Professeur

Chajes

phD Véronique

phD Pietro Ferrari

Claude Universite

Bernard -Lyon

M Le Professeur

Frédéric Fleury

Hamda Ben

Hadid M Le Professeur Didier

Revel M Le Professeur

Philippe Chevalier

M Fabrice

Vallée M Alain Helleu

Composantes Sante

J Etienne

La Mme

C Professeure

Burillon

D Bourgeois

Vinciguerra

M X Perrot

A-M Professeure

Schott

Keywords: Biostatistics, multivariate methods, treelet transform, cancer, nutrition, EPIC, meeting-in-the-middle, PLS, PCA, mediation analysis

Le cadre analytique développé lors de ces deux applications pourrait être réutilisé et ajusté aux besoins d'autres études ayant d'autres types de données « -omiques » ou dans des contextes épidémiologiques similaires. Enfin, nous nous sommes intéressés à une étude cas-témoin sur le BC nichée dans EPIC où 60 mesures d'acides gras (AG) plasmatiques ont été effectuées chez 2 982 cas de BC invasifs et autant de témoins appariés. L'association entre chacun des AG et le risque de BC a été évaluée à travers des régressions logistiques conditionnelles multivariables ajustées. Ces analyses ont été combinées à une correction pour les tests multiples afin de préserver la valeur nominale de significativité des tests statistiques. Ainsi, des niveaux trop élevés en acide palmitoléique et un indice de désaturation DI16 fort ont été associés à une augmentation du risque de BC. Cette étude est l'une des plus larges à cette date se basant exclusivement sur des biomarqueurs en ce qui concerne les expositions des AG, avec une bonne séparation pour AG trans d'origine animale de ceux d'origine industrielle. Elle constitue une première étape dans des analyses plus poussées à venir, notamment des analyses de patterns afin de caractériser le lipidome ainsi qu'une possible application du MITM. Les différentes applications et développements statistiques mis en place lors ce travail de thèse viennent répondre à un besoin d'approches dites holistiques qui visent à intégrer des données de natures différentes et de haute dimension. Cette prise en compte des différents facteurs d'expositions et de risques permettra à l'avenir de mieux appréhender les questions de l'épidémiologie nutritionnelle de nouvelle génération. Cette thèse servira également de base pour des applications multidisciplinaires futures examinant la relation nutrition-cancer.

COMPOSANTES ET DEPARTEMENTS DE SCIENCES ET TECHNOLOGIE

Résumé en Français

La nutrition est un facteur de risque modifiable pour le cancer. Il est estimé qu'un tiers des cas pourraient être évités en adoptant une meilleure alimentation en adéquation avec les recommandations les plus récentes. La relation entre nutrition et cancer est complexe, et son étude est enrichie par les nouveaux défis apportés par les récentes avancées technologiques dans le domaine des « -omiques » auxquels elle doit répondre. Des approches analytiques combinant des informations provenant de questionnaires alimentaires avec ceux de biomarqueurs et de la métabolomique sont actuellement la cible de nombreuses recherches.

Cette thèse avait pour but de développer de nouvelles approches biostatistiques afin d'étudier la relation entre nutrition et cancer au sein de la cohorte EPIC. Pour ce faire, l'applicabilité de nouvelles méthodologies, principalement factorielles, a été étudiée.

Une nouvelle méthode multivariée pour la réduction de la dimensionnalité, le Treelet Transform (TT), a été examinée afin d'extraire des patterns de nutriments issus de questionnaires. Les patterns ainsi obtenus étaient facilement interprétables puisque le TT est un bon compromis entre analyse en composante principale et clustering hiérarchique.

Ensuite, un cadre analytique pour implémenter le concept du « meeting-in-the-middle » (MITM) a été développé et appliqué dans deux études cas-témoin nichées sur le cancer hépatocellulaire avec des données métabolomiques, ciblé et non-ciblée. Le MITM cherche à identifier des biomarqueurs qui soient à la fois des marqueurs de certaines expositions passées et de conditions pathologiques. L'implémentation s'est focalisée sur l'application de la méthode des moindres carrés partiels (PLS) et de l'analyse de médiation. Des signaux métaboliques qui médiaient la relation des expositions vers le cancer ont été identifiés.

Enfin, nous avons examiné la relation entre les niveaux plasmatiques de 60 acides gras issus de biomarqueurs et le risque de cancer du sein dans une étude cas-témoin nichée dans EPIC. Les résultats issus de cette analyse seront un point de départ pour des développements plus poussés.

Cette thèse servira de base pour des applications épidémiologiques futures examinant la relation nutrition-cancer.

Mots-clefs : Biostatistiques, méthodes multivariées, treelet transform, cancer, nutrition, EPIC, meeting-in-the-middle, PLS, PCA, analyse de médiation

English Abstract

Diet is a modifiable risk factor for many cancers. It has been estimated that about a third of cancer cases can be prevented by complying with a healthy diet and adhering to the recommendations in terms of nutrition. The nutrition-cancer relationship is a complex one, and its study is currently at a turning point with the opportunity and challenges brought by the recent technological advances in the fields of « -omics ». New analytical strategies are being sought to combine and explore information collected through dietary questionnaires, biomarkers along with metabolomic data.

The main objective of this thesis was to develop new biostatistical approaches to investigate the diet-cancer relation within the European Prospective Investigation into Cancer and nutrition (EPIC) study. To this end, the applicability of new methodologies in the field of nutritional epidemiology, mainly multivariate and factorial, has been examined. First, a new multivariate dimension reduction method, the Treelet Transform (TT) was applied to extract nutrient patterns relying on questionnaire data. The extracted patterns were easily interpretable as TT is a good compromise halfway between principal component analysis and hierarchical clustering.

Then, an analytical framework was conceived for the « meeting-in-the-middle » (MITM) principle and applied to two nested case-control studies on hepatocellular carcinoma, with targeted and untargeted metabolomic data. The MITM aims to identify overlap biomarkers of past exposures that are at the same time predictive of disease outcomes. The implementation focused on the application of partial least squares (PLS) and mediation analyses. Metabolic signatures were identified that mediated the relation from exposures towards cancer risk.

Last, the association between 60 plasma fatty acids levels assessed from biomarkers and breast cancer risk was examined in a nested case-control study in EPIC. Results from this analysis are a stepping stone towards more sophisticated modelling. This thesis will serve as a basis for future epidemiological applications looking into the nutrition-cancer relation.

This work was the fruit of three years of the most wonderful learning adventure I have been given thus far. It all started with a fortuitous email of a young master's student looking for an internship that transformed into an incredible opportunity to step inside the world of research and pursue a doctoral thesis. I will be eternally grateful to my supervisor Pietro Ferrari for giving me the chance to prove myself, for his teaching and shared expertise, his valuable day-to-day guidance, and most importantly for his humane qualities and kindness. Thank you for having been a tremendous mentor for me.

I would like to express my sincere gratitude to Thierry Philip, who came on board during the second year, and who was abundantly helpful. Thank you for your open-mindedness and your esteemed advice.

I would like to gratefully acknowledge Véronique Chajès who has been particularly supportive and kind-hearted, gave me insightful counsel and always encouraged me.

Vivian Viallon has my deepest gratitude for his support, his availability and bright advice throughout these years, and for making even the most daunting formulas clear as day.

I would like to thank Gianluca Severi and Ioana Tzoulaki who have kindly accepted to act as external reviewers for this work and I am also grateful for the jury members Stefan Michiels, Béatrice Fervers and Rino Bellocco. I thank them all for their time and for the honour of participating to the defence committee.

Additionally, I would like to thank both Béatrice Fervers and David Cox for their active participation to the thesis follow-up committee overseeing the yearly progress. I would also like to acknowledge all my financial supports: The EDISS doctoral school that funded this phD through a Université de Lyon doctoral contract at UCBL1, the International Agency for Research on Cancer for completing my doctoral grant, as well as the INCA who funded some of the hepatocellular carcinoma studies I was fortunate to work on. I would like to warmly thank my colleagues from the Section of Nutrition and Metabolism for their daily support and their patience and for making the workplace a second home. I was very pleased and proud to work alongside all of you, exchanging ideas and learning from you. I looked forward to coming in every morning with the same drive and motivation all because it is the people who make the working environment a great one, not the building or the equipment, not even the reputation. The list of colleagues, past and present, is endless and I am afraid I won't do you justice in forgetting some names but know that I feel truly fortunate and lucky to have met you all. I would like to personally thank each colleague from the NME Section (2013-2016), in the NMB group in particular as well as some other IARC colleagues. Specifically, Amina, Amy, Anne-Sophie, Anouk, Augustin, Behnaz, Bertrand, Carine, Chiara, Diama, Dina, Elom, Elizabeth, Eve, Fiona, Flavie, Graham, Hafed, Heinz, Hwayoung, Isabelle (B and R),

Résumé substantiel en français

La nutrition est un facteur de risque modifiable pour de nombreux cancers.

Environ 35% des cas de cancers pourraient être évités en adoptant une meilleure alimentation en adéquation avec les recommandations les plus récentes. Partant de ce constat, l'épidémiologie nutritionnelle s'est efforcée dans les 30 dernières années d'étudier la relation entre nutrition et cancer, d'appréhender sa complexité et d'en comprendre les mécanismes. Avec les avancées technologiques récentes, notamment dans le domaine de la biologie moléculaire, de nouvelles données dites «-omiques », en particulier les données métabolomiques, ont pu être acquises. Ainsi un nouveau défi s'offre à ce domaine : celui d'allier les nouvelles informations de haute dimensionnalité provenant de la métabolomique aux informations obtenues par des méthodes plus conventionnelles de recueil par questionnaires alimentaires, ainsi qu'avec d'autres biomarqueurs.

Cette thèse avait pour objectif de développer de nouvelles approches biostatistiques dans le but d'étudier la relation entre nutrition et cancer au sein de la cohorte Européenne Prospective sur le Cancer et la nutrition (EPIC). Pour ce faire, l'applicabilité de nouvelles méthodologies, principalement factorielles multivariées, a été étudiée. Tout d'abord, nous avons appliqué une nouvelle méthode multivariée pour la réduction de la dimensionnalité, le Treelet Transform (TT), afin d'extraire des patterns alimentaires, et nous l'avons comparée à l'Analyse en Composante Principale (PCA) qui est une technique de référence. Cette application a été réalisée dans la sous-cohorte de femmes d'EPIC (n=334 850, dont 11 576 cancers de sein incidents) sur 23 nutriments estimés à partir de questionnaires alimentaires. Ainsi, deux patterns principaux ont été identifiés, pour lesquels l'association avec le risque de développer un cancer du sein (BC) a ensuite été évaluée. Un premier profil apparenté à une consommation élevée en produits d'origine animale a été associé à une augmentation non significative du risque de BC. Un second profil associé à un régime riche en vitamines et minéraux a été relié à une diminution significative du risque de BC. Le TT a produit des résultats comparables à ceux obtenus avec des méthodes plus classiques. Ces patterns étaient plus facilement interprétables que ceux de la PCA puisque TT permet d'introduire de la sparsité dans les composantes.

Par la suite, nous nous sommes penchés sur des données métabolomiques issues de deux études cas-témoin sur le cancer hépatocellulaire (HCC) nichées dans la cohorte EPIC, avec 114 cas et 222 témoins appariés pour la première et 147 cas et autant de témoins appariés pour la seconde.

Dans la première étude, nous avons développé un cadre analytique pour l'implémentation du concept dit « meeting-in-the-middle » (MITM). L'idée phare du MITM est d'identifier des biomarqueurs qui soient à la fois des marqueurs de certaines expositions passées et qui soient en même temps prédicteurs de conditions pathologiques. Pour ce faire, un ensemble de 21 variables d'expositions « lifestyle » (alimentaires, de mode de vie, anthropométriques) ont été reliées à un set de 285 variables obtenues par résonance magnétique nucléaire (RMN), correspondant à des pics reconstitués, grâce à l'application de la méthode des moindres carrés partiels (PLS).

La PLS est une méthode multivariée combinant des aspects de l'ACP avec ceux de la régression linéaire multiple. Elle permet de relier deux sets de données et d'en extraire des composantes dont la covariance est maximale. Les facteurs ainsi obtenus ont été reliés par le biais de leurs scores au risque de HCC par l'intermédiaire de modèles de régression logistique conditionnelle. Enfin, une analyse de médiation a évalué si les profils métaboliques obtenus sont des médiateurs de la relation entre les profils de « lifestyle » et le HCC.

Dans la seconde étude cas-témoins nichée portant cette fois-ci sur la métabolomique ciblée, nous avons pu affiner le cadre statistique mis en place précédemment. Dans un premier temps, nous avons limité le nombre d'expositions à 7 variables provenant d'un indice niveau d'adéquation à un mode de vie sain et nous nous sommes focalisés sur un ensemble de 132 métabolites bien identifiés. Ensuite, après une première analyse PLS générale, nous avons procédé à une analyse de PLS multiple pour obtenir des signatures métaboliques spécifiques à chacune des expositions. Enfin, l'analyse de médiation a été étendue et adaptée à notre design d'étude, et les effets directs et médiés ont été estimés grâce à des modèles de régression logistique conditionnelle. 35% of cancers could be avoided by adopting a better diet in western populations [1],

LIST OF TABLES AND FIGURES

the field of nutritional epidemiology strove to investigate nutritional exposures and their link with individual cancer sites. The initial estimate was characterised by a wide range of uncertainty (from 10 to 70%) [2], and the mechanisms through which specific dietary factors contribute to cancer occurrence are still to be understood. Three decades later, the quantitative estimate remained around 30-40% [3]. It has been argued that obesity and physical inactivity accounted for most of the burden of cancer attributable to nutrition, in a broad sense [4]. There is, however, no consensus around these figures since the extent to which diet adds to the burden of cancer remains difficult to assess [3].

Part of this difficulty is imputed to the lack of knowledge with respect to the stage of carcinogenesis on which many nutritional factors may exert their effects and the dose at which they may achieve their protective or harmful impact [5]. Nevertheless, nutritional epidemiology in the past decades has amassed a growing body of evidence establishing diet as an important modifiable risk factor for a substantial proportion of cancers, making it a great public health target for prevention [3,6,7]. Studies in nutrition provided substantial, yet often inconsistent, epidemiologic evidence of the diet-cancer link [7,8] with findings on alcohol consumption [6,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], obesity and weight change [24][25][26][27][28], fat intake [29][30][31][32][33][34][35][36][37][38][39], meat consumption [29,30,[40][41][42][43][44][45][46][47][48], plant foods [49][50][51][52],

glycaemic index/load [53,54], coffee [55][56][57], inter alia. In addition, these studies have canvassed the relationships between a selection of dietary constituents and molecularly [58,59] or anatomically [50,[START_REF] Lee | Combined Genetic and Nutritional Risk Models of Triple Negative Breast Cancer[END_REF][START_REF] Potter | Progesterone and Estrogen Receptors in the Iowa Women's Health Kinds of Breast Cancer and Mammary Neoplasia Study : How Many Are There ?[END_REF] defined subsets of cancer, and evaluated dietary behaviours in relation to cancer [START_REF] Giovannucci | Intake of Fat , Meat , and Fiber in Relation to Risk of Colon Cancer in Men[END_REF] and cancer survival [38].

Nutritional epidemiology is an intricate area due to the fact that diet is not a single simple exposure but rather a complex set of many variables, characterised by profound inter-correlations between dietary constituents. These inter-correlations may arise from food composition, behavioural patterns, e.g. food items are often consumed together, or from differences in the energy balance and total energy intake as people eating a high-energy diet tend to eat a lot of different nutrients [START_REF] Willett | Nutritional Epidemiology : Issues and Challenges[END_REF]. Disentangling the separate effects of each food/nutrient is extremely challenging, largely because of confounding and residual confounding [START_REF] Arija | Biases and adjustments in nutritional assessments from dietary questionnaires[END_REF]. Adding to the methodologic and conceptual complexity are the potential physiological interactions amongst nutrients, e.g. Selenium (Se) and Vitamin E, Vitamin C and Iron (Fe), including food component synergies or antagonisms [START_REF] Hatfield | The Outcome of Selenium and Vitamin E Cancer Prevention Trial (SELECT) reveals the need for better understanding of selenium biology[END_REF][START_REF]Diet and health: implications for reducing chronic disease risk[END_REF][START_REF] Van Dam | New approaches to the study of dietary patterns[END_REF][START_REF] Jacobs | Nutrients, foods, and dietary patterns as exposures in research: a framework for food synergy[END_REF]. Furthering the nutrient assessment challenge is the common exposure misclassification.

In fact, nutritional epidemiology relies on dietary assessment instruments, mainly questionnaires such as food frequency questionnaires or dietary histories, which are subject to random and systematic measurement errors [START_REF] Ferrari | A bivariate measurement error model for nitrogen and potassium intakes to evaluate the performance of regression calibration in the European Prospective Investigation into Cancer and Nutrition study[END_REF]. These errors are frequent in self-reported dietary estimates as a consequence of study subjects' consistent underestimation or overestimation of their dietary intakes.

Traditional approaches initially relied on simple models to evaluate the associations between single dietary constituents, i.e. foods or nutrients, possibly involving statistical adjustment by total energy intake to ensure iso-caloric comparisons [START_REF] Willett | Adjustment for total energy intake in epidemiologic studies[END_REF], and the risk of disease [START_REF] Willett | Nutritional Epidemiology : Issues and Challenges[END_REF]. These models were straightforward to interpret but did not necessarily capture the inherent complexity of individuals' dietary habits, where simultaneous variability of many foods is observed. Approaches became progressively more complex moving towards multivariable models that accounted for more dietary and lifestyle confounders, at times even involving the inclusion of interaction terms.

While these models may better capture the inner sophistication of the diet-disease association, parameters expressing these links are more challenging to interpret. In these models the evaluation of the relation between a given dietary exposure and disease is conditional on all other confounders included in the linear predictor, and it is assumed that they remain constant. This turns out to be an unrealistic assumption that does not factor in the dynamism of an intricate system of synergies between foods, nutrients and other lifestyle variables [START_REF] Van Dam | New approaches to the study of dietary patterns[END_REF][START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF][START_REF] Edefonti | Dietary patterns and breast cancer: a review with focus on methodological issues[END_REF]. The rigorous analysis consistently struggles to find the optimal trade-off between the two extremes: over-simplistic interpretable models on one hand, and increasingly more multifaceted models that progressively lose their ability to provide a realistic overview of individuals' diet on the other, yet involving statistical challenges for their estimation.

In recent years, research focus of nutritional epidemiology has progressively moved towards dietary pattern analysis and the use of multivariate approaches [START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF].

