological conditions (182). Fortunately, the identification of new biomarkers is now accelerated with fast advances in molecular biology, genetics, and computational biology, which enables the research of genomic biomarker as an alternative to traditionally measured characteristics. Consequentially, genetic tests for population screening, diagnostic, predictive, susceptibility testing and pharmacogenomics purposes are being developed (184).Genomic biomarkers are defined as a "DNA or RNA characteristic that is an indicator of normal biologic processes, pathogenic processes, and response to therapeutic or other intervention." They reflect the expression, function or regulation of a gene. DNA characteristics include SNPs, DNA modification (e.g. methylation), insertions, deletions, copy number variations and others. RNA characteristics include RNA sequence, RNA expression levels, micro RNA (miRNA) levels and others (184).

  VEGF-A works as a chemotactic agent for macrophages and granulocytes. Moreover, it induces the proliferation and migration of vascular endothelial cells and protects them against apoptosis to enable their survival in modified conditions (185, 186).

Figure 15 :

 15 Figure 15: VEGF-A role in different (patho)physiological mechanisms. (Source: Selfhacked).

Figure 16 :

 16 Figure 16: Intracellular signalling pathways for VEGF-A (42).

(

  Figure 17) (196), which forms a protective cap that conserves the genetic material during cell divisions and protects it from constitutive exposure to the DNA damage response(197). It is shortening progressively with age (198), as a result of the end replication problem during cell division or oxidative stress. Chromosomes with critically short telomeres are recognized by DNA damage response proteins as a damaged DNA, which leads the cell to controlled telomere-initiated senescence(199). When cells become senescent, they undergo morphological and genetic changes that result in loss of tissue function.

Figure 17 :

 17 Figure 17: The structure of telomere. (Source: Medical news today).

Figure 18 :

 18 Figure 18: Shorter inherited telomeres have been related to advanced atherosclerotic lesions (217).

Figure 19 :

 19 Figure 19: The strategy of personalised medicinebetter diagnostic, prognostic and treatment. (Source: Frost & Sullivan).

Figure 20 : 3 )

 203 Figure 20: The increasing number of GWAS studies and sample sizes in years 2007-2017 (240).

  Synonymous mutations, which affect only DNA or mRNA sequence, but do not cause an apparent change in the protein. They can modify the regulatory mechanisms at the basis of gene expression. Any change in the nucleotide sequence can lead to splicing alterations or to mRNA instability caused by alterations of secondary structure or by altered binding of miRNAs, resulting in decreased protein expression. Splicing, which affects regulatory mechanisms leading to intron retention or exon skipping. Each intronic, as well as an exonic variant, should be assessed for its potential effects on splicing. Missense mutations, which affect a change of a single amino acid in a protein. It is not deleterious if the affected amino acid is not included in the functional domains of the protein or if it is not essential in the protein folding, which is evaluated using the multiple alignment algorithms. Deletion or insertion without reading frameshift, which induces a deletion or an insertion of few amino acids and should be studied with respect to the conservation of involved region and possible alteration of protein structure.1.4.1.4 Mendelian RandomisationEpidemiological studies provide an abundance of information on associations between exposures and outcomes, but they cannot indicate the causal relations due to limitations introduced by confounding and reverse causality (246). Mendelian randomisation (MR) is an established genetic epidemiological approach that uses genetic variants (e.g. SNPs) as instrumental variables to assess causal relationships between exposures and outcomes (e.g. disease). It reports the association of the genetic variants directly associated with exposure, and the outcome (Figure21). The genetic variant used in the study should only affect a single pathway on which the exposure of interest lies, and should not be associated with confounders, which impact on exposure and disease. As such, MR can provide unbiased evidence as to whether an exposure causes the investigated outcome (246).

Figure 21 :

 21 Figure 21: A schematic presentation of MR (247).

  chromosome through modification of gene expression, without changing the underlying DNA sequence. Epigenetic programming is a dynamic process, influenced by environmental exposures during development, and includes several covalent or noncovalent modifications of DNA and histone proteins (Figure22). The major epigenetic mechanisms, i.e. DNAm, histone modifications, chromatin remodelling, and small noncoding RNA (miRNA)-associated gene silencing are involved in complex interactions with each other and with classical transcription factor-based gene regulation (251-253).Epigenetic modifications are critical for the regulation of genes and noncoding RNA expression. In turn, disruption of one of these systems can lead to inappropriate expression or silencing of genes, resulting in alterations in health and disease. Technological breakthroughs enabled large-scale epigenomic studies, which can help to learn how epigenetic marks and mutations are involved in pathophysiological mechanisms. Epigenetic alterations have been demonstrated in many diseases, primarily in cancer, but also in CVD, metabolic diseases and others. The reversibility of epigenetic changes and the discovery of numerous agents that alter methylation patterns or the modification of histone have encouraged the development of new therapeutic options for treatment of such diseases. This resulted in extensive studying in epigenetics for a large spectre of medical applications (254, 255).

Figure 22 :

 22 Figure 22: Major epigenetic mechanisms: chromatin changes, caused by DNA wrapping around a core octamer of histone proteins, DNAm at cytosine rings and miRNAs, involved in post-transcriptional control of gene expression (253).

Figure 23 :

 23 Figure 23: DNA cytosine ring is methylated by enzymes methyltransferases to form 5methylcytosine at CpG sites. (Source: Dnaofbioscience).

  get a deeper understanding of the mechanisms underlying CVDs and enable the development of effective interventions for human cardiovascular disorders, based on DNAm (263).1.4.2.2 Epigenome-wide association studies EWAS are large-scale studies of human disease-associated DNAm variation, which enable identification of methylation markers that are specific for a disease or a particular disease risk factor (264). Genome-wide methylation profile of individuals is assessed through high throughput technology. Currently, Infinium HumanMethylation450 is the most commonly used array, which assesses the methylation status for >450 000 CpG sites located throughout the genome. Differential methylation analysis is performed using common statistical procedures after the required processing of methylation data (filtering of problematic probes and normalisation) (265).

Figure 24 :

 24 Figure 24: Transcription of DNA as a part of gene expression. (Source: Shutterstock).



  In situ hybridisation: a technique used for the high-resolution detection, quantification, and localisation of nucleic acid targets inside cells or tissues. It is based on the hybridisation of sequence-specific fluorescently labelled complementary probes (typically DNA sequences) to their target inside the cell (273).  Run-off transcription assay: in vitro assay to identify transcription start sites (TSS) of a specific promoter. It can be used to quantitatively measure the effect of changing promoter regions on in vitro transcription levels (274).  MS2 tagging: a technique used to monitor the appearance of RNA in living cells, at the site of transcription, or simply by observing the changes in RNA number in the cytoplasm

Figure 25 :

 25 Figure 25: Tools needed for prediction and personalised care (276).

2 .

 2 To investigate the association between DNAm (CpG sites) and intermediate phenotypes such as blood lipids levels and waist circumference, in a healthy population, thus to discover underlying mechanisms linking the DNA methylation with blood lipids metabolism and central obesity, respectively. 3. To investigate the association between DNAm (CpG sites) and VEGF-A levels in a healthy population, thus to discover underlying mechanisms linking the DNA methylation to angiogenesis.

4 . 6 .

