P A Diaz-Valencia

Alain-Jacques Valleron

Pierre " Bougnères

Seminaire

Epidemiology of Type I Diabetes: Global Incidence and Determinants 11

Background

Type 1 Diabetes (T1D) is one of the most common metabolic diseases in childhood Soltesz (298). There is a general agreement that the incidence of T1D has been increasing in children since the 1950's. By 1998, using existing data on 37 studies (from 27 countries), the increase of worldwide incidence of T1D was on average 3.0% per year (Confidence Interval (CI) 95% 2.6-3.3) and it was predicted that it will be 40% higher in 2010 (231) in children aged 0-14 years. The causes of this increase are still under study; this thesis updates the current knowledge on the global incidence of T1D and taking advantage of the increasing availability of public databases examines the correlation between incidence and country characteristics.

Methods

A comprehensive systematic literature search of published articles on the incidence of T1D worldwide was conducted to study global variations of the incidence within countries.

Were included in the study references whose main subjects were epidemiological studies with reports of T1D incidence from population-based data/registers, and reporting incidence by year and age at diagnosis. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) group recommendations (213). The registration number in PROSPERO (International Prospective Register of Systematic Reviews) is:

The initial goal of this thesis was to study the correlation between environmental variables available in public databases with the country incidence of T1D. Seventy-seven independent factors were searched in public databases of the World Health Organization (WHO), the United Nations (UN) and the World Bank (WB) websites and arranged in four domains Climate and environment, Demography, Economy, and Health Conditions.

Professor Pierre Bougnères, previous Director of the Unit 1169 and present Director of the "ISIS-Diab" collaborative group for providing me the research facilities used during this thesis. I wish to warmly thank all the members of the "ISIS-Diab" collaborative group for their enthusiasm in the study of T1D and their support during this thesis.

First of all, I wish to express my deepest gratitude to Professor Alain-Jacques Valleron, Director of this thesis. Professor Valleron's expertise has been essential in this work; his advises always full of new ideas have been available even during his holidays and from different places around the world. Under Professor Valleron's guidance I have consolidated my understanding of the principles of epidemiology but also of the importance of seeing problems with new eyes, with precision and solid bases that are essential for scientific work. Thank you very much for the long conversations about epidemiology, diabetes, methods, analyses and inspiring anecdotes such as "Zorba le Grec". Thank you for accepting my application from the other side of the ocean and always being honest. Also, thank you very for so many delicious coffees! It has been a privilege to work with you. In addition, I wish to thank Professor Pierre Bougnères, Co-Director of this thesis, whose extensive experience in the field of diabetes, and whose enthusiasm and dedication have motivated the study of T1D; his suggestions have been essential in this research. Also, I am infinitely thankful to Professor Pierre-Yves Boëlle, from the UPMC, for his constructive criticisms, welcome advices during the initial phases of this thesis in the validation of the final search equation of the literature for the systematic review presented in chapter 1, and for his help during the statistical analyses of the Age-Period-Cohort models presented in chapter 5. I also wish to thank Sophie Le Fur, coordinator of the "ISIS-Diab" cohort, for her most useful contribution to chapter 6 about the genetic component of T1D.

In addition, I wish to thank all the co-workers of the epidemiology and biostatistics team at the Unit of « Epigénétique et Environnement », and in particular Sophie Valtat whose UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 8 creativity and advices about the use of Tableau and GraphClick have extensively contributed to improve this thesis and for her moral support. Also I wish to express my deepest gratitude to Nga Mai, whose advices about statistical analyses and help with the R program were essential for this work. I also want to sincerely thank Sofia Meurisse for her help during the first analyses about the cumulative incidence of T1D; all my thanks to all three of you for sharing your work place with me, becoming my friends and supporting me emotionally throughout this thesis.

I also sincerely thank Muriel Delacroix administrator at the Inserm Unit 1169 and the secretaries Michèle Taieb, Evelyn Guilloux and Lydi Martorana, for all their help with administrative matters in the laboratories and at the doctoral school; thank you for your kindness and always a positive answers. I also thank Sophie Lesieur at the Hospital Saint Antoine for all her help to retrieve ancient papers used in this thesis. This doctorate would not have been possible without the help of Anne-Lise Haenni from the Institut Jacques Monod, CNRS -Université Paris-Diderot, whose personality, professionalism, intelligence and friendship I admire and who became my family in France.

Infinite thanks to Anne-Lise for her unfailing critical reading of all the materials throughout this thesis, for all her kind and pertinent suggestions, for our monthly academic-friendship meetings "el Trio", for her exceptional friendship and emotional support all through these years. Having the opportunity to share part of the Anne-Lise's time was one of the most fundamental things that happened to me during these years of thesis. I sincerely thank Professor Paul Landais and Professor Régis Coutant for accepting the role of Opponents (Rapporteurs) of my thesis defense and Professor Alfred Penfornis and Professor Fabrice Carrat for accepting the role of Reviewers (Examinateurs) of my thesis defense.

I also wish to warmly thank all my friends in France and in Colombia, too numerous to mention them all, for all the great times we spend together physically and online, for all the interesting discussions, learning, music, food and for all their emotional support.

In particular, I wish to warmly thank my family. I am deeply grateful to my Father Rodrigo Diaz for inspiring me and giving me "wings to fly" and to my Mother Ligia Valencia who has always believed in me and taught me the importance of persistence and UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 9 responsibility. I wish to warmly thank my late brother Rodrigüito 1990-2013 for teaching me a different dimension of love, believing in me and for always having hugs and smiles. Also, I want to thank my brothers Rodrigo Junior, Juan, David, and my sister Katherine for inspiring me.

Finally, I want to thank my dear Juan Ignacio Gallo for all his love, for believing in me and for being an essential part of this project and of my life, and Andrea who was born during this thesis to transform my entire life and become the most important reason for continuing and accepting new challenges.

PS. I also wish to thank all the patients that today suffer from Type 1 Diabetes, especially Pascale, Emilio, Alejandra, for their inspiring courage and endurance. Also, as opposed to what is known in children, we found that the incidence was in most studies larger in males than in females.

Independently of the initial level of incidence, an incidence increase was observed in all continents from 1960 to 2010: Asia (incidence slope = 0.11 per 100.000 per year), Africa (incidence slope = 0.13 per 100.000 per year) and Latin America and the Caribbean (incidence slope = 0.13 per 100.000 per year) show the smallest increases, Europe (incidence 0.22 per 100.000 per year) presents an intermediate increase, and North America (incidence slope = 0.45 per 100.000 per year) and Oceania (incidence slope = 0.53 per 100.000 per year) the largest.

Finally, after having built a 6824 line x 59 column tidy database (Appendix A: Database I), in which we assembled all the incidences by age class and period of the studies from tables, texts and or figures in the 265 articles identified in the systematic review (223 included in the analyses), we extracted data to analyzed information from 2,327,604,529 person-years at risk from 192,741 cases using APC models. A strong cohort effect was observed; in addition the model shows an apparent period effect that decreases after 1987. Also as expected, the incidence of T1D increases with age. These variations are in part explained by the interaction between environmental factors and genetic components of the populations.

For the environmental component, the highest incidences of T1D were noted in the more urbanized, wealthy and cool countries in the world, and with a higher body mass index (BMI) in males. In contrast, exposition to UV radiations showed a significant negative correlation with T1D. Using Stepwise Multiple Linear Regressions (MLR) models the most significant environmental predictors of the country-to-country variation of T1D incidence were UV radiation, number of mobile cellular subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean BMI.

For the genetic component, we then correlated the frequency of HLA alleles retrieved in the Allele Frequency Net with the incidence of T1D found through our systematic review.

High heterogeneity of susceptible and protector haplotypes for T1D within populations was found. Seven significant correlations were noted. Using Stepwise MLR models, significant

Conclusions and implications

During this thesis, using a comprehensive systematic review of the literature, we quantified the country-to-country variation in T1D incidence worldwide in children and adults, and show that a large part of this incidence could be accounted by the variability of environmental factors within the countries.

This thesis was initially motivated by the increase in T1D incidence. Here we could quantify this increase and show that it was even larger than had been predicted in an earlier study (231).

This thesis was also based on the will to take advantage of the increasing availability of public databases providing informing on the country-to-country variation of environmental and genetic factors. For the moment much data are missing, some of them are low quality, in particular in low-income countries, where information on T1D incidence is still lacking. However, we are confident that in the near future high quality information on T1D incidence will be available, as well as genetic and environmental factors that will allow deepening of the approach used here, and will unravel new clues on the gene-environment causality of T1D.

Résumé

Introduction

Le diabète de type 1 (DT1) est une des maladies métaboliques les plus répandues chez les enfants (298). Les recherches s'accordent pour dire qu'il y a une augmentation de l'incidence du DT1 chez les enfants depuis 1950. En 1998, en utilisant les données sur 37 études (de 27 pays), l'augmentation de l'incidence mondiale du DT1 a été calculée à 3,0% en moyenne (intervalle de confiance (IC) 95% 2,6 à 3,3), et il a été prédit qu'elle serait de 40% en 2010 (231) chez les enfants âgés de 0 à 14 ans. Les causes de cette augmentation ne sont pas encore déterminées; cette thèse met à jour les connaissances actuelles sur l'incidence mondiale du DT1, en profitant de la disponibilité croissante des bases de données publiques et en examinant la corrélation entre l'incidence et les caractéristiques des pays.

Méthodes

Revue systématique de la littérature Une recherche exhaustive et systématique des articles publiés sur l'incidence du DT1 dans le monde a été effectuée pour étudier les variations globales de cette incidence dans les divers pays. Ont été inclus dans cette étude, les articles épidémiologiques qui rapportent l'incidence de DT1 dans la population générale, et qui rapportent l'incidence par âge et par année au moment du diagnostic. Nous avons suivi les recommandations de PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) (213). Le numéro d'inscription à PROSPERO (International Prospective Register of Systematic Reviews) est:

Extraction des données

Les données brutes ont été extraites des articles, et introduites manuellement dans une grille de collection de données, « Annexe A: base de données I », en utilisant le logiciel Microsoft Excel. Les données de l'incidence ont été prélevées des études individuelles comme elles ont été signalées dans les publications dans les textes, les tables ou les graphiques; un logiciel a été utilisé pour extraire les données d'origine (x, y) à partir des images (1). La Table 1 présente les principales catégories de l'extraction des données inclues dans la base de données principale (Annexe A: base de données I).

Objectifs de la revue systématique

Étudier les informations publiées sur l'incidence du DT1 au niveau mondial depuis 1950.

Objectifs spécifiques

-Décrire l'incidence mondiale du DT1 au niveau de chaque pays.

-Décrire l'épidémiologie du DT1 chez les enfants et les adultes au niveau mondial.

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Epidemiology of Type I Diabetes: Global Incidence and Determinants 18 -Étudier la co-variation, au niveau des pays, de l'incidence du DT1 avec les déterminants environnementaux et génétiques disponibles dans les bases de données publiques.

-Analyser si la tendance mondiale actuelle de l'incidence du DT1 est compatible avec un effet de période ou de cohorte de naissance.

Statistiques

Les tendances d'incidence ont été corrélées avec des facteurs environnementaux et génétiques, et modélisées en utilisant une approche APC.

Pour les analyses statistiques et graphiques le logiciel R (version 3.0.1) a été utilisé (255), et pour la visualisation des données le logiciel Tableau (262).

Déterminants environnementaux analysés

L'objectif initial de cette thèse était d'étudier la corrélation entre les variables environnementales disponibles dans les bases de données publiques, et l'incidence du DT1 selon les pays. Pour corréler les variables environnementales à l'incidence du DT1, présentées dans le chapitre 3, des facteurs indépendants ont été recherchés dans les bases de données publiques de l'Organisation Mondiale de la Santé (OMS), l'Organisation des Nations Unies (89). L'Allele Frequency Net à http://www.allelefrequencies.net/ (115) a été utilisé pour récupérer des informations sur les fréquences des allèles dans les populations à travers le monde. Les principales méthodes statistiques utilisées étaient des corrélations et des modèles de régression linéaire.

Analyses des tendances de l'incidence mondiale du DT1

Enfin, afin de mieux comprendre les tendances dynamiques du DT1 avec toutes les informations récupérées sur l'incidence du DT1, nous avons utilisé l'APC présenté dans le chapitre 5.

Principaux résultats

Informations recueillies lors de la revue systématique

Tendances de l'incidence mondiale du DT1

L'incidence du DT1 est très variable selon les pays et les régions du monde pour les enfants et les adultes. Dans le groupe d'enfants de 0-14 ans, l'incidence varie de 0,08 / 100.000 personnes / an en Papouasie-Nouvelle-Guinée (230) à 62,42 / 100.000 personnes / an en Finlande (128). Dans le groupe de jeunes de 15-19 ans, la plus faible incidence du DT1 a été signalée dans l'île Maurice, (1,1 / 100.000 personnes / an) (332), et la plus élevée en Estonie (39,9 / 100.000 personnes / an) (157) et dans le groupe d'âge de 70-79 ans, le taux le plus faible a été signalé en Navarre, Espagne (0,8 / 100.000 personnes / an) [START_REF] Forga | En Representacion del Grupo de Estudio de Diabetes tipo 1 de N. [Differences by age and gender in the incidence of type 1 diabetes in Navarre, Spain (2009-2011)[END_REF] et le plus élevé à Kronoberg, Suède (55 / 100.000 personnes / an) (320);

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(n =) nombre de l'ensemble de données récupérées par pays. Symboles géométriques: les triangles indiquent l'ensemble des données de l'étude Diamond (n = 114) (318), les carrés indiquent les bases de données de EURODIAB (n = 87) (90,242,243), les signes "+" indiquent les données de la IDF (n = 28 ) (241, 300) et les cercles sont des autres publications que nous avons récupérée (n = 298); ils sont colorés pour visualiser la tendance de l'incidence (vert: 1958 à rouge: 2009). La ligne noire représente l'incidence moyenne du DT1 de chaque pays. Pour la liste détaillée des études et ensemble des données voir Base de données II. Indépendamment de l'incidence initiale, une tendance à l'augmentation de l'incidence dans tous les continents entre 1960 et 2010 a été observée: l'Asie (pente d'incidence = 0,11 pour 100.000 personés par an), l'Afrique (pente d'incidence = 0,13 pour 100.000 par an) et l'Amérique latine et les Caraïbes (pente d'incidence = 0,13 pour 100.000 par an) ont montré les plus faibles hausses, l'Europe (pente d'incidence de 0,22 pour 100.000 par an) a présenté une augmentation intermédiaire, et l'Amérique du Nord (pente d'incidence = 0,45 pour 100.000 par an) et l'Océanie (pente d'incidence = 0,53 pour 100.000 par an) la plus grande augmentation d'incidence.

Incidence du DT1 chez les jeunes et les adultes

Peu d'études sur l'épidémiologie du DT1 chez les adultes sont disponibles par rapport à celles sur les enfants atteints de DT1. Cependant, même avec la quantité limitée de données disponibles, nous avons pu montrer que les variations géographiques de l'incidence du DT1 chez les adultes suivent une voie parallèle à celles rapportées chez les enfants. Une corrélation géographique importante, mesurée par le coefficient de corrélation de Spearman, a été trouvée entre l'incidence du DT1 chez les enfants de 0-14 ans et chez les adultes dans les classes d'âge 15-19 ans, 20-24 ans, 25-29 ans, 30-34 ans et globalement dans l'ensemble du groupe de 15 à 60 (r = 0,75; p -valeur: 5,7 X10 -10 ). Dans 23 des 35 pays (66%) (55 de 71 études), l'incidence du DT1 était plus élevée dans la tranche d'âge de 0 à 14 ans par rapport aux 15 à 19 ans. Contrairement à ce qui est connu chez les enfants, l'incidence était généralement plus élevée chez les hommes que chez les femmes. Le rapport moyen hommesfemmes chez les adultes utilisé dans notre revue était 1,47 (IC à 95% pour la moyenne de 1,33 à 1,60; SD = 0,49; n = 54; p = <0,0001).

Tendances de l'incidence du DT1 en utilisant un modèle Age-Période-Cohorte

Après avoir construit un base de données comportant 6824 lignes x 59 colonnes, dans

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Age$(years)$/$Last$year$of$the$five3year$period $ 1938 1943 1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 modèle APC un fort effet de cohorte a été observé; le modèle indique également un effet période probable qui diminue après 1987. Finalement, comme prévu, l'incidence du DT1 augmente avec l'âge. Ces variations s'expliquent en partie par l'interaction entre les facteurs environnementaux et les composants génétiques des populations.

L'ensemble des données réunies dans le modèle APC provenaient de pays européens

Introduction

This thesis concerns the study of Type 1 Diabetes (T1D) and is based on the original articles presented in Chapters 3, 4, 5 and 6. Each chapter reproduces the articles with the kind permission of their authors, and in addition supplementary information is also presented at the end of each chapter.

The information has been arranged in six chapters following the chronological order of the study. Global incidence of T1D presented and analyzed in different ways throughout this thesis was retrieved using a systematic literature review presented in chapter 1.

Chapter 2 includes a general review of diabetes classification and the 'state of the art' in the epidemiology of T1D, including reports of the literature before and after 1950. It also, briefly describes the pathogenesis and etiology of T1D seen from an epidemiological point of view.

The first part of chapter 3 contains the analyses of global T1D incidence among children aged 0-14 years. The incidence of T1D was correlated with 77 independent variables covering four domains: Climate and environment, Demography, Economy, Health conditions.

Stepwise Multiple Linear Regression (MLR) models were carried out. The supplementary materials of this chapter also include the Principal Component Analyses (PCA) of the variables included in the final model, as well as preliminary analyses discriminating countries for which information on the incidence of the T1D was -or was not -retrieved. The second part of this chapter includes the analyses of trends, the cumulative incidence from birth cohorts between 1924 and 2004, and the analyses of age at onset calculated in the entire database. A description of epidemiological reports describing changes in incidence trends affecting predominantly the youngest age group is presented at the end of this chapter.

We noticed that in addition to information on T1D incidence in children aged 0-14 years, some of the retrieved studies also reported the incidence of T1D in older age groups.

During exploration of the cumulative incidence of T1D based on the first analyses of this thesis, trends in adults seemed to parallel those in children. Following this observation, we UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants 38 undertook to explore in depth the epidemiology of T1D in adults, as presented here in chapter 4. The main methodological problem related to the study of the epidemiology of T1D in adults is to distinguish this disease from others, such as Type 2 diabetes or Latent Immune Diabetes in Adults (LADA). For that reason, this chapter includes a detailed description of the criteria used to diagnose T1D in adults, which is focused on autoantibody detection.

Comparisons between the incidences retrieved in adults and those retrieved in children are also presented.

Chapter 5 describes the incidence trends of T1D and presents an APC analysis of global trends in incidence of T1D, from data collected between 1975 and 2014. By including a discriminating analysis by country, it was possible to model the effects of the age, the year and the birth cohort at diagnosis of T1D for children aged 0-14 among 89 countries worldwide.

The epidemiology of T1D would not be complete without mentioning the role of genetic causes, mainly the participation of the Human Leukocyte Antigen (HLA) as determinant factor in the development of T1D and in the heterogeneity of levels of incidence retrieved around the globe. Chapter 6 presents the analyses of the genetic components of T1D, considering the latest Type 1 Diabetes Genetics Consortium Families (89); correlations and Stepwise MLR models were carried out as performed for the environmental component. The databases mentioned here will be placed for public examination and access, in the author's Github website at: https://github.com/PaulaDiaz, once the press embargo of the manuscripts expires; Appendix A.

Interactive visualization of the data is available at http://www.isis-diab.org and https://public.tableau.com/profile/paula.diaz -!/.

Chapitre 1

Systematic Literature Reviews of Type 1 Diabetes 1 Systematic literature reviews used in this thesis

Systematic literature reviews have become important in research and are often the starting point of any study. Here we used two systematic literature reviews to obtain data on the incidence of T1D around the world: the first concerns epidemiology of T1D in children, analyzed in chapters 1, 3, 5 and 6, and the second in adults, analyzed in chapter 4.

Heterogeneity of the information in any data collection is a main issue, and is the reason why we followed the PRISMA statement (Preferred reporting for systematic reviews and meta-analyses) (213) recommendations as a method to collect data in a standardized way and to assess the quality of information of the retrieved studies. Each retrieved study was identified and all the information was extracted in a comprehensive format developed in our team. Additional updates of the revisions and the final decision about inclusion or not, and some references were discussed periodically during meetings and group discussions.

Once we had the entire data collection, we selected the information to be included in the analyses that will be presented in the following chapters. Chapter 1 presents the objectives of the systematic reviews and the methodology used to obtain information. In the final section, this chapter presents a comparison of the current review with others reviews on the epidemiology of T1D, and the summary of the information retrieved on global epidemiology of T1D using data collected from 1975 to 2014.

The selected information provided by the current review is presented in chapter 3, that describes the incidence of T1D among children, in chapter 4, the epidemiology among adults, in chapter 5, and the APC approach of the incidence trends of T1D, and in chapter 6 the analyses of incidence of T1D and genetic determinants. For each particular study, we developed large databases that are presented in the respective chapters, and are publicly available as guarantees of the transparency of this research.

Objectives of the systematic reviews

To study the global published information on the incidence of T1D since 1950s.

Specific objectives

-Describe the global incidence of T1D at the country level.

-Describe the epidemiology of T1D among children and adults worldwide.

-Study the co-variation, at the country level, of the incidence of T1D with environment and genetic determinants available in public databases.

-Analyze if the current global trend in incidence of T1D is compatible with a period or birth cohort effect.

Methodology of the systematic literature review

A systematic literature review following the PRISMA recommendations (213) was conducted to retrieve published articles on the incidence and the age at onset of T1D worldwide. Literature search was conducted in the databases: MedLine accessed through PubMed and Science Citation Index Expanded databases accessed via the Web of Science (Thomson Reuters). In addition, general searches and examination of cited references in Google scholar were carried out. Finally, citing references identified through the lecture of the papers were included periodically. The registration number in PROSPERO is:

CRD42012002369

(available on http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002369 ).

Validation of the query equation of the bibliographic search

An exploratory bibliographic search allows the selection of the first 92 articles reporting incidence of T1D published between 1 January 1990 and November 2011 (here called 'query # 0'); Appendix B. On February 3 rd 2012, during a group meeting 3 the query was assessed. After recommendations such as to include new MeSH (Medical Subject Headings) terms and to modify exclusion criteria, we developed a method based on 7 steps, to obtain a query equation in which all mentioned 92 articles previously retrieved were found -Step 3. To check the MeSH terms for irrelevant references, so as to recognize the most common irrelevant MeSH terms.

-Step 4. To introduce at the equation query of step 1 the most common MeSH terms found in irrelevant papers using the builder NOT to exclude irrelevant articles.

-Step 5. To compare references linked in the different alternative queries. For example if we have two queries, query A and query B, using a strategy such as Venn diagrams (A∪B, A∩B), the references linked in the queries will be explored: query A, NOT query B, and query B, NOT query A. The first 60 references in each query will be examined: query A, query B, query A NOT B, and query B NOT A so as to analyze if they are relevant papers, and which one is the most accurate, then to repeat steps 3 to 5 in the selected query until at least 50% (25/60) of the articles examined are pertinent. In this strategy we will identify the queries to which a specific number can be assigned according to the formulation of the queries.

-Step 6. To examine the results of the selected query to verify that all 92 database references are included.

-Step 7. Repeat steps 2 to 7 until a majority of 92 articles are included.

Article selection for the query # 0: the 1 st 92 included papers

The inclusion criteria for preselected references were articles with reports of incidence of T1D from population data registers by year and age at diagnosis, in any country, city or region worldwide. There was no publication language restriction. A minimum degree of caseascertainment was not required. The literature search was finalized removing all articles with at least one of the following exclusion criteria: 1) if the main objective was not the study of incidence of T1D (e.g., genetic factors, complications, treatments); 2) if the length of the report (following registers) of the incidence was ≤5 years; 3) if the study was not population-based, instead it was performed in selected groups, for example, studies based on volunteer subjects or on persons that belong a specific health assurance organization; 4) if the study did not report using the World Health Organization (WHO) (1985 or 1999) or American Diabetes Association (ADA) (1997 or 2011) as criteria of diagnosis; 5) if the study describes the incidence of T1D as a general topic, with no description by year and age at diagnosis, 6) if we could not translate the article; and 7) if the full text of the article remained unavailable.

Bibliographic search used to retrieve incidence of T1D in adults

A modification of the final search query equation (query # 9) presented in Appendix C was introduced to retrieve specific information on adults, the last update dating to January 2015. The search included the following terms: "diabetes mellitus, type 1/epidemiology" AND "incidence" AND "adult", and it was conduced in English, French and Spanish. The entire query used to retrieve information is presented in chapter 4 (session 4.1.1.1) of this thesis.

Data extraction

Raw data were extracted from articles and introduced manually into a standard data collection grid (Appendix A: Database I) using the Microsoft Excel software. Incidence data were taken from the individual studies as they were reported in the publications in text, tables or graphs; a computer program was used to retrieve the original (x, y) data from the images (1). The names of countries corresponding to world regions in the database were both those from the WHO and from the UN. If the information was not available it was identified in the grid as 'No available data' (NA). To further evaluate the quality of the information retrieved, we searched for the reported percentage of completeness (ascertainment): the average percentage of concordance at least between two different independent sources of information containing incident cases of T1D in a specific region and period of time ( 179). Examples of primary sources of registers include data from hospitals, pediatricians, diabetologists, and others, and secondary sources, drug prescriptions, information from diabetes associations, summer camps, or general practitioners.

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Epidemiology of Type I Diabetes: Global Incidence and Determinants 47

Reviews of the literature on global incidence of T1D

Among all retrieved information, seven studies classified as reviews and describing the epidemiology of T1D were published before 2014. A summary is provided in Table 3. Karvonen M., 1997Karvonen M., (160) 1965Karvonen M., -1992 0-14 0-14 This reference shows the total incidence and incidence of T1D for males and females separately of 76 populations from 53 countries using data from the Diamond study. A female predominance of 59% was found in those aged 0-14 years. [START_REF] Onkamo | Worldwide increase in incidence of Type I diabetes-the analysis of the data on published incidence trends[END_REF]Onkamo P., (231) 1960Onkamo P., -1996 0-19 0-19 This study used a systematic search to retrieve information and analyzed 37 studies from 27 countries. A worldwide increase on average of 3.0% (95% CI 2.6,3.3) per year was observed.

Ascher P., 2002 (21) 1990-1994 0-14

The reference describes the incidence trends of TID in Latin America Gale EA., 2002 (105) 1920-2000 0-29

The reference presents a historic review that describes the incidence of T1D before and after 1950. Borchers A.T., 2010 (40) 1995-2009 0-14 and with the studies Eurodiab (90) and Diamond (318) was then performed. Correlations between the current review and the studies were >0.95 in all cases; they are shown in Table 4 and Figure 3. Previous reports vertical (y) axis: in red dots Diamond (318) (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999); in green, Eurodiab (90) (1989-1994); in blue, Onkamo et at. (231) review (1989Onkamo et at. (231) review ( -1994)). In the horizontal axis (x) the current study.

Figure 3:

Correlation of reported incidence of T1D among individuals 0-14 years reported in the thesis vs three previous studies.

Predicted and observed incidence of T1D by 2010

Incidence of T1D has been increasing during the last decades. Predictions performed by Onkamo et al. in 1999 (231) for 37 populations, announced that by 2010 the incidence of T1D would be 40% higher compared with 1998. Using data from the current review, it was possible to compare the predicted and observed incidence of T1D; the observed incidence is higher than the predicted incidence in 7 of 10 studies that reported information of T1D in the group 0-14 years after 2005; Table 5.

Information retrieved in the current review

At the end of this thesis in 2015, among children aged 0-14 years, this systematic review retrieved 237 primary research papers from PubMed and GoogleScholar, covering 86 countries and four "territories", also referred as "90 countries" in this thesis. 28 additional papers, abstracts and updated information were from the International Diabetes Federation (IDF) Atlas (300). From these 265 references, we extracted 527 datasets. Among these 527 datasets, 298 were from individual studies, and 201 were from international collaborative studies (114 from the Diamond study in 2006 ( 318 The WHO sub-regions are: AFRO (Africa), AMRO (Americas), EMRO (Eastern Mediterranean), EURO (Europe), SEARO (South-East Asia) and WPRO (Western Pacific). WHO Member States are divided into high-income (HIGH) or low-and middle-income (LMIC) countries creating 10 groups (359). Non-WHO countries: country not in WHO classification.