Pattern analysis allows for a comprehensive mode taking the full complexity of diet into consideration [START_REF] Jacques | Are dietary patterns useful for understanding the role of diet in chronic diseases?[END_REF]. Two main strategies are often applied: a priori hypothesis-driven patterns and a posteriori data-driven patterns [START_REF] Bamia | Dietary patterns among older Europeans : the EPIC-Elderly study[END_REF][START_REF] Bamia | Dietary patterns and survival of older Europeans : The EPIC-Elderly Study (European Prospective Investigation into Cancer and Nutrition)[END_REF]. A priori techniques often use predefined criteria based on specific health outcomes to construct dietary scores reflecting the degree to which a person adheres to given dietary patterns [START_REF] Van Dam | New approaches to the study of dietary patterns[END_REF][START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF]. These include compliance with guidelines or recommendations such as the WCRF/AICR score [START_REF] Romaguera | Is concordance with World Cancer Research Fund / American Institute for Cancer Research guidelines for cancer prevention related to subsequent risk of cancer ? Results from the EPIC study[END_REF] and the healthy eating index (HEI) [START_REF] Guenther | Update of the Healthy Eating Index: HEI-2010[END_REF], characteristics of established diets such as the Mediterranean diet [START_REF] Buckland | Adherence to the mediterranean diet and risk of breast cancer in the European prospective investigation into cancer and nutrition cohort study[END_REF][START_REF] Agnoli | Italian Mediterranean Index and risk of colorectal cancer in the Italian section of the EPIC cohort[END_REF][START_REF] Couto | Mediterranean dietary pattern and cancer risk in the EPIC cohort[END_REF][START_REF] Couto | Mediterranean Dietary Pattern and Risk of Breast Cancer[END_REF][START_REF] Turati | Mediterranean diet and hepatocellular carcinoma[END_REF][START_REF] De Lorgeril | Mediterranean Dietary Pattern in a Randomized Trial: prolonged survival and possible reduced cancer rate[END_REF][START_REF] Trichopoulou | Conformity to traditional Mediterranean diet and breast cancer risk in the Greek EPIC ( European Prospective Investigation into Cancer and Nutrition) cohort[END_REF], or even agreement with dietary aspects of a more general healthy lifestyle [START_REF] Mckenzie | Healthy lifestyle and risk of breast cancer among postmenopausal women in the European Prospective Investigation into Cancer and Nutrition cohort study[END_REF][START_REF] Mckenzie | Healthy Lifestyle and Risk of Cancer in the European Prospective Investigation Into Cancer and Nutrition Cohort Study[END_REF]. A priori techniques have seen a shift from adherence to a purely dietary predefined pattern towards scores embracing lifestyle factors as healthy eating behaviours are often in conjunction with healthy lifestyle practices [START_REF] Mckenzie | Healthy Lifestyle and Risk of Cancer in the European Prospective Investigation Into Cancer and Nutrition Cohort Study[END_REF].

A posteriori methods rely on data driven methods that often use dimension reduction techniques such as principal component analysis (PCA) or factor analysis (FA) to yield uncorrelated dietary factors based on data covariance or correlation matrices. These analyses have been successful in identifying distinct food/nutrient intake patterns that were related to different cancer endpoints . Statistical research is underway to explore novel multivariate techniques that provide solutions with easier interpretation of the components [129,130] and tools to reduce the number of arbitrary steps involved (number of components to retain, threshold for loadings, etc.) [131]. Investigations are ongoing to assess the validity of these approaches, and evaluate whether they may predict disease risk in studies involving populations characterised by heterogeneous dietary habits and different cancer rates [START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF].

Most of the early results on the role of diet in cancer aetiology stemmed from retrospective case-control studies. These designs however are subject to selection and recall biases [132], making the retrospective studies not the best suited to effectively capture the diet-disease association leading to somewhat inconsistent findings [START_REF] Willett | Nutritional Epidemiology : Issues and Challenges[END_REF]133].

It was suggested that prospective designs were more rigorous and provided a valid solution to minimise methodological biases [3,[START_REF] Willett | Nutritional Epidemiology : Issues and Challenges[END_REF]134,135]. Since information on dietary exposure is collected at baseline in cancer-free individuals illness is less likely to affect the recall of dietary habits. In addition, prospective cohorts provide the opportunity to assess diet over time through repeated measurements and to examine its associations [191][192][193][194][195], pathway analyses [196,197], and approaches to model the "meeting-in-the-middle" concept are instrumental tools providing analytical solutions to fully exploit the multi-dimensional complexity of new generation nutritional epidemiological data.

The methodological work presented in this thesis will draw from already-existing or currently-developing statistical tools, notably multivariate factorial techniques, to explore the associations between diet and cancer. We take on a holistic approach making use of available dietary questionnaire exposures, lifestyle data as well as biomarker and -omics data to explore two cancer endpoints (breast and hepatocellular carcinoma) in an ideal setting to address challenges related to the multi-factorial complexities of dietary exposure. [START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF][START_REF] Jacques | Are dietary patterns useful for understanding the role of diet in chronic diseases?[END_REF].

Dietary patterns have emerged as a tool of choice to depict a broader picture of the effects of overall diet. Conceptually, patterns are more akin to reflect reality than traditional approaches, as people usually consume a variety of foods often containing a complex combination of nutrients. Moreover, some nutrient effects may be too small to detect on their own, thus the cumulative effect of a pattern embracing multiple nutrients may be easier to identify [START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF]212]

MAIN FINDINGS

Two main patterns were retained in both TT and PCA analyses, and were consistent in terms of pattern identification and amount of total variability explained (over 50% of total observed variability). The first TT component (TC1) loaded highly on cholesterol, protein, retinol, vitamins B12 and D, while TC2 reflected a nutrient dense pattern with high contributions for β-carotene, riboflavin, thiamin, vitamins C and B6, fibre, Fe, Ca, K, Mg, P and folate (Figure 1). The TT components were highly correlated with those of PCA (ρTC1, PC1= 0.91, ρ TC2, PC2= 0.86). The first pattern, that was akin to a Western diet, was associated with a non-significant increase of 5% in BC risk, whilst the second pattern was inversely associated with BC risk with HR=0.89(0.83, 0.95). This decrease was also significant for ER+, PR+, PR-and ER+/PR+ tumours. Cut-level (red dashed line) was chosen after using a 10-fold cross-validation. Nutrients related to the treelet components (TC), indicated with numbered circles, have non-zero loadings on the given component.

CONCLUSION

This study investigated the association between nutrient patterns and BC in the international setting of the EPIC study using a new tool in nutritional epidemiology, the Treelet Transform. TT has the advantage of introducing sparsity in factor loadings thus leading to more easily interpretable patterns. When compared to a more standard approach, such as PCA, TT offers a complementary approach yielding comparable nutrient patterns accounting for similar amounts of variability. In essence, there is a sparsity trade-off: TC are easier to interpret but have a lower information resolution than PC, which may lead to disparities in some associations in models with TC scores vs.

PC scores. The findings suggested a protective association for a diet rich in vitamins, minerals and β-carotene, indicating that a diet mostly plant-based decreased BC risk while a nutrient patterns characterized by a diet rich in macronutrients of animal origin, such as cholesterol or SFA, was related to an increase in BC risk, albeit non-significant. Reproduced with permission from the Cambridge University Press.

Online Supplementary Material

A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptordefined breast cancer in the European

CONTEXT

Biosciences in the era of Big Data have undergone a profound change in the way research is focused, structured and executed. Particularly, recent technological advances in the fields of molecular biology and spectrometry resulted in an increased availability of ever-complex high-dimensional -omics datasets. Such data pose logistical challenges pertaining to their storage, their processing but also to analytical approaches to fully exploit them [173]. Aside from the well-established genomics, -omics also encompass a variety of other fields including transcriptomics, epigenomics, proteomics and metabolomics, an opportunity to examine the "exposome" ( i.e., the entirety of lifecourse environmental exposures) in a comprehensive manner [216]. Unlike the genome, the "exposome" is modifiable, and can be explored through exposure-biomarker approach. One such approach has emerged through the "Meeting-In-The-Middle" (MITM) principle, a research strategy that can potentially reveal exposure-specific biomarkers that are at the same time predictive of morbid conditions [162,217] by looking at associations between exposures, intermediate markers and disease, particularly in settings using metabolomics. This is best investigated in prospective studies which are especially well-tailored for this purpose as they rely on biological samples collected before disease onset, often at recruitment, and therefore are marginally influenced by metabolic changes that arise in the disease-development process.

OBJECTIVES

-To conceive a statistical framework for the MITM approach whose aim is to identify biomarkers that are related to specific exposures and that are, at the same time, predictive of disease outcome.

-To include multivariate techniques in the analytical framework for dimensionality reduction and relating different sets of data.

-To apply the analytical strategy within the European Prospective Investigation into Cancer and nutrition (EPIC) where biological samples were collected at baseline in disease-free participants. Untargeted metabolomic data was acquired using NMR techniques from subjects in a nested case-control study on hepatocellular carcinoma (HCC), for which information on lifestyle and dietary exposures was available.

MAIN FINDINGS

PLS allowed the simultaneous identification of relevant lifestyle and metabolic factors whose link can be predictive in the aetiology of chronic diseases. Three PLS factors reflected in a lifestyle and metabolic components were selected. A first lifestyle factor characterized by a healthy pattern with negative loadings for diabetes status, smoking status and lifetime alcohol intake was not associated with HCC risk, neither was its metabolomics counterpart. The lifestyle component of the second PLS factor reflected a 'higher-risk exposures' lifestyle pattern, and showed a significant 54% increase in HCC risk. Likewise, its associated metabolic component displayed a significant HCC risk rise by 11%. The third PLS lifestyle factor included participants with lower vegetables intake, elevated lifetime alcohol consumption, more likely to be ever smokers and have a hepatitis infection; one standard deviation increase of this component was associated with a statistically significant 37% increase in HCC risk. Similarly, its metabolic counterpart characterised by positive signals of ethanol and myoinositol and negative loadings for glucose displayed a 22% significant increase in HCC risk.

CONCLUSION

This integrated framework allowed the use of all potentially informative aspects of high-

dimensional

OBJECTIVES

-To apply the MITM approach in order to explore the components from a modified HLI with respect to serum metabolites in a nested case-control study on HCC within the EPIC cohort. Targeted metabolites were acquired through the BiocratesKit from pre-diagnostic sera samples.

-To further establish and tune the analytical framework previously developed to yield exposure-specific metabolomics profiles through multiple PLS.

-To develop and adapt the mediation analysis structure to accommodate the matched nested case-control design.

APPROACH

Following a similar scheme as in the previous MITM implementation, for 147 HCC cases and their matched controls, 132 metabolites levels were acquired from pre-diagnostic serum samples using standard targeted metabolite profiling protocols (BiocratesKit).

Through PLS analysis, this metabolomics set, including an additional liver damage score,

was

MAIN FINDINGS

In the overall analysis, the lifestyle PLS factor scored high for study subjects

CONCLUSION

Using a multiple PLS scheme within a MITM framework, we were able to yield lifestylespecific metabolomic signatures. These metabolic profiles bridged healthy behaviours to HCC risk through mediation analyses. The models were fine-tuned and metabolomic signals specific to BMI, alcohol intake, diet, smoking and diabetes were found to be mediators on the pathway between each of these exposures and risk of developing HCC.

Future studies applying the MITM should utilize larger sample sizes for improved power. Nevertheless, the present work clearly offers the utility of the MITM in exploring environment-disease associations in an integrated setting with highly-dimensional data.

PAPER

Contribution: First author, discussed the analytical strategy with the supervisor, conducted statistical analyses, wrote the first draft of the manuscript, submitted it to peer-review journals.

The manuscript is currently in draft format. It has been circulated to the writing group, and to EPIC collaborators. After a first unsuccessful submission to the Journal of the National Cancer Institute (JNCI), it has been now submitted and is under consideration at the International Journal of Epidemiology (IJE).

Abstract

Background:

The "meeting-in-the-middle" (MITM) is a principle to identify exposure biomarkers that are also predictors of disease. The MITM statistical framework was applied in a nested case-control study on hepatocellular carcinoma (HCC) within the EPIC cohort where the components of a modified healthy lifestyle index (HLI) were related to serum metabolites.

Methods

Conclusions:

This study refined the analytical framework of the MITM principle as a way to investigate the relations between lifestyle factors and disease risk using metabolomics.

Relevant metabolomic signatures were identified as mediators in the relationship between specific lifestyle exposures and HCC.

Introduction

Metabolomics have become a focal point in epidemiological studies, as a result of large scale collection of biological samples and technological advances in the fields of molecular biology and chemometrics [1][2][3][4]. Metabolomics offers a broad spectrum of potential biomarkers to explore in search of causal and mechanistic pathways in disease development and aetiology. Such endeavours have revealed a number of mechanistic insights in the understanding of disease progression at metabolic levels and led to biomarker discovery [5].

Metabolomic datasets raise challenges from the processing of complex highdimensional data, to the analytical approaches to fully exploit them [1]. New statistical methodologies are increasingly sought to address the multivariate nature of metabolomic data [6] and to discover relevant pathological processes that metabolomics may help investigate. In this scenario, the "meeting-in-the-middle" (MITM) principle [7,8] is used as a research strategy to identify biomarkers that are related to specific exposures and that are, at the same time, predictive of the outcome.

The MITM has been previously implemented in a nested case-control study where intermediate biomarkers were related to nutrients and to colon and breast cancer indicators [9]. The implementation to multivariate modelling was further extended in a Partial Least Squares (PLS) analysis to integrate a set of 21 lifestyle variables and 285 metabolic variables from 1 H NMR spectra in relation to hepatocellular carcinoma (HCC) risk [10].

Since HCC is a multi-factorial disease strongly associated with lifestyle factors [11],

the MITM was applied to identify metabolite signatures related to HCC. The lifestyle components of a modified healthy lifestyle index (HLI) [12,13] were related to specific metabolic signals.

In this study an in-depth proof of concept of the MITM is revisited with a focused strategy to explore the mediating role of metabolic signatures on the path from exposure to disease in a HCC case-control study nested within the European Prospective Investigation into Cancer and nutrition (EPIC) using targeted metabolomic data.

Material and Methods

The nested case-control design Within a nested case-control study of HCC [14,15] in EPIC, this study focused on 147 cases and 147 matched controls with available biological samples identified in the period between subjects' recruitment into the cohort (1993)(1994)(1995)(1996)(1997)(1998) and 2010 [15,16]. Cases of HCC originated from all participating EPIC centres except for Norway and France that were not a part of this study. All subjects were cancer-free at the time of blood collection. Information on population, data collection of dietary and lifestyle data, follow-up, case ascertainment and matching criteria can be read in Supplementary Methods.

The lifestyle variables (X-set of predictors)

The lifestyle variables were the predictors, referred to as the X-set, and included body mass index (BMI) (continuous, kg/m²), average lifetime alcohol intake (continuous, g/day), the diet score (continuous) described in the Supplementary Methods, physical activity (continuous metabolic equivalents of task in MET-h/week), smoking (never, exsmokers quit>10 years, ex-smokers quit <=10 y, current smokers <=15 cig/day, current smokers > 15 cig/day), hepatitis infection (yes/no) and self-reported diabetes at baseline (yes/no). These are the components of a healthy lifestyle index (HLI) [12,13], hereby modified to include hepatitis and diabetes status, as detailed in Supplementary Methods.

The metabolites set (M-set of responses)

Metabolomic data

Metabolic biomarkers from serum samples were measured by tandem mass spectrometry at IARC, Lyon, France, using the BIOCRATES AbsoluteIDQ p180 Kit (Biocrates, Innsbruck, Austria). Details of the sample preparation and mass spectrometry analyses are provided elsewhere [15,17]. Out of 145 metabolites measured in serum, this study included 132 metabolites with at most 40% of missing values. Metabolite nomenclature has been previously described [18] and can be found in Supplementary Methods. Measurements that were below the limit of detection were set to half that value and those below limit of quantification were set to half that limit (applicable to a total of 16 metabolites for 0.3% to 29.3% of participants). Additionally, measurements that were above the highest concentration calibration standards were set to the highest values.

Liver function score

A composite score indicative of liver function identifying the number of abnormal values for six circulating liver blood biomarker tests indicating possible underlying liver dysfunction [10,14,15] was included in the set of metabolites, the M-set, as detailed in Supplementary Methods. These biomarkers were acquired at the same time as the metabolites from the pre-diagnostic blood samples collected at recruitment.

Statistical analyses

Modified HLI and HCC risk

The association between the modified HLI and HCC risk was evaluated through conditional logistic regression models. Odds ratios, and their 95% confidence intervals (OR, 95%CI) were computed to express a change in HCC risk reflecting one standard deviation (1-SD) increase in the index. Unadjusted and liver function score adjusted ORs were estimated.

Principal Component Partial R-squared (PC-PR2) analyses Sources of systematic variability within the X-set of HLI variables and the M-set of metabolites were identified and quantified through the PC-PR2 method [10,19] as described in Supplementary Methods. For both X-and M-sets, residuals on country and batch (M-set only) were computed in univariate linear regression models and used in the PLS analyses.

Primary PLS analyses: overall and individual PLS Exposure variables were related to metabolomic data through PLS analysis that extracts linear combinations, referred to as PLS factors, of predictors (the X-set of lifestyle variables)

and responses (the M-set of metabolites), allowing a simultaneous decomposition of both sets with the aim of maximizing their covariance [20,21]. An overall PLS was conducted using the entire X-set, then a series of individual PLS analyses was further applied using each HLI variable separately as the predictor to yield exposure-specific metabolomics signatures. In an attempt to yield even more specific metabolic signatures, sensitivity PLS analyses using mutually adjusted lifestyle residuals and country for the X-set and with country and batch residuals for the M-set were computed and presented in Supplementary Tables. More details on the process are provided in Supplementary Material.

Mediation analyses

Mediation analysis assessed whether the metabolic profiles mediated the relation between individual lifestyle factors and HCC risk. For the overall and individual PLS analyses, mediating effects were computed for each extracted pair of lifestyle variable and M-score, adapting the formulae from VanderWeele and Vansteelandt [22] to accommodate continuous exposures and mediators and conditional logistic regression for our matched setting. For each examined lifestyle variable, estimates of the natural direct effect (NDE),

Results

Study population characteristics by case-control status are presented in Table 1. One PLS factor was retained after 7-fold cross validation for PLS analysis. The lifestyle PLS factor identified a 'healthy' behavior profile with positive loadings for physical activity, negative loadings for BMI, lifetime alcohol consumption and smoking (Table 2). The corresponding metabolomics PLS factor was characterized by glutamic acid, hexoses and sphingomyelins.

The PLS lifestyle factor was inversely associated with HCC risk, with TE=0. 4).

Individual PLS analyses yielded metabolite signatures for each component of the modified HLI (Table 3). For lifetime alcohol, the signature was negatively related to SM C16:1, SM C18:1, SM(OH) C14:1, SM(OH) C16:1 and SM(OH) C22:2 and positively related to glutamic acid and PC aaC32:1. Metabolites associated with smoking included SM C16:1 and C18:1, SM(OH) C14:1 and C22:2, LysoPC aC28:1 and PC aeC30:2 with negative loadings and hexoses with positive loadings. In the sensitivity analysis, smoking was negatively associated with serine, lysine and biogenic taurine and positively with PC aaC36:1 and aaC40:3 (Supplementary Table 3). Different phosphatidylcholines characterized the metabolic signature related to diet. The metabolic profile of BMI included glutamic acid, tyrosine, PC aaC38:3, the liver function score with positive loadings and glutamine, LysoPC aC17:0 and LysoPC aC18:2 with negative values. Hexoses and amino acids valine, isoleucine and phenylalanine were positively associated with diabetes status.