 46 To identify common physiological pathways among VEGF-A, TREM2, TL and other inflammatory cytokines with genetic association studies. 5. To evaluate the role of TL in personalised medicine and its potential in diagnostics, prognostics and treatment. To be introduced in the working environment and collaboration with clinical diagnostics industry in the field of application of research results to personalised medicine. Study (SFS) is a familial longitudinal cohort, aiming to study the role and the contribution of genetic and environmental factors to cardiovascular status (278, 279). Laboratory and demographic data were collected for 1006 families (two parents and at least two children aged 4 or more) from the East of France, in three recruitments during ten years. Participants were enrolled in the Center for Preventive Medicine (CMP) of Vandoeuvre-lès-Nancy, during a periodical health assessment. All the individuals included in the research were of European-Caucasian origin and supposed healthy, free from any declared acute or chronic disease. More than 600 data variables were collected on each individual with the help of trained professionals, including clinical data and lifestyle information, provided by questionnaires. Measurements were performed in the CMP and other collaborating research laboratories. Waist circumference was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone). Measurements were recorded to the nearest 0.1 cm by trained professionals. BMI was calculated by the Quetelet index formula as weight (kilograms) divided by height² (meters)². Study protocols were approved by the institutional ethics committee CCPPRB de Lorraine (Comité consultatif de protection des personnes dans la recherche biomédicale) and CNIL (Commission Nationale de l'Informatique et des Libertés). All individuals gave written informed consent for their participation in the study. The SFS is part of the BRC IGE-PCV with identification number BB-0033-00051, which was initiated by the UMR INSERM U1122; IGE-PCV.

3. 1 . 4

 14 The TELARTA study Telomeres and Arterial Ageing (TELARTA) is a case-control study, aiming to examine the links of arterial ageing and development of atherosclerosis with TL, as expressed in different tissues (leukocytes, muscle, skin, progenitor cells), and telomere length dynamics, expressed as a difference between TL of muscle and leukocytes. Muscle biopsies of patients with clinically significant atherosclerotic disease and controls without evidence of clinical CVD or history of previous CVD were collected during undergoing surgery or pacemaker/defibrillator implantation. Altogether, 131 atherosclerosis cases and 128 controls were enrolled in two French university hospitals, in Nancy and Marseille. All participants provided written informed consent approved by the Ethics Committee (Comité de Protection des Personnes) of Nancy, France. Subjects with BMI >40 kg/m 2 , a glomerular filtration rate under 30 mL min -1 1.73 m -2 , active malignancy, or history of chemotherapy/radiotherapy for cancer and subjects with aortic aneurysm were excluded from discovery cohort. Replication cohort of the TELARTA study was composed of additional 91 individuals, enrolled in the university hospitals of Nancy and Marseille, and 52 individuals enrolled at 3 Athens hospitals (Onassis Cardiac Surgery Center, Iaso General Hospital, Hippokration Hospital). Moreover, the inclusion criteria for the replication cohort were less strict, and individuals that were excluded from the TELARTA discovery cohort based on BMI >40 kg/m 2 , glomerular filtration rate under 30 mL min -1 1.73 m -2 , malignancy or history of chemotherapy/radiotherapy were included in the replication cohort. Participants enrolled in France provided written informed consent approved by the Ethics Committee (Comité de Protection des Personnes) of Nancy, France. Those enrolled in Athens provided written informed consent approved by the Ethics Committee of the University of Athens and Ethics Committee of each one of the three participating hospitals (81).

  study "Epigenome-Wide Association Study (EWAS) of Blood Lipids in Healthy Population from STANISLAS Family Study (SFS)' gave new insights into epigenetic regulation of TG levels, which could be useful in the future as a diagnostic tool for detecting early dysregulation of cardiovascular risk factors. Moreover, it revealed a link between DNA methylation and TG levels through possible metabolic pathways that could be associated with different diseases and would merit further investigation for future implication in personalised medicine.A limitation of our study could be the use of the combined samples of related children (>6 years) and adults in the discovery population. The reason for such composition was the increased sample size in the combined population, which in turn increased the power of the analysis and helped us to identify the significant association. To avoid the spurious results, we performed the necessary adjustments for age, sex, BMI and family structure along with the analysis. Nevertheless, the replication of a result in the adult-only cohort confirmed that a combination of adult and children samples in the discovery cohort was not limiting for research. Ting Xie * , Vesna Gorenjak * , Maria G. Stathopoulou * , Sébastien Dadé, Eirini Marouli, Christine Masson, Helena Murray, John Lamont, Peter Fitzgerald, Panagiotis Deloukas and Sophie Visvikis-

Figure 27 :

 27 Figure 27: Methylated DNA -a link between environment and genetics (34).

  European-Caucasian origin from 73 families (n=211) from the SFS have been enrolled in this study, including 116 adults and 95 children. Blood DNA methylation assessment was performed by Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA). The R package minfi was used for the analysis of the methylation data and R package CpGassoc was used for the association analyses of methylation values and WC in a linear mixed-effects model adjusted for sex, age, BMI, family structure and individual blood cell counts (neutrophils, lymphocytes, monocytes, eosinophils and basophils) as covariates, and chip array as random effect. The relation between methylation and genetic variants associated with WC was investigated in a linear mixed-effects model using the R package nlme.

  through GWAS (n > 16,000). Despite the well-established studies on VEGF-A genetic determinants, there is only little knowledge of VEGF-A epigenetic regulation. Knowing and understanding the mechanisms of epigenetic regulation of VEGF-A might allow further comprehension of the epigenetic regulation involved in chronic diseases, where VEGF-A plays a crucial role.

Figure 28 :

 28 Figure 28: Structure of the VEGF-A protein: VEGF-A forms an antiparallel homodimer, with symmetrical binding sites for kinase domain receptors at each pole of the protein (297, 298).

  -A levels of 201 healthy participants from the SFS were tested for association with DNA methylation. DNA was extracted by the Miller technique (281) and was stored at -80 °C. Whole-genome DNA methylation was investigated with Illumina HumanMethylation450 bead array (Illumina Inc., San Diego, CA, USA). Minfi R package (Bioconductor) was used to analyse and visualise Illumina Infinium methylation arrays, background correction, colour bias adjustment and Infinium I/II bias correction were carried out with Illumina background correction and SWAN. Finally, CpGassoc (CRAN) was used to test for association between methylation at CpG sites across the genome and VEGF-A concentration in PBMC extracts. The random mixed-effects model included gender, age, BMI, family structure and individual blood cell counts (neutrophils, lymphocytes, monocytes, eosinophils and basophils) as covariates and chip array as random effect.

Figure 29 :

 29 Figure 29: Manhattan plot of significant CpG associations with VEGF-A levels.

ND

  = No Data (ND 1 = ARL4A and ETV1, ND 2 = ZSWIM4 and NANOS3, ND 3 = FOXL1 and C16orf95, ND 4 = ZNF621 and CTNNB1, ND 5 = TBL1XR1 and KCNMB2, ND 6 = TSHZ1 and SMIM21).

Figure 31 :

 31 Figure 31: The relation between inflammatory cytokines and VEGF-A protein/mRNA levels.

  Briefly, 259 French individuals recruited in the discovery cohort of the TELARTA study were included in this population. The aim of the TELARTA study was to measure the TL in skeletal muscle and leukocytes of atherosclerotic CVD (ASCVD) patients and controls, in order to examine the role of telomere dynamics in arterial ageing. All participants provided written informed consent approved by the Ethics Committee (Comité de Protection des Personnes) of Nancy, France. The replication cohort of the TELARTA study included 91 French individuals, recruited under the same conditions as discovery cohort and 52 individuals from an independent Greek population. The samples from three Greek hospitals have been collected at the Laboratory for Experimental Surgery and Surgical Research of the University of Athens. All participants enrolled in Athens provided written informed consent approved by the Ethics Committee of the University of Athens and Ethics Committee of each one of the three participating hospitals.

Figure 33 :

 33 Figure 33: Rs6993770 (red stripe) is located on the intron of the ZFPM2 gene (8q23.1).

7 .