Publication year

Individual studies are represented by dots. 18"

20" 1 9 7 5 " 1 9 7 7 " 1 9 7 8 " 1 9 7 9 " 1 9 8 0 " 1 9 8 1 " 1 9 8 2 " 1 9 8 3 " 1 9 8 4 " 1 9 8 5 " 1 9 8 6 " 1 9 8 7 " 1 9 8 8 " 1 9 8 9 " 1 9 9 0 " 1 9 9 1 " 1 9 9 2 " 1 9 9 3 " 1 9 9 4 " 1 9 9 5 " 1 9 9 6 " 1 9 9 7 " 1 9 9 8 " 1 9 9 9 " 2 0 0 0 " 2 0 0 1 " 2 0 0 2 " 2 0 0 3 " 2 0 0 4 " 2 0 0 5 " 2 0 0 6 " 2 0 0 7 " 2 0 0 8 " 2 0 0 9 " 2 0 1 0 " 2 0 1 1 " 2 0 1 2 " 2 0 1 3 " Since there are no specific and standardized criteria to define T1D, the general classification based on blood glucose levels used in diabetes is applied to T1D. After detecting a blood glucose level compatible with diabetes, aspects of the clinical presentation make it possible to define the etiological classification of diabetes presented in this chapter in section 2.1.1; it is the most recent and extensively used classification used in the retrieved studies analyzed in this thesis. Nevertheless, diagnosis of diabetes has changed over time and epidemiology of T1D, based on the incidence value that could have been affected by the different classifications adopted at different periods; in section 2.1.2 of this chapter we present a summary of the evolution of the diagnosis criteria used to define diabetes. Because of these changes in the diagnosis criteria of T1D over time, and to decide if a given study should be included or not in the systematic reviews presented in chapter 1, we pay particular attention to establish if it was truly about T1D, based on etiological classification and/or diagnosis criteria, especially when they were published before 1985.

Number

Our current knowledge of the epidemiology of T1D is due in large part to intercontinental collaborative projects, such as Diamond, Eurodiab, Diabalt, multicenter studies at the country level such as the SEARCH study, and organizations such as the International Diabetes Federation (IDF), described briefly below in chapter 2.

The last part of this chapter shortly describes several etio-phathological hypotheses mentioned in the literature as causes of T1D, some related with genetic, environmental or gene-environmental factors. A personal summary of the related categories found in the literature is also presented. These categories, are also mentioned in the following chapters:

Diabetes

Diabetes is characterized by chronic disturbances of carbohydrate, fat and protein metabolism leading to long-term damage, dysfunction and failure of internal organs. The most important complications of diabetes include retinopathy, nephropathy, neuropathy and cardiovascular diseases (144). Global estimates of the prevalence suggest that in 2010 more than 285 million adults lived with some form of diabetes in the world (290), and the number of individuals with diabetes worldwide is expected to rise to 472 millions by 2030 (84).

Etiological classification

In 2011, the American Diabetes Association (ADA) (16) adopted the etiological classification of diabetes that includes four types:

-Type 1 diabetes (results from beta-cell destruction, usually leading to total insulin deficiency)

-Type 2 diabetes (results from a progressive insulin secretory defect on a background of insulin resistance) -Gestational diabetes mellitus (GDM) (diabetes diagnosed during pregnancy that is not clearly overt diabetes) -Other specific types of diabetes due to other causes, e.g., genetic defects in B-cell function, genetic defects in insulin action, diseases of the exocrine pancreas (such as cystic fibrosis), and drug or chemically-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

In addition, sub-categories of T1D were introduced, Type 1A for diabetes cellmediated autoimmune attack, and Type 1B for non-immune diabetes generally after ketosis (22). This latter classification is complex, depends on a sophisticated laboratory test, and is not included in the ADA classification proposed in 2011.

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Diagnostic criteria of diabetes

Until the late 1970s, large variations in the level of glucose as diagnosis criterion of diabetes were reported (337). The first intent to standardize the diagnosis and classification system for diabetes worldwide was presented in 1978 by the WHO and the National Diabetes Data Group and modified in 1985 and 1999. Also, in 1995, ADA promoted the formation of an international expert group to review the diagnosis criteria and classification of diabetes, and after two years of discussions, the review and the new criteria were published. At the same time, the WHO convened a new consultation, and published the WHO results in 1999.

The current ADA (2011) criteria for the diagnosis of all kinds of diabetes are the following:

1) symptoms of hyperglycemia including polyuria, polydipsia, weight loss plus random plasma glucose concentration >200 mg/dl (11 mM); 2), fasting (> 8 h fasting), plasma glucose > 126 mg/dl (7 mM); 3) 2 h postprandial glucose >200 mg/dl during an oral glucose tolerance test (OGTT) (22,76). In addition the glycated hemoglobin (AIC) test recommended since 2009 by an International Expert Committee was adopted in 2010 (11,16,22) (Table 7).

Classification of T1D and Type 2 diabetes (T2D) by glucose levels is not always easy.

Clinical manifestations and disease progression could share similarities in both types of diabetes. Some patients with T2D may present ketoacidosis, whereas patients with T1D may have a late onset and very slow progression of the disease and even so they have features of an autoimmune disease. Moreover, T1D can appear at any age and incidence of the disease may be as prevalent in adults as in young people (23). Thus to avoid misclassifications of T1D, the data analyzed here and considered as diagnosis of T1D requires both the necessity of insulin administration and the associated ADA or WHO criteria. All the studies included in this thesis report the incidence of T1D or Insulin-Dependent Diabetes Mellitus (IDDM), for papers published before 1985. Information of other kind of diabetes, such as T2D or secondary diabetes, was excluded.

Type 1 diabetes

T1D represents 5-10% of all the cases of diabetes (22) and is the most prevalent metabolic disease in childhood (298). T1D is defined as a chronic, multi-causal and immunemediated disease characterized by selective loss of insulin-producing beta-cells in the pancreatic islets with as final result, an absolute deficiency of insulin (23,24). T1D has also been known as Juvenile Diabetes or Childhood Diabetes, because during the first studies, the peak expression was seen between 0 and 14 years. It was also called IDDM (insulin dependent diabetes mellitus) in the 80's.

In general, T1D develops slowly. It has a subclinical period that can take a few months to 10 years (219), before the appearance of clinical manifestations, complications or in some cases death, as the first manifestation (23). Nowadays, sophisticated diagnostic methods, such as detection of anti-beta cells autoantibodies, are available. Nevertheless the detection of hyperglycemia based on plasma -either the fasting plasma glucose or the OGTT (2-h value in the 75-g oral glucose tolerance test) -is the main diagnostic criterion.

Epidemiology of T1D

Epidemiology is the study of the distribution and magnitude of diseases in view of understanding their risk factors, causes or providing alternatives to prevent or to manage them. At the very basic, yet no less important level, is the measure of incidence; it quantifies the number of new events or cases of the disease that develops in a population of persons at risk during a specific time interval (118,132). Studying incidence through registration of new cases of T1D has been seen as an opportunity to help people with diabetes.

Only after 1921, when insulin was discovered, could people have access to medical care and better diagnostic processes, and it was then that reports of incidence started; previously only clinical series and mortality statistics had been used to evaluate the incidence of T1D (105).

The first compilation of epidemiology knowledge of diabetes appeared in 1978 in Kelly West's book,'Epidemiology of Diabetes and its Vascular Complications' (355). In this book, the standardization of methodologies as well as diagnostic criteria (377) to define diabetes were proposed. Before that time very few references were available, most of them lacked standardization, different case definitions and age-groups were included, and without any method of ascertainment for data collection (190).

Epidemiology of T1D before 1950

According to Gale (105) knowledge on the epidemiology of T1D before insulin discovery is based on three studies on death rates in children younger than 15 years. The first, in the United States, reported an increase in death rate due to T1D from 1.3 per 100.000 individuals per year in 1890, to 3.1 per 100.000 individuals per year in 1920; the second, from Denmark, reported a change in the death rate for T1D from 2 per 100.000 individuals per year in the period 1905-1909 to 3.1 per 100.000 individuals per year, in 1915-1919; and in the third, from Norway, an increase in the incidence of T1D from 2 to 7 per 100.000 individuals per year between 1900 and 1920 was described (105).

Later, between 1920 and 1950, the introduction of insulin prolonged the survival of patients and the incidence was reported in several countries. The landmark sources of incidence and prevalence for this period were two retrospective surveys in Oslo and Bergen During wartime, in Vasterbötten in Northern Sweden, the highest available rate of T1D incidence was observed: it was 10.2 per 100.000 individuals per year, rising to 37.9 per 100.000 individuals per year during the period 1973-1977. In Finland, the country of the world with the highest incidence of T1D, the annual incidence in 1953 was estimated as 12.5 per 100.000 individuals per year (105).

Epidemiology of T1D after 1950

The evaluation of incidence trends has been more rigorous after the 60s when epidemiological methods were developed and better registration statistics were available.

Onkamo et al. reviewed the incidence of childhood diabetes from studies published from 1960 to 1996. Thirty-seven studies from 27 countries were analyzed, and an increase in incidence of 3.0% per year (CI 95% 2.6-3.3) was reported (231).

In the last 30 years, knowledge of the incidence of T1D has improved substantially thanks to collaborative projects in which information from population-based registers, standardized definitions of T1D, and methods for data collection and validation (298) are used. The Diabetes Epidemiology Research International Group (DERI) was the first group to analyze the data collected on the incidence of T1D between the end of the 1970s and the beginning of the 1980s (79,122), work pursued by studies such as the Diabetes Mondiale study (DIAMOND) (318) and the Europe Diabetes study (EURODIAB) (90). Examples of national projects are the Registry for Type 1 Diabetes Mellitus in Italy (RIDI) study (53,60) in Italy and the SEARCH for Diabetes in Youth (SEARCH) study (282) in the United States.

In addition the Diabetes Atlas of the IDF, an international "umbrella organization" of over 230 national diabetes associations in 170 countries and territories, is committed with the advocacy of global political priority for diabetes and accredited by the WHO and the UN (241, 300). The Diamond project Group started the study of the incidence and trends of childhood T1D worldwide in 1990 as an initiative of the WHO (360). The study aimed to determine the incidence of T1D in the period 1990-1999. It was carried out in 57 countries. In the period 1990-94 the increase in the incidence was 2.8 % (IC 95% 1. 3-3.4%) and between 1995-99 it was 3.4% (IC 95% 2.7-4.3%) (318).

Eurodiab Study

The Eurodiab study was established in 1988 and involved 24 centers (120) in 1992; in 2000 forty-four centers represented most European countries and Israel: the average annual increase in the incidence of T1D between 1889 and 1994 in Europe was 3.4% per year (95% IC 2.5-4.4%) (90). The comparison of the information retrieved from 20 centers for the period 1989-2003 was 3.9% (242) and from 23 centers for the periods 1989-1998 and 1999-2008 was: 3.4% per year and 3.3% per year (243).

Diabalt Study

The Diabalt study is a collaborative project focused on the T1D incidence in countries around the Baltic Sea: Finland, Estonia, Latvia and Lithuania, and started in 1989. Data collected between 1983and 1992, demonstrated a wide variation in the incidence of the disease, Finland had an average age-standardized incidence of 35 per 100.000 individuals per year, Estonia 10.2 per 100.000 individuals per year, Lithuania 7.1 per 100.000 per year and Latvia 6.5 per 100.000 individuals per year. During the period of the study, the incidence only increased in Finland and Lithuania, 1% and 1.4% on average per year, respectively (236).

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Pathogenesis and etiology of T1D

Multiple factors in a complex interaction promote the pathogenesis of T1D.

Pathological studies from necropsies of patients with T1D show the presence of insulitis, characterized by mononuclear cell infiltration (predominantly T cells, macrophages and Blymphocytes) within the pancreas islets with a severe selective loss of beta cells. These studies showed that at the same time there are islets where the beta cells have been destroyed, others that are being destroyed, and some that have not been destroyed (24). Other studies form biopsies of patients with recent onset of T1D suggest an interaction between the proteins of the cell surface that belongs to the tumor necrosis factor and play important role in immune regulation, Fas on beta cells and Fas ligand on infiltrating cells. This interaction could be the trigger of apoptosis of beta cells in the pancreas, as a mechanism of beta-cell destruction (23,24). According to the natural evolution of the autoimmune disease, when the beta-cell mass is considerably lowered, the development of diabetes is irreversible (92); at the end, insulin secretion is reduced to such an extent that it is no longer sufficient to maintain a normal level of blood glucose, and clinical diabetes supervenes.

The mechanisms of beta-cell destruction remain unknown; in humans these mechanisms are related with genetic and non-genetic factors. The genetic factors are of major importance (167). Also, exogenous factors play a major role in the development of T1D for many reasons, such as the following: a) less that 10% of individuals with HLA-conferred diabetes susceptibility progress to clinical disease, b) a pair-wise concordance of T1D of <40% among monozygotic twins, c) a more than 10-fold difference in disease incidence among Caucasians living in Europe, d) considerable increase in incidence over the past 50 years, and e) migration studies indicating that the disease incidence has increased in population groups that moved from a low-incidence to a high-incidence region (167).

Genetic factors associated with T1D

The genetic influence on T1D has been studied and the researches show that some genes provide protection against T1D and others provide susceptibility. Nevertheless, twinand family-based studies have shown that genetic factors play an important role in modifying disease risk in T1D (186); the rapid increase in incidence of T1D is not explained by Mendelian inheritance (202) and is insufficient to explain 35-75% of the cases (202). About (111,158,185,219,358). More details are presented in chapter 5: « Genetic Determinants of T1D ».

Environmental factors associated with T1D

The none-genetic risk factors include the environmental risk determinants and others none-genetic disease-modifying factors. These factors could be promoters of the activation of autoimmunity; this process appears after an apparent imbalance in the immune system of the patient in order to attack foreign pathogens (219) and culminates in the destruction of pancreatic beta cells (76). Despite intensive searches for environmental triggers, the number of candidates being assessed in clinical trials remains small (for example: viruses (40,144,301), proteins from cow milk (66,200,276,315), timing of vaccination (78) and relative lack of vitamin D). However, the exact etiology and pathogenesis of T1D is still unknown (6).

Numerous factors such as epigenetic, microbiome, microbiology, metabolism, nutrient intake, and climatic, and environmental exposures have also been proposed as triggers of T1D (41,69,173). Other environmental determinants are presented here in chapter 2: «Incidence of T1D Incidence and Determinants Among Children».

Interaction between genetic and environmental factors

Our current knowledge on T1D supports the possibility hat the change in incidence trends of T1D around the world could be due to the interaction of genetic and non-genetic risk factors. In this case, non-genetic factors could be a) promoters of the activation of UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 68 autoimmunity, this process appears after an apparent imbalance in the immune system of the host in order to attack foreign pathogens (219) and culminates in the destruction of pancreatic beta cells (76), b) modifiers of disease pathogenesis rather than as triggers, or c) protectors attenuating the disease during different stages of the development, and depending on the quantity and the time of exposure (23). Table 8 lists some examples of non-genetic risk and of protecting factors in childhood at onset of T1D reported in the literature.

Seasonality and T1D

There are differences in the variation of T1D diagnosis depending on the season, a peak in both sexes and in all age groups usually occurring in the winter; it is more pronounced in countries with marked differences between summer and winter temperatures. Knip et al. (167) found that diagnosis of T1D has a seasonal pattern, being more common during the cold season (171, 293), with variations from year to year. In addition, T1D does not necessarily induce beta-cell autoimmunity at the same time in all genetically susceptible siblings in the same family (167). As hypothesis, incidence of T1D could be related with changes in the epidemiology of enterovirus infections, been higher during cold weather (301), and for this reason, the diagnosis of T1D onset could be related to seasons (144,200). Other possible explanations are that seasonal variation of onset could be stimulating factors of the disease processes, or related with nutritional factors that can be different depending on the seasons.

The possibility exists that certain foods harbouring unknown infectious or potentially toxic agents (26) are also implicated.

In this thesis, using information collected up to November 2011, an exploratory analysis of the seasonal and diagnosis of T1D was carried out based on references containing the word "season" in the title or abstract. The season in which T1D was diagnosed was reported in 87% (60 of 69) of the studies; the incidence peak was in winter, followed by autumn, spring and summer; Figure 8.

The season of birth of the patients with T1D diagnosis was reported in 10% (7 of 69) of the studies, spring being the season with most births, followed by summer, autumn and winter; Figure 9.

The month of birth of the patients with T1D was indicated in 7.2% (5 of 69) of the studies, birth in December and January being the months leading to the subsequent highest incidence of T1D diagnosis. The hygiene hypothesis proposed by Strachan (1989) (306) suggests that exposure to pathogens early in life, such as by contact with older siblings, or acquired prenatally from an infected mother by contact with her older children, prevents autoimmune diseases; however, in modern society individuals lack exposure to such factors, they could be genetically predisposed to diseases of the immune system but cannot protect themselves from autoimmune phenomena (301). [START_REF] Bach | The effect of infections on susceptibility to autoimmune and allergic diseases[END_REF] described potential mechanisms affecting the immune system (25). Kolb and Elliot proposed that an improved hygienic environment could be responsible of the increase in incidence trends of T1D (169). Later, Gale concluded that early exposure to micro-organisms, such as parasites and indigenous biota of the gut, could have a protective effect against T1D mediated by regulatory T lymphocytes (104).

Experiments in animal models have shown that infection might inhibit the development of diabetes (66,200). On the other hand, some viral infections appear as triggers of T1D.

Polio Hypothesis

This is an extension of the hygiene hypothesis. It suggests that certain viruses, such as poliovirus, will become less frequent by efficient mass vaccination programs, and by increasing the standards of hygiene. Paradoxically, the incidence of paralytic polio, a complication of acute polio infection, began to increase when the frequency of acute poliovirus infections started to decrease at the beginning of the last century, possibly due to decreased levels of protective maternal poliovirus antibodies transferred transplacentally and through breast milk to the infant. Similarly, diabetes is increasing rapidly in countries such as Finland and Sweden where thanks to vaccination programs the frequency of enterovirus infections has tended to decrease over the last decades (167,346). This situation could however increase the susceptibility of young children to the diabetogenic effect of different enteroviruses (167,200,301).

Virus Hypothesis

Population-based studies have suggested that viruses probably play an important role in initiating autoimmunity toward beta cells and therefore in initiating or accelerating T1D pathogenesis (144,301). In two prospective series of young children an increased genetic susceptibility to T1D after infection by viruses was reported (167). Data from retrospective and prospective epidemiological studies suggest that the principal viruses associated with the development of T1D are viruses of the Enterovirus genus, mainly Coxsackievirus B4 (CoxB4 or CV-B4). These viruses are ubiquitous and are largely transmitted by the fecal-oral route via the ingestion of water or food containing contaminated faeces. Enteroviruses belong to the Picornaviridae family, that includes the Rhinovirus, Hepatovirus, Parechovirus, Cardiovirus, Kobuvirus, Aphthovirus, Erbovirus and Teschovirus genera. CoxB4 has a natural tropism for beta cells and a molecular mimicry which favors the destruction of betacell islets (315). Other viruses associated with T1D although less intensively are Rubella virus, Mumps virus, Cytomegalovirus, retroviruses, and rotaviruses (144, 167). In addition, several studies suggest that possibly many common viral RNA infections may induce islet autoimmunity in genetically susceptible individuals (6). Such infections would begin a vicious circle affecting gut microbiota (the principal intestinal mucosa barrier) and altering intestinal immune responsiveness. This in turn would encourage changes of the composition of the microbiota and disturb the role of gut as modulator of beta-cell autoimmunity and/or as the place to initiate beta-cell autoimmunity. It is conceivable that if a decreased microbial load occurs at an early age, this may have an important impact on the programming of the immune system by altering gut-associated lymphoid tissue (167); finally together with viral infections, these modifications would trigger T1D (336, 354).

Cow's Milk Hypothesis

Antibodies to proteins of cow's milk have been detected in the serum of in children with T1D as of 1988. For Savilahti et al. (276), the introduction of antigenic bovine insulin via cow's milk in food increases the immune reaction leading to the hypothesis that early introduction of cow's milk proteins in the infant's diet may play a role in the development of autoimmunity to T1D (66,200,315). In a study made in Chile the level of anti-BSA-IgG was measured (BSA: bovine serum albumin), as well as of other antibodies. The anti-BSA-IgG was positive (using a cut-off point of 25.6 ng/ml) in 98% of diabetic children and absent from UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 74 the control population (15). On the other hand, some studies support the hypothesis that breastfeeding may play a protective role against the development of T1D (6,200,315).

In babies, beta lactoglobulin, which is present in cow's milk but not in human milk, may initiate an immune response that is cross-reactive with glycodelin (a glycoprotein that belongs to the lipocalin superfamily), and this response could interfere with the ability of glycodelin, to modulate immune responses (66,113). This situation is similar to the one that occurred with the celiac disease and gluten, the wheat-deficient diet in World War II in the Netherlands: its postwar improvement identified the culprit, and autoimmune disease-causing agent (307). It has been proposed that association of HLA in celiac disease, in which HLA DR3-DQ2 is the dominant susceptibility haplotype, can be explained by the superior ability of the DQ2 molecule to bind proline-rich gluten peptides that have survived gastrointestinal digestion and have been deamidated by tissue transglutaminase (166). In agreement with this model, insulin is a crucial autoantigen in T1D because it is the only autoantigen specific of beta cells in postnatal life and because bovine insulin present in cow's milk differs from human insulin by three amino acids: two in the A chain and one in the B chain (167). These formulas generally are the first dietary source of foreign complex proteins to which an infant is exposed in developed countries.

In a multiple logistic regression analysis, Knip et al. (167) observed that IgA antibodies against B-lactoglobulin were strongly associated with an increased risk of diabetes during childhood, independent of the islet cell antibody status and of early weaning to a cow's milk-based formula. There apparently exists an immune response, initially induced by bovine insulin that cross-reacts and may target human insulin in beta cells. These observations could be interpreted as supporting the idea that enhanced consumption of cow's milk before the presentation of clinical T1D is a risk factor (167).

Accelerator Hypothesis

This theory by Wilkin (2001) (362) focus on obesity and insulin resistance as major accelerator of diabetes; it suggests that increasing body mass plays a key role in the development of both T1D and T2D. The combination of beta-cell up-regulation (insulin resistance, determined by weight gain) and autoimmunity (genetically regulated by the HLA genotype) determines the rate of beta-cell apoptosis (361,363). According this hypothesis, UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 75 the increasing worldwide obesity stresses susceptible beta cells, and as consequence its early demise (358).

Spring harvest Hypothesis

This theory is based on the finding that the overall rise in T1D incidence was observed in very young children; according to this hypothesis, environmental factors determine and modulate the progression of diminishing beta-cell mass in susceptible individuals. In this theory Gale (2005) (106) emphasizes T1D progression rather than disease initiation, in which protection against environmental events in early life should be the focus. Ludvigsson (2006) (200) proposed that a patient may have peculiarities or both T1D and T2D, because certain mechanisms lead to islet inflammation and others to insulin demand that at the end cause insulin deficiency -mediated by autoimmune reaction in genetically predisposed individuals -and clinical manifestations of diabetes. According to the author, related with deficiency of immune system are increased hygiene and vitamin D deficiencies, while related with the autoimmune reaction, certain viruses, dietary bovine insulin, or toxic agents could be implicated. [START_REF] Dahlquist | Can we slow the rising incidence of childhood-onset autoimmune diabetes? The overload hypothesis[END_REF] has suggested that environmental determinants, via different mechanisms, "overload beta-cells". Insulin resistance or increased insulin requirement could cause beta-cell overload. If so, the focus should be placed on "accelerating factors rather than potential triggers of autoimmunity" that operate after the initiation of autoimmunity.

Unifying Hypothesis

Overload Hypothesis

Accelerating factors have been proposed as implicated in this process: high growth rate, infections, psychological stress and cold climate (72).

Diet Hypothesis

This theory proposes that dietary factors play a role in the development of T1D.

Intake of agents such as nitrite / nitrate (239, 339) or early introduction of solid foods (cereals) (226,227) are implicated. In contrast, the duration of breastfeeding (66,200,276,315) is a factor associated with reduced risk of T1D.

Human contamination by environmental chemicals

Based on epidemiological evidence, Howard (2012) (137) argues that several environment chemicals, such as N-nitroso compounds, air pollutants and persistent organic pollutants can act as "endocrine disruptors" affecting the development and function of the immune system, and as a consequence contribute to the development of T1D. According to this theory the principles of dose-effect and timing of exposure are determinant in their biological effects.

Multidisciplinary approach

Personal summary of factors related with etiology and incidence of T1D

Throughout this study, several factors related with the etiology of T1D, and mentioned above in various hypotheses, were explored. In view of summarizing the knowledge reviewed during this thesis, three main related categories of determinants were identified: i) autoimmunity, and ii) life style and iii) social determinants of health.

In the autoimmune category, emerging elements are: a) the gut immune mechanisms (affected by diet and composition of the micro / macrobiota flora), b) the lymphocyte T helper (Th1/Th2) response to infections (depending on the kind and timing in which the infection was produced), c) the vitamin D deficiency related with exposure to UV radiation (this could be determined by the effect of seasons, geographic location, outdoor activities, etc.), and d) the accelerated weight gain (producing insulin resistance and an overload of beta cells); Figure 13. In the category related with life style, emerging factors are: a) the current obesity trends and accelerated weight gain childhood and adolescence observed mainly in Western countries (in line with the Accelerator and the Overload Hypothesis), b) the sedentary life style that could be associated with less outdoor activities, less exposition to UV radiation, increased technology dependence, and also with higher obesity rates, and c) the wealth of the countries associated with access to modern facilities, processed food, environment pollution, etc., factors that could be new or unknown triggers or accelerators of autoimmunity processes; also related with more developed countries are higher percentage of urban population and changes in patterns of childhood diseases (related with the Hygiene and Polio Hypothesis); Figure 14. 3 Epidemiology of T1D among children T1D is one of the most common chronic diseases in children: each year 79.000 children develop the disease worldwide (241). The disease has been increasing by 3% per year (318) and despite geographical variations, a global epidemic is now recognized [START_REF] Forlenza | The epidemic of type 1 diabetes: what is it telling us? Current opinion in endocrinology, diabetes[END_REF]. To explain this increase in the incidence and the geographical differences, diverse factors have been implicated; nevertheless the role of these factors remains unclear.

The fist part of chapter 3 is focused on the global conditions as key triggers. The results addressed here were published as an abstract in 2014 in the Revue d'Épidémiologie et de Santé Publique and in 2015, as a full research article in PLoS ONE. In this chapter we propose the analysis of predictors of T1D incidence using information of independent variables retrieved in public databases (the World Health Organization (WHO), the United Nations (UN) and the World Bank (WB)). In these databases we searched for indicators, with as few missing values as possible until a maximum of 5% was reached, that could be correlated with the incidence of T1D in 80 countries. Using regression models and stepwise backward selections, we present significant predictors of T1D among 77 independent variables in four domains: Climate and Environment, Demography, Economy, and Health Conditions. In addition, a comparison between such indicators for countries with and without available information is presented in the Supplemental Materials of this chapter. The significant predictors of T1D described in this chapter are also used to understand the geneenvironment relation shown in unpublished analyses of chapter 5 about APC analyses and in chapter 6 about genetic determinants of T1D.

The second part of chapter 3 is focused on the cumulative incidence of T1D. To study the descriptive epidemiology of T1D, one of basic instruments -but no less important -is the incidence. The measure of incidence quantifies the number of new events or cases of the disease that occur in a population of individuals at risk during a specific time interval. It may be expressed in absolute numbers (cumulative incidence) or in rates (incidence rate or density). Cumulative incidence is the proportion of individuals who become diseased during a specific period of time, and it provides an estimate of the probability, or risk, that an individual will develop a disease during a specified period of time (118,132). The first step in studying the incidence is typically the evaluation of geographic and temporal variations in the disease; this was presented in chapter 1, and chapter 3 will explore such variability in depth.

In addition, chapter 3 presents preliminary analyses on epidemiological changes of T1D by continents and countries, the time course of the cumulative incidence of T1D in successive cohorts of children born since 1924, and a review of studies reporting the early age at onset of T1D observed in some countries (242). Elements that have motivated the development of the APC analyses are presented in chapter 5.

In summary, chapter 3 presents new results through the systematic examination of the correlation of the incidence of T1D in children with various environmental variables and a variety of additional information on the descriptive epidemiology of T1D. The results presented here could be considered as an attempt to show the power of data published in public databases in answering fundamental questions in public health.

Methods

Information on the incidence of T1D and country characteristics were searched for in the 194 World Health Organization (WHO) member countries. T1D incidence was extracted from a systematic literature review of all papers published between 1975 and 2014, including the 2013 update from the International Diabetes Federation. The information on country characteristics was searched in public databases. We considered all indicators with a plausible relation with T1D and those previously reported as correlated with T1D, and for which there was less than 5% missing values. This yielded 77 indicators. Four domains were explored: Climate and environment, Demography, Economy, and Health Conditions. Bonferroni correction to correct false discovery rate (FDR) was used in bivariate analyses.

Stepwise multiple regressions, served to identify independent predictors of the geographical variation of T1D.