All PLS metabolic signatures, with the exception of physical activity and hepatitis infection, were associated with HCC risk, with strong evidence of mediation ( 4). Most of these associations remained statistically significant after FDR correction. With the exception of smoking and, to a lesser extent, lifetime alcohol, the PLS metabolic profiles and estimated associations were virtually unchanged in the sensitivity analysis (Supplementary Tables 3 and4).

Discussion

This study extended the statistical framework of the MITM [10] with a focused strategy to comprehensively explore the mediating role of metabolite signatures in the relationship between HLI and HCC.

In a previous implementation of the MITM [10], 21 lifestyle variables were related to 285 metabolic variables acquired from pre-diagnostic sera 1 H NMR spectra. In this study , the X-set of predictors was restricted to the original components of the HLI, most of which have been previously associated with HCC risk [11,[25][26][27][28][29][30][31][32][33][34]. Variables from the existing index [12,13] were complemented by indicators of hepatitis infection and diabetes status at baseline, which are well-known HCC risk factors [25,26,35]. Alcohol use at recruitment was replaced by lifetime alcohol intake, mainly to address reverse causality. A more focused methodology was further developed building on a similar analytical structure.

PLS analysis was used to relate the sets of HLI variables to metabolites. Preliminarily, an overall factor depicted a lifestyle pattern characterized by low propensity towards smoking, alcohol drinking and obesity, low prevalence of baseline diabetes or hepatitis infection and high levels of physical activity. Mediation analyses indicated the metabolite signature mediated 52% of the association between the healthy lifestyle factor and risk of HCC. In a second phase, individual PLS models were related to specific components of the HLI. The specific metabolite signatures were found to mediate the relation with HCC risk for BMI, lifetime alcohol consumption, smoking, diabetes and diet, with a proportion mediated of 100, 24, 56, 100 and 38%, respectively. These findings suggested that varying proportion of the total effect on HCC is exerted via the metabolite signatures, possibly through specific underlying mechanisms by which the exposure is acting.

Specifically, a recent IARC handbook evaluation on body fatness and obesity reported a positive relationship between BMI and risk of liver cancer [36]. Our study suggests that the increase in HCC risk is entirely mediated by a BMI-specific metabolic signature characterized by phosphatidylcholines (LysoPC aC18:2, LysoPC aC17:0 and PC aeC36:2) and tyrosine. PCs are required for lipoprotein assembly and secretion; in particular acyl-alkyl-PCs were correlated with high-density cholesterol [37,38]. Tyrosine levels imbalance has been previously related to insulin resistance and type 2 diabetes [39][40][41].

Correlation studies conducted in the EPIC-Potsdam cohort exploring the association between lifestyle factors and blood metabolite levels, acquired with the same targeted technology showed similar findings, with serum acyl-alkyl-phosphatidylcholines (PC ae), LysoPC aC17:0, aC18:2 and PC aeC36:2 negatively associated with obesity and BMI whereas tyrosine was positively related to BMI [42][43][44].

The metabolic signature fully mediated the association between diabetes, a wellestablished HCC risk factor [11], and HCC. The contributing metabolites were hexoses, phenylalanine and LysoPCs, consistently with previous studies based on targeted [41] and untargeted [45] approaches. These metabolites were further linked with insulin resistance and involved in glycolysis and gluconeogenesis, and their metabolic alterations was associated with an increased diabetes risk [41].

The metabolomics signature of lifetime alcohol intake was negatively associated with sphingomyelins and positively associated to phosphatidylcholines. Similar metabolites patterns were observed in a study that focused on alcohol-dependent patients [46]. As ethanol has been hypothesised to induce lipogenesis in the liver tissues [47], alcohol can lead to hepatic injuries causing a disruption of the metabolism of fatty acids and phospholipids [48].

The identification of specific metabolic signatures for alcohol and smoking was particularly challenging in our study, as these two factors are strongly correlated [49][50][51]. An overlap between the smoking and alcohol-specific metabolite signatures was observed in the preliminary analysis, where four common sphingomyelins , i.e. SM C16:1, SM C18:1, SM(OH)

C14:1 and SM(OH) C22:2,were identified. In the sensitivity analysis, the different lifestyle exposures were mutually adjusted for prior to PLS, thus leading to a new list of metabolites associated with smoking which included serine, SM(OH) C22:2 and PC aaC36:1, consistently to what was reported in the KORA study [52]. As a result, the estimated proportion of mediation increased from 57 to 100 %, resulting in a metabolic signature capturing smokingrelated metabolic features that is more predictive of HCC.

The application of mediation analysis in this study was another challenging aspect of the analytical framework. A temporal sequence among, in turn, lifestyle exposures, metabolites and outcome is required [53,54] for the NDE and NIE to have a causal interpretation. In our study, while cancer occurrence was assessed during follow-up, lifestyle exposures were assessed at baseline, at the same time of the collection of biological samples that provided metabolomics data. In this respect it is worth noticing that lifestyle and metabolomics reflect different exposure windows. The metabolites likely reflect exogenous and endogenous exposures in a limited timeframe, i.e. between weeks and a few months as the reliability studies that of serum metabolomics data seem to indicate [17,18,55]. The diet score was derived from questionnaires that covered the dietary habits of participants over the past 12 months prior to baseline [56,57]. While lifetime alcohol reflected the history of exposure across adult life, other exposures such as BMI, smoking, physical activity, hepatitis infection and diabetes status were the result of one point in time assessment at recruitment. Our analytical framework study consistently relied on the hypothesis that lifestyle factors were stable over time in the middle-age study populations recruited in EPIC.

Another key aspect of mediation analysis is what is referred to as the 'cross-world assumption', whereby NDE and NIE cannot be identified in the presence of a mediatoroutcome confounder that is affected by the exposure [58]. In our study the composite liver function score, an index compiled from measures of circulating biomarkers of hepatic function indicating underlying liver impairment [14] was likely affected by lifestyle exposure, and was, in turn, likely influencing metabolite levels and HCC risk. The use of weightingbased estimation methods to look at joint mediators to compute randomized interventional effects has been proposed as a solution in the presence of mediator-outcome confounder [58]. In this study the liver function was added to the list of mediators. In this way, the metabolic signatures comprised of relevant information on the liver function, and the link with relevant lifestyle factors was evaluated.

This study was characterized by limited sample size, a direct consequence of the fact that HCC is a rare disease. Findings from this comprehensive approach suggested that certain exposure-specific metabolite profiles are intermediate biomarkers on the metabolic pathway towards hepatocellular carcinogenesis, but replication of these findings in an independent setting is warranted.

This study further refined an endeavor for high-throughput data to integrate metabolomics, lifestyle exposures together with disease indicators. Metabolomics lends itself as a promising tool to identify metabolites bridging the link between exposure(s) and disease, as advocated by the MITM principle [7,8]. The framework we developed allows the identification of informative metabolic signatures, which are useful to elucidate the underlying biological mechanisms in the relationship between lifestyle exposure to risk of cancer risk [59]. * Models were mutually adjusted for all HLI variables with the exception of the overall model including all 7 components of the HLI in PLS analysis. Cases and controls were matched on age at blood collection (±1 year), sex, study centre, date (± 2 months) and time of day at blood collection (± 3h), fasting status at blood collection (<3/3-6/>6h); women were additionally matched on menopausal status (pre/peri/postmenopausal) and hormone replacement therapy. The mediator models were linear. The outcome models were computed through conditional logistic regressions. In the mediation analysis, the exposure was the original modified HLI lifestyle factor (for the overall model the exposure was the X-score), the mediator was the associated M-score (metabolic profile) and the outcome was HCC. ** As the associations were null for direct and indirect effects, the proportion mediated was not computed. NDE and NIE and their 95%CI computed from formulae detailed in Supplementary Methods.

Tables and Figures

* 6 and 21 components were retained to account for 80% (threshold used) of total modified HLI and metabolites variables' variability, respectively. The R 2 value represents the amount of variability in modified HLI/metabolites variable explained by the ensemble of investigated predictors.

Supplementary Table 3:

Metabolites contributing* to two selected modified HLI variable-specific PLS factors: smoking and lifetime alcohol (N=294, X-set= 1 in turn, M-set=133) -Results reported from the primary analysis, using residuals based on country (X-and M-sets) and batch (M-set only), and from the sensitivity analysis, using mutually adjusted lifestyle residuals as well as residuals for country and batch (the latter only in the M-set). * Models were mutually adjusted for all HLI variables with the exception of the overall model including all 7 components of the HLI in PLS analysis. Cases and controls were matched on age at blood collection (±1 year), sex, study centre, date (± 2 months) and time of day at blood collection (± 3h), fasting status at blood collection (<3/3-6/>6h); women were additionally matched on menopausal status (pre/peri/postmenopausal) and hormone replacement therapy. The mediator models were linear. The outcome models were computed through conditional logistic regressions. In the mediation analysis, the exposure was in turn the original HLI lifestyle factor (for the overall model the exposure was the X-score), the mediator was the associated M-score (metabolic profile) and the outcome was HCC. NDE and NIE and their 95%CI computed from formulae detailed in Supplementary Methods.

Primary Analysis

participants from the EPIC-Oxford sub-cohort, although the accuracy of these self-reported data have been validated 5 .

Follow-up and case ascertainment in the nested case-control study

Follow-up started at date of entry to the study and finished at date of diagnosis, death or last completed follow-up (from December 2004 up to June 2010), whichever came first.

Cancer incidence was determined through population cancer registries or through active follow-up as detailed elsewhere 6 . Incident HCC cases were defined as first primary invasive tumours and identified through the 10th Revision of International Statistical Classification of Diseases, Injury and Causes of Death (ICD10) as C22.0 with morphology codes ICD-O-2

"8170/3"and "8180/3". Metastatic cases and other types of primary liver cancer were excluded.

Matching criteria for the nested case-control study

For each HCC case, one control (n=147) was selected by incidence density sampling 7 from all cohort members alive and free of cancer (except for non-melanoma skin cancer), and matched by age at blood collection (±1 year), sex, study centre, time of the day at blood collection (±3 hours), fasting status at blood collection (<3, 3-6,and >6 hours); among women, the pair was additionally matched by menopausal status (pre-, peri-, and postmenopausal), and hormone replacement therapy use at time of blood collection (yes/no).

Modified Healthy Lifestyle Index (HLI) construction

The overall HLI had five initial components and was determined for the entire EPIC cohort as an unweighted sum of the scores of its individual components, each assigned scores of 0 to 4, where a higher score indicated a healthier behaviour 8,9 . This study utilized a modified version of the HLI and included smoking, quintiles of physical activity, BMI, quintiles of the diet score and lifetime alcohol consumption instead of alcohol at recruitment to avoid reverse causality with respect to HCC outcome. In addition, two components reflecting two major risk factors of liver cancer 10-12 were added to the modified index to make it more HCC-specific: diabetes at baseline (No=4, Yes=0) 11 ; and hepatitis infection (No=4, Yes=0, assessed from biomarker measures of hepatitis B and hepatitis C viruses' (HBV, HCV) seropositivity [ARCHITECT HBsAg and anti-HCV chemiluminescent microparticle immunoassays; Abbott Diagnostics, France]) 12 . To some extent hepatitis infection can reflect certain lifestyle exposures and behaviours. Missing values in some of the index components were imputed by an expectation-maximization (EM) algorithm that preserved the variancecovariance structure of the data 13 . Descriptive and scoring details on the modified HLI components can be viewed in Supplementary Table 1.

Metabolomic data nomenclature

Fatty acids side chains are labelled "Cx:y", where x and y are the numbers of carbon atoms and double bonds, respectively. Measured metabolites included 12 acylcarnitines (abbreviated according to the fatty acid side chain), 21 amino acids and 6 biogenic amines (labelled with their full name), 78 phosphatidylcholines (PC) of which there were 11 "LysoPC a" (PCs having one fatty acid side chain with an acyl bound), 34 "PC aa" and 33 "PC ae" (PCs having respectively two acyl side chains [diacyl] and one acyl and one alkyl side chains), a total of 14 sphingomyelins "SM" of which 5 had a hydroxyl group "SM(OH)" (additionally labelled according to the fatty acid side chain) and finally 1 sum of hexoses (including glucose, fructose and galactose). PCs were separated by type of bond and number of fatty acids side chains.

Liver function score construction

This score includes the following tests: alanine aminotransferase >55 U/L, aspartate aminotransferase >34 U/L, gamma-glutamyltransferase: men>64 U/L and women>36 U/L, alkaline phosphatase >150 U/L, albumin<35 g/L, total bilirubin > 20.5 μmol/L; cut-points were provided by the clinical biochemistry laboratory that conducted the analyses (Centre de Biologie République, Lyon, France) based on assay specifications as previously described 6,14 .

The diet score (included in the X-set, continuous and in the modified HLI, categorical)

An a priori score for diet was proposed within EPIC based on dietary components that have been posited to affect risk of cancer 9,8 . The diet score combined six dietary items including cereal fiber, red and processed meats, ratio of polyunsaturated to saturated fatty acids, margarine (used as a surrogate marker for trans-fat from industrial sources), glycaemic load, and fruits and vegetables. Details of the diet score computation are provided elsewhere 9 .

The resulting continuous variable was included in the X-set as previously mentioned.

Statistical Analyses

Principal Component Partial R2 (PC-PR2)

PC-PR2 combines aspects of PCA with the partial R 2 statistic in multiple linear regression models. Briefly, the set under scrutiny is reduced through PCA and a number of components explaining an amount of total variability above a designated threshold (here, 80%), is retained. Multiple linear models are then fitted where each component's variability is explained by regressing it on a list of relevant covariates, yielding an R 2 statistic for each of the latter. The R 2 quantifies the amount of variability each independent variable explains, conditional on all other covariates included in the model. Finally, an overall partial R 2 is computed as a weighed mean for each covariate, using the eigenvalues as components'

weights.

In this study, PC-PR2 was applied to the X-set of 7 exposure variables where the covariates explored for systematic variability were country, age at recruitment and sex. With the similar objective of identifying sources of variability in the metabolite data, another PC-PR2

analysis was run on the M-set and the examined covariates included country, age at blood collection, batch, sex, BMI, diet score, physical activity, alcohol at recruitment, smoking, hepatitis and diabetes at baseline.

After running PC-PR2, a total of 6 and 21 principal components were retained explaining around 80% of total variability among the modified HLI original variables and the metabolites set, respectively. The ensemble of explanatory systematic variables accounted for 10.7 and 29.5% of total variance within the X-and M-sets, respectively. "Country of origin" was the highest contributor with consistently 6.2 and 13.1% in the X-and M-sets, followed by "Batch" with 7.1% in the M-set (Supplementary Table 2). PLS analyses were carried out controlling for these two variables in the respective sets. Sensitivity analyses were also conducted where mutually adjusted lifestyle residuals and country residuals were used in the X-set. Country and batch residuals were used in the M-set (Supplementary Tables 34).

Details on the PLS procedure

PLS is a multivariate method that generalizes features of PCA with those of multiple linear regression 15,16 . Mathematical and computational details of the PLS method and its applicability within the MITM framework have been thoroughly described previously 17 .

Missing values in the M-set were imputed through a simple EM algorithm 18,19 consisting of the two following steps. First, the missing values were replaced by the average of the nonmissing values for each related variable and a PLS model is run. In a second step, the missing data are assigned their predicted values based on the first model and PLS is then rerun. The number of PLS factors to retain was selected after carrying a 7-fold cross-validation to minimize the predicted residual sum of squares (PRESS) statistic, a measure of PLS performance. Details of the process can be found elsewhere 17 . PLS factor loadings, i.e. the coefficients quantifying how much each original variable contributes to the PLS factor, characterize each extracted HLI and metabolomics profile. As the M-set was particularly dense in metabolite variables, the interpretation of the metabolomics profile mainly focused on those most significantly contributing to the PLS component, reporting variables with loading values lower than the 5 th and larger than the 95 th percentiles. One PLS factor was retained in each one of the individual PLS analyses. All lifestyle and metabolomic components of PLS factors where mirrored in their respective PLS-scores (X-and M-scores).

Details on the mediation analyses

The NDE and NIE were produced through two main models: a linear mediator model and a conditional logistic outcome model. HCC being a rare outcome, direct and indirect effects were estimated taking into account the nested case-control design. This is done by running the mediator regression only for the controls 20 . After testing, there was no exposuremediator interaction, the models can then be simply written as follows:

Let x be the exposure, m the mediator, c a set of different confounders, y HCC and j the pair number ranging among the set {1,…, n=147}:

Thus, NDE and NIE are given as follows for a one standard deviation increase in x and m: 95% CI for NDE and NIE were computed through the following formulae:

, where and are the estimated variances of the coefficients and , respectively.

The total effect of X (TE) was computed from the following conditional logistic regressions:

with TE given by: Usually TE can be written as the product of NDE and NIE. However, in our setting employing conditional logistic regression, this is no longer the case because discordant pairs in the model adjusted for the mediator are not the same as the model not including the mediator (TE).

The mediator effect (ME), corresponding to the "independent" effect of the M-score adjusted for its counterpart lifestyle exposure and for confounding variables was given by:

To control for potential confounding, mediation analyses models were adjusted for the modified HLI variables except the one under scrutiny (multiple PLS), with the exception of the models from the overall PLS. P-values for NDE and NIE were inferred from the 95%CI, whereas for the ME and TE, p-values associated with Wald's test for continuous exposure compared with a chi-square distribution with 1 degree of freedom are reported. The false discovery rate (FDR) correction 21 was applied to mediation results stemming from the multiple PLS analyses.

For each mediation analysis the estimates for the NDE, NIE, TE and ME were reported for an increase in the exposure as follows: an increase of 1-SD for the overall PLS analysis and for smoking, an increase of 1-SD among the controls for BMI, physical activity and the diet score, an increase of 1 unit (0 to 1) for diabetes and hepatitis, and finally an increase of 12 g/day (corresponding to one alcohol unit) for lifetime alcohol.

CONTEXT

Breast cancer (BC) is the most frequent cancer affecting women as one in five new cancer cases detected in women is BC, and it is the main cause of cancer death in women worldwide. BC incidence is on the rise and is expected to keep rising as the world population ages [209,222]. BC is a multifactorial disease whose aetiology embraces environmental, lifestyle and dietary risk factors [13,20,22,25,[START_REF] Couto | Mediterranean Dietary Pattern and Risk of Breast Cancer[END_REF][START_REF] Männistö | Dietary patterns and breast cancer risk: results from three cohort studies in the DIETSCAN project[END_REF][223][224][225][226][227][228] cohort -in a nested case-control study with FA biomarker data showed a statistically significant link between industrially produced TFA and increased risk of BC [35]. The following work aims to confirm the findings from the latter study by extending the analysis to a larger nested case-control sample including subjects from all EPIC countries, providing a wider geographical gradient of FA intake.

OBJECTIVES

-To assess the association between biomarkers of dietary FA intake and risk of BC within a large nested case-control study in EPIC.

-To investigate this association by different hormonal receptor status (different BC subtypes) and by menopausal status.