 7 An association between VEGF-A related genetic variant rs6993770 and telomere attrition (defined as LTL/MTL) in an atherosclerosis case-control study, which suggested a common implication of VEGF-A and TL in the aetiology of CVDs.8. A state of the art of the usefulness of TL measurements in future clinical applications in personalised medicine and the need for longitudinal research, starting from childhood. Such study will clarify the telomere biology in the first years of life and will assess the influence of childhood environmental factors on adult TL and susceptibility for common chronic diseases.Personalised medicine has developed as an alternative to classical clinical approaches, based on the principle "one size fits all" for treatment of patients with the same medical condition. It brought new strategies for better management of patients" health and therapies, combining and analysing individual"s genomic information along with other clinical and diagnostic data. This enabled better identification of an individual"s risk of developing a disease, faster detection of illnesses and determination of the most effective interventions(336). In this thesis work, we followed the principles of personalised medicine and used combined clinical and genetic information in research for new possibilities in the fight against common chronic diseases.

First, we investigated

  methylation patterns of TC, LDL, HDL and TG levels in a population of 211 healthy adults and children from the SFS, in research entitled "Epigenome-Wide Association Study (EWAS) of Blood Lipids in Healthy Population from STANISLAS Family Study (SFS)".Deregulated blood lipid levels are among the most important risk factors of CVDs(20). They are influenced by genetics as well as environmental exposures, which can reflect on DNA methylation patterns. Insight into these epigenetic patterns can explain the interindividual variability in lipid phenotypes as well as the underlying regulatory mechanisms(21). Previous studies that investigated the epigenetic regulation of lipids were performed on subjects suffering from a cardiovascular condition. Regulation of lipid traits can differ significantly in healthy population and novel CpG sites involved in gene regulation pathways can be discovered. Using the epigenome-wide approach, this study identified two CpG sites significantly related to TG levels, cg08897188 (chromosome 7q36.1, β=-2.80, P=1.39 × 10 -8 ) and cg04580029 (chromosome 16p13.3, β=3.09, P=5.75 × 10 -9 ).

PRKAG2,

  coding for AMP/ATP-binding subunit of AMP-activated protein kinase, and a noncoding transcript of AC093583.1 gene, LOC644090. PRKAG2 plays a key role in the lipid metabolism via regulation of AMP/ATP-binding subunit of AMP-activated protein kinase (AMPK). AMPK maintains the energy balance within the cell and protects the body against metabolic disorders, such as obesity and type 2 diabetes (338). When activated, it switches off ATP-consuming pathways, i.e. glycogen, cholesterol and fatty acid synthesis and activates ATP-producing pathways, i.e. fatty acid oxidation (339). Mutations in the PRKAG2 gene were previously associated with impaired glucose metabolism and excess glycogen storage in human cells (340) and can result in deformation of myocytes (341). Deformed myocytes can cause arrhythmias and hypertrophic cardiomyopathy-like manifestations (339). CpG cg08897188 could also affect a non-coding transcript of AC093583.1 gene, LOC644090, located on the opposite strand. The effect of cg08897188 on TGs could be exerted via enhancing of the LOC644090 transcription which would, in turn, down-regulate PRKAG2, or could directly affect PRKAG2.Cg04580029 is located in the promoter of KREMEN2 gene, coding for a secreted antagonist of the Wnt/β-catenin signalling pathway dickkopf1 (Dkk1) (342). Wnt signalling pathway is an important regulator of various cellular pathways, including cell migration, cell polarity, neural patterning and organogenesis during embryonic development (343). LRP6 (lowdensity lipoprotein receptor-related protein 6) is a co-receptor of Wnt signalling pathway, previously associated withAD and CAD (344, 345). Methylation at cg04580029 near transcription site could affect expression levels, which might result in modified TG concentrations. In addition, the methylation of the KREMEN2 gene could also help in explaining the links of the Wnt/β-catenin signalling pathway with AD and CAD.

  isoforms (VEGF 121 , VEGF 145 , VEGF 165 and VEGF 189 ) were investigated for their association with the inflammatory molecules IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, INF-γ, TNF-α, MCP-1 and EGF. Associations with VEGF-A protein levels were investigated in 285 individuals from the SFS, while a subset of 110 individuals was used for association study with VEGF mRNA isoforms. The study identified associations of VEGF-A with IL-4, MCP-1 and EGF, associations of VEGF 165 isoform with MCP-1 and IL-1α, and associations of VEGF 189 isoform with IL-4 and IL-6. Detailed bibliographical research has been done to explain the relations of identified cytokines with VEGF-A.

  Besides all the above discoveries, during the preparation of this thesis work, we have actively participated in the European project MAST4HEALTH, HORIZON 2020 Marie Skłodowska-Curie actions -Research and Innovation Staff Exchange (MSCA RISE). Our role in this project was to determine the change in expression of inflammatory biomarkers, such as VEGF-A (VEGF 145 , VEGF 121 , VEGF 165 , VEGF 165b , VEGF 189 ), TNF-α, and IL-6 after the treatment of NAFLD patients with a Greek plant mastiha, known for its antioxidant/antiinflammatory and lipid-lowering properties. In the frames of the project, we have been also involved in 6-month secondment in in vitro diagnostics company Randox Laboratories, where we participated in developing and characterising of monoclonal antibody fragments, which will be used for a precise measurement of inflammatory biomarkers in human serum.This experience was interesting from the scientific point of view; we autonomously prepared numerous experiments and thus contributed to the research of new diagnostic tools. Also, it was important from the collaborative perspective; the exchange of personnel created a tight link between the industrial and our research Unit. We believe that such applicative collaborations between research laboratories and industry can assure the fastest way from the intriguing discoveries towards successful application in patient care. The project MAST4HEALTH is still on course and the final results are expected in 2020. Chapter VI. CONCLUSION AND PERSPECTIVES __________________________________________________________________________________ During the three years of my research work, we used different methodologies to explore the risk factors and molecular pathways of chronic diseases in order to answer the need for biomarkers applicable in personalised medicine. We were building the knowledge based on the rich experiences and discoveries of the research Unit UMR INSERM U1122 IGE-PCV while integrating the most recent approaches for the investigation of the genetic epidemiology of common chronic diseases. VEGF-A is one of the principal targeted biomarkers of our research Unit, which created and led the international VEGF Consortium (http://www.vegfconsortium.org/) that aims to develop a transnational collaborative network dedicated to large integrative and multidisciplinary genomic studies of VEGF. The collaborative projects of the VEGF Consortium, among others, consisted of two large GWAS that identified ten genetic variants related to VEGF-A. The work of this thesis pushed the investigations of VEGF-A further and resulted in 1) identification of new associations with inflammatory molecules, 2) demonstration of genetic association with telomere attrition, and 3) first insights in epigenetic regulation. More precisely, by:  Studying the associations of several molecules that assist VEGF-A and its isoforms in the process of inflammation and angiogenesis. The findings should serve in the development of new therapeutic strategies against VEGF-A, which could be improved by acting on specific isoforms and their associated cytokines.  Searching for the links between TL and VEGF-A. A genetic variant discovered in our study offered a new hypothesis for the explanation of the aetiology of telomere-related chronic diseases.  Determining the epigenetic regulation of VEGF-A. This was the first essay to demonstrate genome-wide methylation mechanisms involved in VEGF-A biology. Epigenetic studies are opening a new era in genetic epidemiology, enabling mechanistic insights into genetic and environmental risk factors for diseases, providing biomarkers of exposure and helping to localise disease-relevant genomic regions to identify potential intervention targets. During the last few years, we were building a strategy to perform EWAS and carried out altogether three studies using the healthy SFS population as discovery cohort. Besides VEGF-A, we focused on epigenetic markers related to intermediate risk factors common in chronic diseases and demonstrated the methylation changes related to WC and to TG levels. Given the large possibilities for clinical application of disease-relevant methylation sites, these results have a high potential for future use in personalised medicine.

Figure 34 :

 34 Figure 34: Schematic presentation of applied methods, investigated intermediate phenotypes, corresponding risk factors, and identified results.