Findings

T1D incidence was estimated for 80 WHO countries. Forty-one significant correlations between T1D and the selected indicators were found. Stepwise Multiple Linear Regressions performed in the four explored domains indicated that the percentages of variance explained by the indicators were respectively 35% for Climate and environment, 33% for Demography, 45% for Economy, and 46% for Health conditions, and 51% in the Final model, where all variables selected by domain were considered. Significant environmental predictors of the country-to-country variation of T1D incidence included UV radiation, number of Funding: This work was supported by grants from the Programme Hospitalier de Recherche Clinique and from Colciencias, the Administrative Department of Science, Technology and Innovation for Colombia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

Covariation of the Incidence of T1D with Country Characteristics

Available in Public Databases

UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants

Introduction

It has long been noticed that the incidence of Type 1 Diabetes (T1D) is highly variable from one country to another. For example, the 62.42/100.000 persons/year incidence found in Finland [1] was 780-fold larger than the 0.08/100.000 persons/year incidence in Papua New Guinea [2]; differences in T1D incidence are also observed between countries where the health care systems are comparable. The variability of T1D incidence is even visible within countries; for example, in Italy, T1D incidence varied between 54.4/100.000 persons/year in Sardinia [3] and 4.4/100.000 persons/year in Lombardia [4]. The reason for these differences is not precisely known, but is most unlikely due to classification bias, as the disease cannot go untreated, and the diagnosis is relative easy to perform in children [5]. The country-to-country T1D variability is known to be partly explained by genetic variations. Indeed, HLA (human leukocyte antigen) and 33 other genes are associated with elevated risk of T1D (T1Dbase Version 4.18 updated on 30/9/2014 available at http://www.t1dbase.org) [5][6][7][8][9][10][11]. The genetic characteristics of several populations have been found to-at least partially-explain the level of their T1D incidence [12]. For example, the low incidence in Japan, and more generally in southeast Asia, was strongly associated with the absence of highly susceptible haplotypes, such as DRB1Ã03-DQB1Ã0201 and DRB1Ã04-DQB1Ã0302 found in Caucasian populations [13] or DRB1Ã030101-DQB1Ã0201 [14] found in Arab populations (Bahrainis, Lebanese, and Tunisians). Instead, the major susceptible HLA haplotypes in the Japanese and Korean populations were DRB1Ã0405-DQB1Ã0401 and DRB1Ã0901-DQB1Ã0303 [15].

Another peculiarity of T1D epidemiology is that a dramatic increase of the incidence (on average 3% per year [16]) was observed over the last decades in many countries, in particular European countries with previously low incidences. This increase cannot be explained by genetic factors, since the genetic structure of these countries cannot have varied greatly over such a short period of time. The reasons are more likely to be found in environmental factors (taking here environment broadly, as encompassing physical, chemical, social and life-style factors). However, no single environmental factor, or configuration of factors, that could explain the patterns of differences has ever been identified. More likely, there are complex networks of environmental causes, and of gene-environmental causes that remain to be discovered.

The search of genetic factors of T1D was facilitated during the last 10 years by the GWAS (genome-wide association studies) technology that replaced the gene candidate approaches and instead scanned the entire genome to find SNPs (Single-Nucleotide Polymorphisms) that were significantly associated with T1D [17]. The discovery of an SNP was not the discovery of a "gene", but was a marker leading to the possible discovery of a gene. Here, we translate this data-driven approach to search for environmental markers related to variations of T1D incidence that might eventually lead to environmental causes, possibly in interaction with genetic factors. One could indeed expect that, in this age of information, plenty of environmental UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 87 characteristics could be readily available, insofar as local and global organizations collect such data, and provide them free to researchers, with easy interface on the Internet. A limitation commonly advocated is that country statistics are in many cases of too low quality. However, for two related reasons, this argument does not hold, or will not hold for long: a) why collect, maintain and publish such statistics if they cannot be used by researchers? and b) how can one encourage a better quality for these statistics-meaning more resources devoted to them-if they are never used?

Here, we present the attempt we made to use open public data to identify climate and environmental, demographic, economic, and health characteristics correlated with variations in T1D incidence between countries.

Material and Methods

Incidence of T1D data by country

The country T1D incidence was obtained thanks to a systematic review following the PRISMA recommendations [18] (S1 Checklist). All relevant original papers published in English between 1975 and 2014 including reviews (Diamond [16] and Eurodiab studies [19][20][21], the International Diabetes Federation (IDF) atlas [22,23]) were analyzed (see Flow diagram of the literature search in S1 Fig., the full search strategy on S1 Table , and the list of selected publications in S2 Table ), registration number in the International Prospective Register of Systematic Reviews (PROSPERO): CRD42012002369 (S1 Protocol).

The T1D incidence value per 100.000 persons/year for individuals in the age group 0-14 years for both sexes used here for each country was obtained as follows. 1) When only one dataset was available for a given country, the figure obtained from this study was used, even if the study was not nationwide. 2) If more than one dataset was available including nationwide and local studies, the most recent published nationwide study in the country was used. 3) If only local studies in a single area within the country were available, the most recent dataset was selected. 4) If local studies reporting incidences were available for different areas in the country, the mean incidence after grouping the most recent published studies for each area was used.

Country indicators

Three open public databases from the World Health Organization (WHO) [24], the United Nations (UN) [25] and the World bank (WB) [26] were used to search indicators in four domains: Climate and environment (land use and physical environment), Demography, Economy (including health resources and expenses), and Health. We only kept indicators with less than 5% missing values in the 80 countries for which we could estimate T1D incidence (see Results, below).

In total we used 77 indicators: two (latitude and longitude) were obtained thanks to Google Maps [27], 29 were retrieved from the WHO [24], three from the UN [25] and 43 from the WB [26]. When several values were available for one indicator, the value obtained in 2012, or on the date closest to 2012 was chosen (see Fig. 1 for a list of the 77 indicators, and S1 Database for the entire database used in the calculation of the correlations).

Statistics

The R software (version 3.0.1) served for statistical and graphic analyses [28]. Spearman correlation was used to compute the correlation between the 77 selected country indicators and the T1D incidence. To account for multiple testing leading to a false discovery rate (FDR), the

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Bonferroni correction was used [29]: a p-value 0.000649 (= 0.05/77 variables) was considered as indicating significance at the 5% level.

We employed Stepwise Multiple Linear Regression (SMLR) methods to obtain the best predictors of T1D incidence within the original list of 77 variables. This process was carried out in two times: first, SMLR served to select independent predictors of T1D within each of the 4 environmental domains considered; second, the final subset of independent variables using the four sets of predictors obtained in the first step was selected.

In the by-domain analysis, all variables were entered for which the p value testing the correlation with incidence was smaller than 0.20. When a couple of variables was correlated with r>0.80, only one of the variables was used in the regression analysis to avoid computational issues associated with colinearity.

Then, starting with the full model, we made a manual backward selection of the variables, selecting at each step one variable. This variable was the one that, after being dropped, maximized the adjusted R 2 . The process was stopped at the step where the adjusted R 2 decreased [30]. Then, taking the list of variables obtained, a new SMLR was performed that identified the smallest subset, based on the Akaike information criterion (AIC) [31]. Model assumptions for linear models were checked by visual inspection of the residuals.

The same process was used in the final analysis, starting with all significant predictors found in the analyses by domain. 10-Fold Cross-validation after bootstrapping [32] served to evaluate the predictive value of the final model. Graphic representation used the DAAG package [33].

Results

Incidences were retrieved for 80 countries; they varied widely between continents, countries and regions (see S2 Table andS1 Database for the incidences). Among children aged 0-14 years the lowest nationwide incidences ( 1 per 100.000/year) were in Eastern Asia (China), South-East Asia (Thailand), Melanesia Oceania (Papua New Guinea), and South America and the Caribbean (Dominican Republic, and Paraguay). The highest nationwide incidences ( 30 per 100.000/year) were in Northern Europe (Finland, Sweden, Norway).

The correlations between the incidence and the 77 selected independent variables grouped into four domains are shown in Fig. 1 (for details and sources of information see S1 Database). Forty-one of 77 variables survived the Bonferroni correction and were significantly correlated with incidence at a significant p-value 0.000649 (= 0.05/77). Thirty-five variables, for which the correlation between any two of them was less than 0.8, and had a correlation coefficient with T1D incidence with p <0.20 were entered in the stepwise regression models.

The detail of the 35 (of 77) variables that were entered in stepwise multiple regressions is shown in Fig. 2 (panel A) and the results of multivariate models by domains in S3 Table . In the Climate and Environment domain, seven of the 18 original variables qualified to be entered in the model; after the SLRM selection, the three selected variables were: UV radiation, CO 2 emissions and outdoor air pollution (particular matter: PM10 μg/m 3 ). The adjusted % of variance explained (R 2 ) in the model was 35.2%. The four excluded variables were: % of agricultural land, latitude, nitrous oxide emissions and annual precipitations. In the Demography domain, six of the 15 original variables qualified to be entered in the model; after the SLRM selection the four selected variables were: adolescent fertility rate, total fertility rate, proportion of the population aged over 65 years, and proportion of urban population; (adjusted R 2 32.7%). The two excluded variables were: life expectancy at birth, and female mean age of childbearing.

In the Economy domain, nine of the 13 original variables qualified to be entered in the model; after the SLRM selection the six selected variables were: energy use, proportion of mobile cellular subscriptions, education expenditure, % of improved sanitation facilities, health expenditure per capita, and % of annual Gross domestic product (GDP) growth; (adjusted R 2 45.0%). The three excluded variables were: GDP per capita, population using solid fuels and external resources for health.

In the Health domain, 13 of the 31 original variables qualified to be entered in the model; after the SLRM selection the seven selected variables were: deaths from chronic respiratory diseases, hepatitis B immunization coverage in <1 year of age, alcohol consumption, mean body mass index (BMI) in male >20, mean total cholesterol in male >25, preterm birth rate, and

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prevalence of undernourishment in the population (adjusted R 2 46%). The six excluded variables were: infant mortality rate, cardiovascular diseases and diabetes, deaths for non-communicable diseases 60, incidence of tuberculosis, haemophilus influenza type B (Hib3) vaccine immunization, and diphtheria tetanus toxoid and pertussis (DTP3) immunization.

In the final model, the five independent predictors were: UV radiation, number of mobile cellular subscriptions, health expenditure per capita, hepatitis B immunization and mean BMI (Table 1, details in S3 Table ). The predicted incidence variation associated with the variation of each predictor can be computed from the values shown in Table 1: for example, for one unit increase in UV radiation in J/mt 2 , a mean 0.2% decrease in the incidence of T1D is expected. For an increase of 1% in the percentage of hepatitis B vaccination among 1 year-olds, an 8.5% decrease in the incidence of T1D is expected; for one unit increase in the percentage of BMI in males, a 1.3% increase in the incidence of T1D can be expected. The predictive value of the regression equation was visualized by comparing the predicted incidences with the observed incidences. Finland is noticeably an outlier in this empirical covariation predicted by the model (Fig. 3). 10-fold cross-validation indicated that the fraction of the variability of the global incidence of T1D was estimated to be 41% (S2 Fig. ).

Discussion

Our work explored whether the information available in public databases could help to study the environmental part of the country-to-country variations of T1D incidence. We found 77 variables with less than 5% missing values in the 80 countries for which the systematic review we carried out provided an estimate of the 0-14 T1D incidence.

These 77 variables explored four domains (Climate and environment, Demography, Economy, Health). In each of these domains, a stepwise multiple regression analyses identified the subset of variables of the domain which were independent predictors of the variation in the geographical T1D incidence. Then, analyzing these 4 subsets of domain variables, we identified five final independent environmental indicators: four of the five were known as risk factors of T1D: UV radiation [34][35][36], health expenditure per capita [34], coverage of hepatitis B vaccination [37] and mean BMI [38]. The last indicator was the proportion of mobile subscriptions, positively correlated with T1D incidence. It is probably a marker of a life-style that was not captured by any of the 77 variables used in the multiple analyses.

Coming to the domain-by-domain analysis Fig. 2 (Panel B), in the Climate and Environment domain, the first indicator that emerged was UV radiation. This may be related to The model included all significant predictors found in the analyses by domain.

(a) Among [39] as reported in previous studies [34]. The second indicator of this domain was CO 2 emissions. An association between air pollution and T1D incidence has been recently described [40].

In the Demography domain, the only indicator that emerged in the final model was total fertility rate. The predictors dropped were: the adolescent fertility rate, the proportion of the population aged over 65 years, and the proportion of urban population. Concerning the proportion of urban population that was positively correlated with T1D incidence in this study as found in Lithuania [41], there are conflicting results in the literature: higher incidences was found in rural areas of the United Kingdom [42], and in semirural areas of Sweden [43], while no urban-rural difference in the incidence of T1D was found in Estonia [44].

In the Economy domain, three variables emerged, among which two were in the final list of independent predictors: proportion of mobile cellular subscriptions and health expenditure per capita. The third one was education expenditure. Higher incidences in wealthy countries, as measured by the GDP [45] and health expenditure per capita [34], have been described previously. In addition, other studies from Sweden [43,46,47], the United Kingdom [42,48], and Canada [49] described similar associations between T1D and socioeconomic variables.

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Within the Health domain, seven independent predictors were identified, among which two were in the final list: mean BMI in male >20, that had already been identified as risk factor [38], and hepatitis B immunization coverage in children <1 year of age, also recently proposed as a potential protector factor [37].

A first limitation of this study is that we could only rely on a relatively short set of variables retrieved in the public databases that met the condition of an acceptable fraction of missing data that we set arbitrarily at 5%. A second limitation was the differences in the temporality of the T1D data collected and the predictors. The mean year of the studies we used to estimate T1D incidence was 1997, while the mean year of the data for the 77 selected predictors was 2009. An important drawback, shared by all ecological studies, is that the correlations cannot be directly interpreted in terms of causality of the disease. However, they can be considered as providing signals that may help to unravel latent, yet unknown causes.

Furthermore, an important part of the variation in T1D incidence cannot be captured by an approach that only focuses on environmental factors. It is well known to be in a large part explained by genetic characteristics of the populations that were not analyzed here. Indeed, ethnic differences in T1D incidence between countries, and even within countries, have long been reported [13,15,50] and the most susceptible and protective haplotype determinants of T1D were identified [50]. However one difficulty is that this genetic information is lacking in many countries of the world, which limits the possibility of extending to genetics the kind of global analysis we presented here. For example, we explored the Allele Frequency Net at http://www. allelefrequencies.net/ [51], a public electronic repository for allele frequency of populations; we found that information on the haplotype DRB1Ã0405-DQA1Ã0301-DQB1Ã0302 for which the T1D Odd Ratio was extremely high (11.37 reported by the Type 1 Diabetes Genetics Consortium Families [50]) was available in only 6 countries. Moreover, the genetic characteristics of a country are much less homogeneous than the environmental characteristics, because of the presence of various ethnic groups within the same country, while many of the environmental variables that we studied can be considered as identical (latitude, geographical characteristics) in the subgroups of a country.

In the future, it can be expected that analyses similar to this one, taking advantage of the increased availability and quality of public databases characterizing the human populations would be a path to help identify some of the still unknown causes, and networks of causes, of TID. Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

UPMC -ED

74, Figure S1, Table S1, Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

74, Figure S1, Table S1.

Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. Table S1, Figure S1, Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

-Protocol S1

Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

-Protocol S1

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. Reported on page # study or outcome level), and how this information is to be used in any data synthesis.

Summary measures

State the principal summary measures (e.g., risk ratio, difference in means). 74 -Protocol S1

Synthesis of results

Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

-Protocol S1

Risk of bias across studies Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

-Protocol S1

Additional analyses Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

RESULTS

Study selection

Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

76, Figure S1

Study characteristics For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

Table S2 Risk of bias within studies Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). Table S2 Results of individual studies For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

Table S2

Synthesis of results

Present results of each meta-analysis done, including confidence intervals and measures of consistency. Table S2, Database S1

Risk of bias across studies Present results of any assessment of risk of bias across studies (see Item 15).

Additional analysis

Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

DISCUSSION

Summary of evidence

Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

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Limitations Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).

Additional analysis of the article: Covariation of the incidence of T1D

with country characteristics available in public databases

Correlations between T1D reported in 80 countries vs. 77 global indicators

Table 12 includes all the information presented in Figure 1 of the paper published in Plos One (81) shown in chapter 3. In addition the number of countries with available information, the summary measures for each variable, and the reference are presented. The tidy database is available on Appendix A: Database III. See Appendix D for the correlation matrix between incidence of T1D and independent variables by domains and Appendix E for the variable selection of the models.

Principal component analyses of the variables included in the final model

Principal Component Analyses (PCA) were performed to evaluate the final full model of the article presented in chapter 3 (81) before the backward selection presented in AP Table 35. The principal components using the set of all significant variables (at p-value ≤0.05) after applying MLM for each domain are presented in SI Figure 18. Related with the incidence of T1D three clusters were identified: associated with the geographic position (circle in blue), with urban conditions (circle in green), and with develop and wealthy (circle in red). The variable that best summarize negative correlations was UV radiation (CE_18) in the geographic cluster, related with lack of sun exposition and vitamin D3 hypothesis (338, 367); for positive correlations, the percentage of urban population (DD_33) and the BMI in males (HR_47), in the urban conditions cluster were noted, related with the "accelerator hypothesis" (361), the "overload hypothesis" (71), and the presence of endocrine disruptors (136, 137), and population aged over 65 years (DD_27) in the developing and wealthy cluster. See Appendix D for the correlation matrix between incidence of T1D and independent variables by domains, and Appendix E for the variable selection of the models.

Résultats

Parmi les 8041 individus inclus, 38,7 % avaient un AHR. Le taux standardisé de détection des AHR était de 136,1/100 000 pour la période prédépistage et 256,6/100 000 pour la période post-dépistage, avec un taux de variation de 89 % entre les deux périodes. Pour les adénomes à bas risque, le taux de variation correspondant était de 68 %.

Conclusion

Il s'agit de la première étude en population générale montrant l'impact marqué du DO sur la détection des AHR puisque les structures d'organisation du DO ne disposent pas de ce type de données sur une grande période de temps et à l'échelle d'un département.

Déclaration d'intérêts

Les auteurs n'ont pas transmis de déclaration de conflits d'intérêts.

Introduction

L'incidence du diabète de type 1 (DT1) varie considérablement selon les pays au niveau mondial ; on observe une augmentation significative de cette incidence au cours des dernières décennies.

Méthodes

Une revue systématique de la littérature a été effectuée. L'incidence du DT1 chez les enfants âgés de 0 à 14 ans a été corrélée avec les données publiques disponibles au niveau mondial dans les domaines de l'environnement, la démographie, l'économie et la santé. Les recommandations de PRISMA ont été respectées.

Résultats

Au total, 527 ensembles de données concernant l'incidence du DT1 ont été obtenus à partir de 265 publications originales . Les articles contenaient des informations sur 90 territoires, 69 % de la population globale des enfants de 0-14 ans. L'incidence varie de 0,08/100 000 en La performance de l'appareil portable A1cNow+ (Bayer) a été tesconditions cliniques habituelles chez 55 patients diabétiques volontaires rvice d'endocrinologie de l'hôpital de référence de l'état de Pernambouc. HbA1c a été mesurée par l'appareil et sur sang veineux, par la méthode s 5,1FS prise comme référence. L'exactitude des résultats de l'A1CNow+ pport à la référence a été jugée par le graphique de Bland-Altman. Son en clinique a été évaluée par le calcul de la sensibilité et de la spécifiar rapport au seuil de 7 % d'HbA1c, en dessous duquel le diabète est jugé ôlé. tats L'écart moyen entre l'A1CNow+ et la méthode Vitros 5,1FS de +0,67 % (IC : +0,52 à +0,81). Les limites d'agrément du graphique nt de-0,45 % (IC :-0,71 à-0,19) à +1,82 % (IC : 1,52 à 2,05). La sensipar rapport au seuil de 7 % était de 100 %, et la spécificité de La prévalence de la maladie rénale chronique (MRC) est incon-Afrique. L'ampleur de cette maladie dans la population noire a été estimée. tude de population à grande échelle doit être menée dans la Région des s-Lacs pour cerner l'ampleur réelle de cette maladie. Nous avons mené tude de population à Bukavu pour identifier la prévalence et les facteurs que de la maladie rénale chronique. ode Un échantillon de 1289 patients âgés de 18 ans a été recueilli de r 2013 à janvier 2014 selon la méthode Steps, interviewé et testé pour fier l'hématurie, l'albuminurie et estimer la fonction rénale. La méthode de cinétique a été utilisée pour la créatinine sérique. Les logiciels SPSS 18.0,

Déclaration d'intérêts

Les auteurs n'ont pas transmis de déclaration de conflits d'intérêts.

Introduction

L'amélioration de la détection des adénomes, lésions précancéreuses courantes chez les personnes âgées de 50 à 74 ans, et leur résection par coloscopie est un des enjeux majeurs du programme de dépistage organisé du cancer colorectal (DO). Notre objectif était d'évaluer à partir des données de Registre, si la mise en place du DO en 2003 a permis d'améliorer en pratique courante le taux de détection des adénomes, en particulier à haut risque de cancérisation (AHR).

Résultats

Parmi les 8041 individus inclus, 38,7 % avaient un AHR. Le taux standardisé de détection des AHR était de 136,1/100 000 pour la période prédépistage et 256,6/100 000 pour la période post-dépistage, avec un taux de variation de 89 % entre les deux périodes. Pour les adénomes à bas risque, le taux de variation correspondant était de 68 %.

Conclusion

Il s'agit de la première étude en population générale montrant l'impact marqué du DO sur la détection des AHR puisque les structures d'organisation du DO ne disposent pas de ce type de données sur une grande période de temps et à l'échelle d'un département.

Déclaration d'intérêts

Les auteurs n'ont pas transmis de déclaration de conflits d'intérêts.

Additional analyses of the epidemiology of T1D among children

Comparison of countries with and without information using 95 indicators

Eighty countries with information on incidence of T1D and 116 without information were compared. For these analyses, the criteria of variable selection did not consider the exclusion of variables when information was unavailable in "less than 5% of the countries". This yielded 95 independent variables in total, 18 more indicators than presented above, see SI Table 13.

When compared to the countries with T1D information, those for which such information was lacking had: a four-fold higher under-five mortality rate (p=3.40×10 -18 ), a two-fold higher premature mortality rate (p=6.28×10 -19 ), 1.5 more deaths labeled as "cardiovascular diseases and diabetes" (p=4.78×10 -11 ); their fraction of urban population was 50% lower (p=1.70×10 -15 ), and their health expenditures per capita (319) were 8.7 fold lower (p=3.25×10 -15 ). To obtain the cumulative incidence of T1D by age at onset and birth cohort, a protocol consisting in 3 steps was proposed and is the following:

SI Table 13: Comparison of 95 indicators in countries with and without information of T1D

Step 1. Grouping data

First, stratification by incidence level proposed in the Diamond study (161,231,318), which is an arbitrary distribution of the cases in five groups: i) very low, <1 case per 100.000 individuals per year; ii) low, 1-4.99 per 100.000 per year; iii) intermediate, 5-9.99 per 100.000 per year; iv) high, 10-19.99 per 100.000 per year; and v) very high, ≥ 20 per 100.000 per year. The identification of the correct level of incidence for each reference was performed after averaging all the data reported in each reference and comparing the result with the respective categories to classify the reference from very low to very high incidence level.

Due to enormous variability in the incidence of T1D, it was conceivable that one country might belong to more than one category of incidence level; for example, in this thesis, 26 references reported the incidence in Italy: Sardinia was classified as very high incidence, the entire nation as high, and the Rome and Lazio region belonged to the intermediate category.

Second, the average of a specific place to summarize the available information in a specific area in a country was generated. The process consisted in pooled studies that belonged to a similar geographic area, such as all nation-wide, regional, or local studies performed in a specific country. For example, also in Italy, the mean for all studies reporting the incidence in Sardinia, the mean of Lombardia, the mean of Turin, and the mean of the whole nation were carried out independently.

Third, using dynamic tables operated by the Microsoft Excel software, six summary tables were obtained, one for each level of incidence (from very low to very high). Each retrieved table then represents the diagonal (Lexis's diagram) of the mean incidence for each category of incidence level. To compare the final results, all final tables presented the same structure of data-frame; each one contains in the rows the age at onset of T1D (listed from 0 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 116 to 40 years of age) and in the columns the mean incidence by each birth cohort (1924 to 2008). To complete the data-frame non-available data were identified as "NA".

Step 2. Generating cumulative incidence

To obtain cumulative incidence the final tables were independently exported to the R program (255). Using the function cumsum, the cumulative sum of incidences by birth cohort and age at onset for each category of analysis was generated. The average of the cumulative incidences each five-years of birth cohorts was obtained later. The result was exported as a list containing three columns each one respectively, with the age at onset from 0 to 40 years, 5-year-periods of birth cohort (starting in 1924 and extending to 2008), and the cumulated incidence. Any treatment was considered for non-available data.

Step 3. Plotting data

Using the library ggplot 2 in the R program (255) and the information exported into the lists described above, the curves representing the mean cumulative incidence country level by 5-year-periods of birth cohorts, and by age at onset were plotted; SI Figures 1920212223.

Findings

Cumulative incidence by birth cohorts dropped after 2004 in countries with very high Cumulative incidence of T1D in persons aged 0-40 years in intermediate incidence countries by birth cohorts Age at onset Cumulative incidenence per 100.000

Birth. Cohort 1924Cohort -1928Cohort 1929Cohort -1933Cohort 1934Cohort -1938Cohort 1939Cohort -1943Cohort 1944Cohort -1948Cohort 1949Cohort -1953Cohort 1954Cohort -1958Cohort 1959Cohort -1963Cohort 1964Cohort -1968Cohort 1969Cohort -1973Cohort 1974Cohort -1978Cohort 1979Cohort -1983Cohort 1984Cohort -1988Cohort 1989Cohort -1993Cohort 1994Cohort -1998Cohort 1999Cohort -2003Cohort 2004

Worldwide Incidence trends of T1D

Independently of the initial level of incidence a positive slope is observed for all continents plotting data published from 1960 to 2010. Asia (incidence slope = 0.11 case per 100.000 individuals per year), Africa (incidence slope = 0.13 case per 100.000 individuals per year) and Latin America and the Caribbean (incidence slope = 0.13 case per 100.000 individuals per year) show the smallest increases, Europe (incidence slope = 0.22 case per 100.000 individuals per year) presents an intermediate increase, and North America (incidence slope = 0.45 case per 100.000 individuals per year) and Oceania (incidence slope = 0.53 case per 100.000 individuals per year) the largest. (SI Figure 24). Since 1980, the speed of the increase had varied depending on the country: comparing only nation-wide studies performed in Europe, the slope of the variation of incidence of T1D with time has varied from a incidence slope = 0.12 case per 100.000 individuals per year (Latvia) to a incidence slope = 0.8 case per 100.000 individuals per year (Czech Republic). (SI Figure 25).

The analysis of the variations of T1D incidence across continents is based on datasets for which the percent of ascertainment was reported. Y-axis: incidence of T1D per 100.000 individuals of both sexes (0-14 years old) per year. X-axis: time of the mean point of duration of each study. Circles represent each retrieved study. The increase in the global trends of T1D is clear, and this seems to be a global phenomenon independent of whether the risk of the population is low or high. Also, the variation in the age at onset of T1D in the last decades has been reported. Since 1955, a little variation of the incidence in the younger age groups in Oslo, Norway had been reported, in a remarkable longitudinal study of the population that was analyzed from data collected between 1925 and 1954 (357).

SI

Today, even though the increase in T1D incidence apparently has affected all age groups worldwide, several research teams have been working on the epidemiology of T1D and have reported that apparently, the age at onset is decreasing, and in most studies the incidence of T1D increased in children between 0 and 14 years, and has been most pronounced in the youngest age group, among of children between 0 and 4 years of age. This is consistent with the greatest rate of increase in childhood onset of T1D incidence in the group of 0-4 years of age ( 224). This situation may be a reflection of a situation of "anticipation" in which individuals with susceptible genotypes may develop the disease at an earlier age than previously (118), a concept first proposed by Kurtz et al. in 1988 (175), by this time called "accelerated onset".

The reason for the continuous decline in the age at onset is unclear. Some authors affirm that the rising incidence of T1D is caused by an earlier clinical presentation rather than due to a global increase in the incidence of T1D in all age categories. Changes in the exposure to environmental factors may promote or accelerate the subclinical disease process in young subjects genetically predisposed to T1D (352), and a more permissive early life environment favorable to the development of T1D could also be implicated (344). Green et al. reported that in Europe during the period 1989-1998 the annual increase in the incidence of T1D was higher in the very young child group (0-4 years of age), than in the 5-9 or 10-14 years age group, 4.5% (CI 95% 3.8-5.9) versus 3.7% (CI 95% 2.9-4.5) and 2.1% (CI 95% 1.4-2.8) respectively ( 121). In the Eurodiab study, a particular finding was that the increase in incidence was particularly rapid in children under 5 years of age. The rates of increases were: 6.3% (CI 95% 4.1-8.5), 3.1% (CI 95% 1.5-4.8) and 2.4% (CI 95% 1.0-3.8) in children aged 0-4, 5-9 and 10-14 years respectively (90). Other multicenter prospective registration of European studies, published in 2007, evaluated the incidence trends in 20 population-based registers in 17 countries. As main finding, the annual incidence of T1D increased for all centers and was on average 3.9% (CI 95% 3.6-4.2), rising to 5.4% (CI 95% 4.8-6.1) in the group 0-4 years, 4.3% (CI 95% 3.8,4.8) to in the group 5-9 years, and to 2.9% (CI 95% 2.5-3.3) in the group 10-14 years of age (242).