-To confirm the findings from the French arm of EPIC -E3N -where evidence showed the detrimental effects of total trans monounsaturated FA, trans palmitoleic and trans elaidic acids on BC risk, using a larger sample size from the whole EPIC cohort with more variability.

-To provide the necessary evidence on the effects of individual FA, particularly

TFAs, prior to moving to more complex frameworks exploring the lipidome in multivariate and pathway analyses.

APPROACH

Within a nested case-control study on BC within EPIC, including 2,982 cases and as many matched controls, sixty fatty acids levels were measured by gas chromatography in pre-diagnostic plasma. For each plasma phospholipid FA, conditional logistic regressions were applied to estimate the odds ratios and associated 95% confidence interval (OR, 95%CI). The models were adjusted for date at blood collection, education level, BMI, height, menopausal hormone use at baseline, alcohol, age at first birth and parity combined, energy intake, and family history of BC. This univariate multivariable approach was additionally used in subgroup analyses where the relationships between FA were investigated by menopausal status and by oestrogen receptor (ER) and progesterone receptor (PR) status in tumours.

MAIN FINDINGS

After controlling for multiple testing through the FDR correction, evidence of an increased overall BC risk was found associated with high levels of palmitoleic acid with OR=1.37 (1.14, 1.64, p-trend<.001, q-value=0.004) comparing the highest quartile with the lowest. High levels of the desaturation index DI16 (16:1n-7/16:0) which is a biomarker of endogenous hepatic synthesis of MUFA, were associated with a statistically significant increase in BC risk by 28%. Contrariwise, high levels of plasma phospholipid n-6 PUFA were associated with a decrease in BC risk with OR=0.81 (0.69, 0.96, p-trend=0.035) but this association did not withstand FDR correction. In subgroup analyses by menopausal status, the results did not markedly differ, whereas specific associations emerged by hormonal receptor status. Specifically, ER-BC cases significantly arose by two-fold in participants with high levels of industrial TFA. This increase was not however present in ER+, PR-and PR+ subtypes.

CONCLUSION

Findings from this study carried out on data from all EPIC participating sub-cohorts showed that an early increase in endogenous synthesis of MUFA might increase BC risk.

This confirmed early findings from E3N, where specific MUFA were linked with an increased BC risk. These results were consistent and independent from menopausal and hormonal receptor status. Dietary industrially-produced TFA increased ER-BC risk.

These results may contribute to issue guidelines for BC prevention, by considerably lowering or eliminating TFA in industrially processed foods. This latter measure would likewise benefit the ER-BC subtype that has one of the highest mortality rates. This analysis is a first stepping stone looking into the associations between FA and BC.

Future analyses will look into the complex lipid interactions at the heart of the lipidome, and disentangle these associations when considering the common metabolic pathways shared by numerous FA, with the scope of looking at BC outcome.

PAPER

Contribution: Co-author, contributed to statistical analyses and to finalizing the report, participated to the addressing reviewer's comments, read and approved the final report.

The manuscript is currently in draft format. It has been circulated to the writing group, and to EPIC collaborators. After a first unsuccessful submission to the Journal of the National Cancer Institute (JNCI), it will be shortly submitted to another target journal.

Introduction

Breast cancer is the most frequently diagnosed cancer among women worldwide with an estimated 1.8 million new cancer cases diagnosed in 2013 (25% of all cancers) (1). While multiple risk factors for breast cancer such as family history, obesity, alcohol, breastfeeding, and reproductive history, are well established, very few additional modifiable risk factors have been identified.

Variation in diet has been suggested to account for up to 25-40% of preventable causes of cancers (2). A potential link between dietary fat and breast cancer has been a focus of intense research; however, overall findings to date are conflicting (3)(4)(5). Epidemiological studies indicate that, rather than total fat intake, subtypes of fatty acids could diversely affect breast cancer risk. A high dietary intake of cis monounsaturated fat (MUFA) ( 6), or long-chain n-3 polyunsaturated fatty acids (PUFA) from marine sources (7), may reduce breast cancer risk. Conversely, a positive association has been reported between dietary intake of saturated fatty acids (SFA) and ER-positive breast cancer (8).

Finally, a high estimated intake of industrial trans fatty acids (ITFA) derived from industriallyproduced hydrogenated vegetable oils may increase the risk of postmenopausal breast cancer (9).

However, overall data on specific fatty acids are still discrepant.

Epidemiological data on biomarkers of exposure to fatty acids and breast cancer risk are also limited.

Meta-analyses of prospective and/or case-control studies have suggested a protective effect of n-3

PUFA on breast cancer risk (7), while some SFA and MUFA have been associated with an increased risk of breast cancer (10). One prospective study showed a significant association between high blood levels of ITFA and increased risk of breast cancer (11). However, in general prospective studies have not shown clear associations between patterns of fatty acids and risk of breast cancer, overall and by hormonal receptor status (12). More epidemiological prospective studies that integrate reliable biomarkers of exposure to fatty acids are needed to further investigate the contribution of different types of fatty acids to the etiology of breast cancer, overall and by hormone receptor subtypes.

the Danish Biobank, samples from Denmark were not included, leading to a total of 2,982 cases. For each case, one matched control was chosen randomly among cohort women without breast cancer.

Controls were matched to cases by center, age at blood donation ( 3 months), menopausal status (pre; surgical post; natural post), time of the day at blood collection ( 1 hour), fasting status ( 3hrs;

3-6 hrs.; 6 hrs.) and phase of the menstrual cycle (early follicular; late follicular; peri-ovulatory; midluteal; other luteal).

The EPIC study was approved by the Ethical Committee of the International Agency for Research on Cancer and individual EPIC centers.

Fatty acid analyses

Fatty acids measured in plasma and erythrocyte membrane phospholipids are highly correlated, and exhibit similar coefficient correlations with dietary fatty acids estimated through questionnaires (13),

suggesting that both matrices can be used as biomarkers of habitual intake. In the present study, fatty acid concentrations were determined in plasma phospholipids, as our previous cross-sectional study within the EPIC study showed that some specific fatty acids measured in this fraction are reliable biomarkers of specific food intakes (14,15).

As previously described (11), total lipids were extracted from plasma samples (200 μl) with chloroform-methanol 2:1 (v/v) containing antioxidant butylated hydroxytoluene and L-Aphosphatidylcholine-dimyristoyl-d 54 as an internal standard. Phospholipids were purified by adsorption chromatography. Fatty acid methyl esters were formed by transmethylation. Analyses were carried out on 7890A gas chromatographs (7890N GC Agilent Technologies). Samples form cases and controls were processed in the same batch, and laboratory staff was blinded to any participant characteristics. Human plasma were used as quality control samples and included in each batch. Fatty acids were identified by their retention times compared with those of commercial standards. The relative concentration of each fatty acid, expressed as percent of total fatty acids, was quantified by integrating the area under the peak and dividing the result by the total area. Fatty acids were also expressed as absolute concentrations in plasma (μmol/liter) based on the quantity of the methyl deuterated internal standard.

Coefficients of variation for fatty acids ranged from 1.81% for large peaks to 9.75% for the smallest peaks.

We calculated the percentage of the following groups: saturated fatty acids (SFA), cismonounsaturated fatty acids (cis-MUFA), ruminant trans fatty acids, industrial trans fatty acids, cis-n-6 polyunsaturated fatty acids (cis-n-6 PUFA), long-chain n-6 PUFA (20:2n-6, 20:3n-6, 20:4n-6, 22:4n-6, 22:5n-6), n-3 PUFA, long-chain n-3 PUFA (20:3n-3, 20:4n-3, 20:5n-3, 22:5n-3, 24:5n-3, 24:6n-3, 22:6n-

3), and ratio of long-chain n-6/long-chain n-3 PUFA. We also determined the desaturation indexes (DI) as the ratio of product to substrate, either oleic acid to stearic acid (DI 18 ) or the ratio of palmitoleic acid to palmitic acid (DI 16 ), as biomarkers of endogenous lipogenesis (16).

Hormonal receptor status

Information on estrogen receptor (ER) expression was available for 2,047 cases (1,649 ER-positive, 398 ER-negative), and on progesterone receptor (PR) expression for 1,729 cases (1,150 PR-positive, 579 PR-negative). Immunohistochemical measurement of ER and PR expression was performed in each EPIC centre. To standardize the quantification of the receptor status, the following criteria were applied for a positive receptor status: 10% cells stained, any 'plus system' description, 20fmo/mg, an Allred score of 3, an IRS 2, or an H-score 10.

Statistical analyses

Baseline characteristics of cases and controls were compared using paired t-tests for continuous variables. For categorical variables, the statistical significance of case -control differences was tested using a chi-square test. All missing values were excluded from calculations.

In order to evaluate the association between fatty acids and breast cancer risk (overall and specific breast cancer subtypes by receptor status), odds ratios (OR) and their 95% confidence intervals (CI)

were estimated using conditional logistic regression models. Plasma fatty acids were categorized into Statistical tests were 2-sided, and P 0.05 was considered significant. All analyses were performed with the SAS 9.2 software (SAS Institute Inc., Cary N. Base SAS® 9.3 Procedures Guide. 2011).

cancer risk (OR [Q4-Q1]=0.81; 95%CI=0.69-0.96; p for trend=0.035), while no further association was detected with total cis n-3 PUFA. However, the association with n-6 PUFA did not withstand correction for multiple testing (q-value=0.259). Further, the ratio of n-6 to n-3 PUFA was not associated with breast cancer development (Table 3).

A higher DI 18 was positively associated with breast cancer (OR [Q4-Q1]=1.16; 95%CI=0.97-1.40; p for trend=0.031, q-value=0.259). Particularly, increased risk of breast cancer was associated with a high DI 16 , even after controlling for multiple testing (OR for the highest quartile compared with the lowest [Q4-Q1]=1.28; 95%CI=1.07-1.54; p for trend=0.002, q-value=0.037).

Plasma phospholipid fatty acids and breast cancer risk by hormonal receptor status

Table 4 presents OR and 95% of breast cancer according to fatty acid groupings, presented by subgroup of hormonal receptor expression. Although not statistically significant, the positive association between breast cancer risk and DI 16 remained irrespective of hormonal receptor status.

Increased risk of ER-negative breast cancer was specifically associated with high levels of ITFA (OR for the highest tertile compared with the lowest [T3-T1]=2.01; 95%CI=1.03-3.90; p for trend=0.047), while no significant association was found with ER-positive breast cancer (p for heterogeneity=0.015).

Discussion

In this large prospective study, we found evidence that higher levels of MUFA, particularly palmitoleic acid, as well as higher DI 16 , were associated with increased risk of breast cancer. In addition, higher levels of ITFA were specifically associated with ER-negative breast cancer.

Nutritional epidemiology has been limited by the assessment of dietary fatty acids through dietary assessment methodologies, prone to substantial measurement error. Measurement of plasma phospholipid fatty acid offer specific biomarkers of past dietary intakes of fatty acids that cannot be endogenously synthesized, irrespective of the source and quality of food (14,15). In contrast, weaker associations were found between dietary intakes and SFA, and MUFA because of endogenous synthesis and complex fatty acid metabolism (15).

Accumulating evidence supports a role of early increased de novo synthesis of MUFA in the development of breast cancer (16,18). Stearoyl-CoA desaturase-1 (SCD-1) is the key enzyme in the synthesis of MUFA from SFA, suggesting the implication of SCD-1 activity in the biological alterations of breast cancer (16,18). In agreement with our findings, some epidemiological studies reported a significant association between increased risk of breast cancer and increasing levels of plasma/serum phospholipid or erythrocyte membrane MUFA (palmitoleic acid and/or oleic acid) (19)(20)(21). Lipid imaging and profiling for tissue samples from different types of cancer reported abundant amounts of MUFA relative to PUFA in the cancer microenvironment compared with the adjacent normal tissue, leading to decreased in membrane fluidity, which, in turns, influences many crucial membrane-associated functions (22). MUFA can serve as mediators of signal transduction and cellular differentiation, and unbalanced levels of these mediators have been also implicated in carcinogenesis (16,18). On the other hand, data available from epidemiological studies have generally shown a negative association between estimated dietary intake of MUFA with breast cancer risk, at least in Mediterranean countries (23,24), suggesting the role of endogenously synthesized MUFA in breast cancer development, rather than exogenous dietary MUFA. Thus, these data support the hypothesis that increased endogenous synthesis of MUFA, rather than exogenous dietary MUFA, may stimulate breast cancer development, and might represent a specific target for breast cancer prevention.

There are limited data on the impact of SFA and MUFA in the DI measured in plasma phospholipids.

In a controlled cross-over study, a high dietary intake of SFA has been shown to increase the DI 16 measured in blood cholesterol esters and phospholipids (25). As a consequence, a high DI 16 in plasma phospholipids that is positively associated with breast cancer risk may be the result of a diet rich in SFA, with concomitant increased hepatic desaturation of dietary SFA to MUFA. In a large cross sectional study within EPIC, a weak correlation was found between dietary intake of oleic acid, the main dietary MUFA, and plasma phospholipid DI 18 , suggesting that dietary MUFA may not be a strong determinant in the DI 18 compared with endogenous synthesis from stearic acid. These data may suggest the effect of dietary SFA rather than dietary MUFA in high DI measured in plasma phospholipids.

We found no significant association between breast cancer risk overall or by hormonal receptor status and levels of n-3 PUFA from marine sources. In contrast, prospective studies conducted in Asian populations consistently reported an inverse association between breast cancer risk and dietary intake or biomarkers of n-3 PUFA (7). Because n-3 PUFA intake in Asian populations is higher compared to Western populations, it was suggested that n-3 PUFA intake from fish might be too low in the EPIC population to reveal a possible protective effect on breast cancer (11). However, in a prospective study conducted in Japan with high intakes of n-3 PUFA, no significant inverse association was found between n-3 PUFA and breast cancer risk, while a negative trend was reported between EPA and ER+PR+ breast cancer (26). Because of the competition between n-3 PUFA and n-6

PUFA for eicosanoids production as an underlying mechanism, ratio of n-3/n-6 PUFA in diet and blood phospholipids has been suggested to play a determinant role in breast cancer risk. Indeed, data from a meta-analysis of prospective studies reported a decreased risk of breast cancer we confirm and refine our previous data on breast cancer (11) by reporting a positive association between plasma phospholipid ITFA isomers and breast cancer risk restricted to the subtype of ERnegative tumours. Few mechanistic data on the effect of ITFA on cancer development are available.

One study showed that elaidic acid, the main ITFA, induces hepatic de novo fatty acid synthesis in vitro through upregulating the SREBP-1 pathway, while cis MUFA and SFA did not show an effect (34). In contrast to ITFA, we found no significant association between natural trans fatty acids and breast cancer risk, overall or by hormonal receptor status.

This study had several strengths including its prospective design, based on a very large number of incident breast cancer cases with detailed clinical and epidemiologic data. Additionally, we were able to separate trans fatty acid isomers from natural and industrial processes. The major limitation of the study is the single collection of blood samples at baseline. Finally, given the longer lifespan of fatty acids in adipose tissue and erythrocytes compared with plasma, it might be suggested that fatty acids measured in these matrices offer a better measure of longer-term intake than fatty acids measured in plasma phospholipids. However, there are data suggesting that plasma fatty acids are correlated with erythrocyte levels (13).

These findings suggest that increased endogenous synthesis of MUFA estimated several years prior to diagnosis may be associated with breast cancer development. The identification of dietary/lifestyle factors as potential regulators of endogenous MUFA synthesis could provide new strategies for breast cancer prevention. ITFA may also specifically increase ER-negative breast cancer risk. The poor prognosis and high burden of ER-negative breast cancer mortality make this subtype a priority for prevention. Eliminating ITFA in industrial processes and in foods could offer a relatively straightforward public health action for reducing non-communicable disease risk. Our approach progressively moved from conventional statistical modelling harbouring multivariable regressions coupled with multiple testing corrections, towards a more holistic scheme embracing multivariate contexts, using increasingly complex mathematical techniques. Evaluations primarily focused on nutrients and cancer association and then moved towards integration of dietary biomarkers, of features of untargeted and targeted metabolomics. These different features were evaluated together with lifestyle exposures, the common denominator of all investigations carried out throughout this thesis, using a methodological challenging integrative strategy to fully exploit a large amount of epidemiological information.

In this chapter, we will discuss different aspects pertaining to the data from different sources exploited within this thesis, addressing some strengths and weaknesses of questionnaire, biomarker and metabolomic data. Advances in lab technology, the importance of the validation of the findings, the necessity of replication as well as the rationale and evolution of the statistical framework that has been developed will be touched upon. considerable resources. This may not be sufficient to describe the evolution of long-term dietary exposure using biomarker measurements. A repeated sampling of biospecimens would be a valuable asset to monitor changes in diet overtime in prospective designs and to better depict dietary intake / nutrient state at baseline and during follow-up [5].

In addition, the potential for bias in biosamples collected in nested case-control studies within prospective design is reduced but not absent. While these samples are collected before diagnosis, the impact of preclinical conditions may impact the biochemical parameters, thus causing spurious associations [START_REF] Willett | Nutritional Epidemiology : Issues and Challenges[END_REF]. shrinking the values of some of the regression coefficient estimates towards zero. These are known as regularized linear regressions and mainly comprise ridge regression, Lasso and its variants as well as Elastic Net. These methods are progressively being applied to -omics data. In particular, multivariate approaches are subject to over-fitting making validation a mandatory step for analytical strategies employing these methods.

Cross-validation techniques that do not call for the appraisal of additional independent Another motivation for a replication of our findings in external studies or using larger samples is the small sample size we had at hand. In the nested case-control studies on hepatocellular carcinoma presented in Chapter 3 and Chapter 4, the sample sizes were very modest with 114 cases and 222 controls, and 147 cases and 147 controls, respectively. We made a rather opportunistic use of the available data that were at our disposal within different nested case-control studies in EPIC where metabolomic data was accessible to investigate the diet-cancer associations or to implement statistical strategies in proof-of-concept designs. In Chapter 5, we looked into associations between levels of 60 plasma phospholipids fatty acids in one of the largest nested case-control studies to date to ascertain fatty acids from biomarkers collected within a prospective study. Due to a flooding that occurred in the Danish Biobank, samples from Denmark were not included, when these will be added to the fold, there will be possibly more power to detect associations that did not withstand multiple correction testing.

Throughout this thesis, we moved from a multivariate problem with dietary data (Chapter 2) to a higher-level multivariate problem integrating biomarkers (Chapter 3)

and then onto a more specific and more tightly defined problem (Chapter 4). We first employed TT, a dimension reduction technique to take on one set of nutrients (Chapter 2), then made use of PLS to best summarise information from two sets of data and then applied a multiple PLS scheme in a more carefully controlled context. We also improved on our usage of mediation analysis from a generic use to evaluate the mediating role played by the extracted metabolic signatures (Chapter 3) to a more refined use adapted sparse-PLS or canonical correlation analysis instead of PLS) and can be tailored to be used with other -omics datasets and disease endpoints. This stems from the conceptual strength of the MITM [162] sustaining that any past exposure may leave alterations, either metabolic, genetic, epigenetic inter alia, that are only expressed far later in time, depending on subsequent exposures. The MITM sets the challenge to first identify these changes that can be recognised as overlap biomarkers mirroring previous exposures and related to pathophysiological conditions, and then to monitor those complex changes at the molecular level and relate them and interpret their effects with respect to the mechanisms of carcinogenesis. These will ultimately lead to a better understanding of the underlying ecology of cancer development in an attempt to connect the external exposures to the palette of internal biochemical modifications.