1 :

 1 relation to TREM2 molecular levels, to determine to which extent it can affect TREM2 plasma concentrations. This would help to explain the mechanisms that are linking rs6918289 to TNF-α and IMT-F. Transcriptomic analyses should be performed afterwards to explain the functionality of the SNP and confirm the proposed hypothesis. The genetic variant should be tested in big atherosclerosis case-control population, to investigate its potential for a susceptibility/risk biomarker in atherosclerotic diseases. Genomic environment of six CpG sites. Dark green regions present a CpG site, numbers on left and right of the box indicate a location, within which CpG can be found. Nearest genomic features upstream (left) or downstream (right) are presented for each CpG. Distance (bp) between each CpG and genomic feature is indicated in light green regions. Turquoise squares present CTCF regions, red promoter region and yellow enhancers. Square brackets [ ] indicate that CpG is located within genomic feature. Seven different PBMC cell types were looked up (Sup.

TABLE OF CONTENTS

 OF

Epigenome-Wide Association Study Detects a Novel Loci Associated with Central Obesity in Healthy Subjects ».

  La population d"étude était composée de 211 adultes et enfants sains de la cohorte SFS. L"obésité est une accumulation excessive de graisses qui augmente le risque de plusieurs comorbidités, notamment le diabète de type 2, les maladies cardiovasculaires et le cancer

de taille et au rapport taille/hanches (ajusté pour l"IMC). Les résultats de ces travaux ont été publiés dans l"article intitulé : «

  (PBMCs) et nous avons discuté, dans l"article, des liens possibles avec les gènes à proximité des sites méthylés. De manière surprenante, aucun site CpG n"a été identifié dans un site de régulation d'un gène directement

impliqué dans la biologie du VEGF-A, tel que VEGF-A (6p21.1),

, KDR (4q12), HIF1A (4q23.2) etc. Ceci pourrait être expliqué par une méthylation de sites non spécifiques induite par les taux élevés de VEGF-A. Cependant, nous avons identifié des sites de méthylation proches de gènes qui pourraient être associés indirectement à la régulation du VEGF-A. Les deux résultats les plus intéressants étaient les sites CpG cg21838233 (gène TPX2) et cg06785213 (gène HAS1). Le gène TPX2 est impliqué dans la mise en place du fuseau mitotique, et est souvent surexprimé dans les cellules de cancer gastrique

  VEGF-A et le niveau d"expression des ARNm des quatre isoformes les plus abondantes du VEGF-A (VEGF 121 , VEGF 145 , VEGF 165 et VEGF 189 ) et les molécules inflammatoires IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8,

IL-10, INF-γ, TNF-α, MCP-1 et EGF a été évaluée. Les interactions avec l"expression protéique du VEGF-A ont été étudiées chez 285 individus de la cohorte SFS, alors qu"un sous-groupe de 110 individus a été utilisé pour l"étude d"association avec les ARNm des isoformes du gène VEGF. Ces travaux ont permis d"identifier des associations entre le VEGF-A et IL-4, MCP-1 et EGF, entre l"isoforme VEGF 165 et MCP-1 et IL-1α, et entre l"isoforme VEGF 189 et IL-4 et IL-6. Des recherches bibliographiques approfondies ont été menées pour expliquer les liens entre les cytokines identifiées et le VEGF-A.

article intitulé « The polymorphism rs6918289 located in the downstream region of the TREM2 gene is associated with TNF-alpha levels and IMT-F », nous avons analysé deux

  6 et IL-1α étaient associées respectivement à l"expression des isoformes VEGF 189 et traits quantitatifs, le taux de TNF-α et IMT-F (épaisseur intima-média fémorale), tous deux des indicateurs importants de l"athérosclérose. Puisqu"il a été récemment démontré que

	Dans l"TREM2 est exprimé dans les lésions d"athérosclérose chez l"homme (69), l"objectif de notre
	étude était d"étudier les variants génétiques localisés à proximité du gène TREM2 et de
	rechercher une association avec le taux de TNF-α et l"IMT-F.
	Tout d"abord, nous avons testé cinq SNP (rs7748777, rs6918289, rs7759295, rs9357347,
	rs6915083) localisés dans la région du gène TREM2 (50 kb en amont et en aval du gène
	VEGF 165 . Il a déjà été montré, lors d"études antérieures en culture cellulaire, que l"IL-6 et TREM2, chromosome 6p21) pour rechercher une association avec le taux de TNF-α chez
	l"IL-1α augmentaient l"expression de l"ARNm du VEGF-A. IL-6 promeut l"angiogenèse 139 enfants de la cohorte SFS. Nous avons identifié un nouvel allèle à risque potentiel (T)
	médiée par le VEGF-A dans les tumeurs du col de l"utérus, les carcinomes gastriques (62) et
	les mésothéliomes malins (63). L"IL-1α stimule la synthèse de l"ARNm du VEGF-A dans les
	PBMCs de manière dose-dépendante. Dans des cellules de cancer du pancréas et dans des
	cellules d"adénome, les deux cytokines, IL-6 et IL-1α (64), participent ensemble à la
	régulation du VEGF-A (65).
	Dans le corps humain, les voies physiologiques constituent des réseaux complexes régulés par
	de nombreuses interactions entre molécules. L"angiogenèse, qui permet le développement de
	nouveaux vaisseaux sanguins, est le principal processus médié par le VEGF-A. Toutefois,
	d"autres mécanismes, notamment l"inflammation, sont étroitement liés à sa régulation (66,
	67). Les interactions entre ces deux mécanismes peuvent donner lieu à de nombreuses
	maladies chroniques courantes et doivent être considérées comme des cibles potentielles pour
	leur prévention.
	•
	La troisième approche méthodologique que nous avons mise en oeuvre lors de cette thèse était
	l"étude des déterminants génétiques permettant d"évaluer les facteurs de risque intermédiaires
	et les voies biologiques impliquées dans différentes maladies chroniques. Les caractéristiques
	de la variation génétique quantitative ont été utilisées pour identifier des biomarqueurs
	génétiques destinés à prédire le risque d"athérosclérose, un processus pathologique commun à
	la plupart des maladies cardiovasculaires (68).

objectif de ces travaux intitulés « Discovering the Relation between Telomere Length and VEGF-A » était

	Les cinq variants génétiques étudiés ont été sélectionnés sur la base de leur proximité avec le
	gène TREM2 et non d"après une association identifiée auparavant lors d"études GWAS. Une
	telle décision se justifiait parce que tous les SNP n"étaient pas disponibles dans notre
	population génotypée. Nous avons choisi les variants génétiques d"après leur localisation qui
	présupposait qu"ils pourraient être impliqués dans la régulation du gène TREM2. D"autres
	travaux dans lesquels des variants TREM2 identifiés lors d"études GWAS seraient utilisés
	pour explorer l"association entre TREM2, TNF-α et IMT-F sont nécessaires. Toutefois, une
	étude de réplication dans du tissu adipeux avec une population d"étude plus grande doit
	d"abord être effectuée pour confirmer nos résultats et évaluer leur éventuelle utilité clinique.
	•
	L"étude des variants génétiques a été poursuivie pour explorer l"association entre deux
	biomarqueurs, la longueur des télomères (LT) et le VEGF-A. L"d"évaluer l"association entre la longueur des télomères des leucocytes (LTL), la longueur des
	télomères des cellules musculaires (LTM) et l"attrition des télomères (calculée comme
	LTL/LTM) et 10 variants génétiques liés au VEGF-A (rs10761741, rs10738760, rs6921438,
	rs7043199, rs6993770, rs4416670, rs114694170, rs34528081, rs4782371 et rs2639990)