The results of the European studies are consistent with the hypothesis of the decrease of the age at onset of childhood diabetes. Other unaggregated studies, conducted in Europe and North America provided similar results. An article published in 2008 in Germany, concluded that although the absolute incidence was highest in the group of 10-14 years of age, the age at onset of T1D has decreased, and the highest rise in incidence occurred in the group of 0-4 year-old patients (5.8%, CI 95% 2.5-9.3). In the group of 5-9 and 10-14 year-old patients, it was 3.4% (CI 95% 0.8-6.0) and 2.7% (CI 95% 0.3-5.1), respectively (85). Also in 2008, an article was published reporting that in Aquitaine, France, the annual increase of incidence was highest in the group of 0-4 years, 7.59% in comparison with the group of 5-9 (4.06%) and 10-14 years (1.28%) (28). In a Swedish study published in 2011, on 14.721 incident cases of T1D aged 0-14 years in the period 1978-2007, confirms that children aged 0-5 years are the most affected by T1D. This publication argues that the rise in the incidence of T1D is coincident with a birth cohort effect (33). Also, a Czech study published in 2000 registered that the age at onset of diabetes had presented a significant log-linear increasing trend in children (in those aged 0-4 years the increment was 6.9%, (p = 0.033) and in the group of 5-9 years it was 4.8% (p = 0.038)) (64). In Colorado, USA, the incidence of T1D in children aged 0-4 years had increased more compared with the other age groups: 1.96 (CI 95% 1.6,2.4) in the 0-4 year-old group, in the 5-9 year-old group, 1.5 (CI 95% 1.3-1.7) in the 10-14 year-old group, and 1.6 (CI 95% 1.2-2.1) in the 15-17 year-old group (344).

In Israel, the annual increase in incidence in the period 2000-2008 was 5.82% (CI 95% 1. 80-9.98) and the group of children aged less than 5 years had the highest increase; it increased by 104%, from 5.41 (CI 95% 3.44-7.37) per 100.000 person years to 11.04 (CI 95% 8.27-13.81) per 100.000 person years in the periods 2000-2002 and 2006-2008 respectively. As a consequence, the age at onset of T1D decreased from 10.21 (SD 4.48) to 9.25 (SD 4.54) (p = 0.07) in this time interval (284).

In contrast with the above reports, others studies carried out in Italy (43), Finland (177) and Western Australian (130), observed an increase in T1D in all groups of age. These results are against of the observation of an earlier age of onset of T1D (40).

Median age at onset of T1D

In Aquitaine, Franc, the median age at onset of diabetes was 10.04 years (CI 95% 6. 64-12.53) in the period 1988-1996 and8.83 years (CI 95% 5.48,11.73) in the period 1997-2004 (28). In the study of Northern Italy, the incident cases were identified in the a majority of cases during puberty, and with respect to the group aged 0-4 years, the Relative Risk (RR) was 1.64 (CI 95% 1.41-1.91) (46).

Using data retrieved in this thesis, the distribution of the age at onset of T1D was explored among 1690 data reporting the incidence of T1D by classes of age: 0-4, 5-9 and 10-14 years. The analyses show that the mean incidence for the age group was 10.0 cases per 100.000 individuals per year in the age group 0-4, 16.8 cases per 100.000 individuals per year in the group 5-9, and 19.2 cases per 100.000 individuals per year in the group 10-14; SI This thesis was initially focused on the study of T1D in children only because one usually thinks that the so-called "juvenile" diabetes is a disease specific of the youngest age groups. Nevertheless, no limit of age at onset of T1D was detected during the data collection for this thesis, as mentioned in chapter 1. With time, interesting papers appeared reporting the incidence of T1D in older individuals in several countries, and then in preliminary analyses, we observed that cumulative incidence among individuals older than 15 years followed patterns similar to those observed in children, as mentioned in section 3.4 of chapter 3. It was then that we decided to study the epidemiology of T1D in adults as presented here.

All possible studies reporting T1D in people aged over 15 years and published in indexed databases by the time this thesis was undertaken, were searched. Special attention was focused on the diagnostic criteria used to define T1D in adults as well as on the use of autoantibodies and detection of the C-peptide, a sub-product of insulin, as criteria to define T1D.

Finally, even though the map of the epidemiology of T1D in children as described in chapters 1 and 2 is as yet incomplete, we could see that this statement was even more valid for the epidemiology of T1D in adults, since available information was retrieved in only 17% (35 of 196) of the world countries. Moreover, interesting results were found, such as high correlation between incidences in children and adults, a same pattern of variability observed in children and a surprising male predominance of T1D among adults (82). The worldwide epidemiology of childhood Type 1 diabetes (T1D) was extensively described in the 6th edition of the International Diabetes Federation (IDF) [1]. Data were retrieved in approximately 45% of the countries [1][2][3][4]. In contrast, we are unaware of a similar review on the worldwide epidemiology of adult T1D diabetes, although T1D is known to occur even late in adults [5][6][7]. A major limitation of the epidemiology of T1D in adults is certainly the difficulty there is to distinguish it from Type 2 diabetes (T2D) requiring insulin treatment or from Latent Autoimmune Diabetes in Adults (LADA), when specific markers of autoimmunity are not searched.

Here, our primary objective was to describethrough a systematic review of the literaturethe available published information on adult T1D incidence, and the diagnostic criteria used for case definition. A secondary

Methods

Literature review

A systematic review was conducted according to the PRISMA recommendations to retrieve original papers published in English, French and Spanish up to November 6th, 2014, in peer-reviewed journals reporting the incidence of T1D among individuals aged more than 15 years, in population-based studies (i.e. collected in a defined geographic area [8]) and reporting the diagnostic criteria used to define T1D.

The databases used for the literature search were Medline (PubMed), Google Scholar and Thomson Reuters (Web of Knowledge). The protocol of the search was registered in the International Prospective Register of Systematic Reviews (PROSPERO) and is available on http://www.crd.york.ac.uk/PROSPERO/display_ record.asp?ID=CRD42012002369 (Registration number: 2012:CRD42012002369). Figure 1 presents the flow diagram of the bibliographic search, Additional file 1 for the full electronic search strategy, and Additional file 2 for the PRISMA checklist.

Data collection

For each study, the following information was extracted:

the identification of the study: authors, title, journal, publication year, -the period and country of study. The country was categorized by its World Health Organization (WHO) region and economic level: high-income (HIGH) or low-and middle-income (LMIC) [9],

Articles screened based on title and abstract n = 178

133

the geographic coverage of the study: nationwide (when the study was performed in the entire nation) and local (when it was restricted to a given region, city, or a geographically defined population), -the diagnostic criteria used to define T1D in adults: detection of autoantibodies against beta-cells (such as: islet cell antibody (ICA), insulin autoantibody (IAA), islet antigen-2 autoantibody (IA-2), anti-glutamic acid decarboxylase antibodies (GAD)), measurement of the fasting C-peptide level [7], need for permanent insulin therapy, time when the administration of insulin therapy was started, and clinical signals of T1D diabetes such as ketosis, ketonuria and weight loss, -the sources of data/registers reporting T1D incidence in the studies, defined according to LaPorte et al. [10] as: primary source of information: a "well-established system of standardized registries for identifying new cases", for example national or regional registers, secondary source of information: other different sources of cases "that would provide a check on the degree of ascertainment", for example medical records or hospital discharges, and tertiary source of information: a third approach for identifying cases, for example, through surveillance system or death certificates, -the reported percentage of completeness/ ascertainment between sources of information reporting incidence [10], -the incidence rates reported in the text, tables or graph (expressed as new cases per 100.000 persons/year) by sex and age classes, -additional information such as those concerning rural/urban, or ethnic differences.

Data analyses

The country distribution of the T1D incidence information and the analysis of the diagnostic criteria used were performed on the entire set of articles retrieved. For the few papers for which the results were presented by ethnic origin, we estimated the mean value of the incidence for the given period in the countries/regions concerned.

Correlation between adult and children T1D incidences

In the geographical correlation analyses between children and adult incidences, we considered for each country the more recent nationwide study published, or if not available, the last published set of local studies retrieved from a given area in the country; in addition, we included all published papers reporting autoantibodies against beta-cells or C-peptide. To obtain an estimate of the incidence of T1D in children in the countries for which the adult incidence was available, we used the data provided by the same adult paper, when available. The incidence of T1D in children was not available in 9 of these papers included in the geographical correlation analyses. In this case, it was estimated through a separate systematic review focused on the corresponding countries and periods (see Additional file 3).

Statistics

Data were extracted from graphs using GraphClick [11].

The country-to-country co-variation of children and adult incidences was quantified by the Spearman correlation and a linear regression.

The R software (version 3.0.1) was used for statistical and graphic analyses [12].

Results

Description of the information obtained from the systematic review on adult T1D

Seventy articles reporting incidence of T1D in young adults and adults aged over than 15 years concerned one country, and one article concerning two countries were retrieved in this systematic review, resulting in a total of 71 studies covering 35 countries (Table 1). Twenty-four of the 71 studies were nationwide; 43 papers provided information on the T1D incidence in the age class 15-29 years, 26 in the age class 30-59 years, and 6 in the persons aged >60 years.

A primary source of information was reported in 99% (70 of 71) of the studies: among these reported sources, 60% (42 of 70) were from medical/hospital records, 36% (25 of 70) from national or regional registers, and 4% (3 of 70) from other sources, such as communitybased surveys; a secondary source of information was reported in 90% (64 of 71) of the studies: among these reported sources, 58% (37 of 64) were from medical/ hospital records, 16% (10 of 64) from associations of patients, 14% (9 of 64) from drug or supplies prescription registers, 8% (5 of 64) from national or regional registers, and 5% (3 of 64) from death certificates and schools registers; finally, a tertiary source of information was reported in 21% (15 of 71) of the studies: among these reported sources, 27% (4 of 15) were from national or regional registers, 27% (4 of 15) from associations of patients, 20% (3 of 15) from death certificates, 20% (3 of 15) from drug or supplies prescription registers, and 7% (1 of 15) from medical registers; see details in Table 1. Percentage of ascertainment (completeness) between sources of information was evaluated in 53 of 71 (75%) studies. The mean percentage of ascertainment of these 53 studies was 94% (Table 1).

In the group of young adults (15)(16)(17)(18)(19), the lowest incidence of T1D was reported in Mauritius, (1.1/100.000 persons/year) [13], and the highest in Estonia (39.9/ 100.000 persons/year) [19]. In the 70-79 year age group, the lowest incidence was reported in Navarra, Spain (0.8/100.000 persons/year) [57] and the highest in Kronoberg, Sweden (55/100.000 persons /year) [63]. The details of all retrieved incidence by study and age classes are in Additional file 4: Table S1.

Diagnostic criteria used to define T1D in adults reported in 71 epidemiological studies Autoantibodies against beta-cell antigens or the C-peptide were included in the T1D diagnostic criteria in 14 studies [15,[30][31][32]34,35,45,46,54,56,57,63,74,81], detection of ICAs was reported in 9 studies [15,[30][31][32]34,45,46,54,63], IAA in 4 studies [30][31][32]54], IA2 in 5 studies [30][31][32]56,57], and GAD in 11 studies [30][31][32]35,45,46,56,57,63,74,81]. The C-peptide was measured in 7 studies. In one paper difference of auto-antibodies by age group (0-19) was explored but no significant differences were detected [74]. The other reported diagnostic criteria for T1D were the need for insulin therapy (reported in 70 of 71 studies), clinical symptoms of diabetes (reported in 56 of 71 studies), low or normal body weight (14 of 71 studies), and ketosis or ketonuria (26 of 71 studies). The details are shown in Table 1.

Comparison of adult and children T1D incidences

The variations of incidence of T1D in adults with country and age were studied in each area for which we retrieved information on a geographically defined population. This concerned 35 countries.

Variation of T1D incidence with age in adults

In 23 out of 35 (66%) countries (55 of 71 studies), the incidence of T1D was higher in the age range of 0-14 compared with 15-19 years. When restricted to the 14 reports for which the criteria of diagnosis of T1D were auto-antibodies against beta-cells or C-peptide detection, the variation of adult incidence with age showed a consistent decrease after the age of 14 years (Figure 2 and Additional file 4: Table S1).

Geographical correlation of adult and child T1D incidence

A significant geographical correlation, as measured by the Spearman correlation coefficient, was found between adult T1D incidence and 0-14 incidence in the age classes 15-19 years, 20-24 years, 25-29 years, 30-34 Figure 2 Age variation of incidence from childhood to adult age. On this figure, the adult estimates of incidence were taken from the 14 reports of the systematic review using the autoantibodies/C-peptide as diagnostic criteria. Full lines correspond to articles from which both child as well as adult information could be retrieved. The dotted lines are those for which the child information was searched in the same country as in the adult paper, but was from a different paper (see Additional file 3 for details on this literature search). The corresponding countries are shown as: BE1: Belgium (2007) [30]; BE2: Belgium (2002) [31]; BE3: Belgium (1997) [32]; DK: Denmark [34]; ES1: Spain, Catalonia [54]; ES2: Spain, Navarra (2014) [56]; ES3: Spain, Navarra (2013) [57]; FI: Finland [35]; IR: Iran (Islamic Republic of) [15]; IT: Italy [45,46]; SE: Sweden [63], TW: Taiwan [81]; US: United States of America [74]. ). The correlation was not significant in the oldest class where sparse data were available, but the relation was similar (Figure 3).

Comparison of male and female T1D adult incidences T1D incidence was larger in males aged 15 to 39 years than in females in 44 (81%) of the 54 studies reporting incidence by sex (Additional file 5: Table S2). The mean male-to-female ratio in our review was 1.47 (95% CI for mean 1.33-1.60, SD = 0.49, n = 54, p = < 0.0001).

Discussion

A first result of this systematic review is the paucity of data available on adult incidence of T1D as compared to those concerning children. The 71 studies retrieved provided information on adult T1D in only 35 countries, 40% of the 88 countries with primary childhood T1D incidence information in the 6th IDF atlas [1].

A second result is that only a small proportion (n = 14) of the 71 studies used detection of specific autoantibodies and/or dosage of C-peptide [83] as diagnostic criteria of adult T1D.

A third result was that in a majority of the retrieved studies, adult T1D incidence was greater in men than in women, which contrasts with incidence of T1D in children where sex ratio is around one [2,84]. Using comparative data, Karvonen et al. also described a male excess among young adults in the 15-39 years of age [85]. Sex differences in exposure to possible environmental triggers of T1D, in hormonal/genetic susceptibility, in lifestyle have been proposed as possible explanations for this difference [62].

A last striking observation of the current analysis is the strong geographical correlation of the incidences in adults and children. This correlation may be explained by the fact that adults with T1D share the gene alleles known to be associated to incidence of T1D in children, [86,87], and/or some predisposing environmental causes [4]. For example, in a previous study on incidence of T1D in children, a significant positive correlation was detected between the percentage of urban population and the incidence of T1D in children (r = 0.41 p-value: < 0.0001) [4]; in this review a significantly higher urban proportion of T1D incidence among adults was found in 4 of the 7 studies reporting differences between rural vs urban areas [15,21,42,75].

There was an overall decrease of incidence with age in adults and young adults after the age of 14. A second peak of T1D around the age of 50, as described by Krolewski et al. [88], was only reported in 7% (4 of 58) of the studies [18,63,80,89].

The paucity of data made it impossible to document an increase in adult T1D incidence that would parallel the dramatic increase observed in children [2,3,90]. Indeed, successive studies in the same region over different periods reporting incidence in people aged >30 years of age were only found for Belgium [30][31][32], Lithuania [20][21][22] and Sweden [58][59][60][61][62]. Similarly, this review did not dispose of sufficient data to document differences in the clinical presentation of T1D of adults and children as suggested elsewhere [32,40]; indeed only two of the 71 studies describe differences in clinical presentation of T1D between adults and children [89,91].

Improving the quantity and quality of information on adult T1D is not only useful to better understand the epidemiology and natural history of T1D, but can have practical consequences, as delay of T1D diagnosis may mean retardation in insulin treatment, lost opportunities for potential prevention of acute and chronic complications, and even death [92]: in Croatia [18], 14% of the incident cases were identified solely through death certificates, and high mortality was found in the newlydiagnosed T1D aged over 50.

Conclusions

Overall, the results of this systematic review should encourage the launching of epidemiological studies of adult T1D with specific diagnostic criteria. Note 1. The last terms of the equation (after "Diabetic Neuropathies" were added to focus the bibliographic search on incidence papers by removing all those dealing, for example, with the incidence of complications of T1D.

Note 2: Through a general search, we searched in other languages than English. We identified 115 articles published in French and 70 in Spanish, from which we assessed full text for eligibility of 7 papers published in French and 14 in Spanish. At the end, one paper in French and two in Spanish were included in the analysis. 4 Glossary for PubMed search:" [MAJR]: MeSH Major Topic, a MeSH term that is one of the main topics discussed in the article; [MeSH]: MeSH Terms, the National Library of Medicine's controlled vocabulary of biomedical terms that is used to describe the subject of each indexed journal article in MEDLINE; [SH]: MeSH Subheadings; it describes more completely a particular aspect of a subject; [TIAB]: Title/Abstract, the search include words and numbers included in the title, abstract, and other abstract of a citation; [PT]: Publication Type, it describes the type of material the article represents (e.g., Review, Clinical Trial, Retracted Publication, Letter).

117, 118

Eligibility criteria Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

117, 118

Information sources Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

117, 118

Search Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

Additional file 1

Study selection State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

118, Figure 1 Data collection process Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

119, Figure 1 Data items List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

119

Risk of bias in individual studies

Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

Summary measures

State the principal summary measures (e.g., risk ratio, difference in means). 119

Synthesis of results

Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

119

Risk of bias across studies Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

Additional files 4 and 5

Additional analyses Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

RESULTS

Study selection

Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

Figure 1 Study characteristics For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

120

Risk of bias within studies Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 120, Table 1 Results of individual studies For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

Additional files 4 and 5, Table 1 Synthesis of results Present results of each meta-analysis done, including confidence intervals and measures of consistency.

Risk of bias across studies

Present results of any assessment of risk of bias across studies (see Item 15).

Additional analysis

Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

DISCUSSION

Summary of evidence

Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers). 15: List of selected papers reporting incidence of T1D in 0-14 year-olds in 9 countries This list was obtained through a separate review focused on 9 countries considered (1st column). These values were used in the child-adult correlations. ---------------------- ----------------------------

--- --- --- --- --- --- --- --- --- --
--- --- --- --- --- --- --- --- --
--- --- --- --- --- --- --- --- ---
--- --- --- --- --- --- --- US Virgin Islands: NW Washington R. E., 2013 † (351) 0-19 2001-2010 11.2 (a) 19.1 (b) --- --- --- --- --- --- --- --- --- ---
Incidence was per 100.000 persons per year. T1D: Type 1 Diabetes. NW: Nation-wide study. HIGH, LMIC: High, Low-Medium Income Level. Highlighted: reports of the systematic review using the autoantibodies/C-peptide as diagnosis criteria. Among 71 studies described in chapter 3, ethnicity differences were reported in 7 studies. In Mauritius, the incidence was similar among individuals of Asian Indian, Chinese or Creole origin (332). In Israel, a significant difference was found between Jews and Arabs (39,171). In Unites States, in Pennsylvania, the incidence was higher in non-Whites than in Whites (191), in Alabama the opposite was found (349, 350), whereas, in Colorado no significant differences were found after exploring the incidence of T1D in different periods (344).

Rural-urban differences in T1D Incidence of T1D in Adults

Only ten percent (7 of the 71) of the studies explored the differences in the incidence of T1D in rural vs urban areas. A significantly higher urban proportion of the incidence of T1D among young adults and adults was found in 4 of these 7, (Iran (Fars) (248), Lithuania (nation-wide) (254), Italy (Pavia) (317), and United States of America (Wisconsin) ( 13)). In another study from Poland (Bialystok), a significantly rising incidence trend was found in the rural areas but not in the urban regions (172). In the Province of Rzeszow no differences were found between the rural and urban incidences (123), nor in Turin, Italy (45). After performing the APC analyses among individuals aged 0-14 years, a clear and strong birth cohort effect was observed. In addition, a calendar period effect that has been changing over time was also observed. The highest rate of incidence was observed for those [START_REF] Derrick | Observations on (1) errors in age in the population statistics of England and Wales and (2) the changes in mortality indicated by the national records[END_REF] (77) first proposed the analyses of cohorts to study the effect of the birth cohort on mortality. The history of the first 30 years of the cohort analyses is presented in [START_REF] Case | Cohort analysis of mortality rates as an historical or narrative technique[END_REF] (56) historical article: "cohort analysis of mortality rates as a historical or narrative technique". With time, the APC models were improved with the introduction of the age (of death, in the case of mortality studies) and period (in which the death/event of interest was produced), as described by Gilg Soit Ilg in her doctoral thesis (1999), supervised by Professor Alain-Jacques Valleron: "La méthode âge-période-cohorte, outil prévisionnel application au mesothéliome en France" at the UPMC (ED 393) (110). In chapter 6, thanks to Professor Pierre-Yves Boëlle from the UPMC, HAPC-CCREM were proposed to explain the effect of the age, the period and the cohort in the study of T1D using Poisson regression models, and with a country-level random level intercept according the Holford method (133, 134).

Some examples of APC analyses used in public health

In spite of limitations of the APC models this method was used extensively in mortality analyses (mainly in cancer research), as well as in other domains of public health such as phycology, social sciences, psychology, stomatology, ophthalmology, and demography (110).

APC analysis has been proposed to answer fundamental questions the public health through different methodological approaches. As examples see the following studies:

-The study: "Estimation of the past and future burden of mortality from mesothelioma in France" (Gilg Soit Ilg et al. 1998) (140), states and predicts the mortality due to mesothelioma in France using a classical approach of the APC models.

-The study: "Intrinsic Estimator for Age-Period-Cohort analysis: what it is and how to use it", (Yang et al. 2008) (368), addresses the problem of the APC analysis of age-by-time period tables of rates or proportions.

UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants 166 -The study: "Aplicación del estimador intrínseco a tasas de mortalidad por cancer de mama", [START_REF] Arnesi | Aplicación del estimador intrínseco a tasas de mortalidad por cancer de mama[END_REF] (19), contrasts the use Generalized Linear Models and IE in an APC analysis.

-The study: "A mixed models approach to the age-period-cohort analysis of repeated cross-section surveys, with an application to data on trends in verbal test scores", (Yang and Land, 2006) (370), uses HAPC-CCREM models applied to sociology.

-The study: "Trends in smoking in Sweden from 1968 to 2002: Age, period, and cohort patterns" [START_REF] Ahacic | Trends in smoking in Sweden from 1968 to 2002: age, period, and cohort patterns[END_REF] (8), uses HAPC-CCREM models applied to the study of tobacco consumption.

-The study: "Is old age depressing? Growth trajectories and cohort variations in late life depression", (Yang, 2007) (369), uses APC approaches in psychology, applied to longitudinal data of an initial sample of individuals from a broad array of ages.

Examples of APC analyses used to study T1D trends

Few studies have used APC models to study incidence trends of T1D. Classical approaches of APC models from Denmark, the United Kingdom and Sweden, have described a calendar period effect in the incidence of T1D (119, 141, 217, 233, 310), observed when trends affect all age groups at the same time. In contrast, in other studies from Norway, Sweden and the United States a birth cohort effect (3,74,156) was described. Only two studies report the analyses of the three interrelated variables: age-period-cohort: the study: Analyses of 1990Analyses of -2003 Incidence time trends of childhood diabetes in Italy, the RIDI Study" From Italy, [START_REF] Bruno | Age-period-cohort analysis of 1990-2003 incidence time trends of childhood diabetes in Italy: the RIDI study[END_REF] (43), in which neither period nor cohort effect was observed, and the study: "Spatiotemporal trends and Age-Period-Cohort modeling of the incidence of Type 1 diabetes among children aged <15 years in Norway 1973-1982and 1989[START_REF] Cherubini | RIDI: the registry of type 1 diabetes in Italy[END_REF]" from Norway, (Aamodt et al. 2007)(3), in which a birth cohort effect was described.

"Age-Period-Cohort

Abstract

Background: Type 1 Diabetes (T1D) has generally been increasing worldwide. In contrast, in Scandinavian countries, the decline in cumulative incidence in recent years reflects a possible break in the trend.

Objective: to disentangle the effect of age, calendar period, and birth cohort on the temporal trends of T1D incidence in children aged 0-14 years.

Methods: the incidence of T1D worldwide was retrieved through a systematic review of the literature. To model the effects of age, calendar time and birth cohorts on the variation of T1D incidence, a Hierarchical Age-Period-Cohort cross-classified Random Effects Model (HAPC-CCREM) was then proposed using Poisson regression.

Findings: Information on the incidence of T1D was retrieved in 89 countries worldwide and increase in the incidence of T1D with age was examined among those aged less than 14 years. Global trends of T1D increase with age show increasing incidence in successive birth cohorts over the period of analysis. After allowing a cohort effect, little change due to period was present.

Background

The incidence of T1D has been studied in several countries worldwide showing an enormous variation in trends (90,122,241,243,300,318), that is not explained by either genetic or social factors or by health care systems (81). In the 1980s, the increase of the T1D incidence was recognized ( 105) and calculated to be on average 3% per year (318).

T1D is a complex disease with multifactorial causes (338,342,343). In the etiology of the disease, genetic (89) factors are implicated as well as environmental factors (104,306); in which autoimmune (338, 367) mechanisms are involved. Previous studies have described a calendar period effect in the incidence of T1D (119, 141, 217, 233, 310), observed when trends affect all age groups at the same time. In contrast, in other studies a birth cohort effect (3,74,156) was described, in which the increase of the incidence trends was observed in all age groups from the same birth cohort. The importance of clarifying whether increases in temporal trends of T1D are due to period or cohort effects is fundamental to better understand the etiology of this disease. A calendar period effect has been related with short or long-term changes and more stable increases in incidence, in which an abrupt exposition to an environmental trigger could reflect a non-linear patron, whereas a birth cohort effect has been related with long-term changes, in which rapid and unstable changes in the incidence are comparable to an epidemic pattern.

Evidence of the influence of age, period, or birth cohort effects affecting the incidence trends of T1D has been studied in some countries; nevertheless, very few studies report the analyses of these three interrelated variables: age-period-cohort (3,43), in part because during short periods of time, only a limited number of observations are available in most countries and are also due to methodological issues, such as the linear dependence between the variables age, calendar period and birth cohort (232). Among the studies reporting an APC analysis, neither period nor cohort effects were observed in the Bruno study (43), whereas a birth cohort effect was found in the Aamodt study (3). Here, using a systematic review of the literature, we have generated a large database containing global information on the incidence of T1D; then using a methodological approach based on Mixed Poisson Regression Models (MPRM) and introducing a random effect by country, we were able to estimate the APC rates of the global incidence of T1D, considering all three variables: age, calendar period, and birth cohort.

Global incidence of T1D data

A systematic literature search was conducted to obtain data to model trends in T1D worldwide. The recommendations of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Group were followed (213) (SI Eurodiab studies (90,242,243), and the International Diabetes Federation (IDF) atlas (241, 300).

The search, conducted on 28/11/2011 and updated in November 2014 included the following terms: "diabetes mellitus, type 1/epidemiology" AND "incidence" AND "age" (OR "age distribution" OR "age factors" OR "age groups"). All abstracts were read for reports of T1D incidence. If present, the full-text was obtained and the article included in the analysis if it reported incidence data in the general population at a regional or country level. Figure 27 shows the flow diagram of the bibliographic search. The complete search strategy is in:

Supporting information 5.2.5.1. For the entire list of selected references see SI Table 20.

Data abstraction

T1D incidence rates or counts up to the age of 14 years were extracted from the texts, tables or graphs in the articles. We also extracted the country where the study had been performed, the period (calendar year) and the age or age class. When available, we also extracted the population at risk (in person years).

Preparation of data

We searched for duplications in the data extracted. When two papers presented the same data, we included the more detailed version (in age and period). When local studies were included in studies performed on a larger scale, only the largest, up to the country level UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants 170 were included. Finally, the data was grouped in age classes of 5 years each (0-4, 5-9, 10-14) and in 5 years period classes (starting in 1935). In all cases, the data was converted back to number of cases and populations at risk. If the population was not available, we used estimates from the United Nation population (335), and otherwise we checked that the population sizes from these references agreed with those reported in the published articles.

The final database is presented in Appendix A: Database IV.

The classification of incidence-country-level was arbitrarily proposed as: very low, low, intermediate, and high incidence level, corresponding to countries with incidence levels of: ≥2 to <6, ≥6 to <10, ≥10 to <42 and ≥ 42 cases per 100.000 persons per year.