In our evaluation of whether the metabolic signals mediated the association between a given exposure or a lifestyle profile and HCC risk, we resorted to mediation analysis (Chapters 3 and 4). Mediation analysis is an increasingly utilised technique, widely used across many disciplines, to explore various causal pathways, beyond the estimation of simple associations. Mediation analysis investigates the mechanisms that underlie an observed relationship between an exposure variable and an outcome variable and examines how they relate to a third intermediate variable, the mediator [195]. Rather than hypothesizing only a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the exposure variable causes the mediator variable, which in turn causes the outcome variable. The direct and the indirect (through the mediator) levels of association levels are then estimated from the outcome and mediator models [193].

Although mediation analysis has become very popular in social sciences, its use remains challenging. Over simplistic regression models, the possibly greatest merit of mediation analysis is that it allows the synergistic structure of the relationship between exposure, mediator and outcome variables to be captured and quantified. By introducing more complex functional relationships between variables, thus mimicking features of pathway analysis, the interpretation of model parameters needs to account for the large amount of underlying hypotheses subjacent each mediation model. Very strong assumptions are required for such an ambitious causal endeavour, they must be met and confounders must be accounted for in order to have a causal interpretation of the findings [192,193].

We were faced with some of these challenges that we addressed especially in Chapter 4.

One such example relates to temporality; the exposure must precede the mediator that Statistical innovations and new methodologies to analyse increasingly highdimensional, biologically complex data will be key to pursue the investigation of the diet-disease relationship, a relation that evolves in time and crystallizes many alreadyestablished components, but that will inevitably pick up new contributing factors along the way.

Faculté

  Techniques des Activités Physiques et Sportives Observatoire des Sciences de l'Univers de Lyon Polytech Lyon Ecole Supérieure de Chimie Physique Electronique Institut Universitaire de Technologie de Lyon 1 Ecole Supérieure du Professorat et de l'Education Institut de Science Financière et d'Assurances Directeur : M. F. DE MARCHI Directeur : M. le Professeur F. THEVENARD Directeur : Mme C. FELIX Directeur : M. Hassan HAMMOURI Directeur : M. le Professeur S. AKKOUCHE Directeur : M. le Professeur G. TOMANOV Directeur : M. le Professeur H. BEN HADID Directeur : M. le Professeur J-C PLENET Directeur : M. Y.VANPOULLE Directeur : M. B. GUIDERDONI Directeur : M. le Professeur E.PERRIN Directeur : M. G. PIGNAULT Directeur : M. le Professeur C. VITON Directeur : M. le Professeur A. MOUGNIOTTE Directeur : M. N. LEBOISNE

Figure 1 :

 1 Figure 1: Cluster tree produced by the Treelet Transform.

29 PAPER

 29 Contribution: First author, discussed the analytical strategy with the supervisor, conducted statistical analyses, wrote the first draft of the manuscript, submitted it to the journal and replied to reviewers' comments.

APPROACHFigure 2 :

 2 Figure 2: General original scheme to model the MITM principle.

:

  Lifestyle and targeted metabolomic data were available from 147 HCC cases and 147 matched controls. Partial Least Squares (PLS) analysis related 7 modified HLI variables (diet, BMI, physical activity, lifetime alcohol, smoking, diabetes, hepatitis) to 132 serummeasured metabolites, and a liver function score. Exposure-specific signatures were also extracted with PLS models. Mediation analysis evaluated the role of metabolomic PLS scores in the relationship between the modified HLI and HCC risk.Results:The overall PLS factor's lifestyle component was negatively associated with lifetime alcohol, BMI, smoking, diabetes and positively associated with physical activity. Its metabolic counterpart was positively related to SM(OH) C14:1, C16:1 and C22:2, and negatively to glutamate, hexoses, and PC aaC32:1. The lifestyle and metabolomics components were inversely related to HCC risk. The PLS scores expressing metabolic signatures mediated the association between smoking and lifetime alcohol and HCC with Natural Indirect Effects respectively equal to 1.22(95% confidence interval [CI]=1.04 to 1.44) and 1.09(95%CI=1.03 to 1.15).

Chapter 3 ,

 3 samples are typically used to internally validate the findings. In this procedure, the data is randomly partitioned into a training set used to build a given model and a test set that is removed, usually with a 90%-10% fold proportion. The process is then iterated until each sample has served as a test set once. It is a model validation technique evaluating the accurate predictive performance of the model in practice and its robustness in face of data perturbations[242,250,251]. Yet cross-validation does not guarantee good performance across different populations and may even lead to an overestimation of the discriminatory classifier performance likely due to biases introduced in the process[251,252]. The direction is now in favour of an external independent validation of results that would produce more conservative results, but alas even such external validations can possibly be subject to some biases, selective reporting and optimism causing them to be inflated[251,253]. Validation has become an issue of special concern with the exponential growth of -omics that powered expectations for a cutting-edge era of personalized medicine. The current recommendation is to adopt routine external validation of biomarkers and metabolites, preferably in much larger studies than in current practice, and if possible by different teams[252]. Given the inherent complexity of biomarker data, it is essential to differentiate true signals from false positives and assess the generalizability of metabolic signatures that arise from analyses[251,252]. In an internal cross validation procedure was performed to evaluate the predictive performance of the PLS models. The receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) were determined from conditional logistic models including progressively the PLS scores, separately for lifestyle and metabolomic signatures. The AUC unavoidably increases with the number of covariates added to the conditional logistic model. A resampling scheme was devised to compute objective unbiased estimates of the AUC inspired from the work of Uno et al[254]. For each one of the 1000 drawn bootstrap samples, a 10-fold cross-validation was performed, repeated 10 times to remove variation due to random partitioning of data and to yield more stable estimates. The predicted values from each of the conditional logistic models in the training set were used to derive AUC values in the test set. A replication of these findings in independent studies is needed.

  to our study design (Chapter 4). More specifically, in the different stages of the development of the statistical framework for the MITM implementation different factors and exposures were considered. We first embraced a multitude of exposures in the first application of the MITM, with 13 main EPIC food groups out of 21 diverse lifestyle exposures in Chapter 3. In the next exercise presented in Chapter 4, we simplified the exposure to diet by using a diet score constructed based on 6 dietary items, this may have been a simplification but it reduced the dilution / dispersion of information by having one factor for diet, and possibly resulted in a more specific metabolic factor in relation to dietary exposure. The framework developed is flexible and can accommodate other statistical methods that can fit like block parts and replace those in use (e.g.

  in turn precedes the outcome to satisfy the chronological ordering assumption. In EPIC and most observational epidemiology settings, most variables of interest, including the exposures and mediators under study, were simultaneously assessed at baseline, together with the collection of biological samples. Yet, lifestyle and metabolomics reflect exposure windows of different nature and time length, thus our working assumption was to consider these factors as relatively stable in EPIC. A number of issues still require further investigation including intermediate confounders, multiple mediators and their inter-correlations and mediator-outcome confounders that are affected by the exposure to mention a few. These scenarios may not be trivial to handle, and current research is focusing on such challenging aspects and solutions are emerging[192][193][194][255][256][257].Nonetheless, mediation analysis remains a tightly controlled environment where every variable entering the DAG and every association arrow that is drawn has to comply with strict hypotheses[258].To overcome challenges related to confounding and reverse causality in aetiological models, a Mendelian randomization (MR) method was developed as a way to use genetic variants as an instrumental variable for the exposure of interest[259,260]. The rationale is that, due to the random heritability of genetic traits brought by the random assortment of alleles at the time of gamete formation [260], if a genetic variant alters some dietary or lifestyle exposure, including the level of a biomarker, then the direct association of the variant with cancer risk would strongly suggest that the biomarker-cancer relationship is not confounded by other factors, and that the primary link between the exposure of interest and cancer is causal[261]. Aside from establishing causal associations, MR provides estimates of the magnitude of effect between exposure and outcome[259]. MR could be used in the diet-biomarker-cancer relationship by including information on genetic variations upstream (for instance, with singlenucleotide polymorphisms). The current knowledge on how genetic variations influence dietary habits, nutrient metabolism or how they affect mechanism, bioavailability, adsorption or biotransformation of nutrients is progressively growing[262]. It is noteworthy to remember that MR, similarly to mediation analysis, also embraces a series of assumptions to account for in order to be implemented. Bias can arise when the genetic variant targets an exposure that is different from the one of interest[259]. In this case the instrumental variable is invalid, either because 1) the variant is not predictive of the exposure, 2) is also related to confounding factors of the exposureoutcome association or 3) is also indirectly related to the outcome, conditional to the exposure and confounders. The latter assumptions refer to pleiotropy (multiple effects of a single gene), which in essence requires that the genetic variant be strictly linked to the exposure of interest, and nothing else[260,263]. Current MR developments are striving to fill the methodological gap in order to obtain causal estimates and to evaluate MR performance when using invalid instruments[264]. New research is also joining efforts between mediation analysis and MR to focus into causal pathways, by investigating more complex networks of relationships between variables, through the integration of regression-based methods and structural equation models along with the use of genetic variants as instrumental variables[263]. In the context of MR this new development allows to estimate the direct and indirect effects even in the presence of unmeasured confounding. Both mediation and MR analyses tackle causality with different approaches but both are rigorous concepts limiting variables amongst them, and where a set of assumptions on the exposure, mediator, instrument and outcome are required for mediation effects to be interpreted as causal irrespective of the statistical models used[193,195].Alternatively, pathway analysis has been suggested as a valuable way to investigate etiological mechanisms[197,265]. Pathway analysis employs what is referred to as mixed-method research to search for mechanisms, exploiting the principle is that quantitative and qualitative studies have complementary strengths that can be used to explore underlying relationships between some explanatory variable and an outcome, controlling for other factors[265]. A critical aspect of pathway analysis is the need for an a priori knowledge of the expected relationship between the exposure and the outcome, the nature of the outcome, and the state of knowledge about causal pathways, which is often limited and uncertain. Another degree of complexity is that mechanisms in the context of pathway analysis are treated analogously to mediators or intermediate variables in standard mediation approaches [266-268], i.e. that the mechanism is caused by the exposure and causes the outcome [265]. The implementation of pathway analysis is not straightforward and many approaches are being developed to adequately apply it [269-271]. A number of metabolic pathway analysis tools which includes pathway enrichment analysis [272] can reveal underlying complex biological processes and connectivities, and are now used for metabolomics data [273,274].

Table 1 :

 1 Number of EPIC study subjects by country with questionnaires information and availability of blood samples

	Figure 1: Cluster tree produced by the Treelet Transform
	Figure 2: General original scheme to model the MITM principle
	ABBREVIATIONS
	95%CI 95% Confidence Intervals
	BC	Breast Cancer
	BMI Body Mass Index
	DQ	Dietary Questionnaires
	E3N Etude Epidémiologique auprès de femmes de l'Education Nationale
	ENDB EPIC Nutrient Database
	EPIC European Prospective Investigation into Cancer and nutrition
	ER	Estrogen Receptor
	FA	Fatty Acids or Factor Analysis
	FDR False Discovery Rates
	Fe	Iron
	FFQ Food Frequency Questionnaires
	HLI	Healthy Lifestyle Index
	HCC Hepatocellular carcinoma
	HR	Hazard Ratio
	MET Metabolic Equivalent of Task
	MITM Meeting-in-the-Middle
	MLR Multiple Linear Regression
	MR	Mendelian Randomisation
	MS	Mass Spectrometry
	MUFA Monounsaturated Fatty Acids
	NCD Non-Communicable Diseases

INTRODUCTION 15 Ever since Doll and Peto's comprehensive review of 1981 estimating that 30 to

  These instruments are then utilized to provide estimations of frequency of consumption, portion sizes and total energy intake. Long-term assessment can be complemented by short-term instruments, which include food diaries, food records, and 24h dietary recalls. These types of assessments are meant to collect deeper aspects of individuals' Other markers classified as "predictive" biomarkers are markers of early response/effect and are used to monitor early changes preceding disease occurrence[160,163]. Last, markers of susceptibility can be used in cancer epidemiology to identify subgroups in the population with greater susceptibility to cancer[139,[161][162][163].

	vastly relying on existing methods, has been developed to analyse this new wave of
	overwhelming and promising data [179,181-187]. These range from standard
	Biomarkers can be quantified in biological samples of serum, blood, plasma, urine. It is procedures of metabolome-wide association studies (MWAS) operated through
	diet, like, for example, detailed information on portion sizes, timings of meals, recipes recognised that these quantities are also affected by random and systematic adequate multiple statistical regression models coupled with multiple testing
	and possibly cooking methods [139]. All self-reported dietary instruments rely on the measurement errors, but these errors are assumed to be independent of errors corrections to multivariate dimension reduction techniques and approaches for variable
	existence of adequate food and nutrient composition databases, to convert food amounts associated with self-reported dietary assessments [139,160,163]. As such, they can also selection [179]. Some of these techniques are customised for supervised and
	into nutrient and macronutrient contents [135,139]. All dietary assessment methods in be used as a means of validation of dietary instruments to estimate the magnitude of unsupervised analyses of-omics data, in particular involving metabolomics [188-190].
	large scale epidemiological investigation rely on study participants' ability to recall their systematic and random errors in questionnaires. Their use in calibration studies of

with a wide array of diseases with appropriate statistical power, if a sufficiently large number of study subjects is enrolled

[135]

. If the latter condition applies and if the follow-up is carried out for several years, prospective designs allow the investigation of rare outcomes. The Nurses Health Studies

[136]

and the EPIC cohort

[137,138]

were among the first large-scale retrospective cohorts expressly designed to explore the dietcancer association. In such large sized investigations diet is assessed through the use of structured, self-administered questionnaires

[135]

, which include food frequency questionnaires (FFQs) for estimation on long-term, or habitual, dietary exposure, i.e. referring to study subjects' diet during a 12-month period preceding its administration. diet, and are therefore prone to systematic and random measurement errors [133,139,140]. Measurement errors can be substantial and can, in turn, bias estimates of associations between diet and cancer risk [139-142], and lead to loss of statistical power to detect associations [142]. It has been argued that a large proportion of inconsistencies and null results observed in population-based studies of diet and cancer could be the consequence of poor dietary assessments [143]. One compelling example is the downgrading by the 2003 IARC Handbooks of Cancer Prevention on Fruits and Vegetables [144] and by the 2007 update of the World Cancer Research Fund (WCRF) comprehensive report [145], of the cancer protective role of intakes of fruits and/ or vegetables from 'convincing' to 'probable', depending on the cancer site, which were established in the 1997 WCRF comprehensive review [146]. Research in the field of nutrition has strived to develop better methods to ascertain eating behaviours and their reporting [147-154] and to account for measurement errors in self-reported dietary measurements [155-159]. However, in the absence of an "ideal" reference instrument and in order to obtain "objective" observations of food consumption, the use of biomarkers emerged as a valuable research instrument. This motivated the collection of study subjects' biological material in population based studies [160]. Dietary biomarkers are biochemical indicators that can be viewed as an index of short to long-term dietary intake, of nutrient metabolism or markers of the biological consequences of food intake [161]. Biomarkers have been introduced in cancer epidemiology with the idea of relying on markers of relevant internal dose and markers of biologically effective dose to improve exposure assessment [162]. These markers are also known as "concentration" or "recovery" biomarkers [139,163]. diet/disease association has been advocated but seldom pursued [163]. A great extent of cancer research has developed around biomarkers with studies focusing on their validation [161,164-166], their methodological challenges [161,167], and their use in aetiological models [162,168-172]. The recent technological advancements in highthroughput technologies, particularly in the field of molecular biology, generated a slew of new round of metabolites, which can be acquired in biological samples collected in large-scale epidemiological studies [173,174]. Metabolomics is the branch of ''-omics'' concerned with the high-throughput identification and quantification of small molecule metabolites present in the human metabolome i.e. the ensemble of all metabolites [175,176]. It provides a complete picture of metabolic status and biochemical events happening within an organism [177]. These data have the potential to bring useful tools to improve our understanding of the role of diet in cancer research [175,178]. Biomarker research supports causal reasoning by linking exposures with disease via mechanisms. This is the premise on top of which the "meeting-in-the-middle" concept was proposed [162]. It aims to find overlap biomarkers that are indicative of a given exposure and that are, at the same time, predictive of disease outcome. This complementary approach sheds light on the mechanisms through which individual dietary (or more generally environmental) exposures diverge towards risk of cancer development by investigating life-course biological pathways using -omics technologies [162]. To achieve this, new statistical methodologies are being developed to provide holistic approaches for the combination of dietary questionnaires and biomarker data to be later used in aetiological models and to tackle the challenges brought on by theomics data [179]. These data are characterised by high-dimensionality, a correlated structure and a general lack of a priori biological hypotheses resulting in challenges for results interpretability [180]. Methodology that conceives a novel use of statistical tools, Unsupervised learning methods' main aim is to explore, summarize and discover groups or trends that are entailed within the data, they need only a few prior assumptions and a little to no a priori knowledge [177]. These include techniques such as PCA, k-means clustering or hierarchical clustering. Supervised techniques are methods largely used in biomarker discovery, classification, and prediction and usually deal with sets of data with response variables. They mainly include partial least squares and support vector machine analyses and are now often used in metabolomics data analysis [177,179]. The use of mediation

Table 1 .

 1 Approval for this study was obtained from the ethical review boards of the International Agency for Research on Cancer and from all local institutions.

	These principles were applied in the European Prospective Investigation into
	Cancer and nutrition (EPIC), an on-going multicentre prospective cohort study, mainly
	designed to study the relationship between nutrition and cancer [198]. Over 521,000
	participants, aged between 25 and 70 years, were recruited between 1992 and 2000
	across 23 centres spanning 10 European countries including: France, Germany, Greece,

Table 1 : Number of EPIC study subjects by country with questionnaires information and availability of blood samples.