  Durant mon temps de travail chez Randox Laboratoires, une société qui propose des outils de diagnostic in vitro, nous avons développé et caractérisé des fragments d"anticorps monoclonaux qui serviront à mesurer de manière précise les biomarqueurs inflammatoires dans le sérum humain. Nous estimons que de telles collaborations de mise en application entre La recherche de liens entre la longueur des télomères (LT) et le VEGF-A. Un variant génétique a été identifié et offre de nouvelles hypothèses quant à l"étiologie de maladies chroniques liées aux télomères. "intérêt particulier que nous portons à LT chez les enfants nous a amené à concevoir une étude destinée à faire la lumière sur les déterminants génétiques et environnementaux de la LT pendant l"enfance et à combler les lacunes de connaissances dues au manque d"études longitudinales. Enfin, cette étude testera également l"hypothèse d'un lien

	lipides. les laboratoires de recherche et l"industrie représentent la voie la plus rapide pour passer des  L"identification de mécanismes de régulation épigénétique du VEGF-A. Il s"agissait de la première étude pangénomique cherchant à mettre en évidence des mécanismes inflammatoires. Lentre une attrition rapide de LT pendant l"enfance et la survenue de maladies chroniques chez
	découvertes en recherche à une application réussie en pratique clinique. de méthylation impliqués dans la biologie du VEGF-A. l"adulte. La publication d"un article de revue rassemblant les informations les plus pertinentes
	sur les télomères pendant l"enfance a constitué la première étape de cette démarche.
	Les études épigénétiques ouvrent une nouvelle ère dans le domaine de l"épidémiologie
	génétique en offrant un aperçu des mécanismes d"action des facteurs de risque génétiques et Enfin, les protéines de la famille TREM ont fait l"objet, ces dernières années, de recherches Conclusion et perspectives environnementaux dans la physiopathologie des malades, des biomarqueurs correspondant à approfondies au sein de notre unité. Les résultats sur TREM2 présentés dans cette thèse font
	). Son rôle dans la prédiction, le diagnostic et le traitement de différentes expositions et contribuant à la localisation de régions génomiques impliquées afin partie des travaux qui ont permis de démontrer un rôle important pour les variants génétiques
	maladies a été étudié au cours des dernières décennies, mais sans encore d"application clinique prometteuse (101). Pendant ces trois années de travaux de recherche, différentes méthodologies ont été mises en liés à TREM dans les maladies cardiovasculaires. TREM2 est un des membres les moins d"identifier des cibles potentielles. oeuvre pour explorer les facteurs de risque et voies moléculaires des maladies chroniques afin connus de cette famille des protéines et représente un potentiel important de découvertes
	avons contribué aux connaissances actuelles qui viennent renforcer l"expérience déjà réalisation d"études EWAS et, en tout, nous avons réalisé trois études sur une population de Nous croyons que les télomères recèlent encore de nombreux secrets et nous avons consacré de répondre au besoin de nouveaux biomarqueurs utilisables en médecine personnalisée. Nous Au cours des dernières années, nous avons travaillé sur le développement d"une stratégie de éventuellement applicables en médecine personnalisée.
	deux articles de revue à l"état actuel des connaissances sur la longueur des télomères et leur conséquente et le savoir de l"unité de recherche UMR INSERM U1122 IGE-PCV, tout en découverte d"individus sains issus de la cohorte SFS. En plus du VEGF-A, nous avons Tous les travaux entrepris dans le cadre de cette thèse étaient destinés à contribuer à
	potentiel. Plus important, ces deux revues nous ont permis de souligner les nombreuses intégrant les approches les plus récentes d"étude de l"épidémiologie génétique de maladies cherché des marqueurs épigénétiques associés aux facteurs de risque intermédiaires communs l"amélioration de la médecine personnalisée : compréhension des voies biologiques et de la
	lacunes de connaissances qui nous empêchent de comprendre complètement la biologie des chroniques courantes. à différentes maladies chroniques et nous avons mis en évidence des modifications du profil régulation génétique des biomarqueurs, mécanismes épigénétiques des facteurs de risque et
	télomères et, si elles étaient comblées, pourraient contribuer à la réussite de la médecine de méthylation associées au tour de taille et au taux de TG. Les possibilités d"applications associations entre molécules. Un résumé des résultats obtenus dans les principaux projets de
	personnalisée. Nous concluons que LT pourrait constituer un biomarqueur prospectif, mesuré systématiquement dès la naissance. En raison de la grande variabilité inter-individu, une mesure simple de LT ne serait pas représentative, alors que la détection d"une attrition accélérée pourrait apporter des informations précieuses et alerter sur d"éventuelles Le VEGF-A est un des biomarqueurs principaux ciblé par les travaux de recherche de notre recherche est présenté sous forme graphique dans la figure 34 et montre les liens identifiés cliniques sont nombreuses pour des sites de méthylation liés à une maladie ; par conséquent, il unité qui a créé et dirige le Consortium VEGF international (http://www.vegfconsortium.org/) entre les biomarqueurs, les facteurs de risque et les voies moléculaires étudiés. est hautement probable que ces résultats soient utilisés dans l"avenir en médecine destiné à mettre en place un réseau de collaboration transnational dédié à de grandes études génomiques intégratives et pluridisciplinaires du VEGF. Parmi les projets collaboratifs nés de personnalisée. L"étude des associations entre le VEGF-A et les molécules inflammatoires a mis au jour des
	modifications pathologiques chez un individu. Des études longitudinales à long terme portant ce Consortium se trouvent deux grandes études GWAS qui ont permis d"identifier 10 variants LT, le biomarqueur le plus étudié après le VEGF-A, est également une des cibles des travaux liens intéressants, la plupart desquels ont déjà été décrits dans d"autres études. Toutefois, des
	un intérêt particulier à la période de l"enfance seront nécessaires pour tester cette hypothèse. génétiques associés au VEGF-A. Ces travaux de thèse ont approfondi les résultats de ces scientifiques de notre unité. Notre participation aux projets de Lorraine Université travaux supplémentaires sont nécessaires pour une recherche systématique de voies
	études et ont conduit à 1) l"identification de nouvelles associations avec des molécules d"Excellence (LUE), soit les projets GEENAGE (Functional Genomic, Epigenomic and moléculaires communes entre le VEGF-A et les molécules inflammatoires. Tout d"abord, nos
	• inflammatoires, 2) la démonstration d"une association génétique avec l"attrition des télomères, Environment interplay to impact the understanding, diagnosis and management of healthy résultats doivent être confirmés sur un plus grand échantillon d"individus en bonne santé, avec
	and pathological Ageing) et TELARTA (Telomeres and Arterial Ageing) nous a encouragé à en parallèle la mesure des valeurs plasmatiques pour les mêmes biomarqueurs. Cela et 3) les premières explications quant à sa régulation épigénétique. En plus de tous les résultats décrits précédemment, nous avons participé activement, pendant poursuivre nos explorations de la biologie des télomères et a donné lieu à des collaborations permettrait de comparer la production de cytokines et de VEGF-A dans les PBMCs et les
	toute la durée de ces travaux de thèse, au projet européen MAST4HEALTH, un programme Plus précisément, les travaux ont porté sur : fructueuses. L"étude des associations génétiques de LT et de VEGF-A est l"une de ces concentrations dans le plasma auxquelles contribuent tous les types cellulaires qui sécrètent
	d"échange de personnel de recherche et d"innovation faisant partie des actions Marie collaborations. des cytokines. Par la suite, des PBMCs devraient être mises en culture et stimulées par des  L"étude des associations de plusieurs molécules qui interviennent avec le VEGF-A et Skłodowska-Curie (MSCA RISE) du programme-cadre HORIZON 2020. Notre rôle dans ce projet était de déterminer les modifications de l"expression de biomarqueurs inflammatoires, tels que VEGF-A (VEGF 145 , VEGF 121 , VEGF 165 , VEGF 165b , VEGF 189 ), TNF-α, et IL-6, chez des patients atteints de NAFLD traités par la plante grecque Pistacia lenticus (arbre à mastic), connue pour les propriétés antioxydantes, anti-inflammatoires et de réduction du taux de facteurs angiogéniques ou inflammatoires pour mesurer la production de molécules et ses différentes isoformes dans les processus d"inflammation et d"angiogenèse. Les Afin de surmonter les premiers obstacles sur la voie de l"utilisation de la longueur des d"isoformes d"ARNm dans des conditions spécifiques. Les molécules associées pourraient résultats sont destinés à contribuer au développement de nouvelles stratégies télomères en tant que biomarqueur en médecine personnalisée, nous avons effectué des ensuite être mesurées chez des patients spécifiques (cancer, maladies cardiovasculaires) pour thérapeutiques contre le VEGF-A, qui pourront être améliorées encore en ciblant des important dans certaines maladies et leurs associations avec d"autres molécules isoformes spécifiques et les cytokines qui leur sont associées. recherches bibliographiques exhaustives pour comprendre leur régulation génétique, leur rôle évaluer leur utilité potentielle en tant que cibles thérapeutiques.