Analyses

We used an APC model, specifically a HAPC-CCREM using Poisson regression, to analyze the incidence rates over periods and ages. In the APC models, incidence is described as depending on age, calendar period, and birth cohort. Here age was the age at diagnosis of T1D -the year of the first insulin injection-reported by the studies, calendar period was the year of diagnosis, and birth cohort was the year of birth of the patient (obtained as periodage). To avoid over-parametrization, we used a 3-nodes linear spline to model the period and cohort components. We analyzed all data jointly, using a country-level random level intercept. More precisely, the model for analysis was:

log ! ! !,!,!,! !(!,!,!,!) = !!! ! ! + !!!! ! ! !+!!!! ! ! + !! !
where D(a,p,c,y) was the number of incident cases at age a, period p and birth cohort c in country y, fA(a), fP(p) and fC(c) were the age, period and cohort components, N(a,p,c) the number of persons at risk and b y a normally distributed country level effect with mean 0 and variance ! ! ! .

The R software (version 3.0.1) was used for statistical and graphic analyses (255).

Packages Epi, Splines, Lme4 and Grid were required.

Retrieved global information on T1D

Thanks to our systematic review, worldwide incidence trends of T1D were retrieved from 265 references (223 included in the APC model) between 1975 and 2014 that contained information of incident cases of T1D reported during the period 1938-2008 in individuals aged less than 14 years. There are enormous variations in incidence trends of T1D after stratification of the analysis according to incidence-country-level. Incidences varied widely between continents, countries, and regions. Among children aged 0-14 years, the lowest nationwide incidences (≤ 1 case per 100.000 individuals/year) were in Eastern Asia (China), South-Eastern Asia (Thailand), Melanesia Oceania (Papua New Guinea), and South America and the Caribbean (Saint Kitts and Nevis, Dominican Republic, and Paraguay). (Finland, Sweden, Norway). SI Table 20 presents the variability of the incidences within a country, the duration of each study, the number of cases and the population at risk taken from the information used during these analyses.

APC analyses of the global incidence of T1D

The mean incidence of T1D was 13.7 cases (95% CI 13.1-14.2) per 100.000 persons per year. North America, Europe and Oceania are the regions with the highest incidences, in contrast Asia, Africa and Latin America and the Caribbean, showed the lowest incidences.

Europe is the region in the world with most incidences registered: 147,220 cases and more person-years of following at risk: 1,048,075,945. See Table 18. Retrieved datasets were predominantly from European countries (n = 2007), followed by North America (n = 527) and Asia (n = 499). 1989-1993 was the period reporting the largest number of datasets (n = 844); Figure 28.

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Epidemiology of Type I Diabetes: Global Incidence and Determinants 173 Green-to-red gradient represents the number of datasets from the lower to the higher. Age is the mean for each age-class (0-4, [5][6][7][8][9][10][11][12][13][14], each period correspond to 5 calendar years.

Discussion

The incidence of T1D is highly variable among the 89 countries and regions reporting information. Regarding incidence trends, the incidence of T1D increases with age; it presents the highest rate for those aged 10-14 years. In addition a birth cohort effect and a calendar period effect were observed. The curve representing the birth cohort effect shows a steady increase after 1975, been the RR = 1 in the 1980s and RR = 2.5 in the 2000s. Surprisingly, a decrease in the curve representing a calendar period effect appears after the peak in 1987; this tendency is strongly influenced by the effect of Finland, a very special country in which the epidemiology of T1D differs in genetic and maybe in environmental parameters and which provides a large amount of information, compared with other countries in this study.

In general, we observed that the incidence of T1D shows dynamic trends around the world. Large and sudden variations in the epidemiology over relatively short periods of time have also been described. Studying the ancient literature of T1D, Gale suggests that T1D showed a stable low incidence over the first half of the century (105). But in the 90s, collaborative studies such as Diamond (318) and Eurodiab (90,242,243) reported an increase in incidence trends of T1D worldwide. Our results are congruent with this finding and also with a nation-wide Swedish study, in which a reversed trend of cumulative incidence of T1D after 2000 (p < 0.01) was observed (33).

It is known that T1D is a multi-causal disease, and several hypotheses have been proposed as explanations. Nevertheless most authors agree with the theory that there is a strong influence of the environment among the causes of this disease. For example, Dahlquist (74) and Jaroz-Chobot (148) strongly support the idea that the most important promoters of the increase of T1D are the availability of fast food and the 'westernisation' of lifestyles (overweight, high growth rate, and having sedentary lifestyles), conditions that are associated with other environmental factors (e.g. enterovirus (314, 373)) and could be triggers in the initiation of the disease (probably by accelerating the beta-cell destructive process) due to a direct influence or to lack of protective agents (74,148).

Our findings support the hypothesis that external factors including environmental risk determinants, and other non-genetic causes could be promoters or modifying factors implicated in the change of incidence trends of T1D worldwide. In a previous study, ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 175 significant predictors of T1D incidence were: UV radiation, mobile cellular subscriptions, health expenditure per capita, immunization against Hepatitis B and mean BMI (body mass index) (81). Nevertheless, more analyses of gene-environment as well as social determinants of health for T1D are still necessary.

Our study has several limitations. We found publications only in 46% (90 of 196) of the recognized countries. The variation of the duration of studies -between 1 and 40 years -, reflects enormous differences in surveillance and epidemiological reports of T1D worldwide.

Few studies were carried out in Central and Latin America, Central Asia, and Sub-Saharan Africa, geographic regions where the epidemiological surveillance systems are concentrated in other priority concerns of health care systems or have under-developed data reports and registration systems. Nonetheless, part of the regional differences could also be due to different genetic ( 89) backgrounds of the populations not shown here. This could explain the extreme differences observed between and within countries, such as for example, the high incidence of Sardinia in Italy or in Newfoundland in Canada, and also the differences between ethnic groups, as for example Ashkenazi Jews, or non-Ashkenazi Jews in Israel, or among Hispanic or non-Hispanic populations in the United States.

To minimize the risk of bias, we only considered for the quantitative analyses performed here, references with population data-registers and in which an assessment of all included references at the study-level and outcome-level was rigorous; nevertheless, data plots and summarized results from heterogeneous populations could imply a bias. Also, to avoid duplications of the subject, we carefully selected the information to be entered in the analyses, excluding repetitions of data from a same region during a specific time period; however it is impossible to verify in all cases if any doubling exists. Similarly, we selected information reported for a given country or area during a specific period of time only once.

In addition, there are methodological issues of the APC models, here were treated the over-parametrization by introducing an external random factor, in our case the "country". Using this method it was possible to present a separate analysis excluding Finland, the country with the highest incidence in the world. Also, data analyses were started in 1970, a period with more available information for most of the countries. The birth cohort effect we show here should be explored. Researches focusing on pregnancy, and neonatal period are necessary to clarify causal factors of the birth cohort effect, for example, the implications of congenital infections as well as the absence of exposure to protective factors related to the way of delivery and the first moths of life of the newborns. Based on the period effect we report here, quick changes such as weight gain during early childhood (91) or adolescence could be risk factors of T1D and a manifestation of a calendar period effect.

Our results show that for children 0-14 years old, the global incidence of T1D increases with age, with a tendency of steady increase after the 1975 birth cohort. However, observing the period effect, after reaching a peak in 1987, the tendency shifted downwards, mainly influenced by the effect of information from Finland. Our remaining questions are if we are face a true slowdown in the tendency of T1D incidence; in this case, is it still necessary to understand the reasons why. Nevertheless, this apparent reversal trend could be due to miss or insufficient reporting of the disease in most countries during the last decades.

Keeping the collection of standardized information, the collaborative studies performed worldwide and the promotion of public repositories with available data on the incidence of T1D remain valid and are still necessary to better understand the epidemiology of T1D. Note 1. The last terms of the equation (after "Diabetic Neuropathies" were added to focus the bibliographic search on incidence papers by removing all those dealing, for example, with the incidence of complications of T1D. 5 Glossary for PubMed search:" [MAJR]: MeSH Major Topic, a MeSH term that is one of the main topics discussed in the article; [MeSH]: MeSH Terms, the National Library of Medicine's controlled vocabulary of biomedical terms that is used to describe the subject of each indexed journal article in MEDLINE; [SH]: MeSH Subheadings; it describes more completely a particular aspect of a subject; [TIAB]: Title/Abstract, the search include words and numbers included in the title, abstract, and other abstract of a citation; [PT]: Publication Type, it describes the type of material the article represents (e.g., Review, Clinical Trial, Retracted Publication, Letter).

!

INTRODUCTION

Rationale

Describe the rationale for the review in the context of what is already known. 158

Objectives Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).

158

METHODS

Protocol and registration

Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number.

3, 5

Eligibility criteria Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.

159

Information sources Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.

159 Search Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. 167

Study selection State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

161, Figure 27 Data collection process Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

159, Figure 27 Data items List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

159

Risk of bias in individual studies

Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.

Summary measures

State the principal summary measures (e.g., risk ratio, difference in means). 160

Synthesis of results

Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2 ) for each meta-analysis.

160

Risk of bias across studies Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

160

Additional analyses Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.

RESULTS

Study selection

Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

Figure 27

Study characteristics For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.

Figure 29, Table 20 Risk of bias within studies Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). Table 20 Results of individual studies For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

Table 20

Synthesis of results

Present results of each meta-analysis done, including confidence intervals and measures of consistency.

Risk of bias across studies

Present results of any assessment of risk of bias across studies (see Item 15).

Additional analysis

Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]).

DISCUSSION

Summary of evidence

Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).

164

Limitations Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias). 21 compare the retrieved intercept of the incidence of T1D retrieved using the APC model presented above, in which the age, the period and the birth cohort of the retrieved incidences were adjusted, and the incidence retrieved in our systematic review and described in chapter 3 (81). In chapter 3, we describe the covariation of T1D incidence worldwide in children with environmental factors that could be retrieved thanks to public databases. We found that 51% of the variation of T1D incidence could be accounted for by country-to-country variation in environmental factors.

SI
T1D has an important genetic component. HLA (Human Leukocyte Antigen) which is the best known and best studied locus and alone explains the 40-50% inheritable T1D risk (212) was the main subject of this chapter.

Here, we assessed the proportion of the variability of T1D incidence that could be explained by the country-to-country variation of the frequency of susceptible and protective HLA alleles and haplotypes identified as T1D determinants. We relied on the Allele Frequency Net at http://www.allelefrequencies.net/ (115), a public electronic repository that provided 1007 gene/allele HLA and 370 haplotype data for 1025 worldwide populations. In this website, we searched for the frequency of susceptible and protective HLA alleles and haplotypes and validated based on different populations around the world by the Type 1 Diabetes Genetics Consortium Families report (2008) (89); we then compared the retrieved data with the incidence of T1D retrieved throughout the systematic review using methods similar to those presented in chapter 3.

After Stepwise Multiple Linear Regression (MLR), the significant variables were the Bivariate and multivariate analyses were used to determine the correlations between the incidence of T1D and selected independent predictors of the geographical variation of T1D within each domain and overall. PROSPERO Registration number: CRD42012002369

Background

It has long been noticed that the incidence of Type 1 Diabetes (T1D) is highly variable from one country to another (81). For example, the 62.42 per 100.000/ per year incidence found in Finland (128) was 780-fold larger than the 0.08 per 100.000/ per year incidence in Papua New Guinea (230); the variability of T1D incidence is even visible within countries; for example, in Italy, T1D incidence varied between 54.4 per 100.000/ per year (Sardinia (102)) 4.4 per 100.000/ per year in Lombardia (108). The reason for these differences is not precisely known, but is most unlikely due to classification bias, as the disease cannot go untreated, and the diagnosis was relative easy to perform in children, at least before the recent rise in obesity rates (263). Moreover there are also differences in incidence within countries where the health care systems are comparable. Environmental factors have been implicated either in addition or as cofactors to genetic determinants. In a previous study we reviewed 77 independent variables; significant environmental predictors of the country-to-country variation of T1D incidence included UV radiation, number of mobile cell-phone subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean body mass index (BMI) (81). The country-to-country T1D variability may be partly explained by genetic variations.

The major and most studied loci of susceptibility are located in HLAs (Human Leukocyte Antigens) in the major histocompatibility complex (MHC) on chromosome 6p21, that encodes immune response proteins. HLAs alone contribute by 40-50% of the family clustering observed in T1D (inheritable T1D risk) (212). Among the three classes of HLAs, class II HLAs DR and DQ haplotypes have been associated with a genetic risk of T1D or the protection profile within different populations (89).

One could indeed expect that in this age of information, the country-to-country covariations of T1D incidence could be readily correlated with a variety of genetic Eurodiab studies (90,242,243), the International Diabetes Federation (IDF) atlas (241, 300))

were analyzed and described in detail elsewhere (81).

HLA DQ/DA haplotype frequencies by country

Thanks to the report of the Type 1 Diabetes Genetics Consortium Families (2008) (89), that collects and analyzes samples of families with T1D from various populations around the world, the most susceptible and protective haplotype determinants of T1D have been identified.

To retrieve global information of the frequency of these haplotypes, we explored the Allele Frequency Net at http://www.allelefrequencies.net/ (115), a public electronic repository that stores allele frequency of worldwide populations. For HLAs, on 11/20/2014 this website contained data from 1025 population studies, 1007 gene/allele data and 370 haplotype data. Using the tool "haplotype frequency search" we examined the frequency in all populations of the most susceptible haplotypes reported by the T1D Genetics Consortium and reported in percentages (%).

In addition, using the tool "allele frequency search" we examined the four-digit resolution (high resolution four subtyping i.e. DRx/DQx*xx00) of each of the alleles contained in the haplotypes mentioned above. After retrieve a database containing 6867 lines, we then analyzed information from the 80 geographical regions for which we retrieved information on T1D incidence as follows. 1) For each of the 80 countries, only one population reporting the HLA allele frequency was selected among all those reported in the Allele Frequency Net; the same population was used across all the alleles explored. The population selected was the one for which we retrieved information for most of the alleles studied (12 susceptible and 9 protective HLA alleles related to T1D; 21 alleles in total). 2)

When for a given country, more than one population was retrieved informing in a similar among of alleles, the selected population was the one with the highest sample size and less among of "0" values. 3) If no information was available for a given country, this country was excluded from the analyses. See Appendix A: Database VI for the final HLA alleles frequency database.

Statistics

Spearman correlation was used to compute the correlation between the 21 alleles and the T1D incidence.

Stepwise Multiple Linear Regression model (MLR) methods were used to select the best HLA alleles independent predictors of T1D incidence. We started with all variables for which the p value tested the correlation whose incidence was smaller than 0.05, and we successively removed the variables until the maximum value of the adjusted R 2 was obtained.

When a couple of variables was correlated with r >0.80, only one variable was used in the regression analysis to avoid the computational issues associated with colinearity. Model assumptions for linear models were checked. 10-Fold Cross-validation after bootstrapping 22, the details in SI Table 24 -26, the backward selection process in AP Table 34, and the graphic validation of the model in AP Figure 71-72). 10-fold crossvalidation indicated that the fraction of the variability of the global incidence of T1D was estimated to be 30% (SI Figure 48). See Appendix F for the validation of the model. 27282930The incidence of T1D retrieved from 56 countries reporting information on HLA alleles and five independent predictors found in a previous study (81) For the allele DRB1*08:01 the correlation coefficient at the p-value <0.05 dropped after controlling for: mobile cell-phone subscriptions and hepatitis B immunization; and increased after controlling for BMI in 3% (p = 0.001).

Discussion

Thanks to the information available in public databases, we identified genetic markers that were predictors of the variation in the geographical incidence of T1D.

Conclusions and Perspectives

"In laboratory science, technological advances typically have meant new instruments or techniques. However, in diabetes epidemiology, the population is our laboratory. Can there be a revolution in diabetes epidemiology based on new techniques to facilitate measurement? Can this directly lead to a revolution in how we view and quantitate health? We believe so". LaPorte, et al. Diabetes Care, 1993 (179) T1D is a chronic pathological condition that affects millions of children and young people around the world, with devastating consequences on patients, parents, and society. The well-known genetic component of T1D cannot explain the increase in the incidence and the decreased age at onset that has been observed worldwide. The geographic variability and the changes in temporal trends of incidence of T1D are influenced by still unknown environmental factors affecting the natural course of childhood diabetes. Understanding these environmental effects as they interact with the genetic component in T1D would be critical to identify potential modifiable factors of this disease.

In chapter 2 of this thesis, we show that countries with information on T1D incidence present lower temperatures and UV radiation and are more "urbanized", characterized by reduced rural population, higher obesity rates and overweight, higher CO 2 emissions per inhabitant, as well as stronger economies (higher Gross National Income (GNI), improved sanitary facilities, and more expenses in health resources).

Then, observing in depth the countries with high T1D incidence for which we could collect information, three main related categories of environmental determinants were identified. First, higher incidences were present in wealthy countries with a large national income and a "western" life style, defined here as countries with high concentrations of urban population and more CO 2 emissions, in addition to higher rates of obesity and overweight; such factors may be implicated (or may cooperate) in accelerated beta-cell destruction, as proposed in the accelerator hypothesis (361) and the overload hypothesis (71)-. Second, high incidences were observed in countries in northern latitudes, in countries with traditionally low temperatures and with low UV exposition, factors that could be associated, for example, with a vitamin D deficiency, currently described in autoimmunity and alterations in the regulation of gene expression (367). Third, higher incidences were also found in countries with better health care, large education expenditure, and a more aged population, factors that could mirror better health care registers.

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Epidemiology of Type I Diabetes: Global Incidence and Determinants 225 Paradoxically, countries with low incidence or without available information could be the result of an under-diagnosis and lack of notifications rather than real absence of incident cases. Nevertheless, it is also possible that both situations are present in the epidemiology of T1D: lower incidence countries presenting higher rates of under-diagnosis or missnotifications of T1D incidence levels.

This enormous geographic variability in the incidence of T1D, and the changes in the incidence trends shown here in chapters 1 and 5, remains a challenge for the scientific community. The variety of climatic, environmental, economic, and health factors that seem correlated with T1D incidence presented in chapter 3 of this thesis deserve deeper exploration. The strong geographical correlation of the incidence in adults and children as observed in chapter 4 may be explained by the fact that adults with T1D could share the same genetic alleles known to be associated to juvenile T1D, for example the alleles DRB1*04:01 and DRB1*08:01 that we found here to be significant predictors of the incidence of T1D in children described in chapter 6, but also because they share some predisposing environmental causes (81).

The string birth cohort effect presented in chapter 5 allow us to suggest that research focused on pregnancy, and the neonatal period is required to recognize the factors implicated in T1D reflecting the birth cohort effect. In addition, epidemics due to infection diseases, environmental expositions or quick changes, such as weight gain during early childhood (91) or adolescence, could be risk factors of T1D and a manifestation of a calendar period effect also observed. If the downward tendency observed when we observe the model with out the effect of Finland is real, it means that variations of environmental factors could induce changes in the incidence of T1D in relatively short periods. Revealing all the factors associated with T1D incidence is not a feasible task for the moment, since most of them are unknown and/or information is unavailable. However, this thesis showed that some environmental and genetic factors available publicly, could be associated at the level of the global epidemiology of T1D. We are confident that in the near future high quality information on the incidence of T1D will be accessible, as well as genetic and environmental indicators that will allow deepening the approach used here, and will unravel new clues on the geneenvironment causality of T1D and potential target factors for preventive measures. Definition of used terms at the end of this appendix.

PubMed

Advanced search was used, for published articles in the literature between 1 January 1990 and November 2011.

The equations for the query in the search history section were:

-Search #1 ("diabetes mellitus, type 1/epidemiology" [Mesh Terms]) AND -At the end the equation was search ((#1) AND #2) AND #3). We will call this query #0.

Web of Science

Search articles published during the period 1 January 1990 and November 2011 using -Searching the Title Field: enter Title terms to limit your search to article titles.

-Lemmatization: it finds (On) or not (Off) alternative forms of the search term, for example, tooth and teeth.

- All MeSH terms reported in these 8 references were listed and arranged using Excel software, 100 in total. The MeSH term "Blood Glucose" introduced in the query #2 was found in the article No.1 [START_REF] Bruno | Comparison of incidence of insulin-dependent diabetes mellitus in children and young adults in the Province of Turin, Italy, 1984-91. Piedmont Study Group for Diabetes Epidemiology[END_REF]. Also, the report of groups of age (adolescent, child, etc.) was a predominant condition of the MeSH terms in the lost references.

Two articles of this list have particular conditions: the article No.3 [START_REF] Carrasco | Type 1 diabetes mellitus incidence in Santiago, Chile. Analysis by counties in the period 2000-2004[END_REF] was published in Spanish, and the article No. 6 (Skordis, 2002) did not report any MeSH term.

As consequence of this analyses, the decision was to modify the query #8, excluding Repeating step 2: Query # 9 using the main MeSH terms retrieved from articles retrieved in step 6. See Table 12 for name of codes. 22 andSI Table 24- Table 1: Information extraite des documents et inclue dans la base de données ..................... 17! Table 2: Introduction : Le diabète de type 1 (DT1) est une des maladies métaboliques les plus répandues chez les enfants (298). L'augmentation de l'incidence mondiale du DT1 a été calculée à 3,0% par année en moyenne et il a été prédit qu'elle serait de 40% en 2010 (231) chez les enfants âgés de 0 à 14 ans. Les causes de cette augmentation ne sont pas encore déterminées; cette thèse met à jour les connaissances actuelles sur l'incidence mondiale du DT1, en profitant de la disponibilité croissante des bases de données publiques et en examinant la corrélation entre l'incidence et les caractéristiques des pays.

List of Tables

Méthodes : Une recherche exhaustive et systématique des articles publiés sur l'incidence du DT1 dans le monde a été effectuée pour étudier les variations globales de cette incidence dans les divers pays. Nous avons suivi les recommandations de PRISMA 8 (213). L'objectif initial de cette thèse était d'étudier la corrélation entre les variables environnementales disponibles dans les bases de données publiques, et l'incidence du DT1 selon les pays. Nous avons également réalisé une analyse similaire à celle décrite ci-dessus concernant les variables environnementales, pour les composantes génétiques du DT1. La fréquence des haplotypes de HLA (Human Leukocyte Antigens) qui confèrent la susceptibilité à, ou la protection contre le DT1, a été exploré (89). Les principales méthodes statistiques utilisées étaient des corrélations et des modèles de régression linéaire. Enfin, afin de mieux comprendre les tendances dynamiques du DT1 avec toutes les informations récupérées sur l'incidence du DT1, nous avons utilisé l'Âge-Période-Cohorte (APC). Pour les analyses statistiques et graphiques le logiciel R (version 3.0.1) a été utilisé (255), et pour la visualisation des données le logiciel Tableau (262).

Principaux résultats : Grâce à la revue de la littérature publiée entre 1975 et 2014, 265 références ont été sélectionnées, qui contenaient les données épidémiologiques sur les cas incidents de DT1 chez les individus âgés de 0-14 ans portant sur 90 pays. Chez les personnes de plus de 15 ans, 70 références ont été sélectionnées portant sur 35 pays. L'incidence du DT1 est très variable selon les pays et régions du monde pour les enfants et les adultes. Contrairement à ce qui est connu chez les enfants, chez les adultes l'incidence était généralement plus élevée chez les hommes que chez les femmes. Indépendamment de l'incidence initiale, on a trouvé une tendance à l'augmentation de l'incidence dans tous les continents entre 1960 et 2010. Finalement, nous avons extrait des données à analyser (223 articles sélectionnés) en utilisant des modèles Age-Période-Cohorte, des informations équivalentes à 2,327,604,529 années-personnes à risque de 192,741 cas de DT1. Avec le modèle APC un fort effet de cohorte a été observé; le modèle indique également un effet période probable qui diminue après 1987. Comme prévu, l'incidence du DT1 augmente avec l'âge. Ces variations s'expliquent en partie par l'interaction entre les facteurs environnementaux et les composants génétiques des populations. Pour la composante environnementale, en utilisant des modèles de régressions linéaires multiples (MLR) Stepwise, les facteurs prédictifs environnementaux les plus importants de la variation de l'incidence du DT1 d'un pays à l'autre étaient: les rayons UV, le nombre d'abonnements aux téléphones mobiles dans le pays, les dépenses de santé par habitant, la vaccination contre l'hépatite B et l'IMC (indice de masse corporelle) moyen. Pour les composants génétiques, en utilisant des modèles de MLR Stepwise, les variables significatives étaient les allèles sensibles: DRB1*04:01 (p = 0,0001) et DRB1*08:01 (p = 0,003). Aussi, une grande hétérogénéité des haplotypes susceptibles ou protecteurs face au DT1 au sein des populations a été trouvée.

Conclusions et implications :

Au cours de cette thèse, nous avons quantifié la variation pays par pays de l'incidence du DT1 dans le monde chez les enfants et les adultes, montrons qu'une grande partie de cette incidence pourrait s'expliquer par la variabilité de facteurs environnementaux d'un pays à l'autre. Cette thèse a également profité de la disponibilité croissante de données publiques sur la variation des facteurs environnementaux et génétiques de pays à pays, qui peuvent être impliqués dans le fort effet de cohorte de naissance qui a été observé ; il est conseillé d'approfondir la recherche sur la grossesse et la période néonatale et son implication dans DT1. Nous avons pu quantifier que l'augmentation de l'incidence du DT1 était encore plus grande que ce qui avait été prédit dans une étude antérieure (231).

Mots clés :

Le diabète de type 1, l'incidence, corrélations, modèles de régression linéaire, modèles de Âge-Période-Cohorte, Revue systématique. Background: Type 1 Diabetes (T1D) is one of the most common metabolic diseases in childhood (298). The increase of worldwide incidence of T1D was calculated to be on average 3.0% per year and it was predicted that it would be 40% higher in 2010 (231) in children aged 0-14 years. The causes of this increase are still under study; this thesis updates our current knowledge on the global incidence of T1D, and taking advantage of the increasing availability of public databases examines the correlation between incidence and country characteristics.

Methods: A comprehensive systematic literature search of published articles on the incidence of T1D worldwide was conducted to study global variations of the incidence within countries. We followed the PRISMA 9 group recommendations (213). The initial goal of this thesis was to study the correlation between environmental variables available in public databases with the country incidence of T1D. We also achieved a similar analysis of the genetic component of T1D. The frequency of major HLA susceptible and protective haplotypes reported elsewhere (89) was explored. The principal statistical methods used were correlations and multiple linear regressions (MLR). Finally, we used the Age-Period-Cohort (APC) approaches. For statistical and graphical analyses the R software (255), and for data visualization the Tableau software (262) were used.

Key findings:

Through the literature review, 265 selected references that contained epidemiological data about incidence cases of T1D among individuals aged 0-14 years, and 70 references of T1D incidence in those aged over 15 years, published between 1975 and 2014, were collected. Information was available on 90 countries and in 35 countries for those over 15 years of age. The incidence of T1D is highly variable among countries and regions worldwide in both children and adults. T1D incidence in adults paralleled those reported in children. Also, as opposed to what is known in children, we found that the incidence was in most studies larger in males than in females. Independently of the initial level of incidence, an increase in incidence was observed in all continents from 1960 to 2010. Finally, using the Age-Period-Cohort model we analyzed information from 2,327,604,529 person-years at risk from 192,741 cases. A strong effect cohort was observed; in addition the model shows an apparent effect period that decreases after 1987. Also as expected, the incidence of T1D increases with age. These variations are in part explained by the interaction between the environmental factors and the genetic components of the populations. For the environmental component, using Stepwise MLR models the most significant environmental predictors of the country-to-country variation of T1D incidence were UV radiation, number of mobile cellular subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean BMI. For the genetic component, using Stepwise MLR models, significant variables were the susceptible alleles: DRB1*04:01 and DRB1*08:01. High heterogeneity of susceptible and protector haplotypes for T1D within populations was found.

Conclusions and implications:

During this thesis, we quantified the country-to-country variation in T1D incidence worldwide in children and adults, and show that a large part of this incidence could be accounted for by the variability of environmental factors within the countries taking advantage of the increasing availability of public databases. Environmental factors may be involved in the strong cohort effect we found; it is advisable to deepen the research on pregnancy and neonatal period and its implications on T1D. Here we could quantify that the increase of T1D was even larger than had been predicted in an earlier study (231).

Keywords : Type 1 Diabetes, incidence, correlation, multiple linear regressions, Age-Period-Cohort model, systematic literature review.

  PS. I wish to thank Thierry Duroselle and Adona Fouchard at the Société Centrale d'Apiculture at the Jardin du Luxembourg for allowing me to discover the wonderful world of bees! This doctorate was financially supported by the award: « Becas Francisco José de Caldas, 2011-2012 » From Colciencias, Administrative Department of Science, Technology and Innovation of Colombia. With the additional financial support of the « Sapiensa -Enlaza mundos » Program, Alcaldía de Medellín, Colombia. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 13 geographical variations of T1D incidence in adults paralleled those reported in children.

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 14 variables were the susceptible alleles: DRB1*04:01 (p-value = 0.0001) and DRB1*08:01 (pvalue = 0.0032).

CRD42012002369 2 .