 1

	The present thesis aims to investigate the applicability of multivariate statistical
	methods in the investigation of the relationship between nutrition and cancer, using
	questionnaires and biomarker data available from the EPIC study.
	In a first study described in Chapter 2, we explored the applicability of a new
	dimension-reduction technique that has been recently introduced to the field of
	nutritional epidemiology: the Treelet Transform (TT). We investigated the relationship
	between the extracted nutrient patterns and risk of developing breast cancer overall and
	by hormonal-receptor status in the EPIC Study. Initially developed by Lee et al. [206], TT
	has been conceived as a statistical method aiming to reduce multidimensional datasets
	by harnessing features of PCA and combining them with those of hierarchical clustering.
	TT yields orthogonal components (eigenvectors of the correlation or covariance matrix
	of the data), that are linear projections of the starting variables while introducing
	sparsity in the component loadings, by making some of these loadings exactly equal to
	zero. In this way, TT produces components that are easier to interpret than in the well-
	established PCA [207], where findings' interpretation is complicated by the fact that all
	component loadings are nonzero. Additionally, TT returns a hierarchical tree reflecting
	the internal structure of the data. These elements make it a very promising technique in
	that respect as it allows for an easier interpretation of the findings and to spot the

CHAPTER II: NUTRIENT PATTERNS AND BREAST CANCER IN EPIC CONTEXT

	Breast Cancer (BC) is the most frequent type of cancer affecting women worldwide; it is
	the most prevalent form of cancer in the world and the leading cause of mortality from
	cancer in women both in developed and developing countries [209]. Among modifiable
	risk factors, diet may account for up to 40% of preventable causes of cancer. In
	particular an estimated 50% of BC deaths are attributed to diet although despite
	substantial research, the relationship between diet and BC is still open to debate
	[2,6,210,211]. Usual approaches have often assessed the role of single dietary items i.e.
	micro/macronutrients, foods, energy and alcohol mostly through standard univariate
	analyses, and these have yielded significant results [145]. However, due to the
	complexity of diet and the potential interactions between different dietary components,
	approaches that focus on individual foods or restricted list of nutrients / dietary
	constituents may miss information on the role of diet in disease aetiology

Prospective Investigation into Cancer and Nutrition study. SupplementaryTable 1 :

 1 TT (cut-level 16) loadings of the third and fourth components.

	Variables *	TT 16 loadings TC3 TC4
	Calcium (Ca)
	β-Carotene
	Cholesterol	-0.178	0.448
	MUFA
	PUFA
	SFA
	Iron (Fe)
	Fibre
	Potassium (K)
	Magnesium (Mg)
	Phosphorus (P)
	Protein	-0.052	0.132
	Retinol	-0.410	-0.609
	Riboflavin
	Starch
	Sugar
	Thiamin
	Vitamin B 6
	Vitamin B 12	-0.254	0.641
	Vitamin C
	Vitamin D	0.856
	Vitamin E
	Folate
	Explained variance	9%	6%
	TC3, treelet component 3. TC4, treelet component 4.

* log-transformed nutrient variables.

Supplementary

Table 2 :

 2 PCA loadings of the 4 derived components.

	Variables * †	PC1	PCA loadings PC2 PC3	PC4
	Calcium (Ca)	-0.024	0.12	-0.136	0.314
	β-Carotene	-0.275	0.601	-0.121 -0.495
	Cholesterol	0.276	0.07	-0.172	0.064
	MUFA	0.018	-0.043	-0.123 -0.148
	PUFA	-0.006	0.102	0.131	-0.211
	SFA	0.119	-0.031	-0.155 -0.105
	Iron (Fe)	-0.054	0.102	-0.019	0.048
	Fibre	-0.131	0.145	0.136	0.006
	Potassium (K)	-0.065	0.174	0.065	0.169
	Magnesium (Mg)	-0.045	0.142	0.042	0.115
	Phosphorus (P)	0.003	0.108	0.01	0.19
	Protein	0.042	0.077	-0.003	0.159
	Retinol	0.601	0.271	-0.295 -0.275
	Riboflavin	0.004	0.206	-0.131	0.322
	Starch	-0.004	-0.112	0.137	-0.068
	Sugar	-0.098	0.073	0.01	0.175
	Thiamin	-0.076	0.174	0.133	0.183
	Vitamin B 6	-0.075	0.177	0.072	0.189
	Vitamin B 12	0.362	0.254	-0.266	0.306
	Vitamin C	-0.276	0.316	-0.033	0.126
	Vitamin D	0.431	0.25	0.796	0.006
	Vitamin E	-0.098	0.153	0.068	-0.256
	Folate	-0.141	0.249	-0.014	0.105
	Explained variance	28%	22%	10%	8%
	PC1, principal component 1. PC2, principal component 2. PC3, principal component 3. PC4,
	principal component.

* log-transformed nutrient variables † In bold are PCA loadings >0.20 Supplementary

Table 3 :

 3 HRs (95%CI) for BC by quintiles of pattern scores (1 st and 2 nd components of TT cut-level 16) for PR positive and PR negative tumours in EPIC women.

				First component					Second component
	Model*	PY	BC cases	HR (95% CI)	P-LRT a	P-trend b	PY	BC cases	HR (95% CI)	P-LRT a	P-trend b
	PR Positive
	Q1 723,730	611	1.00 (ref)			738,063	801	1.00 (ref)
	Q2 729,055	850	1.12 (1.01,1.25)			727,815	823	0.96 (0.86,1.06)
	Q3 726,226	805	1.10 (0.98,1.22)	0.31	0.28	720,137	827	0.95 (0.85,1.05)	0.17	<0.01
	Q4 726,869	800	1.10 (0.98,1.23)			716,542	766	0.90 (0.81,1.00)
	Q5 717,755	812	1.10 (0.97,1.24)			721,078	661	0.87 (0.77,0.98)
	PR Negative
	Q1 722,296	386	1.00 (ref)			735,796	467	1.00 (ref)
	Q2 726,449	468	0.98 (0.86,1.13)			725,303	449	0.89 (0.78,1.02)
	Q3 723,483	433	0.91 (0.79,1.06)	0.46	0.10	717,455	434	0.84 (0.73,0.96)	0.10	0.03
	Q4 724,668	468	0.99 (0.85,1.15)			714,395	454	0.90 (0.78,1.03)
	Q5 715,243	435	0.90 (0.77,1.06)			719,189	386	0.84 (0.72,0.98)
	P-heterogeneity	c			0.07					0.36

HR: hazard ratio. 95%CI, 95% confidence interval. BC, breast cancer. PR, progesterone receptor. PY, person-years. a P-LRT, p-values for the likelihood ratio test (LRT), that was used to evaluate overall significance of a score variable in quintile categories compared with a chi-square distribution with 4 df. b P-trend values were obtained by modelling score variables with quintile-specific medians as continuous variables.

c P-heterogeneity values for BC risks across PR status on 1 df were obtained using a data augmentation method.

*Models were stratified by study centre and age in 1-y categories and adjusted for baseline menopausal status (premenopausal and perimenopausal [reference] or postmenopausal and women who underwent an ovariectomy), baseline alcohol intake (never drinkers [reference], former drinkers, drinkers only at recruitment, lifetime drinkers, unknown), height (continuous), BMI (below [reference] or above 25), schooling level (none, primary [reference], technical/professional/secondary, longer education, unknown /unspecified), age at first full-term pregnancy (nulliparous [reference], ≤ 21years, 21-30 years, > 30 years, unknown or missing), age at menarche (≤ 12 years [reference], 12-14 years, >14 years, missing), age at menopause (≤50 years [reference], > 50 years, pre-menopause or missing), use of hormones (never[reference], ever, unknown), levels of physical activity (inactive [reference], moderately inactive, moderately active, active, unknown) and alcohol-free energy(continuous).

Table 4 :

 4 HRs (95%CI) for BC by quintiles of pattern scores (1 st and 2 nd components of PCA) for overall, ER positive and ER negative tumours in EPIC women.

				First component					Second component
	Model*	PY	BC cases	HR (95% CI)	P-LRT a	P-trend b	PY	BC cases	HR (95% CI)	P-LRT a	P-trend b
	Overall
	Q1 729,222	1,843	1.00 (ref)			748,437	2,143	1.00 (ref)
	Q2 736,877	2,292	1.03 (0.96,1.09)			737,177	2,339	1.03 (0.97,1.10)
	Q3 734,382	2,445	1.06 (1.00,1.13)	0.29	0.07	732,009	2,280	0.98 (0.92,1.04)	0.15	0.046
	Q4 735,659	2,478	1.06 (1.00,1.13)			727,730	2,354	0.98 (0.99,1.05)
	Q5 734,300	2,509	1.07 (1.00,1.15)			725,087	2,460	0.96 (0.89,1.02)
	ER Positive
	Q1 723,700	882	1.00 (ref)			741,994	1,087	1.00 (ref)
	Q2 730,480	1,201	1.07 (0.98,1.17)			730,010	1,142	1.00 (0.92,1.09)
	Q3 727,426	1,260	1.09 (0.99,1.19)	0.27	0.09	725,034	1,113	0.94 (0.86,1.03)	0.46	0.10
	Q4 728,361	1,286	1.11 (1.01,1.22)			720,800	1,173	0.94 (0.86,1.03)
	Q5 726,145	1,201	1.09 (0.99,1.21)			718,273	1,315	0.95 (0.86,1.04)
	ER Negative
	Q1 719,177	215	1.00 (ref)			736,399	280	1.00 (ref)
	Q2 724,194	287	1.01 (0.85,1.22)			724,298	312	1.10 (0.93,1.30)
	Q3 720,958	333	1.13 (0.94,1.35)	0.56	0.91	719,335	301	1.05 (0.88,1.25)	0.02	0.11
	Q4 721,850	306	1.01 (0.83,1.22)			714,609	245	0.83 (0.69,1.00)
	Q5 720,190	297	1.04 (0.85,1.27)			711,728	300	0.96 (0.80,1.16)
	P-heterogeneity	c				0.80				0.13

HR: hazard ratio. 95%CI, 95% confidence interval. BC, breast cancer. ER, estrogen receptor. PY, personyears.

a P-LRT, p-values for the likelihood ratio test (LRT), that was used to evaluate overall significance of a score variable in quintile categories compared with a chi-square distribution with 4 df. b P-trend values were obtained by modelling score variables with quintile-specific medians as continuous variables.

c P-heterogeneity values for BC risks across ER status on 1 df were obtained using a data augmentation method.

*Models were stratified by study centre and age in 1-y categories and adjusted for baseline menopausal status (premenopausal and perimenopausal [reference] or postmenopausal and women who underwent an ovariectomy), baseline alcohol intake (never drinkers [reference], former drinkers, drinkers only at recruitment, lifetime drinkers, unknown), height (continuous), BMI (below [reference] or above 25), schooling level (none, primary [reference], technical/professional/secondary, longer education, unknown /unspecified), age at first full-term pregnancy (nulliparous [reference], ≤ 21years, 21-30 years, > 30 years, unknown or missing), age at menarche (≤ 12 years [reference], 12-14 years, >14 years, missing), age at menopause (≤50 years [reference], > 50 years, pre-menopause or missing), use of hormones (never[reference], ever, unknown), levels of physical activity (inactive [reference], moderately inactive, moderately active, active, unknown) and alcohol-free energy(continuous).

Table 5 :

 5 HRs (95%CI) for BC by quintiles of pattern scores (1 st and 2 nd components of PCA) for ER & PR positive and ER & PR negative tumours in EPIC women.

				First component						Second component
	Model*	PY	BC cases	HR (95% CI)	P-LRT	a	P-trend	b	PY	BC cases	HR (95% CI)	P-LRT	a	P-trend	b
	ER and PR Positive
		Q1 721,384	525	1.00 (ref)					718,901	161	1.00 (ref)
		Q2 727,780	775	1.15 (1.03,1.29)					723,803	211	1.00 (0.81,1.23)
		Q3 724,554	805	1.16 (1.03,1.31)	0.07	0.04		720,508	242	1.09 (0.89,1.35)	0.77	0.65
		Q4 725,315	790	1.16 (1.03,1.31)					721,445	224	0.98 (0.79,1.23)
		Q5 723,543	758	1.17 (1.03,1.33)					719,832	212	0.99 (0.78,1.25)
	ER and PR Negative
		Q1 739,692	743	1.00 (ref)					736,067	215	1.00 (ref)
		Q2 727,688	774	1.03 (0.93,1.14)					723,975	241	1.10 (0.91,1.32)
		Q3 722,601	720	0.96 (0.86,1.07)	0.38	0.09		718,949	214	0.97 (0.80,1.19)	0.06 <0.05
		Q4 717,804	694	0.94 (0.84,1.05)					714,277	180	0.82 (0.66,1.02)
		Q5 714,791	722	0.94 (0.84,1.06)					711,222	200	0.90 (0.72,1.12)
	P-heterogeneity	c					0.45							0.12

HR: hazard ratio. 95%CI, 95% confidence interval. BC, breast cancer. ER, estrogen receptor. PR, progesterone receptor. PY, person-years. a P-LRT, p-values for the likelihood ratio test (LRT), that was used to evaluate overall significance of a score variable in quintile categories compared with a chi-square distribution with 4 df. b P-trend values were obtained by modelling score variables with quintile-specific medians as continuous variables.

c P-heterogeneity values for BC risks across ER\PR status on 1 df were obtained using a data augmentation method.

*Models were stratified by study centre and age in 1-y categories and adjusted for baseline menopausal status (premenopausal and perimenopausal [reference] or postmenopausal and women who underwent an ovariectomy), baseline alcohol intake (never drinkers [reference], former drinkers, drinkers only at recruitment, lifetime drinkers, unknown), height (continuous), BMI (below [reference] or above 25), schooling level (none, primary [reference], technical/professional/secondary, longer education, unknown /unspecified), age at first full-term pregnancy (nulliparous [reference], ≤ 21years, 21-30 years, > 30 years, unknown or missing), age at menarche (≤ 12 years [reference], 12-14 years, >14 years, missing), age at menopause (≤50 years [reference], > 50 years, pre-menopause or missing), use of hormones (never[reference], ever, unknown), levels of physical activity (inactive [reference], moderately inactive, moderately active, active, unknown) and alcohol-free energy(continuous).

Supplementary Figure

1

: Relations between PCA nutrient patterns and BC risk (and associated 95%CI) obtained by using restrictive cubic splines with values of 1 st and 99 th percentile and medians of quintiles 1, 3 and 5 used as knots.

Models were stratified by study centre and age in 1-y categories and adjusted for baseline menopausal status (premenopausal and perimenopausal

[reference] or postmenopausal and women who underwent an ovariectomy), baseline alcohol intake (never drinkers [reference], former drinkers, drinkers only at recruitment, lifetime drinkers, unknown), height (

continuous)

, BMI (below [reference] or above 25), schooling level (none, primary [reference], technical/ professional/ secondary, longer education, unknown / unspecified), age at first full-term pregnancy (nulliparous [reference], ≤ 21years, 21-30 years, > 30 years, unknown or missing), age at menarche (≤ 12 years [reference], 12-14 years, >14 years, missing), age at menopause (≤50 years [reference], > 50 years, pre-menopause or missing), use of hormones (never[reference], ever, unknown), levels of physical activity (inactive [reference], moderately inactive, moderately active, active, unknown) and alcohol-free energy(continuous). P-linearity was obtained by evaluating the joint significance of variables other than the linear one in the model by using Wald's test with 3 df.

Table 2 :

 2 Results from the sensitivity analysis run on a subsample (N=271, 92 cases, 179 controls) excluding sets where cases were diagnosed within the first two years of follow-up (X-set=21, Y-set=285). Lifestyle and NMR cluster variables contributing to each PLS factor.

		Mean / N*	sd / %*	p5	p95	N missing
	Dietary Variables (g/day)
	Potatoes and other tubers	100.57	78.15	9.34	266.97	0
	Vegetables	194.20	143.22	45.03 473.45	0
	Legumes	9.85	18.03	0.00	41.18	0
	Fruits, nuts and seeds	232.80	197.94	23.55 585.22	0
	Dairy products	334.40	261.46	49.92 777.48	0
	Cereal and cereral products	227.04	117.67	76.39 458.94	0
	Meat and meat products	115.97	62.29	37.83 236.32	0
	Fish and shellfish	32.88	32.26	3.78	81.43	0
	Egg and egg products	18.67	18.72	1.88	55.57	0
	Fat	34.61	18.48	11.01	70.76	0
	Sugar and confectionary	47.26	51.51	1.93	138.73	0
	Cakes and biscuits	41.33	49.68	0.00	147.26	0
	Non-alcoholic beverages	1053.91	793.31	85.00 2391.90	0
	Anthropometric variables
	BMI (kg/m2)	27.41	4.41	21.22	36.16	0
	Height (cm)	169.70	9.99	152.00 184.80	0
	Lifestyle Variables
	Lifetime alcohol intake (g/day)	23.27	41.38	0	91.998	61
	Physical activity (Mets/h)	77.13	49.45	11.5	173.63	0
	Highest Education Level
	None or primary school completed	167	49.7	-	-	-
	Technical/professional school	75	22.32	-	-	-
	Secondary school	27	8.04	-	-	-
	Longer education (incl. university degree)	62	18.45	-	-	-
	Unspecified or Unknown	5	1.49	-	-	-
	Smoking status
	Never	124	36.9	-	-	-
	Former	125	37.2	-	-	-
	Current smoker	85	25.3	-	-	-
	Unspecified or Unknown	2	0.6	-	-	-
	Pathology variables indicative of lifestyle
	Hepatitis status					1
	No	291	86.87	-	-	-
	Yes	44	13.13	-	-	-
	Diabetes					0
	No	307	91.37	-	-	-
	Yes	29	8.63	-	-	-

data including untargeted metabolomics, dietary and lifestyle exposures and disease outcome resulting in intermediate biomarker signatures discovery. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data possibly highlighting the intersection of relevant markers of exposure with predictive markers of disease outcome. This implementation of the MITM was applied towards the investigation of HCC determinants; it can be easily extended to similar aetiological contexts and to settings characterized by high-dimensional data, increasingly frequent in the -omics generation.

PAPER

Contribution: First author, discussed the analytical strategy with the supervisor, conducted statistical analyses, wrote the first draft of the manuscript, submitted it to the journal and replied to reviewers' comments.

Reproduced with permission from the Oxford University Press.

*Mean and standard deviation (sd), were reported for continuous variables and frequencies and percentages (%) were reported for categorical variables. p5: 5th percentile, p95:95th percentile. Supplementary ‡ CS: 1 H chemical shift (in ppm) of the cluster (center value). **Some of the identified clusters were found to be background noise during the annotation phase and were removed from this table.

Supplementary Table 3:

Results from the sensitivity analysis (N=271, 92 cases, 179 controls) conducted excluding sets where cases were diagnosed within the first two years of follow-up (X-set=21, Y-set=285). Area under the curve (AUC), sensitivity, specificity, accuracy and positive predictive value (PPV) of ROC models (with 95% CI).

*The model is run on the adjustment covariates (ADJ) including the C-reactive protein concentration, alphafetoprotein concentration and a composite score for liver damage. ** AUC b is the bootstrapped-cross validated estimate of the AUC. X1, X2 and X3 are the lifestyle component scores of the first, second and third PLS factors, respectively. Y1, Y2, and Y3 are the metabolomics component of the first, second and third PLS factors, respectively. originating from different high-dimensional sets. Building on these previous implementations of the MITM, and using targeted metabolomic data, we further refined and developed the analytical scheme by focusing on a restricted set of exposures and by adapting the mediation analysis to matched case-control study designs. The application looked yet again into determinants of HCC, the most common form of liver cancer, which ranks as the 2 nd most frequent cause of cancer death worldwide

[209]

. HCC being a multi-factorial disease strongly associated with lifestyle factors and with dietary habits

[221]

, components of a modified Healthy Lifestyle Index (HLI) scores' link with serum metabolites are jointly investigated to possibly identify modifiable lifestyle exposure patterns and metabolite signatures related to HCC that may ultimately lead to the identification of targeted cancer prevention schemes.