Table 2 .

 2 Association of methylation sites with TG levels.

	CpG	Gene Name	Chromosome	Effect size	SE	P-Value	FDR
	cg08897188	PRKAG2	7q36.1	-2.80	0.47	1.39 × 10 -8 0.049
	cg04580029	KREMEN2	16p13.3	3.09	0.51	5.75 × 10 -9 0.049

SE:

Standard error, FDR: False discovery rate (< 0.05)

Table 3 .

 3 Replication of the significant results in adipose tissue.

	CpG	Gene Name	Effect size	SE	P-Value*
	cg08897188	PRKAG2	-0.0008	0.0016	0.6265
	cg04580029	KREMEN2	0.0084	0.0036	0.0196

SE: Standard error, *adjusted P-Value

Table 4 .

 4 Association of methylation site with WC level, adjusted for BMI.

	CpG	Gene Name Chromosome Effect size	SE	P-Value	FDR
	cg16170243 AC090241.2	18q21.1	2.32	0.41	0.13 × 10 -8 0.048
	SE: Standard error
	Bioinformatics analysis showed that the probe cg16170243 corresponds to a 50 bp-length
	human methylation oligoprobe located on the chromosome 18q21.1, in the forward strand of
	the ST8SIA5 (Alpha-2,8-sialyltransferase 8E) antisense gene (AC090241.2). On the opposite
	strand, the probe cg16170243 is located 245 base pairs upstream of the ST8SIA5 gene, in the
	promoter flank, where it overlaps eight transcription factors binding sites (TFBS).

Table 5 .

 5 Novel associations between DNA methylation and VEGF-A PBMCs levels.

	CpG	Gene Name	Chromosome	Effect size	P-Value
	cg05739757	RPL31	2q11.2	0.00176	5.64 x 10 -15
	cg23333878	GLIS3	9p24.2	0.00270	3.86 x 10 -14
	cg21838233	TPX2	20q11.21	0.00191	4.01 x 10 -12
	cg18815539	SEPSECS	4p15.2	-0.00197	1.70 x 10 -12
	cg21968169	LOC338799	12q24.31	-0.00189	1.11 x 10 -12
	cg16333561	ND 1	7q11.23	-0.00130	7.02 x 10 -11
	cg20547575	AUTS2	7q11.22	-0.00349	1.52 x 10 -10
	cg15014826	ND 2	19p13.12	-0.00157	1.12 x 10 -10
	cg00117600	PIGB	15q21.3	0.00118	8.84 x 10 -09
	cg08759276	ND 3	16q24.1	-0.00153	3.31 x 10 -09
	cg05275012	ND 4	3p22.1	0.00176	8.81 x 10 -08
	cg10517202	ND 5	3q26.32	-0.00103	8.74 x 10 -08
	cg09614565	IL17RD	3p14.3	-0.00283	5.69 x 10 -08
	cg13689591	KALRN	3q21.1-q21.2	0.00199	2.58 x 10 -08
	cg06934988	USP43	17p13.1	-0.00188	1.96 x 10 -08
	cg06785213	HAS1	19q13.4	0.00087	1.06 x 10 -08
	cg13332754	ND 6	18q22.3	0.00164	1.39 x 10 -07
	cg03551607	ESD	13q14.2	0.00115	1.34 x 10 -07
	cg24364967	CLHC1	2p16.1	0.00122	1.22 x 10 -07
	cg15057061	SOX2OT	3q26.33	-0.00151	1.08 x 10 -07

Table 6 .

 6 Significant associations between VEGF-A protein levels and inflammatory molecules. Effect sizeA correlation study has been performed between VEGF-A mRNA levels and inflammatory molecules. A significant trend was observed between VEGF 121 and IL-4, between VEGF 165 and IL-4, IL-6, and between VEGF 189 and IFN-γ, IL-1β, IL-4, IL-6.

	Two VEGF-A mRNA isoforms were significantly associated with four inflammatory
	molecules. VEGF 165 was associated with MCP-and IL-1α, whereas VEGF 189 was associated
	with IL-4 and IL-6 in a linear regression model adjusted for age and gender (Table 7).

SE:

Standard error, β:

Table 7 .

 7 Significant associations between the expression of VEGF-A mRNA isoforms and inflammatory molecules.

		VEGF 165			VEGF 189
	β	SE	P	β	SE	P

SE:

Standard error, β: Effect size

Table 9 .

 9 Association of rs6918289 with TNF-α levels in the discovery and replication populations.

	Population	Model	N	β	SE	P-value
		Additive	809	0.13	0.04	0.0026*
	The SFS	Dominant	809	0.11	0.05	0.022*
		Recessive	809	0.49	0.15	0.0017*
		Additive	916	0.042	0.02	0.073
	Replication	Dominant	916	0.03	0.03	0.189
		Recessive	916	0.20	0.09	0.023*

SE:

Standard error, β: Effect size, P-value threshold is P<0.01, *significant P-value

Table 10 .

 10 Association of rs6918289 with IMT-F in the discovery population.

	Population	Model	N	β	SE	P-value
		Additive	350	0.02	0.009	0.026*
	The SFS	Dominant	350	0.024	0.01	0.026*
		Recessive	350	0.024	0.025	0.34

SE:

Standard error, β: Effect size, P-value threshold is P<0.01, *significant P-value

Vesna Gorenjak ,

 Gorenjak Alexandros M. Petrelis, Maria G. Stathopoulou and Sophie Visvikis-Siest

	CCLM, September 2019

Table 11 .

 11 Results of MAF and HWE analysis for ten VEGF-A related genetic variants in the study population.

	SNP	Chromosome	MAF	HWE (P-Value)
	rs114694170	5	0.04602	0.5802
	rs34528081	6	0.3505	0.3788
	rs6921438	6	0.4325	1
	rs4416670	6	0.4527	0.4209
	rs6993770	8	0.2873	1
	rs7043199	9	0.206	0.7591
	rs10738760	9	0.4739	0.617
	rs10761741	10	0.4439	0.01131*
	rs4782371	16	0.3282	0.1659
	rs2639990	18	0.1136	0.6178

MAF:

Minor allele frequency, HWE: Hardy-Weinberg equilibrium, *significant P-Value

Table 12 .

 12 Association between VEGF-A related genetic variants and LTL.

	SNP	Chromosome	β	SE	P-Value
	rs114694170	5	0.002083	0.007452	0.7799
	rs34528081	6	-0.001541	0.003261	0.6369
	rs6921438	6	-0.0004508	0.003173	0.8871
	rs4416670	6	-0.002907	0.00323	0.3687
	rs6993770	8	0.007	0.003496	0.04594
	rs7043199	9	-0.00173	0.003846	0.6531
	rs10738760	9	0.002185	0.003119	0.4841
	rs4782371	16	0.002397	0.003291	0.4669
	rs2639990	18	-0.001899	0.004965	0.7023

SE:

Standard error, β: Effect size, P-value threshold is P<0.0012

Table 13 .