 2 Nous avons développé une méthode comportant sept étapes pour obtenir une équation de recherche bibliographique, dans laquelle les 92 articles précédemment récupérés ont été trouvés (voir Annexe B), mais en ajoutant de nouvelles références qui sont identifiées ici UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

(

  ONU) et la Banque Mondiale (WB), et disposés dans quatre domaines : Climat et environnement, Démographie, Economie, et Etat de Santé. Nous avons examiné tous les indicateurs présentant une relation plausible avec le DT1 et ceux qui précédemment on été rapportés dans la littérature comme corrélés avec le DT1, et pour lesquels il y avait moins de 5% de valeurs manquantes. Ceci a donné 77 indicateurs. Des corrélations et des régressions linéaires multiples parmi les indicateurs récupérés, lors de la revue systématique de ces domaines ont ensuite été réalisées. Le pourcentage de la variance expliquée par des variables environnementales a été évalué par la méthode de 10 fois la validation croisée. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 19 Analyse des déterminants génétiques Nous avons également réalisé une analyse similaire à celle décrite ci-dessus concernant les variables environnementales, pour les composantes génétiques du DT1 présentés au chapitre 6. Pour les analyses génétiques, la fréquence des haplotypes de HLA (Human Leukocyte Antigens) qui confèrent la susceptibilité à, ou la protection contre le DT1, correspondant à 21 HLA allèles DR/DQ (DR/DQ sont des sous-unités d'antigènes chez les humains), rapportés par le Type 1 Familles génétiques du Consortium diabète (2008) a été exploré

1 .

 1 Grâce à la revue de la littérature publiée entre 1975 et 2014, 265 références ont été sélectionnées, qui contenaient les données épidémiologiques sur les cas d'incidences de DT1 chez les individus âgés de 0-14 ans. Chez les personnes de plus de 15 ans, 70 références ont été sélectionnées. Parmi les enfants âgés de 0 à 14 ans, cette revue systématique a récupéré 237 documents de PubMed et GoogleScholar, couvrant 86 pays et quatre «territoires», et 28 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 20 documents supplémentaires, des résumés et des informations mises à jour pour la IDF (International Diabetes Federation) dans l'Atlas de la IDF (300) ont été joints. De ces 265 références, nous avons extrait 527 ensembles de données. Parmi ces 527 ensembles de données, 298 étaient des études individuelles, et 201 étaient des études de collaboration internationale (114 de l'étude Diamond en 2006 (318), 44 de l'étude EURODIAB en 2000 (90), 21 de l'étude EURODIAB en 2009 (242), 22 de l'étude EURODIAB en 2012 (243)), et 28 de l'Atlas de l'IDF (300) ; Figure Parmi les jeunes de plus de 15 ans, 70 articles rapportant l'incidence du DT1 dans le pays, et un article concernant deux pays ont été récupérés dans cette revue, résultant en un total de 71 études portant sur 35 pays, et 24 des 71 études étaient à l'échelle nationale. 43 documents ont fourni des informations sur l'incidence du DT1 dans la classe d'âge 15-29 ans, 26 dans la classe d'âge 30-59 ans et 6 chez les personnes de plus de 60 ans.

Figure 1 .

 1 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

Figure 1 :

 1 Figure 1: Variation de l'incidence du DT1 dans 90 pays

  laquelle nous avons recueilli toutes les incidences par classe d'âge et la période des études, Ces données ont été prélevées de tables, textes et/ou des figures contenus dans les 265 articles identifiés par la revue systématique. Nous avons extrait des données à analyser (223 articles sélectionnés) en utilisant des modèles APC, des informations équivalentes à 2,327,604,529 années-personnes à risque et de 192,741 cas de DT1 ont été analysés. Avec le UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

2 .

 2 (n = 2007), suivie par l'Amérique du Nord (n = 527) et l'Asie (n = 499). La période de 1989 à 1993 fut celle comportant le plus grand nombre d'ensembles de données (n = 844), suivie de 1984 à 1988 (n = 643) et de 1994 à 1998 (n = 550); Figure Le gradient du vert au rouge représente le nombre d'ensemble de données trouvé par classe d'âge et vert foncé <10 au rouge> 20. L'âge est la moyenne pour chaque classe d'âge (0-4, 5-9, 10-14), chaque période correspondant à cinq années calendrier.

Figure 2 :

 2 Figure 2: Répartition de l'information (bases de données) utilisée dans le modèle APC

Final

  considerations and perspectives are presented in the Conclusions section.Tables and figures are presented in order of appearance and are designated SI Table/Figure for those related with Supporting Information of the main publication of each chapter and for those presented in Additional Analyses, that include analyses or results unsuitable for publication but with additional information pertinent to this thesis, and AP Table/Figure for those published in the Appendix Section.

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 42 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 43

- 1 .- 2 .

 12 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 44 but adding new references. The entire processes aimed at obtaining the most sensitive and specific bibliographic search equation to retrieved original publications reporting incidence of T1D worldwide; here identified as 'query # 9'. Search equations were updated periodically, the last update dating to November 2014. Details of the seven proposed steps are presented in Appendix C. Summary of the seven steps used to obtain the final query search: Step To perform an analysis of the MeSH terms presented in the 92 selected articles included in the database. Step To carry out new queries in the MeSH Advanced Search Builder of PubMed taking into account the main MeSH terms found in step 1.

48 1. 4

 484 Comparison of incidence of T1D retrieved in the current review with other collaborative studies or reviews By 2012, this thesis included the information available worldwide on the incidence of T1D from 226 populations, from 68 countries, published in 161 articles between 1975 and 2011; a comparison of the retrieved incidence of T1D with the Onkamo et al. (231) review,

  ), 44 from the Eurodiab study in 2000 (90), 21 from the Eurodiab study in 2009 (242), 22 from the Eurodiab study in 2012 (243)), and 28 from the IDF Atlas (300).

Figure 4 andFigure 4 :

 44 Figure 4: Geographical distribution of countries where T1D incidence has been reported in the literature 1975-2014

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 52 In parenthesis: n = number of retrieved datasets by country. One dataset corresponds to the unique data retrieved from the literature. Geometric symbols: triangles indicate datasets from the Diamond study (n=114) (318), squares indicates databases from Eurodiab (n=87) (90, 242, 243), plus signs indicate data from the International Diabetes Federation (n=28) Atlas (241, 300) and circles are from the other publications that we retrieved (n=298); they were colored to visualize the trend in incidence (green: 1958 to red: 2009). The black line represents the mean country T1D incidence. For the detailed list of the studies / datasets see Appendix A: Database II.

Figure 5 :

 5 Figure 5: Variation of T1D incidence found in 90 countries

Figure 6 :Figure 7 :

 67 Figure 6: Publication year by World Region

Chapter 2

 2 briefly describes the state of the art in the epidemiology of T1D. This chapter has three main parts: in the first, a definition of diabetes is given, in the second, we present a review of the epidemiology of T1D before and after 1950, and in the third part, different hypotheses about the etiology of T1D are described.

chapter 3

 3 about epidemiology in children, chapter 4 epidemiology in adults, chapter 5 on global incidence trends of T1D using an APC model and chapter 6 about genetic determinants of T1D. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 58 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

59

 59

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 64 in Norway. The Oslo survey identified 4251 individuals aged less than 30 years with T1D, and the incidence remained relatively constant over the period 1925-1954; the average incidence was 4.1 per 100.000 individuals per year in children aged less than 15 years. In the Bergen survey, during the period 1925-1939 the incidence was 7.9 per 100.000 individuals per year in individuals under 20 years; at that time, the rise in incidence was attributed to the increasing age of the population (105).

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 67 85% of patients with recent diagnosis have no family history of diabetes (23) and T1D only appears in approximately 6% of siblings of patients with the disease (358). Only 2 chromosomal regions has been associated with T1D: the major histocompatibility complex (MHC) human leukocyte antigen (HLA) class II region on chromosome 6p21 [insulin-dependent diabetes mellitus 1 (IDDM1)] and the insulin gene region on chromosome 11p15 [insulin-dependent diabetes mellitus 2 (IDDM2)] (219). Genes located within the HLA-II account for approximately 50% of genetic risk of T1D. The HLA alleles associated with T1D susceptibility include HLA DR3, DR4, DQ2 (DQB1*0201-DQA1*0501-DRB1*03), and DQ8 (DQB1*0302-DQA1*0301-DRB1*04). These alleles are present in 90% of the children with T1D (111), whereas, other HLA alleles are associated with disease protection, such as DR2, DQA1*0102, DQB1*0602, and children with these diabetes-protective HLA are unlikely to develop T1D

69

 69

Figure 10 .

 10 UPMC -ED 393 -2015Epidemiology of Type I Diabetes: Global Incidence and Determinants

70

 70

Figure 8 :

 8 Figure 8: Peak of incidence of T1D by season of onset in the literature

Figure 9 :Figure 10 :

 910 Figure 9: Season of birth in patients with T1D in the literature

71 2. 4 . 3 . 2

 71432 Figures 11 and 12 show the distribution of recruited patients aged 0-16 years of the Isis-Diab cohort, an ongoing prospective cohort of French patients with T1D since 2007. More information about the Isis-Diab cohort on: http://www.isis-diab.org.

Figure 11 :

 11 Figure 11: Month of diagnosis of patients with T1D in the Isis-Diab cohort

Figure 12 : 2 . 4 . 4

 12244 Figure 12: Month of birth of patients with T1D in the Isis-Diab cohort

  Vehik et al. (2013) (342) discuss how advances in epidemiology, genetics, microbiology and immunology have provided new frontiers to understand the etiology of T1D. The relationship between early life environment -in utero and early postnatal period -, the development of gut immunity (microbiome) and epigenetic factors affect the risk of T1D (mediated by the innate or adaptive immune system). Environmental factors involved are for example a diet early in life, vitamin D levels, mode of delivery and enteroviral infections. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

77

 77

Figure 13 :

 13 Figure 13: Tentative clarification of the autoimmunity related factors with T1D

Figure 14 :

 14 Figure 14: Tentative clarification of the life style related factors with T1D

Figure 15 :Social

 15 Figure 15: Tentative clarification of the social determinants and T1D

3. 3 . 5 !

 35 Median!age!at!onset!of!T1D!.............................................................................................................!125! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 82 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

83

 83

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

84

 84

Fig 1 .

 1 Fig 1. Correlations between T1D incidence and 77 country indicators. The correlations were computed in the 80 WHO countries where T1D incidence could be estimated. Dots: no significant correlations. Squares: significant correlations with p0.2. Significant correlations after Bonferroni correction (p-value 0.000649) are highlighted. Abbreviations: PM: particular matter, UV: ultraviolet, GDP: Gross Domestic Product, GNI:

Gross

  National Income, BMI: Body Mass Index, CVD: Cardiovascular Diseases, CA: Cancer, DM: Diabetes Mellitus, CHRD: Chronic Respiratory Diseases, Adj. R2: Adjusted-R 2 . Red: positive correlations, blue: negative correlations. (a) Age-standardized estimate; (b) Per 100,000 individuals; (c) Coverage among 1year-olds (%). See S1 Database for the entire database. doi:10.1371/journal.pone.0118298.g001 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 90

Fig 2 .

 2 Fig 2. Stepwise identification of predictors of T1D Incidence (a) by domain and (b) final. The lines are the 35/77 variables with p<0.2. Green bars indicate variables that were excluded during the stepwise multiple regression (the length of the bar indicates the number of steps at which it was excluded). Blue bars indicate that variables were selected after SMLR was applied. The dark blue bars indicate that the independent predictors of T1D were highly significant; panel (A): in the by-domain analysis; panel (B): in the final model. The final analysis was performed on the variables selected in the by-domain analysis (shown in dark blue in panel (A). Abbreviations: Dom: Domain, PM: particular matter, UV: ultraviolet, GDP: Gross Domestic Product, BMI: Body Mass Index, CVD: Cardiovascular Diseases, CA: Cancer, DM: Diabetes Mellitus, CHRD: Chronic Respiratory Diseases, Adj.R2: Adjusted-R2. (a) Variable dropped after applied Akaike information criterion (AIC). doi:10.1371/journal.pone.0118298.g002

Fig 3 .

 3 Fig 3. The predicted incidence of T1D among 80 countries vs. the observed incidence. Red dot: Finland. See S2 Fig. for predicted indices obtained after 10-fold Cross-validation. doi:10.1371/journal.pone.0118298.g003

  393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 94 primary and secondary sources of registers. Data collection process reported in the article: (P) prospective-incident cases collected prospectively-, (H) historical-incident cases collected retrospectively-. First author and year of publication. Number of reference. NW: Nationwide study. Note: the age range for the reported incidence was 0-14 years, except in the following cases: (a) 0-12, (b) 0-13, (c) 0-18, (d) 0-19, (e) updated International Diabetes Federation (IDF) 2013. (DOCX) S3 Table. Models by domains and final summary model. CI, Confidence Intervals. (a) Agestandardized estimate; (b) per 100,000 individuals; (c) Coverage among 1-year-olds (%). (DOCX) S1 Protocol. PROSPERO protocol. International Prospective Register of Systematic Reviews. (PDF) S1 Database. Database global incidence of T1D and independent variables. Excel table containing the database used in the correlations of 77 independent variables and the incidence of T1D among 80 countries. (XLSX) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 95 10. Smyth DJ, Cooper JD, Bailey R, Field S, Burren O, Smink LJ, et al. A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nature genetics. 2006; 38(6):617-9. PMID: 16699517 11. Roach JC, Deutsch K, Li S, Siegel AF, Bekris LM, Einhaus DC, et al. Genetic mapping at 3-kilobase resolution reveals inositol 1,4,5-triphosphate receptor 3 as a risk factor for type 1 diabetes in Sweden. American journal of human genetics. 2006; 79(4):614-27. PMID: 16960798 12. Dorman JS. Molecular epidemiology of insulin-dependent diabetes mellitus. Epidemiol Rev. 1997; 19 (1):91-8. PMID: 9360906 13. Dorman JS, Bunker CH. HLA-DQ locus of the human leukocyte antigen complex and type 1 diabetes mellitus: a HuGE review. Epidemiologic reviews. 2000; 22(2):218-27. PMID: 11218373 14. Stayoussef M, Benmansour J, Al-Jenaidi FA, Nemr R, Ali ME, Mahjoub T, et al. Influence of common and specific HLA-DRB1/DQB1 haplotypes on genetic susceptibilities of three distinct Arab populations to type 1 diabetes.

  of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the Protocol S1 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 98

78 Conclusions 72 From: 16 : 3 . 1 . 1 . 3

 7872163113 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 78 FUNDING Funding Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097 For more information, visit: www.prisma-statement.org. Flow diagram literature search for T1D incidences UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 100 Publication list of the incidence of T1D by country and area SI

105 3 . 1 . 1 . 5

 3115 Visualisation of 10-Fold Cross-Validation 10 fold-Cross validation of the final model Small symbols show cross-validation predicted values of T1D incidence. Raw R 2 = 0.55, 10-Fold crossvalidated R 2 =0.409 SI Figure 17: 10-Fold Cross-Validation UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 106

  109

CE_10:

  CO 2 emissions (metric tons per capita); CE_18: UV radiation (J/mt2, 2004); DD_19: Adolescent fertility rate (births per 1,000 women aged 15-19); DD_21: Fertility rate, total (births per woman); DD_27: Population aged 65 and above (% of total); DD_33: Urban population (% of total population); ED_41: Mobile cellular subscriptions (per 100 persons); ED_36: Adjusted savings: education expenditure (% of GNI); EH_45: Health expenditure per capita, PPP (constant 2005 international $); HI_59: Hepatitis B (HepB3) immunization coverage among 1-year-olds (%); HR_47: Mean BMI (kg/m2) (age-standardized estimate) Male >20 years of age. SI Figure 18: Variables Factor Map (PCA): significant variables for all domains -393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

  Méthodes À partir du seul Registre enregistrant tous les polypes colorectaux sur l'ensemble d'un département depuis 1976, tous les résidents de Côte-d'Or âgés de 50 à 74 ans ayant eu un premier adénome ou carcinome in situ entre 1997 et 2008 ont été inclus. Les taux standardisés de détection des adénomes ont été étudiés selon deux périodes « pré-dépistage (1997-2002) » et « postdépistage (2003-2008) », en distinguant les individus ayant participé au DO des non-participants et les individus avec test positif des faux négatifs.

  http://dx.doi.org/10.1016/j.respe.2014.06.174 P5-4 Incidence mondiale de diabète de type 1 : revue systématique et corrélation avec des bases de données publiques P.A. Diaz-Valencia a,b , P. Bougnères c,d,e , A.-J. Valleron a,b a Inserm Unité 986, Le Kremlin-Bicêtre, France b Université Pierre et Marie Curie-Paris 6, Paris, France 3.2.3 Published Abstract: Incidence mondiale du diabète de type 1: revue systématique et corrélation avec des bases de données publiques A preliminary version of the analyses presented in the paper: Covariation of the incidence of T1D with country characteristics available in public databases (81) was presented during the VI International Congress of Epidemiology, and published in French in the Revue d'Epidémiologie et de Santé Publique (80). UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 111

40

 40 incidence, and around 1994 in countries with intermediate, low and very low incidence of T1D. The SI Figures 19-23 presented below show the increase in the incidence of T1D with successive birth cohorts; nevertheless, after the period 1994-1998, in countries with very high, intermediate and very low incidence levels, new cohorts exceed the previous cohort indicating a relative stabilization or a new tendency of T1D worldwide. The negative trend of T1D is persistent after stratification by country-incidence-level. There are enormous variations in incidence trends of T1D in individuals aged 0-40 years after stratifying the analysis according to incidence-country-level. Maximum cumulative incidence of T1D per 100.000 persons was respectively, 649.5 for countries with very high-incidence level, 394.8 for high, 220.4 for intermediate, 56.2 for low, 10.3 for very low-incidence countries, and 486 after cumulating all countries. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes:Cumulative incidence of T1D in persons aged 0-40 years in very high incidence countries by birth cohorts

40

 40 Cumulative incidence of T1D in persons aged 0-40 years in low incidence countries by birth cohorts (1924-2008) Age at onset Cumulative incidenence per 100.000 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Cumulative incidence of T1D in persons aged 0-40 years in very low incidence countries by birth cohorts (1924-2008) Age at onset Cumulative incidenence per 100.000 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants

  120

Figure 24 : 3 . 3 . 4

 24334 T1D incidence trends in major UN areas among studies reporting ascertainment UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 121 The analysis of the variation of T1D incidence with time in European countries was restricted to nationwide studies having reported the percentage of ascertainment. Y-axis: incidence of T1D per 100.000 individuals of both sexes (0-14 years old) per year. X-axis: time of the mean point of duration of each study. Circles represent each retrieved study. SI Figure 25: T1D incidence trends in Europe among nation-wide studies reporting ascertainment UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 122 Anticipation phenomenon in the age at onset of T1D

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 124

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 125

Figure 26 26 : 1 !! 4

 262614 Figure 26.

!

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants132objective was to study how the variations of T1D incidence in adults mirrored those in children.

113 Excluded 18 ArticlesFigure 1

 181 Figure 1 PRISMA Flow diagram bibliographic search strategies. * Kumar P, et al. Indian Med Assoc. 2008;106(11):708-711. ** The article: Radosevic B, et al. Pediatr Diabetes. 2013;14(4):273-4 gives information from two countries: 1) Bosnia and Herzegovina: Republic of Srpska and 2) Slovenia, Nationwide.

!

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 134

WHO

  Member States are divided into high-income (HIGH) or low-and middle-income (LMIC) states[30]. AA: autoantibodies, NW: Nation-wide study, NA: Unavailable data. (a) When there were diagnostic doubts, (b) Only for patients aged over 40 years at onset, (c) Not performed in all cases; the author of this study was contacted to confirm the proportion of these cases, but by the time of submission of this paper no answer was available. T1D: Type 1 Diabetes. Highlighted: reports of the systematic review using the autoantibodies/C-peptide as diagnosis criteria. ( † ) Studies used in the statistical analyses. (*) Data were not available but researchers assumed that patients have had T1D based on their average of age. ( § ) Ohio (8counties), Washington State (5 counties), South Carolina, Colorado, California. Diaz-Valencia et al. BMC Public Health (2015) 15:255 ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 140

!Figure 3 (

 3 Figure 3 (See legend on next page.)

1

 1 Search equation used for the bibliographic analysis of incidence of T1D in adults We used the following equation to search the electronic database Medline on November 6 th 2014: "diabetes mellitus, type 1/epidemiology"[MAJR] AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND (adult[TIAB] OR adult[MeSH Terms]) AND "humans"[MeSH Terms]) AND (Journal Article[PT] NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms] OR "Vascular Diseases" [MeSH Terms] OR "Arteriosclerosis" [MeSH Terms] OR "Lipoproteins" [MeSH Terms] OR "Vascular Endothelial Growth Factors" [MeSH Terms] OR "Comorbidity" [MeSH Terms]) ! AND English[Lang]: 178 Retrieved articles 4 .

149 4 . 1 . 1 . 3

 4113 List of additional papers used in the child-adult correlations SI Table

4 . 1 . 1 . 5

 4115 (a) 0-9 years of age, (b) 10-19 years of age, (c) 10-18 years of age, (d) 15-17 years of age, (e) 15-18 years of age, (---): unavailable data. (*): Data was retrieved from a different study; for details see Additional file 3. ( †) Studies used in the geographical correlation analyses. ( ‡) Special population. ( §) The five areas were Ohio (8 counties), Washington State (5 counties), South Carolina, Colorado and California; the table presents the mean incidence calculated, retrieved from 5 populations: African American, Asian Pacific Islander, Navajo, Hispanic and non-Hispanic young. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 154 Incidence of T1D by sex in young adults and adults SI

  First author and publication year in reports of the systematic review using the autoantibodies/C-peptide as diagnosis criteria are highlighted. Inc: incidence per 100.000 persons per year. NW: Nation-wide study. HIGH, LMIC: High, Low-Medium Income Level. ( †) Studies used for analyses. ( §) The five areas were Ohio (8 counties), Washington State (5 counties), South Carolina, Colorado and California; the table presents the mean incidence calculated retrieved from 5 populations: African American, Asian Pacific Islander, Navajo, Hispanic and non-Hispanic young. Incidence was calculated as the mean of retrieved information: (a) in Jews and other non-Arabs and Arabs; (b) in White and Black populations; (c) in Non-Hispanic Whites and Hispanic Whites. (d) Study giving the total incidence by sex, not by age classes. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes:

5

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 158 This page was intentionally left blank. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 161 Global incidence trends of T1D A classical epidemiological method to disentangle the possible effects of environmental factors influencing the incidence of T1D at different ages, at different periods and in different birth cohorts is the Age-Period-Cohort (APC) method, first proposed by Derrick in 1927 (77) in studies of mortality, then extended to other domains of public health such as social sciences, psychology, stomatology, ophthalmology, demography, and studies of environmental factors related with cancer (110). The first part of this chapter briefly describes the model and gives some examples of the APC approaches used to answer different questions in the domain of public health. The second part of this chapter presents the APC approach we used to explain the increase in the incidence of T1D worldwide, as documented in chapters 1, 2 and 3. In the APC model presented in this chapter included 2,327,604,529 person-years at risk and 192,741 cases retrieved from 265 worldwide studies published between 1975 and 2014, reporting T1D incidence among individuals aged 0-14 years and collected through a systematic review.

aged 10 -

 10 14 years. Environmental factors are no doubt implicated. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 162 This page was intentionally left blank.

  The Norman Ryder's classical article published in 1965 (272): "The cohort as a concept in the study of social change", argues that a cohort membership could determine behavior as other social structures such as socioeconomic status, and describes in the abstract, the cohort analysis as follows: Later, in 1973, Mason et al. (206) specified the Age-Period-Cohort (APC) "identification problem" due to the linear relationship of the variables age, period and birth cohort in the model, in their classical article: "Some methodological issues in cohort analysis of archival data"; see abstract below: UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 164 During the last years, several articles and replies about the methods on APC analyses were published and detailed by Kenneth C. Land in the seminar: "Disentangling Age-Period-Cohort Effects: New Models, Methods, and Empirical Applications" (178). Land argues that recent methodological contributions such as developments of statistics (such as mixed (fixed and random) effect models, Markov Chain Monte Carlo (MCMC) estimation of Bayesian models) 'can lead to better methods for APC analyses that can be applied by ordinary [social] scientists'. Recommendations for the estimation of APC models include: a) graphic descriptive analysis of the model, b) model fitting procedures, and when the analyses suggest that all three dimensions (age, period and cohort) are operating in the model: c) the introduction of an external factor, (e.g., apply the procedure of the Intrinsic Estimator (IE)) (178). However, in APC analyses based on repeated cross-section sample surveys or data registers, such as the data we collected on T1D in this thesis, individual-level data are not available, then other research designs have been recently proposed. These new approaches of the APC model can use a different temporal grouping for the age (A), period (P), and cohort (C) dimensions that breaks the linear dependency: UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 165 -Single year of age -Time periods correspond to years in which the surveys are conducted -Cohorts can be defined either by five-or ten-year intervals On this last APC based on population data from surveys or registers, to assess the possibility that individuals within the same periods and cohorts could share unobserved random variances, and to determine if a period and/or cohort heterogeneity can be explained by the given period and/or cohort characteristics, the Hierarchical Age-Period-Cohort (HAPC) Models were proposed as an alternative solution. The HAPC models use as analytical methods: a) mixed (fixed and random) effect models or hierarchical linear models (HLM) and b) cross-classified random effect models (CCREM) (178).

  analysis of the Global Incidence trends of Type 1 diabetes P. A. Diaz-Valencia, P. Y. Boëlle, P. Bougnères, A. J. Valleron.

Conclusions:

  The incidence of T1D increases over the 14 first years of life. During the last century, dynamic changes of the incidence trends of T1D were observed. Both calendar period and birth cohort effects were associated. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 168

Figure 27 :

 27 Figure 27: Flow diagram bibliographic search strategy

Figure 28 :

 28 Figure 28: Distribution of information (datasets) used in the APC model by main geographical areas

Figure 29 :

 29 Figure 29: APC analyses using data groped in 5*5 groups

!

  UPMC -ED 393 -2015Epidemiology of Type I Diabetes: Global Incidence and Determinants 176

1

 1 Search equation used for the bibliographic analysisWe used the following equation to search the electronic database Medline on November 6 th 2014:("diabetes mellitus, type 1/epidemiology"[MAJR]) AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND (age[Title/Abstract] OR "age distribution"[MeSH Terms] OR "age factors"[MeSH Terms] OR "age groups"[MeSH Terms] ) AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Autoantibodies"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms] OR "Vascular Diseases" [MeSH Terms] OR "Arteriosclerosis" [MeSH Terms] OR "Lipoproteins" [MeSH Terms] OR "Vascular Endothelial Growth Factors" [MeSH Terms] OR "Comorbidity" [MeSH Terms]) 5 .

  interpretation of the results in the context of other evidence, and implications for future research. 164FUNDINGFundingDescribe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.

From: 5 . 2 . 5 . 3

 5253 Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. doi:10.1371/journal.pmed1000097 For more information, visit: www.prisma-statement.org. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 180 Selected papers reporting incidence of T1D in 0-14 year-olds SI

!SI Figure 30 :SI Figure 31 :SI Figure 32 : 5 . 3 . 1 . 2

 3031325312 UPMC -ED 393 -2015Epidemiology of Type I Diabetes: Global Incidence and Determinants 186 5.2.5.4 Observed vs predicted incidence of T1D by countries using an APC model USA: United States of America; ITA: Italy; POL: Poland; SWE: Sweden; GBR: United Kingdom; AUT: Austria; CAN: Canada; JPN: Japan; BGR: Bulgaria. For each country there are several studies, dots represent these studies. Information from a particular study are in the same color in each country, color through different countries are independent. Observed vs predicted incidence of T1D in countries with large incidence rates using an APC mode ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 187 FRA: France; FIN: Finland; HUN: Hungary; SVK: Slovakia; THA: Thailand; NOR: Norway; AUS: Australia, ISR: Israel; CZE: Czech Republic. For each country there are several studies, dots represent these studies. Information from a particular study are in the same color in each country, color through different countries are independent. Observed vs predicted incidence of T1D by countries with medium incidence rates using an APC mode ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 188 BRA: Brazil; CHL: Chile; ESP: Spain; DEU: Germany; KWT: Kuwait; MLT: Malta; CHN: China; CYP: Cyprus; VIR: United States Virgin Islands. Observed vs predicted incidence of T1D by countries with small incidence rates using an APC mode ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 190 Incidence trends of T1D and temporal changes on the economic growth of countries In an exploratory analysis, temporal trends of T1D incidence by each one of the 89 countries were analyzed. Comparing the resulting drift of each country allows to distinguish differences in the evolution of the T1D by birth cohort in each country. The following figures show the results retrieved for the countries with a higher incidence growth with respect to countries with an average growth: Canada (CAN), Chile (CHL), Hungary (HUN), Poland (POL), Singapore (SGP), Slovakia (SVK), Switzerland (CHE), and Thailand (THA).