  Their respective NIE was equal to 1.56[1.24, 2.96], 1.09[1.03, 1.15], 0.85[0.74, 0.97], 1.22[1.04, 1.44] and 5.11[1.99, 13.14].

	equal to
	0.53[95% CI: 0.39, 0.71] and mediator effects adjusted for the exposure OR=0.30[0.19,
	0.47] per 1-SD change in components' scores, respectively. There was evidence of
	mediation between this overall "healthy" pattern and HCC through its metabolic
	counterpart with NIE=0.62 [0.50, 0.77]. Results from multiple PLS, showed that specific

characterised, on average, by low propensity towards smoking, alcohol drinking and obesity. Its metabolic counterpart was positively related to sphingolipids with hydroxyl group including SM(OH) C14:1, SM(OH) C16:1 and SM(OH) C22:2, and negatively with glutamic acid, hexoses, PC aaC32:1 and liver damage score. Both components displayed decreased HCC risks quantified with total effects through with odds ratios (OR) metabolic signatures of BMI, alcohol intake, diet, smoking and diabetes were found to be mediators of the relationship between corresponding HLI variables and HCC risk.

Table 4 )

 4 , whereas the HLI score was not related to HCC with OR=0.93, 95%CI=0.84 to 1.02, P value =0.117 (results not shown). The PLS metabolic profile showed a strong inverse association with HCC risk, with ME (Mediator Effect) equal to 0.30

	53 (95%CI=0.39-

(95%CI=0.19 to 0.47, P value =1.94E-07). The association of the lifestyle factor with HCC risk was mediated by the metabolic profile, with NIE=0.62 (0.50 to 0.77, P value =2.12E-05), with an estimated mediated proportion of 52% (Table

Table 4 )

 4 . In particular, for both diabetes and BMI, a positive association for the NIE, equal to 5.11 (1.99 to 13.14, P value =6.99E-04) and 1.56 (1.24 to 1.96, P value =1.72E-04), respectively, was observed, together with a lack of association for the NDE, thus suggesting that the relationship between these two variables and HCC risk was fully mediated by the corresponding metabolic signatures. As for smoking, diet and lifetime alcohol, the mediated proportions were 56%, 38% and 24%, respectively, with NIE equal to 1.22 (1.04 to 1.44, P value =0.018), 0.85 (0.74 to 0.97, P value =0.025) and 1.09 (1.03 to 1.15, P value =0.002), respectively.

	The TE estimates showed strong associations for lifetime alcohol (1.40, 95%CI=1.14
	to 1.72, P value =1.40E-03), diet score (0.66, 0.47 to 0.92, P value =0.014) and hepatitis infection
	(16.70, 4.82 to 57.84, P value =8.92E-06) (Table

Noteworthy, the NIE estimate for smoking in the sensitivity analysis was 1.98 (1.34 to 2.92, P value =5.65E-04), and the relation between smoking and HCC was fully mediated by the Mscore (Supplementary Table

4

).

Table 1 :

 1 Baseline characteristics of the study population of the EPIC nested case-control study on hepatocellular carcinoma.

	Cases	Controls
	(N=147)	(N=147)

Table 2 :

 2 Exposure variables of the modified HLI and corresponding metabolites contributing to the first PLS factor (N=294, X-set= 7, M-set=133). Results from the overall analysis using residuals based on country (X-and M-sets) and batch (M-set only).

	Exposure Variable	Loadings	Metabolites	Loadings*
	BMI	-0.385	Glutamic Acid	-0.192
	Lifetime Alcohol	-0.695	Hexoses	-0.191
	Diet score	-0.058	SM(OH) C14:1	0.196
	Physical activity	0.297	SM(OH) C16:1	0.179
	Smoking	-0.409	SM(OH) C22:2	0.214
	Hepatitis Infection	-0.176	PC aaC32:1	-0.184
	Diabetes	-0.282	Liver function score	-0.186
	* Metabolite variables contributing to each PLS factor were selected based on extreme loading
	values, i.e. below or above the 2.5 th and 97.5 th percentiles.

Table 3 :

 3 Metabolites contributing to the PLS factor of each HLI component (N=294, X-set=1, M-set=133)*. Results from multiple PLS models performed using residuals based on country (X-and Msets) and batch residuals (M-set only).

	Metabolite	Loadings	Metabolite	Loadings	Metabolite	Loadings
	BMI		Lifetime alcohol		Diet score
	Glutamine	-0.186	Glutamic Acid	0.170	PC aaC36:1	-0.178
	Glutamic Acid	0.230	SM C16:1	-0.171	PC aaC38:0	0.195
	Tyrosine	0.243	SM C18:1	-0.167	PC aaC38:6	0.230
	LysoPC aC17:0	-0.218	SM(OH) C14:1	-0.180	PC aaC40:6	0.204
	LysoPC aC18:2	-0.236	SM(OH) C16:1	-0.184	PC aaC42:2	0.263
	PC aeC36:2	-0.203	SM(OH) C22:2	-0.211	PC aeC34:1	-0.195
	Liver function score	0.191	PC aaC32:1	0.211	PC aeC40:6	0.167
	Physical activity		Smoking		Hepatitis infection
	Biogenic Creatinine	-0.199	Hexoses	0.136	SM C20:2	-0.179
	Biogenic Taurine	-0.181	SM C16:1	-0.238	SM(OH) C16:1	-0.178
	Glutamic Acid	-0.212	SM C18:1	-0.194	PC aaC32:2	0.188
	PC aaC34:2	-0.188	SM(OH) C14:1	-0.214	PC aaC34:1	0.184
	PC aeC34:2	0.209	SM(OH) C22:2	-0.182	PC aaC34:3	0.180
	PC aeC34:3	0.176	LysoPC aC28:1	-0.204	PC aaC34:4	0.197
	PC aeC36:3	0.193	PC aeC30:2	-0.264	PC aaC36:5	0.189
	Diabetes status
	Biogenic Alpha AAA	0.236
	Isoleucine	0.168
	Phenylalanine	0.158
	Valine	0.211
	Hexoses	0.551
	Lyso PC aC16:1	-0.145
	Liver function score	0.226

* Metabolite variables contributing to each PLS factor were selected based on extreme loading values, i.e. below or above the 2.5 th and 97.5 th percentiles.

Table 4 :

 4 Results

	% mediated	52	100	24	38	**	57	0	100
	NDE P value FDR NIE P value FDR Total Effect P value FDR ME P value FDR	0.64 (0.44,0.92) 0.015 -0.62 (0.50, 0.77) 2.12E-05 -0.53 (0.39,0.71) 2.64E-05 -0.30 (0.19,0.47) 1.94E-07 -	0.85 (0.60,1.20) 3.44E-01 4.81E-01 1.56 (1.24,1.96) 1.72E-04 1.20E-03 1.23 (0.93,1.62) 1.49E-01 1.74E-01 4.04 (2.22,7.36) 4.77E-06 3.17E-05	1.31 (1.06,1.61) 1.20E-02 4.20E-02 1.09 (1.03,1.15) 2.40E-03 4.67E-03 1.40 (1.14,1.72) 1.40E-03 3.50E-03 2.50 (1.57,3.97) 1.00E-04 2.48E-04	0.77 (0.54,1.11) 1.68E-01 3.92E-01 0.85 (0.74,0.97) 1.80E-02 2.52E-02 0.66 (0.47,0.92) 1.40E-02 3.27E-02 0.61 (0.41,0.89) 1.10E-02 1.54E-02	0.98 (0.72,1.35) 9.18E-01 9.18E-01 0.97 (0.87,1.09) 6.17E-01 6.17E-01 0.98 (0.71,1.34) 8.84E-01 8.84E-01 0.90 (0.60,1.35) 6.15E-01 6.15E-01	1.17 (0.77,1.77) 4.59E-01 5.36E-01 1.22 (1.04,1.44) 1.77E-02 2.52E-02 1.42 (0.99,2.03) 5.82E-02 1.02E-01 3.33 (1.96,5.66) 9.04E-06 3.17E-05	17.99 (5.15,62.80) 5.87E-06 4.11E-05 0.94 (0.83, 1.06) 2.98E-01 3.48E-01 16.70 (4.82,57.84) 8.92E-06 6.24E-05 1.22 (0.88,1.69) 2.23E-01 2.60E-01	0.46 (0.11,1.93) 2.87E-01 4.82E-01 5.11 (1.99,13.14) 6.99E-04 2.45E-03 2.45 (0.84,7.18) 1.01E-01 1.41E-01 2.75 (1.59,4.78) 3.17E-04 5.55E-04
	Models*	Overall, 7 components	BMI	Lifetime Alcohol	Diet score	Physical activity	Smoking	Hepatitis Infection	Diabetes

from the mediation analyses, with natural direct effect (NDE), natural indirect effect (NIE), total effects (TE), mediator effects (ME) and their associated 95% confidence intervals, using residuals based on country (X-and M-sets) and batch (M-set only).

Table 4 :

 4 Results from the mediation analyses, with natural direct effect (NDE), natural indirect effect (NIE), total effects (TE), mediator effects (ME) and their associated 95% confidence intervals in the sensitivity analysis, using mutually adjusted lifestyle residuals as well as residuals for country and batch (the latter only in the M-set).

	Lifetime Alcohol	Smoking
	Metabolites	Loadings	Metabolites	Loadings
	SM C16:1	-0,173	Lysine	-0,173
	SM C18:1	-0,175	SM C16:1	-0,218
	SM(OH) C14:1	-0,205	SM C18:1	-0,176
	SM(OH) C16:1	-0,193	SM(OH) C14:1	-0,196
	SM(OH) C22:2	-0,212	SM(OH) C22:2	-0,171
	LysoPC aC28:1	-0,170	LysoPC aC28:1	-0,170
	PC aeC30:2	-0,177	PC aeC30:2	-0,235
		Sensitivity Analysis
	Lifetime Alcohol	Smoking
	Metabolites	Loadings	Metabolites	Loadings
	SM C18:1	-0.161	Biogenic Taurine	-0.201
	SM(OH) C16:1	-0.168	Lysine	-0.211
	SM(OH) C22:1	-0.168	Serine	-0.189
	SM(OH) C22:2	-0.203	SM(OH) C14:1	-0.195
	LysoPC aC16:1	0.162	PC aaC36:1	0.23
	PC aaC32:1	0.234	PC aaC40:3	0.202
	Acylcarnitine C2	0.152	PC aeC30:2	-0.206

* Metabolite variables contributing to each PLS factor were selected based on extreme loading values, i.e. below or above the 2.5 th and 97.5 th percentiles.

Supplementary

.52 (1.22,1.89) 1.72E-04 1.20E-03

	100
	1.35E-05 4.73E-05
	3.22 (1.90,5.46)
	1.49E-01 1.74E-01
	1.23 (0.93,1.62)
	1.50E-02 5.25E-
	1.30 (1.05,1.60)
	Lifetime Alcohol

02 1.10 (1.04,1.17) 1.68E-03 2.94E-03 1.40 (1.14,1.72)

	27	41	72
	8.28E-05 1.93E-04	5.74E-03 8.04E-03	1.21E-02 1.41E-02
	2.60 (1.62,4.18)	0.57 (0.38,0.85)	0.57 (0.36,0.88)
	1.43E-03 5.00E-03	1.39E-02 3.24E-02	8.84E-01 8.84E-01
		0.66 (0.47,0.92)	0.98 (0.71,1.34)
		2.16E-01 4.12E-01 0.85 (0.75,0.97) 1.40E-02 1.96E-02	7.30E-01 7.30E-01 0.85 (0.74,0.98) 2.48E-02 2.89E-02	3.15E-01 4.12E-01 1
		0.79 (0.54,1.15)	0.94 (0.68,1.32)	0.71 (0.36,1.39)
		Diet score	Physical activity	Smoking

.98 (1.34,2.92) 5.65E-04 1.32E-04

	100	2
	1.42 (0.99,2.03) 5.82E-02 1.02E-01 11.63 (4.33,31.28) 1.16E-06 8.12E-06	0.88 (0.64,1.21) 4.17E-01 4.17E-01 15.56 (4.44,54.54) 1.78E-05 1.25E-04 1.07 (0.91,1.26) 4.33E-01 4.33E-01 16.70 (4.82, 57.84) 8.92E-06 6.24E-05	0.43 (0.10,1.87) 2.61E-01 4.12E-01 5
		Hepatitis Infection	Diabetes

.39 (2.07,14.0) 5.53E-04 1.32E-04 2.45 (1.50,3.88)

	100
	2.39E-04 4.18E-04
	2.83 (1.63,4.94)
	1.01E-01 1.41E-01

Table 1 .

 1 Baseline characteristics of control and cancer subjects in the EPIC Study

	Characteristic

Table 2 .

 2 Mean plasma phospholipid fatty acids at baseline among control and case subjects in the

	EPIC Study
	Fatty acids (% of total fatty acids)	Controls	Cases
		(n=2,982)	(n=2,982)
		Mean (SD)	Mean (SD)
	14:0 (myristic acid)	0.27 (0.09)	0.27 (0.09)
	15:0 (pentanoic acid)	0.18 (0.06)	0.18 (0.06)
	16:0 (palmitic acid)	25.53 (2.23)	25.62 (2.23)
	17:0 (heptanoic acid)	0.39 (0.08)	0.39 (0.08)
	18:0 (stearic acid)	14.09 (1.64)	14.03 (1.55)
	16:1n-7 (palmitoleic acid)	0.64 (0.25)	0.66 (0.27)
	18:1n-5	0.16 (0.12)	0.16 (0.13)
	18:1n-7 (cis-vaccenic acid)	1.50 (0.39)	1.52 (0.34)
	18:1n-9 (oleic acid)	10.32 (2.09)	10.42 (2.10)
	16:1n-7/9 (palmitelaidic acid)	0.44 (0.47)	0.43 (0.44)
	18:1n-9/12 (elaidic acid)	0.36 (0.24)	0.36 (0.22)
	18:1n-7 (vaccenic acid)	0.30 (0.15)	0.29 (0.14)
	18:2n-6 (linoleic acid)	22.10 (3.22)	21.97 (3.25)
	18:3n-6 ( -linolenic acid)	0.10 (0.05)	0.11 (0.47)
	20:3n-6 (di-homo--linolenic acid)	3.34 (0.83)	3.34 (0.84)
	20:4n-6 (arachidonic acid)	10.97 (2.21)	10.98 (2.17)
	22:4n-6 (adrenic acid)	0.37 (0.54)	0.38 (0.57)
	22:5n-6 (osbond acid)	0.25 (0.10)	0.26 (0.11)
	CLA9cis,11trans (conjugated linoleic acid)	0.22 (0.38)	0.22 (0.36)
	18:2ct, 18:2tc, 18:2tt (trans linoleic acid)	0.18 (0.11)	0.18 (0.10)

Table 3 .

 3 Odds ratios (OR) and 95% confidence intervals (CI) of breast cancer by quartiles of plasma phospholipid fatty acids (percentage of total fatty acids) Conditional logistic regression adjusted for date at blood collection, years of education, Body Mass Index, height, menopausal hormone use at baseline, alcohol at baseline, age at first birth and parity combined, energy intake, family history of breast cancer. ‡ ‡ FDR: false discovery rate

		Q-value	(FDR corrected p-	values)					0.794
	Quartiles of plasma phospholipid fatty acids	1 2 3 4 P for trend	(reference) on	means † †			< 0.20 [0.20-0.26[ [0.26-0.32[ ≥ 0.32	726/746 807/804 696/727 753/705	1.00 1.05;0.90-1.23 1.00;0.85-1.18 1.13;0.95-1.34 0.279		< 0.15 [0.15-0.18[ [0.18-0.21[ ≥ 0.21	854/816 778/772 663/684 687/710
	Plasma phospholipid	fatty acids		Saturated fatty acids	(SFA)	14:0 (myristic acid)	Range (%)	Cases/controls (n)	OR; 95% CI	15:0 (pentanoic acid)	Range (%)	Cases/controls (n)

Table 4 .

 4 Odds ratios (OR) and 95% confidence intervals (CI) for the highest tertile compared to the lowest of breast cancer by plasma phospholipid fatty acid groupings according to hormonal receptor status at blood collection, years of education, Body Mass Index, height, menopausal hormone use at baseline, alcohol, age at first birth and parity combined, energy intake, family history of breast cancerIn this work we have explored aspects of nutritional epidemiology by combining self-reported dietary and lifestyle information together with biomarker measurements to deeply investigate features of the diet and cancer association. Our main objective was to develop novel statistical frameworks for the application of multivariate statistical techniques. This work was made possible by exploiting the availability of data and the unique features of the European Prospective Investigation into Cancer and nutrition study. Different themes were tackled, ranging from nutrient patterns to use of metabolomics and fatty acids, different endpoints, including carcinomas of the breast and the liver. This thesis focused on the use of multivariate analytical solutions to make full use of available exposure data, thus extracting relevant information that could improve our understanding of cancer aetiology in the field of nutritional epidemiology.