 13 Association between VEGF-A related genetic variants and MTL. Effect size, P-value threshold is P<0.0012

	SNP	Chromosome	β	SE	P-Value
	rs114694170	5	0.1713	0.1132	0.1312
	rs34528081	6	-0.07143	0.04946	0.1495
	rs6921438	6	-0.06758	0.04827	0.1623
	rs4416670	6	0.007034	0.04927	0.8866
	rs6993770	8	-0.02124	0.05354	0.6917
	rs7043199	9	-0.08772	0.05874	0.1361
	rs10738760	9	0.03656	0.04753	0.4422
	rs4782371	16	0.02819	0.05035	0.5758
	rs2639990	18	0.06804	0.07592	0.3707

SE:

Standard error, β:

Table 14 .

 14 Association between VEGF-A related genetic variants and LTL/MTL.

	SNP	Chromosome	β	SE	P-Value
	rs114694170	5	-0.01288	0.009716	0.1856
	rs34528081	6	0.004154	0.004246	0.3285
	rs6921438	6	0.004166	0.004146	0.3156
	rs4416670	6	-0.005934	0.004207	0.1592
	rs6993770	8	0.0148	0.004516	0.001143*
	rs7043199	9	0.004878	0.005062	0.3358
	rs10738760	9	0.0003862	0.004077	0.9246
	rs4782371	16	0.002534	0.004284	0.5546
	rs2639990	18	-0.008642	0.006481	0.1832

SE:

Standard error, β: Effect size, P-value threshold is P<0.0012, *significant P-value

Table 15 :

 15 Multiple regression analysis of VEGF-A plasma concentration with LTL, MTL and LTL/MTL. Dependent variable: logVEGFA.

	Independent Variable	Regression Coefficient ± SE	R²	P-Value Level
	LTL	-0.034 ± 0.029	0.4%	0.24
	MTL	-0.045 ± 0.029	0.7%	0.12
	LTL/MTL	0.000 ± 0.003	0.0%	0.99

SE: Standard error

Table 1

 1

representation of regulation elements in the vicinity of the CpG

  ). in each box of particular genomic region is indicating in what extent this genomic feature is presented in PBMCs. Diagrams on the top are presenting patterns of genomic features that can be found in the genomic environment of CpGs. We can see that in the immediate proximity of CpG enhancers are the most common and that with distance, genetic features become less common (regions of non-coding DNA).

	total number of Cumulative occurrence total number of total number of total number of CpG occurrence CpG occurrence CpG occurrence CpG	occurrence	total number of CpG	occurrence	total number of CpG	occurrence	total number of CpG	occurrence	total number of CpG	occurrence	total number of CpG	occurrence	total number of CpG
	Promoter	2	6	2	3	3				2		1		1	1	1
	Enhancer	0		1	1	1				0,5		1		0	0	0
	CTCF	0		0	1	2				3,5		1		0	0	0
	Any	4		3	1	0		6		0		3		5	5	5
	Zone -4		Zone -3	Zone -2	Zone -1					Zone +1		Zone +2		Zone +3	Zone +4	Zone +5
						1	1	1	1	1	1	1	1
	x-origin							60000
								25	25
	relative coordinates				22722	23321	59975	60025	64073	64272	106273	107738
	verif					ok		50
							Sequential

representation of regulation elements in the vicinity of the CpG

	Sup. Figure 2
	100%				100%			100%			100%						100%		100%	100%	100%	100%
	90%				90%			90%			90%						90%		90%	90%	90%	90%
	70% 80%				70% 80%	6		70% 80%	6		70% 80%	6			6		70% 80%	6	70% 80%	6	70% 80%	6	70% 80%	6	70% 80%	6
	60%				60%			60%			60%						60%		60%	60%	60%	60%
	50%				50%			50%			50%						50%		50%	50%	50%	50%
	40%				40%			40%			40%						40%		40%	40%	40%	40%
	30%				30%			30%			30%						30%		30%	30%	30%	30%
	20%				20%			20%			20%						20%		20%	20%	20%	20%
	10%				10%			10%			10%						10%		10%	10%	10%	10%
	0%	Zone -4		0%	Zone -3		0%	Zone -2		0%	Zone -1					0%	Zone +1	0%	Zone +2	0%	Zone +3	0%	Zone +4	0%	Zone +5
	promoter Enhancer		promoter Enhancer		promoter Enhancer		promoter Enhancer					promoter Enhancer	promoter Enhancer	promoter Enhancer	promoter Enhancer	promoter Enhancer
	CTCF		Any		CTCF	Any		CTCF	Any		CTCF	Any					CTCF	Any	CTCF	Any	CTCF	Any	CTCF	Any	CTCF	Any
	windows (kb)			Distance (bp) from CpG			Distance (bp) from CpG			Distance (bp) from CpG			Distance (bp) from CpG	CpG	Distance (bp) from CpG			Distance (bp) from CpG	Distance (bp) from CpG	Distance (bp) from CpG	Distance (bp) from CpG
											CTCF						CTCF	Enhancer
											ENSR00000324887		cg16333561		ENSR00000324889	ENSR00000208618
											1/7						1/7		5/7
	116										13765801	13766400	36679 13803079	13803128	4073	13807201	13807400	46273	13849401	13850866
	promoter		promoter					[prom						oter]	promoter
	ENSR00000107436		ENSR00000107437		promoter		ENSR00000107447					ENSR00000107447	promoter	promoter	promoter	ENSR00000107453
		2/7			6/7		ENSR00000287547		6/7						6/7		ENSR00000107450	ENSR00000107451	ENSR00000107452	6/7
								7/7											2/7	6/7	7/7
	promoter		promoter		promoter		promoter					CTCF
	ENSR00000089179		ENSR00000089180		ENSR00000089183		ENSR00000089191		cg08759276		ENSR00000281859
	110	7/7			6/7		7/7			5/7						1/7
	86563201	86564600	46056	86565602	86570799	39857	86575277	86579907	30749	86604401	86605400	5256 86610656	86610705	36496	86647201	86648400
								CTCF		promoter					Enhancer	CTCF
								ENSR00000303625		ENSR00000151190					ENSR00000151191	ENSR00000303631
	110							1/7			2/7						3/7		3/7
								40568601	40569000	50738	40615334	40616200	3538	cg05275012	3684	40623471	40624117	9014	40628801	40629800
																	CTCF
																	ENSR00000303630
																	1/7
														40619738	40619787	3814	40623601	40625000
								Enhancer		CTCF						promoter
								ENSR00000162307		ENSR00000307656		cg10517202		ENSR00000162347
	114							6/7			6/7						3/7
								177426801 177428000	41455 177456601 177458400	11055 177469455 177469504	68298 177537802 177539600
					Enhancer		promoter		Enhancer					CTCF
					ENSR00000105063		ENSR00000105064		ENSR00000105068		cg13332754		ENSR00000286717
	120				1/7		6/7			1/7						3/7
					75244601	75244800	45350	75246002	75250399	39751	75277001	75277801	12349 75290150	75290199	35002	75325201	75328000

:

  Genetic environment of fourteen CpG sites.

	Cumulative

representation of regulation elements in the vicinity of the CpG

			total												total		total		total		total
			number of		total number		total number		total number				total number		number of		number of		number of		number of
	occurrence	CpG	occurrence	of CpG	occurrence	of CpG	occurrence	of CpG	occurrence	total number of CpG	occurrence	of CpG	occurrence	CpG	occurrence	CpG	occurrence	CpG	occurrence	CpG
	Promoter	3,5	1 4	5		7		1 2				1 1		3		4		1		1
	Enhancer	0		0		1		0				0		2		1		0		0
	CTCF	2,5		3		4		1				2		5		0		1		0
	Any	8		6		2		1		1 4		1		4		9		1 2		1 3
	Zone -4			Zone -3		Zone -2		Zone -1				Zone +1		Zone +2		Zone +3		Zone +4		Zone +5
									Sequential

representation of regulation elements in the vicinity of the CpG 1 1 0

		[prom		oter]
	promoter ENSR00000120750	ENSR00000120755 7/7	cg05739757	ENSR00000120755
	1/7

Table 3 :

 3 Summary table explaining the potential functionality and biological plausibility of each of the 20 significant CpGs and their nearby genes.