!SI Figure 34 :

 34 UPMC -ED 393 -2015Epidemiology of Type I Diabetes: Global Incidence and Determinants 191 CAN: Canada, CHL: Chile, HUN: Hungary, POL: Poland, SGP: Singapore, SVK: Slovakia, CHE: Switzerland, and THA: Thailand. Countries with a higher incidence growth respect to countries with an average growthThe following figures show the results retrieved for the countries with a lower incidence growth respect to countries with an average growth: China (CHN), Denmark (DNK), Israel (ISR), Kuwait (KWT), Libya (LBY), Norway (NOR), and Romania (ROU).

! 5 . 3 . 1 . 4

 5314 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 196 Comparison of environmental correlations using the intercept of the T1D incidence retrieved in the APC model and the incidence of T1D retrieved in the systematic review Table

!6

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 202 This page was intentionally left blank. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 203 Genetic determinants of T1D

6 . 1

 61 susceptible alleles: DRB1*04:01 (p = 0.0001) and DRB1*08:01 (p = 0.003). Only the protective allele DQB1*06:02 had a significant positive correlation with the geographical repartition of T1D incidence. The adjusted % of variance explained (R 2 ) in the model was 61% and 30% after 10-fold cross validation. Correlation coefficient on partial correlations between the incidence of T1D and HLA alleles after controlling environmental variables dropped in most cases. A combination of genetic predisposition and environmental factors is required to produce the clinical manifestation of T1D. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 204 This page was intentionally left blank. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 205 Geographical covariation of T1D incidence and genetic markers P. A. Diaz-Valencia, Sophie Le Fur, P. Bougnères, A. J. Valleron. The incidence of Type 1 Diabetes (T1D) in children varies dramatically between countries and has increased during the last decades. Methods: Information on the incidence on T1D and on country characteristics were searched for the 194 WHO member countries. T1D incidence was extracted from a systematic literature review of all papers published between 1975 and 2014, including the 2013 update from the International Diabetes Federation, and was estimated for 80 WHO countries. The frequency of the major HLA (Human Leukocyte Antigen) haplotype determinants to T1D was searched for those countries where incidence was retrieved was searched in the Allele Frequency Net.

Findings:

  The frequency of documented susceptible and protector HLA haplotypes was investigated. We found heterogeneity in the effect of some haplotypes, conferring protection or susceptibility among different study populations. Seven significant correlations were detected in 21 alleles tested (12 conferring susceptibility to T1D and 9 conferring protection). After Stepwise Multiple Linear regression the significant variables were the susceptible alleles: DRB1*04:01 (p = 0.0001) and DRB1*08:01 (p = 0.003). Only the protective allele DQB1*06:02 had a significant positive correlation with the geographical repartition of T1D incidence. Conclusions: The frequency of susceptible and protective haplotypes toward T1D was highly variable around the world. Susceptible HLA alleles to T1D are frequent that protective ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 206 alleles; among 21 alleles studied, DRB1 04:01 and DRB1 08:01 alleles are determinant in the risk of T1D.

!

  UPMC -ED 393 -2015Epidemiology of Type I Diabetes: Global Incidence and Determinants 207 characteristics, insofar as several global organizations collect such data, and provide them free to researchers, with easy interface on the Internet. value per 100.000 persons/year for individuals in the age group 0-14 years for both sexes used here for 80 countries worldwide was obtained through a systematic review following the PRISMA recommendations (213). All relevant original papers published in English between 1975 and 2014 including reviews (Diamond (318) and

  Families: DRB1*0301-DQA1*0501-DQB1*0201 (OR 3.64), DRB1*0405-DQA1*0301-DQB1*0302 (OR 11.37), DRB1*0401-DQA1*0301-DQB*0302 (OR 8.39), DRB1*0402-DQA1*0301-DQB1*0302 (OR 3.63), DRB1*0404-DQA1*0301-DQB1*0302 (OR 1.59), DRB1*0801-DQA1*0401-DQB1*0402 (OR 1.25) and of the most protective haplotypes: DRB1*1501-DQA1*0102-DQB1*0602 (OR 0.03), DRB1*1401-DQA1*0101-DQB1*0503 (OR 0.02) and DRB1*0701-DQA1*0201-DQB1*0303 (OR 0.02) (89), (Appendix A: Database V). In the website Allele Frequency Net all haplotype frequencies were calculated ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 208 taking the total number of copies of the haplotype in the population sample (haplotypes/2n)

(

  273) was used to evaluate the predictive value of the final model. Graphic representation used the DAAG package (203). ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 209 Partial correlations were used to evaluate the effect of genetic factors controlled by environmental factors. The first-order partial correlations rXY.Z for the three intercorrelated variables were calculated using the application First-Order Partial Correlations at the VassarStats website available at: http://vassarstats.net/par.html (2). The R software was used for statistical and graphic analyses (255).

  of HLAs haplotypes found are in Appendix A: Database V. With the little information available, it was only possible to perform the correlation between the incidence of T1D and the susceptible haplotype: DRB1*0301-DQA1*0501-DQB1*0201 (OR 3.64, Spearman correlation (Sp. Cor.) = 0.18, p = 0.71, n = 7), and the protective haplotypes DRB1*15:01-DQA1*01:02-DQB1*06:02 (OR 0.03, Sp. Cor = 0.48, p = 0.36, n = 6), DRB1*1401 -DQA1*0101 -DQB1*0503 (OR 0.02, Sp. Cor = 0.66, p = 0.18, n = 6), and DRB1*0701-DQA1*0201-DQB1*0303 (OR 0.02, Sp. Cor = 0.40, p = 0.75, n = 4); it was possible correlate the 21 alleles contained in the DR-DQ haplotypes associated with T1D and the incidence of T1D retrieved in this review (Appendix A: Database VI). For susceptible haplotypes, significant positive correlations were found for the alleles, DRB1*03:01, DRB1*04:01, DRB1*08:01 and DQB1*02:01, and significant negative correlations for the alleles, DRB1*04:05 and DQB1*04:01. For protective alleles, only the allele DQB1*06:02 shows a significant (positive) correlation with incidence of T1D.

Figure 41 and

 41 Figure 41 and SI Figure 44-47 for graphs of retrieved correlations.

Figure 41 :

 41 Figure 41: Correlations between T1D incidence in 56 countries and susceptible/protective HLA alleles with respect to T1D

211 6 . 1 . 5 . 3

 6153 Partial correlations between incidence of T1D and HLA alleles controlling environmental factorsPartial correlations between incidences of T1D and selected HLA alleles after adjustment, controlling on independent environmental predictors were established; Appendix A: Database VII, SI Table

  were: UV radiation, number of mobile cell-phone subscriptions in the country, health expenditure per capita, hepatitis B immunization and mean body mass index (BMI). The selected HLA alleles were those of the four alleles that qualified to be included in the final model after checking for colinearity: DRB1*03:01, DRB1*04:01, DRB1*04:05, DRB1*08:01. Partial correlations between incidence of T1D and HLA alleles, and level of statistical significance, at the p-value <0.05, dropped after controlling for environmental variables for the allele DRB1*03:01 controlling for: UV radiation, number of mobile cell-phone subscriptions in the country, health expenditure per capita and BMI; only controlling for hepatitis B immunization the correlation coefficient increase in 1% (p = 0.0003).For the alleles DRB1*04:01 and DRB1*04:05 the correlation coefficient at the pvalue <0.05 dropped, after controlling for all five environmental predictors for: mobile cellphone subscriptions, hepatitis B immunization and BMI.

!

  UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 216 Sp.$Cor$=$0.46.,$p-value$=$0

! 45 : 46 : 47 : 6 . 1 . 7 . 3

 4546476173 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 217 Sp.$Cor$=$)0.20,$p)value$=$0Major genetic determinants alleles at HLA-DQ for susceptibility of T1D Red dot: Finland. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 218 Sp.$Cor$=$0.12.,$p-value$=$0Major genetic determinants alleles at HLA-DR protective against T1D Red dot: Finland. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 219 Sp.$Cor$=$0.08.,$p,value$=$0Major genetic determinants alleles at HLA-DQ protective against T1D Red dot: Finland. ! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 220 Details of the final Stepwise MLR model SI

221 6 . 1 . 7 . 4

 6174 Visualisation of 10-Fold Cross-Validation of the final model Small symbols show cross-validated predicted values of T1D incidence vs. HLA alleles. Raw R 2 = 0.664, 10-Fold cross-validated R 2 =0.299. SI Figure 48: 10-Fold Cross-Validation 10 fold-Cross validation of the final model

8 . 2

 82 UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 226 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 249 Appendix B: First search query of the literature (Query # 0) Two independent searches performed during December 2011, which allow the identification of the initial 92 references. The first in the database MedLine access through PubMed and the second in Thompson Reuters through Web of Science. AP Figure 49.

(

  "incidence" [MeSH Terms] OR "models, statistical"[MeSH Terms] OR cohort studies [MH]) -Search #2 ((trend [TI] AND incidence [TI]) OR (trends [TI] AND incidence [TI]) OR (onset [TI] AND Incidence [TI]) OR "birth cohort"[TIAB] OR "age period cohort"[TIAB] OR age-period-cohort [TIAB]) OR "incidence" [TIAB]) -Search #3 Journal Article [PT] NOT (Letter [PT] OR comment [PT] OR editorial [PT] OR news [PT]) AND ("1990/1/1" [PDAT]: "2011/11/28" [PDAT]). Limits: Humans.

AP Figure 49 :- 8 . 2 . 3 . 2

 498232 the following search equation: -Topic = (cohort studies OR (trend* OR incidence* OR tendency*) OR epidemiology*) AND -Title = ("type 1 diabetes" OR "IDDM" OR "childhood diabetes" OR "juvenile diabetes" OR "diabetes") AND UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 250 -Title = ((trend AND incidence) OR (trends AND incidence) OR (onset AND Incidence) OR "birth cohort" OR "age period cohort" OR age-period-cohort). -The search was refined excluding: document type = (proceedings paper OR editorial material OR letter OR correction OR note). Databases=SCI-EXPANDED. Lemmatization=Off. Initial search query of the literature Articles retrieved from literature searh in combinade database n = 985 677 articles excluded based and title or abstract review. 10 references that pottencially satisfying inclusion criterya by title were excluded because the abstract was not available, and 26 because there were meeting abstract. 308 articles remaining 112 articles were excluded: 94 because we could not retrieved the full text, and 18 because we could not translate the article. 196 articles remaining 104 articles excluded: 24 because the main objective was not the study of incidence of T1D, 54 because the length of the study was ≤5 years, 3 because the study did not follow the WHO-1985, WHO-1999, ADA-1997 or ADA-2011 as criteria of diagnosis of T1D, 6 because the study was performed in special populations, 10 because the study is a review or a general topic, and 7 because the data were not from original studies. MeSH Major Topic [MAJR]: a MeSH term that is one of the main topics discussed in the article denoted by an asterisk on the MeSH term or MeSH/Subheading combination, e.g., Cytokines/physiology*. See MeSH Terms [MH] below. -MeSH Subheadings [SH]: MeSH Subheadings are used with MeSH terms to help describe more completely a particular aspect of a subject. For example, the drug therapy of asthma is displayed as asthma/drug therapy, see MeSH/Subheading Combinations in MeSH Terms [MH] below. -MeSH Terms [MH]: the NLM Medical Subject Headings controlled vocabulary of biomedical terms that is used to describe the subject of each journal article in MEDLINE. MeSH terms are arranged hierarchically by subject categories with more specific terms arranged beneath broader terms. MeSH terms in PubMed automatically include the more specific MeSH terms in a search. -Publication Type [PT]: describes the type of material the article represents (e.g., Review, Clinical Trial, Retracted Publication, Letter); see the PubMed Publication Types, e.g., review[pt]. -Title/Abstract [TIAB]: words and numbers included in the title, abstract, and other abstract of a citation. English language abstracts are taken directly from the published article. If an article does not have a published abstract, NLM does not create one. -Publication Date [DP]: the date that the article was published. -Source and details in: http://www.ncbi.nlm.nih.gov.gate2.inist.fr/books/NBK3827/ Web of Science: -SCI-EXPANDED: Science Citation Index Expanded (--1975-present) -SSCI: Social Sciences Citation Index (--1975-present) -A&HCI: Arts & Humanities Citation Index (--1975-present) -CPCI-S: Conference Proceedings Citation Index-Science (--1990-present) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 252 -CPCI-SSH: Conference Proceedings Citation Index-Social Science & Humanities (--1990-present) -Searching the Topic Field: enter Topic terms to search the following fields within a record: title, abstract, author keywords, Keywords Plus ® . The product returns every record containing all your search terms.

1 .AP Figure 50 :

 150 MeSH term reported in the articles retrieved from query# 0All MeSH term included in the 92 database references were listed and ordered using Excel. Nine hundred and ninety-one (991) MeSH terms were found using relevant MeSH terms (for example Diabetes Mellitus, Type 1) reported in each article. One hundred and fourteen (114) irrelevant terms as Kuwait/epidemiology or Hospitalizations/statistics or numerical data were discarded. AP Figure50presents the distribution of the main MeSH terms reported in the first 92 articles. Frequency of MeSH terms included in selected articles

AP Figure 51 :

 51 reported i the first 92 articles UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 254 groups"[MeSH Terms] 6 AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] : "2011/11/28"[PDAT])) 8.3.3 Step 3. Frequent MeSH term retrieved from irrelevant papers The query # 1 showed 9817 references arranged in 491 pages into PubMed Website. I examined all MeSH terms reported in the first irrelevant reference each 50 pages, starting on the first page (for example: reference 1 in page 1, reference 981 in page 50, reference 1981 in page 100, … reference 8889 in page 450). Using Excel software, 167 MeSH terms were listed and arranged. Following criteria of relevance and frequency, 8 MeSH terms were selected as irrelevant. See AP Figure 51. Frequency of irrelevant MeSH terms included in query #1

Figure 52 .AP Figure 52 : 3 ii

 52523 Figure 52.

3

 3 NOT Query # 4 = 90 references Query # 4 NOT Query #3 = 0 references Note: In the Query # 3 NOT Query # 4 = 90, were found 2 relevant references in the first 60 examined. The common MeSH term of these references was immunology. In consequence, a new query to keep the Subheading immunology was necessary. mellitus, type 1/epidemiology"[MAJR] OR "type 1 diabetes"[TIAB]) AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND age[Title/Abstract] OR "age distribution"[MeSH Terms] OR "age factors"[MeSH Terms] AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] : "2011/11/28"[PDAT])) NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Autoantibodies"[MeSH Terms] OR "Blood Glucose"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms]) 915 Query #5 is equal to query #4, except for the exclusion of the term: "immunology" [Subheading]Repeating step 3 for frequent MeSH term in irrelevant papers of query # 5.

53 .AP Figure 53 :

 5353 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 258 The query # 5 showed 915 references arranged in 46 pages into PubMed Website. I examined all MeSH terms reported in the first irrelevant reference each 5 pages, starting on the first page. Using Excel software, 188 MeSH terms were listed and arranged. Following criteria of relevance and frequency, 6 MeSH terms were selected as irrelevant. See AP Figure Frequency of irrelevant MeSH terms included in query #5 iii) Query #5 vs. query #6

  Query # 5 NOT Query # 6 = 310 references Query # 6 NOT Query # 5 = 0 references Note: In the Query # 5 NOT Query # 6 = 310, were found 4 relevant references in the first 60 examined. The common MeSH term of these references was risk factors. In consequence, a new query to keep the MeSH term Risk Factors was necessary. mellitus, type 1/epidemiology"[MAJR] OR "type 1 diabetes"[TIAB]) AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND age[Title/Abstract] OR "age distribution"[MeSH Terms] OR "age factors"[MeSH Terms] AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] : "2011/11/28"[PDAT])) NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Autoantibodies"[MeSH Terms] OR "Blood Glucose"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms] OR "393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 260 Diseases" [MeSH Terms] OR "Arteriosclerosis" [MeSH Terms] OR "Lipoproteins" [MeSH Terms] OR "Vascular Endothelial Growth Factors" [MeSH Terms] OR "Comorbidity" [MeSH Terms]) Repeating step 3 for frequent MeSH term in irrelevant papers of query # 7. The query # 7 showed 776 references arranged in 39 pages into PubMed Website. We examined all MeSH terms reported in the first irrelevant reference each 5 pages, starting on the first page. Using Excel software, 115 MeSH terms were listed and arranged. Following criteria of relevance and frequency, 0 MeSH terms were selected as irrelevant. As consequence, we decided to exclude only the term "type 1 diabetes"[TIAB] of the equation in order to analyze if was possible obtain a query more specific but keeping sensitivity. mellitus, type 1/epidemiology"[MAJR]) AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND age[Title/Abstract] OR "age distribution"[MeSH Terms] OR "age factors"[MeSH Terms] AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] : "2011/11/28"[PDAT])) NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Autoantibodies"[MeSH Terms] OR "Blood Glucose"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms] OR "Vascular Diseases" [MeSH Terms] OR "Arteriosclerosis" [MeSH Terms] OR "Lipoproteins" [MeSH Terms] OR "Vascular Endothelial Growth Factors" [MeSH Terms] OR "Comorbidity" [MeSH Terms]HD, Bojovic BM, Sipetic SB, Adanja BJ, Jarebinski MS, Radmanovic SZ, et al.Insulin dependent diabetes mellitus: incidence in childhood inBelgrade 1982-92. J Epidemiol Community Health. 1995 Feb;49(1):107-8.

8 . 3 . 7 Step 7 .

 8377 the term: "Blood Glucose"[MeSH Terms] of the equation and including the term: "age groups"[MeSH Terms]. Repetition of steps 2 to 7 until the majority of 92 articles are included.

1

 1 mellitus, type 1/epidemiology"[MAJR]) AND (("incidence"[MeSH Major Topic] OR incidence[Title/Abstract]) AND (age[Title/Abstract] OR "age distribution"[MeSH Terms] OR "age factors"[MeSH Terms] OR "age groups"[MeSH Terms] ) AND "humans"[MeSH Terms]) AND (Journal Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] : "2011/11/28"[PDAT])) NOT ("Diabetic Neuropathies"[MeSH Terms] OR "Diabetic Nephropathies"[MeSH Terms] OR "Autoantibodies"[MeSH Terms] OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR "histocompatibility antigens class ii"[MeSH Terms] OR "insulin"[MeSH Terms] OR "Vitamin D"[MeSH Terms] OR "Diabetic ketoacidosis"[MeSH Terms] OR "Vascular Diseases" [MeSH Terms] OR "Arteriosclerosis" [MeSH Terms] OR "Lipoproteins" [MeSH Terms] OR "Vascular Endothelial Growth Factors" [MeSH Terms] 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 263 With the query # 9 we lost 2 of 92 database references: the article No.3 (Carrasco E, 2006) because it was published in Spanish, and the article No. 6 (Skordis, 2002) because it did not report any MeSH term. We retrieved 57 additional papers expanding the search for all published articles before 1990/01/01. After 2011/11/28, there were not new indexed references by February 2012. Updated searches were performed on November 2014 and January 2015. With the methodology proposed in this document, the best query equation for conduct a literature review of the study of incidence of T1D considering the age at onset worldwide is the query # 9. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 264 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 265 Correlation matrix incidence of T1D and climate and environment domain Selected variables: CA_1 + CE_10 + CE_15 + CE_16 + CE_8 + CE_11 + CE_18.

AP Figure 54 :

 54 Correlation matrix: environment domain (Agriculture)

55 :AP Figure 56 : 58 : 3 AP Figure 59 : 60 :AP Figure 61 : 8 . 4 . 4 63 :AP Figure 64 :AP Figure 65 : 2

 55565835960618446364652 Correlation matrix: climate and environment domain (Environment 1 of 2) Correlation matrix: climate and environment domain (Environment 2 of 2) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 267 Correlation matrix: demography domain (Demography 2 of 2) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 269 Correlation matrix incidence of T1D and economic factors domain Selected variables: ED_39 + ED_41 + ED_42 + ED_36 + ED_37 + EH_43 + EH_45 + EC_33 + EC_34. See Table 12 for name of codes. Correlation matrix: economic factors domain (Country economy) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 270 Correlation matrix: economic factors domain (Development) Correlation matrix: economic factors domain (Health economy) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 271 Correlation matrix incidence of T1D and health conditions domain Selected variables: HD_68 + HD_71 + HD_72 + HI_60 + HI_57 + HI_59 + HI_58 + HM_75 + HR_56 + HR_47 + HR_51 + HR_54 + HR_55. See Table 12 for name of codes. AP Figure 62: Correlation matrix: health conditions domain (Risk factors for non-communicable diseases) Correlation matrix: health conditions domain (Deaths from non-communicable diseases) Correlation matrix: health conditions domain (Infant and maternal mortality rates) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 273 Correlation matrix: health conditions domain (Infectious diseases and immunization) UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 274 This page was intentionally left blank. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 280 Correlation matrix of HLA alleles against T1D included in the final model For details see Table

26 .AP Figure 72 :

 2672 Correlation matrix of HLA alleles against T1D included in the final model

51 : 52 : 53 : 54 : 55 : 56 : 57 : 58 : 59 : 60 : 61 : 62 : 63 : 64 : 65 : 66 : 67 : 68 : 69 :

 51525354555657585960616263646566676869 Frequency of irrelevant MeSH terms included in query #1 .......................... 254! AP Figure Frequency of irrelevant MeSH terms included in query #3 ......................... 256! AP Figure Frequency of irrelevant MeSH terms included in query #5 .......................... 258! AP Figure Correlation matrix: environment domain (Agriculture) ................................ 265! AP Figure Correlation matrix: climate and environment domain (Environment 1 of 2) 266! AP Figure Correlation matrix: climate and environment domain (Environment 2 of 2) 266! AP Figure Correlation matrix: demography domain (Demography 1 of 2) .................... 267! AP Figure Correlation matrix: demography domain (Demography 2 of 2) .................... 268! AP Figure Correlation matrix: economic factors domain (Country economy) ............... 269! AP Figure Correlation matrix: economic factors domain (Development) ...................... 270! AP Figure Correlation matrix: economic factors domain (Health economy) ................. 270! AP Figure Correlation matrix: health conditions domain (Risk factors for noncommunicable diseases) ......................................................................................................... 271! AP Figure Correlation matrix: health conditions domain (Deaths from noncommunicable diseases) ......................................................................................................... 272! AP Figure Correlation matrix: health conditions domain (Infant and maternal mortality rates) ....................................................................................................................................... 272! AP Figure Correlation matrix: health conditions domain (Infectious diseases and immunization) ......................................................................................................................... 273! AP Figure Graphic validation of the best model for the climate and environment domain ................................................................................................................................................ 275! AP Figure Graphic validation of the best model for the demography domain ............... 276! AP Figure Graphic validation of the best model for the economic factors domain ........ 277! AP Figure Graphic validation of the best model for the health conditions domain ........ 279! AP Figure 70: Graphic validation of the final summary model with all significant variables ................................................................................................................................................ 280! AP Figure 71: Graphic validation of the final model ............................................................ 281! AP Figure 72: Correlation matrix of HLA alleles against T1D included in the final model 282! UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 285

3 : 4 : 5 : 6 : 7 : 8 :UPMC

 345678 Extracted information from the papers and included in the database ........................ 46! Table Reviews reporting global incidence of T1D .............................................................. 47! Table Comparison of age-standardized incidence of T1D in children 0-14 years of age reported in four studies ............................................................................................................. 48!Table Predicted and observed incidence of T1D in 10 studies ............................................ 49! Table Distribution of the information available on the incidence of T1D worldwide ........ 51! Table Comparison of diagnosis criteria of diabetes according to the WHO and the ADA . 61! Table Risk and protectors related to T1D ............................................................................ 68! SI Table 9: CONSORT PRISMA checklist ............................................................................. 97! SI Table 10: Publication list of the incidence of T1D by country and area .......................... 100! SI Table 11: Models by domains and final summary model ................................................. 104! Table 12: Spearman Correlation: Type 1 Incidence reported in 80 countries vs.77 global variables .................................................................................................................................. 106! SI Table 13: Comparison of 95 indicators in countries with and without information of T1D ................................................................................................................................................ 111! SI Table 14: CONSORT PRISMA checklist ......................................................................... 147! SI Table 15: List of selected papers reporting incidence of T1D in 0-14 year-olds in 9 countries .................................................................................................................................. 149! SI Table 16: Geographic repartition, and reported adult T1D incidences found in the SR ... 150! SI Table 17: T1D incidences by sex in young adults and adults found in the systematic review ..................................................................................................................................... 154! Table 18: Incidence rates of T1D among children aged 0-14 years retrieved from 89 countries during the years 1975-2014 by main geographical areas ....................................................... 172! SI Table 19: CONSORT PRISMA checklist ......................................................................... 178! SI Table 20: List of selected countries and areas reporting incidence of T1D in 0-14 year-olds included in the APC model ..................................................................................................... 180! SI Table 21: Comparison environmental correlations and incidence of T1D by countries, adjusted using am APC model and crudes ............................................................................. 196! Table 22: Final Stepwise MLR model ................................................................................... 210! SI Table 23: Susceptible and protective HLA haplotypes against T1D vs incidence of T1D ................................................................................................................................................ 214! SI Table 24: Final Stepwise MLR model .............................................................................. 220! SI Table 25: Analysis of variance (Anova) ........................................................................... 220! SI Table 26: Residuals ........................................................................................................... 220! SI Table 27: Partial correlation for DRB1*03.01 .................................................................. 222! SI Table 28: Partial correlation for DRB1*04.01 .................................................................. 222! SI Table 29: Partial correlation for DRB1*04.05 .................................................................. 223! SI Table 30: Partial correlation for DRB1*08.01 .................................................................. 223! AP Table 31: Manual backward selection of the variables of climate and environment domain .................................................................................................................................... 275! AP Table 32: Manual backward selection of the variables of demography domain ............. 276! AP Table 33: Manual backward selection of the variables of economic factors domain ..... 277! AP Table 34: Manual backward selection of the variables of health conditions domain ..... 278! AP Table 35: Manual backward selection of all significant variables .................................. 280! AP Table 36: Manual backward selection of the HLA alleles against T1D .......................... 281!

8UPMC

  Le numéro d'inscription à PROSPERO est : CRD42012002369. Disponible sur http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002369

Table 1 :

 1 Information extraite des documents et inclue dans la base de données

	Informations sur l'étude	Informations sur l'incidence
	Nombre d'identifications de la référence	Incidence moyenne par pays / zone
	Nom du premier auteur	L'âge du début et la période au cours de laquelle le DT1
		a fait son apparition
	Date de publication (reçu / accepté / publié)	Incidence par genre: masculin, féminin et les deux
		ensemble.
	Région du monde / pays / domaine d'étude	Augmentation annuelle de l'incidence
	Période du début de l'étude / date de fin	Nombre de cas incidents
	Durée de l'étude	Population à risque pendant la période
	Tranche d'âge couverte par l'étude	Critères diagnostic de DT1
	Source d'information des données de la population	informations Complémentaires
	Conception de l'étude: historique, prospective ou	Indicateur sur la gestion du diabète en 2010 (OMS)
	les deux
	Les analyses statistiques utilisées dans l'étude	Signaler si l'information a été standardisée
	Pourcentage de détermination	Sources de l'information extraite (table, texte ou figure)

Table 2

 2 reports the main categories of data extraction included in the main database.

UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants

Table 3 :

 3 Reviews reporting global incidence of T1D

Table 4 :

 4 Comparison of age-standardized incidence of T1D in children 0-14 years of age reported in four studies

	Study	Ref.	Study	n ref. * Correlation
			period
	Current Review (A)		1975-2011	161
	Diamond study (B)	(318)	1990-1999	112	0.95
	Eurodiab (C)	(90)	1989-1994	45	0.96
	Onkamo (D)	(231)	1960-1996	36	0.97

Ref. Reference (*) Number of references by country and area reported in each study.