			P for	heterogeneity		0.449		0.822		0.451		0.013		0.208
	PR-negative	(n=579 cases)	OR P trend	95% CI on	means † †	0.79 0.279	0.51-1.22	1.07 0.779	0.75-1.53	1.14 0.457	0.81-1.60	0.79 0.223	0.55-1.14	0.76 0.183	0.52-1.09
	PR-positive	(n=1,150 cases)	OR P trend	95%CI on	means † †	0.96 0.795	0.72-1.29	0.99 0.970	0.77-1.30	0.94 0.603	0.75-1.19	1.29 0.062	1.00-1.64	1.06 0.641	0.83-1.36
			P for	heterogeneity		0.182		0.460		0.835		0.006		0.473
	ER-negative	(n=398 cases)	OR P trend	95% CI on	means † †	0.76 0.315	0.45-1.29	1.24 0.331	0.80-1.92	0.93 0.682	0.62-1.39	0.66 0.067	0.42-1.01	0.82 0.356	0.54-1.26
	ER-positive	(n=1,649 cases)	OR P trend	95% CI on	means † †	1.13 0.299	0.90-1.43	1.04 0.749	0.83-1.29	1.01 0.928	0.83-1.24	1.17 0.140	0.95-1.44	0.99 0.979	0.81-1.22
	Fatty acids (% of	total fatty acids)				SFA*		cis MUFA †		cis n-6 PUFA ‡		Long-chain cis n-6	PUFA §	cis n-3 PUFAǁ

  the ten EPIC countries following standardized procedures[203,235,236]. Thus, analysis described in Chapter 2 featured 23 nutrients and total energy, the predictors set in the MITM implementation outlined in Chapter 3 included 13 main EPIC food groups, and the diet score used in the study presented in Chapter 4 was Standard dietary assessment methods, like food frequency questionnaires are feasible and cost-effective to be administered in large epidemiological studies, but are prone to exposure misclassification[133]. Measurement error may account for some of the lack of consistency that has been pointed out in findings within and across studies relying on data from FFQs examining diet and cancer risk[143]. Some of the early results found in large cohorts were not confirmed with long-term follow-up [237] and many strong findings on the nutrition-cancer relationship unveiled in case-control studies could not be replicated in clinical trials[238,239] or in cohort studies[240]. Metabolic profiles were identified that were the overlap signals in the MITM principle: biomarker signatures that are related to specific exposures and are predictive of cancer risk at the same time. The evaluation outlined in Chapter 5 used biomarker data as the primary exposure of interest where 60 plasma fatty acids concentrations were examined in relation with breast cancer risk. These were

	constructed based on six dietary items known to be related to cancer risks [86]. In quantified through an improved gas chromatography procedure that allowed a good
	addition the variables for alcohol consumption (e.g. alcohol at baseline and lifetime separation of trans fatty acids. Combining questionnaire with biomarker data provided
	alcohol intake), used either as part of the main exposures (Chapters 3 and 4) or as us with an unprecedented opportunity to deeply investigate the complex relationships
	adjustment confounders (Chapters 2 and 5) were also appraised from lifestyle between diet and the risk of cancer, using increasingly sophisticated statistical
	questionnaires [199]. techniques.
	An interesting property of dietary biomarkers measured in biological samples is
	that some of them reflect a great number of endogenous factors influencing foods and
	nutrients (e.g. involvement in metabolic pathways, genetic characteristics, excretion,
	Questionnaires are nonetheless a valuable tool for large-scale dietary assessment and Different classes of dietary biomarkers can be identified: the "recovery"
	remain the standard measure for diet in epidemiologic research [5,143]. Much research biomarkers provide unbiased estimates of absolute dietary intakes and are therefore
	is taking on the challenge of evaluating FFQs and enhancing the quality of their reporting suitable to be used as reference measurements to assess the accuracy of dietary
	[143,241]. assessments [165]. These markers often reflect the short-term nutritional status and
	Regardless, new strategies are sought to move from traditional nutritional display moderate correlation values with estimates of dietary intake [139,163].
	epidemiology that focuses on self-reported dietary and lifestyle factors towards ways to However, only a few recovery biomarkers are available, i.e. urinary doubly labelled
	investigate the aetiology of diseases not relying on study participants' capacity to recall water for total energy intake, and urinary nitrogen and potassium for dietary protein
	previous habits, yet exploiting objective measures to assess exposure [143]. Biomarkers and potassium intakes, respectively [244]. Blood samples are usually collected in cohort
	Features of mediation analysis, that holds a central part measured in biological specimens are increasingly being used for this scope [139,163]. studies at recruitment, largely because collecting many replicates of biosamples requires
	in our MITM implementation, are extensively explained. Finally, future perspectives are Dietary biomarkers and -omics technologies provide a very promising means to
	evoked whereby the tools investigating the diet-cancer relation can be further extended quantify dietary and other environmental exposures [242].
	to embrace Mendelian randomisation or through more complex pathway analyses. The work developed in this thesis utilized biomarker measurements, either to
	estimate the diet-disease risk associations, or as a complementary tool to combine

A large part of the evidence assessed in this thesis relied on dietary information originating from validated questionnaire data, whereby nutrients and total energy were estimated from harmonised food composition tables, the ENDB, compiled from national databases of evidence from different sources. In Chapters 3 and 4 analytical frameworks that integrated, respectively, untargeted NMR and targeted MS data with dietary and lifestyle questionnaire data are described. tissue turnover, absorption effects, etc.) that affect the correlation of a biomarker with its corresponding dietary exposure

[139]

. Additionally, they also reflect more closely the dietary compound's bioavailable dose, the latter being the relevant parameter in any metabolic process they are involved in

[243]

. With all this in mind, valuable additional information of dietary exposure can be obtained through biomarker assessment.

  Concentration values of dietary biomarkers may be difficult to compare across different studies, mainly due to heterogeneity in laboratory processes that may introduce systematic bias affecting the biomarker measurement[139,242]. These include the type of biological specimens exposure or response variables or alternately with disease outcomes.Multivariate techniques of dimension reduction applied to large metabolomics sets mainly aim to summarize information into a restricted number of latent variables known as the principal components. PCA and its derivatives are the most widely used methods, while Discriminant Analysis (DA) partitions observations with respect to the investigated outcome by maximising the ratio of intergroup to intragroup variation. PLSbased multivariate approaches combine PCA and MLR to identify latent factors capturing as much variation in predictors and responses by extracting linear combinations maximising the covariance of the latter sets. Variable selection techniques entail a penalisation introduced in regression approaches to ensure sparsity by

	obtained, the differences in sample handling (e.g. procedures of collection, storage,
	thawing), the methodologies employed to measure the biomarker (machinery, precision,
	limits of detection and quantification, day-to-day drifts, etc.) [139].
	With recent advances in technology, many elements related to the laboratory
	settings have improved [245-247]. For example, a method (the group-batch profile -
	GBP method) has been developed to adjust NMR data for systematic variations
	introduced by sample work-up prior to spectral data acquisition [248]. The PC-PR2
	method has been conceived to identify and quantify the contribution of relevant sources
	of variation in metabolomics data prior to investigation of etiological hypotheses [183].
	in international settings. Such harmonisation efforts have started in international
	collaborations such as the The COnsortium of METabolomics Studies (COMETS), a
	partnership among prospective cohort studies involved in the acquirement of
	metabolomics profiling. International consortia face the need to provide
	interdisciplinary solutions to investigate complex data, at a time when epidemiologic
	investigations are accumulating -omics data [249].
	The unique attributes of metabolomics data and the increase in the amount of
	information they bring make them an appealing opportunity to take on the challenge
	brought by highly dimensional, collinear, nonlinear and non-normal data. With such
	overwhelming sets of data to process, there is an increased demand for statistical
	methodologies and modelling approaches that are needed for better analysis of data.
	After pre-processing and exploratory steps, data analyses of metabolomics
	currently rely mostly on regression-based methods including multivariable regression

This technique has been used in studies described in Chapters 3 and 4. Considerable efforts are currently underway to harmonize metabolomics data in order to allow pooling data together from different studies, to ensure a better comparability of results models, multiple testing correction procedures, use of multivariate dimension reduction techniques, and to a lesser extent variable selection approaches

[179,242]

. Univariate approaches are employed in the first instance to uncover simple associations between metabolites and

Acknowledgments

Acknowledgements:

The authors wish to thank Dr Joshua Sampson from the Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA, for useful discussions and insightful comments on this work. The authors would like to extend their thanks to Mr Bertrand Hémon and Ms Carine Biessy from the International Agency for Research on Cancer for their kind help with issues related to data management.

Acknowledgments

The authors gratefully acknowledge Mrs Mélanie Collin and Mrs Anne-Sophie Gross within the Lipidomic Platform (Plateforme de Biologie Intégrée) at Institut Gustave Roussy, and Mrs Béatrice Vozar at the International Agency for Research on Cancer, for their outstanding assistance with laboratory measurements of plasma fatty acids.

Funding

This work was supported by the French National Cancer Institute (L'Institut National du Cancer; INCA) [grant number 2009-139; PI: M. Jenab]. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Nordic Centre of Excellence programme on Food, Nutrition and Health. (Norway); Health Research Fund (FIS), PI13/00061 to Granada), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk and MR/M012190/1 to EPIC-Oxford) (United Kingdom). The work undertaken by N Assi was supported by the Université de Lyon I through a doctoral fellowship awarded by the EDISS doctoral school. "For information on how to submit an application for gaining access to EPIC data and/or biospecimens, please follow the instructions at http://epic.iarc.fr/access/index.php" Financial support This work was supported by World Cancer Research Funds, and the Institut National du Cancer (INCA). The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); UiT The Arctic University of Norway; Health Research Fund (FIS), PI13/00061 to Granada), Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Scientific Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk) (United Kingdom).

A prospective evaluation of plasma phospholipid fatty acids and breast cancer risk in the EPIC study

CHAPTER III: A STATISTICAL FRAMEWORK FOR THE "MEETING-IN-THE-MIDDLE" APPLIED TO UNTARGETED METABOLOMIC DATA

Supplementary Tables

A Statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study. Supplementary Table 1: Summary statistics of the predictors variables (X-set) of the study subjects in the EPIC liver nested case-control study (N=336, 114 Cases, 222 Controls).

CHAPTER IV: A REFINEMENT OF THE "MEETING-IN-THE MIDDLE" FRAMEWORK WITH AN APPLICATION IN TARGETED METABOLOMICS

The meeting-in-the-middle framework using metabolomics in the relationship between lifestyle factors and hepatocellular carcinoma risk Nada Assi 1 , Duncan Thomas 2 , Michael Leitzmann 3 , Magdalena Stepien 1 , Véronique Chajès 1 , Thierry Philip 4 , Paolo Vineis 5 , Christina Bamia 6,7 , Marie-Christine Boutron-Ruault 8,9 , Torkjel M Sandanger 10 , Amaia Molinuevo 11,12 , Hendriek Boshuizen 13 , Anneli Sundkvist 14 , Tilman Kühn 15 , Ruth Travis 16 , Kim Overvad 17 , Elio Riboli 5 , Marc Gunter 1 , Augustin Scalbert 1 , Mazda

Jenab 1 , Pietro Ferrari 1* , Vivian Viallon 18 1 Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), Lyon, France. 2 University of Southern California, Los Angeles, CA, USA. 3 Department of Epidemiology and Preventive Medicine, Regensburg University, Regensburg, Germany. 4 Unité Cancer et Environnement, Centre Léon Bérard, 28 rue Laennec, 69373, Lyon 08 Cedex, France. 5 Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place W2 1PG London, UK. 6 Hellenic Health Foundation, Athens, Greece. France. 9 Gustave Roussy, F-94805, Villejuif, France. 10 Department of Community Medicine, UiT the Arctic University of Norway, Tromsø, Norway. 11 Public Health Division of Gipuzkoa, Regional Government of the Basque Country, Spain. 12 CIBER of Epidemiology and Public Health (CIBERESP), Spain. 13 National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands. 14 Department of Radiation Sciences Oncology, Umeå University 901 87 Umeå, Sweden. 15 Division of Cancer Epidemiology, German Cancer

Research Center (DKFZ), Heidelberg, Germany. 16 Cancer Epidemiology Unit, University of Oxford, Oxford OX3 7LF, UK. 17 The Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark. 18 Université de Lyon, Université Claude Bernard Lyon1, Ifsttar, UMRESTTE, UMR T_9405, F-69373, LYON the natural indirect effect (NIE), and the total effect (TE) were obtained, along with the effect of the corresponding M-score adjusted for its counterpart lifestyle exposure and for confounding variables and referred to as the mediator effect (ME). For more details, see

Supplementary Methods.

All statistical tests were two-sided and p-values < 0.05 were considered statistically significant. Statistical analyses were performed using PROC PLS in SAS [23] for PLS analyses and the R Software [24] for linear and conditional logistic regressions and mediation analyses.

Conflict of Interest

None to declare.

Supplementary material:

Supplementary

Supplementary Methods

Material and Methods

The EPIC Study

EPIC is a multicentre prospective study designed to investigate the link between diet, lifestyle and environmental factors with cancer incidence and other chronic disease outcomes. Over 520,000 healthy men and women aged 25-85 were enrolled between 1992 and 2000 across 23 EPIC administrative centres in 10 European countries including Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom 1 . In most of EPIC centers, participants were recruited amongst the general population with the following exceptions: for France, women were enrolled from a health insurance scheme for school and university employees; in Utrecht, The Netherlands and in Florence, Italy, participants came from breast cancer screening programs; some centers in Italy (Turin and Ragusa) and Spain recruited blood donors; and the Oxford sub-cohort (United Kingdom) included mostly health-conscious individuals recruited throughout the UK.

Finally, the French, Norwegian and Naples (Italy) cohorts comprised only women. Extensive details of the study design and recruitment methods have been previously published 1,2 .

Data collection of dietary and lifestyle data

During the enrolment period, participants gave informed consent and completed questionnaires on diet, lifestyle and medical history. Approval for this study was obtained from the ethical review boards of the participating institutions and the International Agency for Research on Cancer (IARC). Biological samples were collected for approximately 80% of the cohort prior to disease onset. Serum samples were stored at IARC, Lyon, France in -196°C liquid nitrogen for all countries, with the exception of samples originating from Sweden (-80°C freezers) and Denmark (-150°C nitrogen vapour). Usual diet over the previous 12 months was assessed for each individual through validated country-specific dietary questionnaires (DQs) 1 . Nutrient intakes were then estimated using a common harmonized food composition database across EPIC countries (EPIC Nutrient Database, ENDB) 3,4 . Information on sociodemographic data including education, smoking and alcohol drinking histories as well as physical activity were gathered in lifestyle questionnaires.

Anthropometric characteristics were directly measured by trained study personnel for most of the participants 1 , but were self-reported in baseline questionnaires for a subset of Since TE=NDE*NIE does not hold in our setting, the mediated proportion was computed using the following formula:

Indeed, the proportion mediated makes real sense only when NDE and NIE have the same direction of association and is bounded between 0% and 100%. In this case our formula reduces to:

When NDE and NIE have opposite directions, the mediated proportion is not well-defined.

For example, if and so that , it is not clear what the mediated proportion would be. In our results, NDE and NIE always had the same direction when they were both statistically significant. For example, in our analyses for diabetes (or equivalently for BMI), the NIE is significantly associated with an increased risk of HCC and the NDE was not significant and had the opposite direction of association. This suggested that TE=NIE and using our first formula above we get the appropriate value of 100%. while no association was found for ER-positive tumors (P-heterogeneity =0.01). These findings suggest that increased endogenous synthesis of palmitoleic acid estimated many years prior to diagnosis is associated with higher breast cancer risk. Dietary trans fatty acids derived from industrial processes may specifically increase ER-negative breast cancer risk.

The purpose of the current study was to investigate associations between plasma phospholipid fatty acids and risk of breast cancer, overall and by hormonal receptor status, in a large case-control study nested within the prospective EPIC cohort.

Materials and Methods

The EPIC STUDY

The EPIC study includes 519,978 participants in 10 European countries: Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom. Participants gave informed consent and completed questionnaires on diet, lifestyle, and medical history. In most centers, participants were recruited from the general population. Exceptions were the French cohort (women of the health insurance scheme covering teachers), the Utrecht cohort (women attending breast cancer screening), the Ragusa cohort (blood donors and their spouses), and one-half of the Oxford cohort (vegetarians and health-conscious volunteers). Following a standardized protocol, blood samples were collected (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002), aliquoted into plasma, serum, white blood cells and erythrocytes, and stored in liquid nitrogen.

Outcome assessment

Incident breast cancer cases were identified through population cancer registries or by active follow up using health insurance records, cancer and pathology registries, and contacts with participant.

Subjects were followed up until cancer diagnosis (except non-melanoma skin cancer), death, emigration, or the end of the follow-up period.

Nested case-control study

Of 367,993 women, the present analysis excluded women with prevalent cancers at any site children; first birth after age 30y), energy intake (as a continuous variable), and family history of breast cancer (yes; no). Tests for trend were computed using the quartile-or tertile-specific means of each fatty acid.

Additionally, a forward selection procedure was run on all fatty acids including groupings, to select fatty acids that mostly contribute to the aetiological model. Adjustment variables mentioned above were forced into the model and fatty acids considered as explanatory effects are tested. Chi-Square statistic was computed for each variable not in the model, if it is significant at the entry level=0.05, the corresponding fatty acid was then added to the model. The procedure was repeated until none of the remaining variables meets with the entry criterion.

Sub-analyses were conducted according to hormonal receptor status (ER-positive, ER-negative, PRpositive, PR-negative), and tests of heterogeneity of associations were performed. Formal tests of heterogeneity were based on chi-square statistics, calculated as the deviations of logistic betacoefficients observed in each of the subgroups relative to the overall beta-coefficient.

The false discovery rate (FDR, q-values) was computed for results from the multivariable models from the main analysis using the Benjamini-Hochberg correction to control for multiple comparisons (17).

Results

Characteristics of participants

Baseline characteristics of cases and controls are presented in Table 1. Cases had a significantly higher BMI, adult height, a lower number of full term pregnancies and an older age at first full term pregnancy.

Plasma phospholipid fatty acids in cases and controls

Mean plasma phospholipid fatty acid levels in cases and controls are provided in Table 2. Palmitic acid is the main SFA, oleic acid the main cis-MUFA, and linoleic acid the main n-6 PUFA, with a ratio of n-6 to n-3 PUFA higher than 2. Elaidic acid, the main ITFA, represents a higher percentage than vaccenic acid, the natural trans fatty acid.

Plasma phospholipid fatty acids and overall breast cancer risk

Table 3 presents OR and 95% CI of overall breast cancer according to quartiles of fatty acids, expressed as percent of total fatty acids. SFA were not statistically significantly associated with breast cancer risk. Higher levels of cis-MUFA were associated with increased risk of breast cancer (OR for the highest quartile compared with the lowest [Q4-Q1]=1.17; 95%CI=0.98-1.39; p for trend=0.042, q-value=0.259). Only palmitoleic acid remained statistically significantly related to breast cancer risk after FDR correction (OR [Q4-Q1]=1.37; 95%CI=1.14-1.64; p for trend=0.0001, q-value=0.004).

Consistently, palmitoleic acid (16:1n-7) was the only fatty acid retained by the forward selection procedure (data not shown).

No significant association was found between overall breast cancer and levels of trans-MUFA or trans PUFA from natural ruminant sources or industrial sources (Table 3).

Levels of individual cis n-6 or n-3 PUFAs were not significantly associated with breast cancer incidence (Table 3). However, levels of total cis n-6 PUFA were inversely associated with breast associated with increasing ratio of n-3/n-6 PUFA measured in diet or in serum phospholipids (27).

However, no significant association remained among European populations (27). In agreement with this latter finding, we failed to report a significant inverse association between n-3/n-6 ratio and breast cancer risk within the EPIC study. In a prospective study conducted in a French population, breast cancer risk was not related to any dietary PUFA overall (28); however, opposite associations were seen according to food sources of PUFA (28), emphasizing the importance of considering food sources of PUFA. If long chain n-3 PUFA originates mainly from fish sources, we cannot distinguish the contribution of different food sources (vegetable oils, meat, processed foods) to n-6 PUFA levels in plasma phospholipids. This high level of heterogeneity between epidemiological studies may suggest that other micronutrients and biochemical pathways may modulate the relationship between PUFA and breast cancer. In support of this hypothesis, one prospective study showed that antioxidant supplementation modified the association between PUFA and breast cancer risk (29).

Further epidemiological studies should incorporate markers of micronutrient intake and other metabolic factors linked to breast cancer (e.g. insulin, inflammatory markers).

Trans fatty acids are classified as natural or industrially produced. Natural trans fatty acids are produced by the gut bacteria of ruminant animals and are found in small amounts in the food products from these animals. ITFA are formed when fats and oils are partially hydrogenated during industrial processing techniques, and these fatty acids are found in fast foods, industrially-produced products, snack, deep-fried foods, and baked goods. There is evidence that ITFA significantly increases the risk of coronary heart disease more than any other dietary component (30). The average intake of ITFA in many European countries is now relatively low; however, as the majority of the European countries still do not limit the content of ITFA in food, a large number of products containing high levels of ITFA are still available in Europe (31).

Some epidemiological studies have reported a positive association between intake of ITFA and risk of breast cancer (11), ovarian cancer (32), colon cancer, and prostate cancer (33)