	1 4	1 4

Chapter IV. PUBLICATIONS -RESULTS

Acknowledgements Chapter IV. PUBLICATIONS -RESULTS

Publication under redaction

Discovering the Relation between Telomere Length and VEGF-A.

Int J Mol Sci, February 2019, 26;20(5) The prevention of CVDs requires a fast detection of dysregulation of cardiovascular risk factors. Blood lipids, i.e. HDL, LDL, TC and TGs, are strongly associated with adverse cardiovascular outcomes, as well as with other chronic diseases, such as diabetes (246,289). Angiogenesis is an important physiological mechanism that enables the formation of new blood vessels through VEGF-A signalling pathways and is implicated in the pathology of a wide range of diseases [START_REF] Carmeliet | Angiogenesis in cancer and other diseases[END_REF]177). Besides VEGF-A-induced angiogenesis, other pathways are associated with progression of vessel-growth. For instance, inflammatory cells are likely to drive angiogenesis by contributing to tumour growth, progression, and immunosuppression (299). The elucidation of common pathways between VEGF-A isoforms and inflammatory molecules could serve to provide new biomarkers used for stratification of patients under an anti-angiogenic therapy, and in the development of novel targeted therapies for treatments of chronic pathologies related to these processes. PBMCs are highly specialised immune cells (Figure 30), consisting of populations of B cells (~15 %), T cells (~70 %), monocytes (~5 %), and natural killer (NK) cells (~10 %) (301).

PBMCs are secreting VEGF-A, as well as other important inflammatory proteins involved in complex molecular pathways that impact on physiological balance in the human organism [START_REF] Turner | Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease[END_REF]. Therefore, PBMCs extracts might be a good source for studying the association of VEGF-A with other inflammatory markers. Moreover, the utilisation of the healthy population could help to elucidate common regulatory pathways in normal physiological mechanisms, which would, in turn, facilitate the understanding of pathophysiological processes.

Objective

The goal of this study was to investigate the associations of eleven molecules, commonly implicated in inflammation with VEGF-A mRNA levels of four most abundant VEGF-A isoforms (VEGF 165 , VEGF 189 , VEGF 145 and VEGF 121 ) and with VEGF-A protein levels in PBMCs, in order to determine common physiological pathways that could impact on the angiogenic process.

Populations and analysis

Participants from the SFS (n=285) were selected for the measurement of twelve molecules derived from PBMCs extracts (IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, MCP-1, TNF-α,

INF-γ, EGF and VEGF-A).

Moreover, a subset of 110 participants was used for quantification of four specific VEGF-A mRNA isoforms from total RNAs derived from PBMCs (i.e. VEGF 121 , VEGF 145 , VEGF 165 and VEGF 189 ).

Non-parametric correlation analysis (Spearman correlation) and linear regression models were applied, adjusted for age and gender, to test for possible associations between VEGF protein levels and inflammatory molecules.

VEGF-A protein levels were pre-tested for normal distribution using the Kolmogorov-Smirnov test of normality. Consequently, VEGF-A protein levels were log-transformed to TREM2 was previously associated with diverse inflammatory molecules which are known mediators of physiological and pathophysiological processes, thus with decreased infiltration of TNF-α and IFN-γ, but with increased secretion of IL-4 and IL-13 [START_REF] Choi | Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies[END_REF]. Recent studies showed that TREM2 expression was found in human atherosclerotic lesions (Figure 32), suggesting its involvement in the control of macrophage-mediated inflammation in atherosclerosis [START_REF] Debette | Identification of cis-and trans-acting genetic variants explaining up to half the variation in circulating vascular endothelial growth factor levels[END_REF]. TNF-α is a key proinflammatory cytokine with an important role in CVD [START_REF] Urschel | TNF-α in the cardiovascular system: from physiology to therapy[END_REF]. Its role in the formation of atherosclerotic plaques has been previously demonstrated (304), however, no relation between TNF-α and TREM2 has been reported up to date.

Objective

The goal of this study was to investigate the association of genetic variants located in TREM2 gene region with TNF-α and intima-media thickness of the femoral artery (IMT-F), in order to examine the role of TREM2 in the development of atherosclerotic plaques.

Populations and analysis

TNF-α

Discovery cohort consisted of 139 children, recruited in the SFS, where genotype information for the TREM2 gene region was readily available through the Infinium CoreExome Illumina GWAS assay. Subsequently, 393 adults and 277 additional children were genotyped for the significant SNPs identified in the initial children sample and were included in the analysis. De novo genotyping was conducted by the Laboratory of the Government Chemist (LGC), using a PCR-based KASP assay. The association between the SNPs and TNF-α were tested through linear regression adjusted for age, gender and BMI. Analyses were performed using the R package GWAF (Genome-Wide Association/Interaction Analysis and Rare Variant analysis with Family Data), taking into account familial resemblance.

The replication cohort included 916 unrelated adults of French origin. De novo genotyping and analysis performed in the replication cohort corresponded to discovery cohort. A meta-analysis of discovery and replication cohort was performed using the GWAMA software and a random-effect method.

TNF-α of discovery and replication cohort was measured in plasma by a commercially available ELISA (R&D Systems, UK), according to manufacturer instructions.

IMT-F

SNPs significantly associated with TNF-α in the analysis of the children cohort were tested also for association with IMT-F, in 350 adults from SFS, where measurements of IMT-F were available. IMT-F was measured in the right and left femoral arteries, examined with a 7.5

MHz probe, according to a protocol previously described (305). For each individual, two IMT-F measurements were obtained and right and left measurements were used to calculate the mean IMT-F (in mm).

Results

Five SNPs (rs7748777, rs6918289, rs7759295, rs9357347, rs6915083) located in the TREM2 gene region (6p21) were tested for the association with TNF-α in 139 children from the SFS (Table 8). The minor allele (T) of rs6918289 was significantly associated with increased concentration of TNF-α (p=0.0015). To increase the discovery population, rs6918289 was de novo genotyped in 393 adults and 277 additional children.

Because the underlying model of the genetic determinant of a trait was not known, in all analysis, three classical genetic models were used to test the association of the minor allele

Statistical Analysis

Minor allele frequencies (MAF) and Hardy-Weinberg equilibrium (HWE) were calculated for ten VEGF-A related genetic variants (rs10761741, rs10738760, rs6921438, rs7043199, rs6993770, rs4416670, rs114694170, rs34528081, rs4782371 and rs2639990). The SNP rs10761741 did not follow the HW equilibrium and was excluded from further analyses.

The direct effects of VEGF-A related genetic variants on three phenotypes of interest (LTL, MTL and LTL/MTL) were tested using the PLINK toolset in the additive genetic model.

Epistatic interactions were tested using PLINK and R package CAPE. LTL was logtransformed to follow a normal distribution.

The significance level for the direct effects of the 9 SNPs and the three tested phenotypes (LTL, MTL and LTL/MTL) was calculated as 0.05/9/3= 0.0012. For epistatic interactions, the significance level for the nine SNPs and three phenotypes was calculated as 0.05/36/3= 0.00046.

Multiple regression analysis was performed to study the association of VEGF-A plasma concentration with LTL, MTL and LTL/MTL. As VEGF-A concentrations were not normally distributed, a log-transformation was performed. VEGF-A plasma concentrations were compared between atherosclerosis cardiovascular disease (ASCVD) case and control groups using a non-parametric Mann-Whitney test.

In silico Analysis

The genomic environment of the significant SNPs was explored using Ensemble browser of the human genome (GRCh38.p12) and NCBI dbSNP.

Results

Association between VEGF-A related genetic variants and TL

Ten VEGF-A related genetic variants were tested for association with LTL, MTL and telomere attrition (LTL/MTL). Values of MAF and results of HWE analysis are presented in