Table 5 :

 5 Predicted and observed incidence of T1D in 10 studies

	Predicted incidence of T1D
	by 2010
	(
	Country, area

Onkamo et al., (231)) Observed incidence of T1D in 10 studies

								Observed
								-
	Mean T1D Inc.*	Based on incidences from the study period	Mean T1D Inc. (a)	Based on period incidences from the study	Author, Publication year	Ref.	Predicted incidence of T1D (b)
	Austria	12.8	1979-1993	17.5	2004-2008	Patterson C, 2012	(243)	4.7
	Finland	50.2	1965-1996	62.42	2006-2011	Harjutsalo V, 2013	(128)	12.22
	Hungary	20.1	1978-1987	18.3	2004-2008	Patterson C, 2012	(243)	-1.8
	Israel, Yemenite Jews	10.1	1965-1993	11.1	1997-2010	Blumenfeld O., 2014	(39)	1
	Lithuania	7.5	1983-1992	14.2	2004-2008	Patterson C, 2012	(243)	6.7
	Malta	16.8	1980-1996	23.87	2006-2010	Formosa N, 2012	(100)	7.07
	Norway	41.8	1973-1982	32.8	2004-2008	Patterson C, 2012	(243)	-9
	Sweden	32.2	1978-1992	32.3	1983-2007	Dahlquist G. G., 2011	(74)	0.1
	UK, Oxford	33	1985-1995	25.10	2004-2008	Eurodiab, 2012	(243)	-7.9
	UK, Yorkshire	21	1978-1992	25.50	2004-2008	Eurodiab, 2012	(243)	4.5
	(a) Mean incidence of T1D per 100.000 per year (b) Difference between predicted and observed
	incidence of T1D. Ref.: Reference.
	UPMC -ED 393 -2015			50	Epidemiology of Type I Diabetes:
					Global Incidence and Determinants

Table 6 :

 6 Distribution of the information available on the incidence of T1D worldwide

	WHO region	AFRO	AMRO	EMRO	EURO	SEARO	WPRO	Non-
	WHO categories of income	LMIC	HIGH	LMIC	HIGH	LMIC	HIGH LMIC	LMIC	HIGH	LMIC	WHO Countries	TOTAL
	Number of countries in the region	45	3	29	5	16	22	28	11	6	16	0	194
	Countries and territories with incidence report	6	3	15	3	8	22	19	3	4	2	5	90
	Countries with Nation-wide studies	1	1	8	2	2	18	12	1	4	1	3	53
	0-14 years old population (thousands)	350,355	67,978 163,233	10,830 189,816 67,243	88,940 534,895	30,933 335,038	7,413 1,846,674
	0-14 year old
	population with published information	37%	100%	83%	86%	68%	100%	63%	74%	77%	79%	11%	69%
	on T1D incidence (%)

Table 7 :

 7 Comparison of diagnosis criteria of diabetes according to the WHO and the ADA

	ADA 2011	ADA 1997	WHO 1999	WHO 1985
	A1C ≥ 6.5%. The test	…	…	…
	should be performed in
	a laboratory using a
	method that is NGSP
	certified and
	standardized to the
	DCCT assay.*
	FPG ≥ 126 mg/dl (7.0	FPG ≥ 126 mg/dl (7.0	FPG ≥ 126 mg/dl (7.0	FPG ≥ 140 mg/dl (7.8
	mmol/l). Fasting is	mmol/l). Fasting is	mmol/l). Fasting is	mmol/l). Fasting is
	defined as no caloric intake for at least 8 h.*	defined as no caloric intake for at least 8 h.*	defined as no caloric intake for at least 8 h.*	defined as no caloric intake for at least 8 h.*
	2-h plasma glucose ≥ 200	2-h plasma glucose ≥ 200	2-h plasma glucose ≥ 200	2-h plasma glucose ≥ 200
	mg/dl (11.1 mmol/l)	mg/dl (11.1 mmol/l)	mg/dl (11.1 mmol/l)	mg/dl (11.1 mmol/l)
	during an OGTT. The test should be performed as described by the WHO, using a glucose load containing the	during an OGTT. The test should be performed as described by the WHO, using a glucose	during an OGTT, using a glucose load containing the equivalent of 75 g	during an OGTT, using a glucose load containing the equivalent of 75 g
	equivalent of 75 g	load containing the	anhydrous glucose	anhydrous glucose
	anhydrous glucose	equivalent of 75 g	dissolved in water.	dissolved in water.***
	dissolved in water.*	anhydrous glucose
		dissolved in water.**
	In a patient with classical	In a patient with classical	In a patient with classical	In a patient with classical
	symptoms of	symptoms of	symptoms of	symptoms of
	hyperglycemia or during a hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dl (11.1 mmol/l).	hyperglycemia or during a hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dl (11.1	hyperglycemia or during a hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dl (11.1	hyperglycemia or during a hyperglycemic crisis, a random plasma glucose ≥ 200 mg/dl (11.1
		mmol/l).	mmol/l).	mmol/l).

*In the absence of unequivocal hyperglycemia, the result should be confirmed by repeat testing. ** Not for routine test. *** Only recommended when the patient's glucose level is not determined. Note. One criterion is enough to made the diagnosis. ADA: American Diabetes Association, WHO: World Health Organization, A1C: Glycated hemoglobin, NGSP: National Glycohemoglobin Standardization Program, DCCT: Diabetes Control and Complications Trial Reference Method, FPG: fasting plasma glucose, OGTT: oral glucose tolerance test. UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 62

Table 8 :

 8 Risk and protectors related to T1D

Factors associated with increased risk of T1D

	Age at day-care entry (proxy of maternity leave and viral load)
	Caesarian section delivery
	Climatic influences
	Early infant diet
	Fall in birth-weight
	Food processing consumption
	High maternal age
	Improvement of socio-economic conditions
	Increased body mass
	Lifestyle habits
	Linear growth during early childhood
	Overweight and obesity
	Psychological stress
	Relative lack of Vitamin D, E and Omega-3 fatty acids
	Reduced frequency of early infections
	Stressful life events
	Toxins
	Vaccine administration (probably coincidental)
	Viral infection during pregnancy
	Viral infections during childhood

Factors associated with reduced risk of T1D

	Atopic diseases
	Breastfeeding/ duration of breastfeeding
	Early age at preschool day-care entry
	Early introduction of Vitamin D supplementation
	Healthy gut micro and macro biota
	Low birth weight
	Short birth length
	For more details see the following paragraphs in this chapter 2.
	UPMC -ED 393 -2015	Epidemiology of Type I Diabetes:
		Global Incidence and Determinants

3.2! Additional,analysis,of,the,article:,Covariation,of,the,incidence,of,T1D,with,country, characteristics,available,in,public,databases,............................................................................,106!

	1! Supporting!Information! .......................................................................................................................! 97!
	3.1.1.1! CONSORT!PRISMA!checklist!.......................................................................................................................! 97!
	3.1.1.2! Flow!diagram!literature!search!for!T1D!incidences!.........................................................................! 99!
	3.1.1.3! Publication!list!of!the!incidence!of!T1D!by!country!and!area! ....................................................!100!
	3.1.1.4! Details!of!the!models by domains and final summary model! .........................................................!104!
	3.1.1.5! Visualisation!of!10LFold!CrossLValidation!10!foldLCross!validation!of!the!final!model!.!105!
	3.2.1! Correlations!between!T1D!reported!in!80!countries!vs.!77!global!indicators! ..........!106!
	3.2.2! Principal!component!analyses!of!the!variables!included!in!the!final!model! ..............!109!
	3.2.3! Published!Abstract:!Incidence!mondiale!du!diabète!de!type!1:!revue!systématique!et!
	corrélation!avec!des!bases!de!données!publiques! ................................................................................!110!
	3

.3! Additional,analyses,of,the,epidemiology,of,T1D,among,children, ............................,111!

	3.3.1! Comparison!of!countries!with!and!without!information!using!95!indicators! ...........!111!
	3.3.2! Analyses!of!cumulative!incidence!of!T1D! ..................................................................................!115!
	3.3.2.1! Steps!performed!to!obtain!cumulative!incidence!of!T1D! ............................................................!115!
	3.3.2.2! Findings!............................................................................................................................................................!116!
	3.3.3! Worldwide!Incidence!trends!of!T1D!...........................................................................................!120!
	3.3.4! Anticipation!phenomenon!in!the!age!at!onset!of!T1D!.........................................................!122!

3.3.4.1! Epidemiological!reports!showing!the!change!in!incidence!trend!in!the!youngest!age! groups! 123!

Table 1 .

 1 Final Stepwise MLR model.

	Code	Coefficients

  Kawabata Y, Ikegami H, Kawaguchi Y, Fujisawa T, Shintani M, Ono M, et al. Asian-specific HLA haplotypes reveal heterogeneity of the contribution of HLA-DR and-DQ haplotypes to susceptibility to type 1 diabetes. Diabetes. 2002; 51(2):545-51. PMID: 11812768 16. The DIAMOND Project Group. Incidence and trends of childhood Type 1 diabetes worldwide 1990-1999. The DIAMOND project Group. Diabet Med. 2006; 23(8):857-66. PMID: 16911623 17. Pociot F, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, et al. Genetics of type 1 diabetes:

	74
	Protocol S1

Clinical and vaccine immunology: CVI. 2009; 16(1):136-8. doi: 10.1128

/CVI.00215-08 PMID: 19005023 15. what's next? Diabetes. 2010; 59(7):1561-71. doi: 10.2337/db10-0076 PMID: 20587799 18. Moher D, Liberati A, Tetzlaff J, Altman DG, Group. TP. Preferred Reporting Items for Systematic Reviews and Meta -Analyses: The PRISMA Statement. BMJ. 2009; 339:332-6 19. EURODIAB ACE Study Group. Variation and trends in incidence of childhood diabetes in Europe. Lancet. 2000; 355(9207):873-6. PMID: 10752702 20. Patterson CC, Dahlquist GG, Gyurus E, Green A, Soltesz G, Group ES. Incidence trends for childhood type 1 diabetes in Europe during 1989-2003 and predicted new cases 2005-20: a multicentre prospective registration study. Lancet. 2009; 373(9680):2027-33. doi: 10.1016/S0140-6736(09)60568-7 PMID: 19481249 21. Patterson CC, Gyurus E, Rosenbauer J, Cinek O, Neu A, Schober E, et al. Trends in childhood type 1 diabetes incidence in Europe during 1989-2008: evidence of non-uniformity over time in rates of increase. Diabetologia. 2012; 55(8):2142-7. doi: 10.1007/s00125-012-2571-8 PMID: 22638547 22. Soltesz G, Patterson C, Dahlquist G. Diabetes in the young: a global perspective. Global trends in childhood type 1 diabetes. International Diabetes Federation. Diabetes Atlas fourth edition2009. 23. Patterson C, Guariguata L, Dahlquist G, Soltesz G, Ogle G, Silink M. Diabetes in the young-a global view and worldwide estimates of numbers of children with type 1 diabetes. Diabetes Res Clin Pract. 2013; 103(2):161-75. doi: 10.1016/j.diabres.2013.11.005 PMID: 24331235 24. WHO. Global Health Observatory Data Repository 2013 [cited 2012 11-08]. Available: http://apps.who. int/gho/data/?vid=2472. 25. United Nations, Department of Economic and Social Affairs. Population Division (2013). World Population Prospects: The 2012 Revision, DVD Edition. [cited 2014 April 16]. Available: http://esa.un.org/ unpd/wpp/Excel-Data/population.htm. 26. World Bank Indicators. [Access date: 30-11-2013] [Internet]. 2013. Available: http://data.worldbank.org/ indicator. 27. Google Maps API. Geographical coordinates 2013. [Acsess date: 11-08-2012]. Available: http:// universimmedia.pagesperso-orange.fr/geo/loc.htm. 28. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. R version 3.0.1 (2013-05-16). Available: http://www.r-project.org/ ed. Vienna, Austria, 2013. 29. Abdi H. Bonferroni and Šidák corrections for multiple comparisons. In: Salkind NJ, editor. Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage, 2007. p. 103-7. 30. Weisberg S. Applied Linear Regression. US: Wiley Series in Probabilty and Statistics; 2005. 31. Sakamoto Y, Ishiguro M, Kitagawa G. Akaike information criterion statistics. Tokyo: Reidel Publishing Company; 1986. 32. S original, from StatLib, by Rob Tibshirani. R port by Friedrich Leisch. Package: bootstrap. Version 2014.4. Data Analysis And Graphics data and functions. p. Functions for the Book "An Introduction to the Bootstrap". UPMC -ED 393 -2015 Epidemiology of Type I Diabetes: Global Incidence and Determinants 96 INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known. 72 Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS). 73 METHODS Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number.

Table 10 :

 10 Publication list of the incidence of T1D by country and area

	Country, Area	Study period	Mean T1D Inc*	Inc. by area	Ascer tainm ent %	Info. Source	Data on collecti	Author, Pub yr. Ref.
	Algeria
	Oran	1990_1999	8.60	8.60	NA	PBDR P or H	Diamond, (318)
	Argentina
	Cordoba	1991_1992	7.55	7.00	90.0	PBDR P or H	Diamond, (318)
	Avellaneda	1990_1996	7.55	6.30	94.0	PBDR P or H	Diamond, (318)
	Tierra del Fuego	1993_1996	7.55 10.30 100.0	PBDR P or H	Diamond, (318)
	Corrientes	1992_1999	7.55	6.60	95.0	PBDR P or H	Diamond, (318)
	Australia
	NW	2000_2006	22.50 22.50	97.1	PBDR	P	Catanzariti L, (58)
	Austria
	NW	2004_2008	17.50 17.50	97.2	PBDR	P	Patterson C, (243)
	Bahamas
	NW	2001_2002	10.10 10.10	NA	MBR	P	Peter S A, (246)
	Barbados
	NW	1990_1993	2.00	2.00	NA	PBDR P or H	Diamond, (318)
	Belarus
	Gomel, Minsk	1997_2002	5.60	5.60 100.0	PBDR	NA	Zalutskaya A, (374)
	Belgium
	Antwerp	2004_2008	15.90 15.90	94.9	PBDR	P	Patterson C, (243)
	Bosnia and Herzegovina
	Tuzla Canton	1995_2004	7.53	6.93 100.0	PBDR	P	Tahirovic H, (312)
	Republic of Srpska	1998_2010	7.53	8.13 100.0	PBDR	P	Radosevic B, (256)
	Brazil
	Sao Paulo (Bauru)	1986_2006	8.83 10.66	94.7	PBDR	P	Negrato C, (221)
	Rio Grande do Sul (Passo
	Fundo)	1996_1999	8.83	7.00	82.5	PBDR P or H	Diamond, (318)
	Bulgaria
	Eastern	1989_1994	8.53	6.80	99.9	PBDR	P	Eurodiab, (90)
	Varna	1990_1999	8.53	8.10 100.0	PBDR P or H	Diamond, (318)
	Western	1990_1999	8.53 10.70	99.5	PBDR P or H	Diamond, (318)
	Canada
	Toronto (c)	1976_1978	21.73	9.00	97.2	PBDR	P, H	Ehrlich R M, (86)
	Manitoba	1985_1993	21.73 20.65	95.0	PBDR	P	Blanchard J F, (36)
	Prince Edward Island	1990_1993	21.73 24.50 100.0	PBDR P or H	Diamond, (318)
	Alberta (Edmonton)	1990_1996	21.73 23.30	85.5	PBDR P or H	Diamond, (318)
	Calgary	1990_1999	21.73 20.60 100.0	PBDR P or H	Diamond, (318)
	Québec	1989_2000	21.73 15.34	NA	PBDR	P	Legault L, (184)
	Newfoundland and Labrador	1987_2010	21.73 38.68	NA	PBDR	P, H	Newhook L A, (225)
	Chile
	IX Region	1980_1993	3.84	1.37	97.0	PBDR	P	Larenas G, (180)
	Santiago of Chile (Communes
	of Metropolitan region)	2000_2005	3.84	6.30 100.0	PBDR	P	Torres-Avilés F, (323)
	NW	1988_1994	0.47	0.47	93.0	PBDR	P, H	Yang Z, (371)
	Colombia
	Bogota	1990_1990	2.15	3.80	97.0	PBDR P or H	Diamond, (318)
	Cali	1995_1999	2.15	0.50	NA	PBDR P or H	Diamond, (318)
	Croatia
	NW	1995_2003	8.90	8.90	98.5	PBDR	P	Stipanic G, (305)
	Cuba
	NW	1990_1999	2.30	2.30	62.5	PBDR P or H	Diamond, (318)
	Cyprus
	NW	1990_2009	12.34 12.34	50.0	PBDR	NA	Skordis N, (294)
	Czech Republic
	NW	2004_2008	19.30 19.30	97.4	PBDR	P	Patterson C, (243)
	Dem. People's Republic of Korea
	Seoul	1990_1991	1.10	1.10	NA	PBDR P or H	Diamond, (318)
	Denmark

4 Details of the models by domains and final summary model

	Model and environment d error: 7.834 on 73 d R2: 0.3519; F-76 on 3 and 73 DF, p-e-07 y d error: 7.948 on 75 d R2: 0.3274; F-61 on 4 and 75 DF, p-7; AIC=336.5 ctors d error: 7.362 on 66 d R2: 0.4499; F-81 on 6 and 66 DF, p-e-08 itions d error: 7.23 on 65 d R2: 0.4604; F-76 on 7 and 65 DF, p-e-08 ary Model* d error: 6.84 on 70 d R2: 0.5114; F-26 on 6 and 70 DF, p-e-10; AIC=302.76 3.1.1.CI, Confidence Intervals. (a) Age-standardized estimate; (b) per 100,000 individuals; (c) Coverage among 1-year-olds (%). Code Coefficients Estimate CI 2.5% CI 97.5% Std. Error (Intercept) 18.113 13.071 23.155 2.530 CE_10 CO2 emissions (metric tons per capita) 0.462 0.189 0.735 0.137 CE_16 Outdoor air pollution (Annual PM10 [µg/m3]) -0.008 -0.060 0.045 0.026 CE_18 UV radiation (J/mt2, 2004) -0.003 -0.005 -0.002 0.001 (Intercept) -12.909 -79.608 11.928 6.690 DD_19 Adolescent fertility rate (births per 1,000 women ages 15-19) -0.121 -0.199 -0.019 0.043 DD_21 Fertility rate, total (births per woman) 4.071 0.956 8.011 1.716 DD_27 Population ages 65 and above (% of total) 0.744 0.181 1.125 0.217 DD_33 Urban population (% of total population) 0.147 -0.032 0.237 0.049 (Intercept) -1.488 -11.044 8.068 4.786 ED_39 Energy use (kg of oil equivalent per capita) 0.001 0.000 0.001 0.000 ED_41 Mobile cellular subscriptions (per 100 people) 0.105 0.039 0.172 0.033 ED_36 Adjusted savings: education expenditure (% of GNI) 1.131 0.052 2.209 0.540 ED_37 Improved sanitation facilities (% of population with access) -0.097 -0.225 0.031 0.064 EH_45 Health expenditure per capita, (constant 2005 international $) 0.002 0.001 0.003 0.001 EC_33 GDP growth (annual %) -0.473 -1.028 0.083 0.278 (Intercept) -105.151 -163.500 -46.802 29.216 HD_71 Chronic respiratory diseases, deaths. (a)(b) Male 0.074 -0.018 0.166 0.046 HI_59 Hepatitis B (HepB3) immunization (c) -0.118 -0.174 -0.062 0.028 HR_56 Total alcohol in liters of pure alcohol >15yr. -0.194 -0.640 0.251 0.223 HR_47 Mean BMI (kg/m2) (a) Male >20 yr. 3.319 1.411 5.228 0.956 HR_51 Mean Total Cholesterol in mmol/l (a) Male >25yr. 8.205 -0.026 16.437 4.122 HR_54 Preterm birth rate (per 100 live births) -0.604 -1.489 0.281 0.443 HR_55 Prevalence of undernourishment (% of population) 0.189 -0.168 0.545 0.178 (Intercept) -25.240 -55.934 5.460 15.390 CE_18 UV radiation (J/mt2, 2004) -0.002 -0.003 0.000 0.001 DD_21 Fertility rate, total (births per woman) 2.181 -0.430 4.793 1.309 ED_41 Mobile cellular subscriptions (per 100 people) 0.066 0.012 0.121 0.027 EH_45 Health expenditure per capita (constant 2005 international $) 0.001 0.000 0.003 0.001 HI_59 Hepatitis B (HepB3) immunization (c) -0.085 -0.139 -0.032 0.027 HR_47 Mean BMI (kg/m2) (a) Male >20 yr. 1.263 0.045 2.480 0.611	t-value 7.160 3.371 -0.288 -4.427 -1.930 -2.776 2.373 3.430 2.986 -0.311 1.761 3.151 2.092 -1.506 2.999 -1.700 -3.599 1.615 -4.208 -0.872 3.474 1.991 -1.363 1.058 -1.640 -1.952 1.666 2.428 2.401 -3.187 2.068	p-value 0.000 0.001 0.774 0.000 0.057 0.007 0.020 0.001 0.004 0.757 0.083 0.002 0.040 0.137 0.004 0.094 0.001 0.111 0.000 0.387 0.001 0.051 0.178 0.294 0.106 0.055 0.100 0.018 0.019 0.002 0.042
	SI
	UPMC -ED 393 -2015	104	Epidemiology of Type I Diabetes:
			Global Incidence and Determinants

Table showing the list of publications reporting T1D incidence used in the analyses (*). Information source: (PBDR) population-based data register, (MBR) medical-based record, (OPD) other population denominators, (NS) non-specified. % Ascertainment: percentage of completeness between primary and secondary sources of registers. Data collection process reported in the article: (P) prospective -incident cases collected prospectively -, (H) historical -incident cases collected retrospectively -. First author and year of publication. Number of reference. NW: Nationwide study. Note: the age range for the reported incidence was 0-14 years, except in the following cases: (a) 0-12, (b) 0-13, (c) 0-18, (d) 0-19, (e) updated International Diabetes Federation (IDF) 2013.

Table 11 :

 11 Models by domains and final summary model

UPMC -ED 393 -2015

Epidemiology of Type I Diabetes: Global Incidence and Determinants

Table 12 :

 12 Spearman Correlation: Type 1 Incidence reported in 80 countries vs. 77 global variables Historical Data. Available from http://data.worldbank.org/developers/climate-data-api, accessed 11-08 2012; (WHO) WHO (2012) Global Health Observatory Data Repository. Available from http://apps.who.int/gho/data/?vid=2472, accessed 11-08 2012; (UN) United Nations, Department of Economic and Social Affairs (2011) World Population Prospects, the 2010 Revision. Available from http://esa.un.org/wpp/Excel-Data/population.htm, accessed May 31 2012; (GM) Google Maps API Geographical coordinates 2013. Available from http

				Countries
	C at eg or y	Code	Variable	with variable available inf. on each T1D for	Spearman Estimate/ p-value Correlation:	Mean	SD	Ref
				(n of 80)
				Agriculture
		CA_1	Agricultural land (% of land area)		80	0.07 5.29E-01	37.41	21.00	WB
		CA_2	Arable land (% of land area)		80	0.10 3.96E-01	17.03	14.28	WB
		CA_3	Cereal production (metric tons)		79 -0.03 8.02E-01 27600000 80200000	WB
		CA_4	Cereal yield (kg per hectare)		78	0.33 2.77E-03	4419.00	2079.00	WB *
		CA_5	Fertilizer consumption (kilograms per hectare of arable land)		76	0.02 8.63E-01	233.10	487.80	WB
		CA_6	Forest area (% of land area)		80 -0.06 5.85E-01	28.53	19.39	WB
	Climate and environment	CA_7 CE_8 CE_9 CE_10 CO 2 emissions (metric tons per capita) Permanent cropland (% of land area) Annual precipitation by country: values in millimeters. (Mean 1961-1999) Annual temperatures by country: values in degrees Environment Celsius. (Mean 1961-1999) CE_11 Latitude CE_12 Longitude CE_13 Methane emissions (kt of CO 2 equivalent)	79 -0.36 1.08E-03 77 -0.16 1.71E-01 77 -0.44 5.45E-05 80 0.61 1.38E-09 80 0.56 5.31E-08 80 -0.14 2.15E-01 75 -0.18 1.32E-01 81100.00 1.83 845.60 13.76 7.16 29.88 15.08	2.79 582.50 WB-C * WB * 8.48 WB-C * 6.57 WB * 26.30 GM * 59.45 GM 219000.0 0 WB *
		CE_14	Nitrous oxide emissions (thousand metric tons of CO2 equivalent)	75 -0.15 1.94E-01 30500.00 79200.00	WB *
		CE_15	Nitrous oxide emissions in industrial and energy processes (% of total nitrous oxide emissions)		75	0.32 5.69E-03	17.68	12.29	WB *
		CE_16 Outdoor air pollution (Annual PM10 [ug/m 3 ])		77 -0.42 1.38E-04	52.07	39.62	WHO *
		CE_17 PM10, country level (ug/m 3 )		76 -0.32 5.25E-03	32.57	26.88	WB *
		CE_18 UV radiation (J/mt2, 2004)		80 -0.63 6.68E-10	3153	1395	WHO *
	Demography	DD_19 DD_20 Female mean age of childbearing (years) Adolescent fertility rate (births per 1,000 women ages 15-19) DD_21 Fertility rate, total (births per woman) DD_22 Life expectancy at birth, female (years)	Demography	80 -0.47 1.06E-05 80 0.44 3.62E-05 80 -0.25 2.65E-02 80 0.58 1.94E-08	26.89 28.88 1.99 78.60	25.70 1.59 0.78 5.62	WB * UN * WB * WB *

://universimmedia.pagesperso-orange.fr/geo/loc.htm.

  See also Figure2in the Plos One paper.

	UPMC -ED 393 -2015	Epidemiology of Type I Diabetes:
		Global Incidence and Determinants

  Méthodes À partir du seul Registre enregistrant tous les polypes colorectaux sur l'ensemble d'un département depuis 1976, tous les résidents de Côte-d'Or âgés de 50 à 74 ans ayant eu un premier adénome ou carcinome in situ entre 1997 et 2008 ont été inclus. Les taux standardisés de détection des adénomes ont été étudiés selon deux périodes « pré-dépistage (1997-2002) » et « postdépistage (2003-2008) », en distinguant les individus ayant participé au DO des non-participants et les individus avec test positif des faux négatifs.

	es est difficile.	cancérisation (AHR).
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	néphrologie et
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	menée dans la Région des
	aladie. Nous avons mené
	prévalence et les facteurs
	e 18 ans a été recueilli de
	, interviewé et testé pour
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	e. Les logiciels SPSS 18.0,
	s pour l'analyse statistique.
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	vement de 38,8 % (95 %
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	t de l'hématocrite dans la
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	duction de la MRC, basées
	Kivu ; Bukavu
		110

5 Prévalence de la schizophrénie au sud du Bénin

  HbA1c) s'avère difficile dans certaines zones du l éloignées des centres urbains et ayant un accès restreint à des laboratoires lyses médicales. Des appareils portables mesurant l'HbA1c en instantané e goutte de sang capillaire pourraient rendre service dans ce cadre. odes

		VI e Congrès International d'Épidémiologie / Revue d'Épidémiologie et de Santé Publique 62S (2014) S213-S254	S225
	uction	La surveillance du contrôle glycémique des patients diabétiques
	hémoglobine glyquée (
				dépressifs, la manie et les
				était d'étudier la prévalen
				Ouidah Kpomassè Tori-Bo
				Méthodes	Il s'agissait d
				descriptive et analytique. L
				de 15 ans et plus résidant d
				Résultats	Les 1530 suje
		Discussion	Les incidences de DT1 sont plus hautes dans les pays à revenu	sujets de sexe masculin et
		élevé. Les variations entre les pays sont encore inexpliquées. La disponibilité	ratio de 0,77. Parmi les e
		d'informations sur l'incidence et l'accès aux bases de données publiques, per-	majeur actuel (EDM) et la
		mettra de faire plus d'analyses systématiques d'association et ainsi d'identifier	femmes (28,0 %) contre 17
		les facteurs associés à la maladie.	était de 3,1 % et 3,0 % des
		Mots clés	Épidémiologie ; Incidence ; Revue systématique ; Diabète de	maniaques a été de 3,1 %
		type 1		3,2 % chez les sujets de sex
		Déclaration d'intérêts conflits d'intérêts.	Les auteurs n'ont pas transmis de déclaration de	de 3,7 % des enquêtés et e élevée chez les sujets fémi Discussion Il ressort de
				était élevée dans la zone sa
				sodes maniaques et les trou
				demeuraient présents dans
				Déclaration d'intérêts
				conflits d'intérêts.
			a	c	c

S226 VI e Congrès International d'Épidémiologie / Revue d'Épidémiologie et de Santé Publ Papouasie-Nouvelle-Guinée à 62,42/100 000 en Finlande. En effectuant une régression multiple, on met en évidence une corrélation positive entre l'incidence du DT1 et l'indice de masse corporelle ainsi que deux variables liées aux revenus du pays (la consommation d'énergie électrique et le nombre d'abonnés à la téléphonie mobile). Des corrélations négatives ont été trouvés avec : le rayonnement UV, les émissions de CO 2 , la consommation d'alcool, et la vaccination contre l'hépatite B (toutes les valeurs de p < 0,05). http://dx.doi.org/10.1016/j.respe.2014.06.175 P5-J. Ezin Houngbé a , M.G. Gansou a , R. Agongbonou b , Y. Amonles a , http://dx.doi.org/10.1016/j

alence et facteurs de risque de la maladie le chronique dans une population noire : ntillon de la Clinique régionale du Kivu

			67,7 %.
	atients sur 55 étaient mal classés par l'A1CNow+, tous étant des faux
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	s habituelles ne sont pas suffisantes pour proposer son utilisation dans les
	où l'accès à un laboratoire d'analyses médicales est difficile.
	clés	Hémoglobine glyquée ; Diabète sucré ; Analyseur portable ;
	primaires de santé
	ration d'intérêts	Les auteurs n'ont pas transmis de déclaration de
	ts d'intérêts.
	/dx.doi.org/10.1016/j.respe.2014.06.172
	ula a , L. Maroyi b , M. Burnier c , O. Devuyst d
	ique régionale du Kivu, Service de néphrologie et dialyse, R.D. Congo
	ique régionale du Kivu, Service d'épidémiologie et dialyse, R.D. Congo
	tre hospitalier universitaire vaudois, Service de néphrologie et
	tension, Suisse
	tre de physiologie rénale, Université de Zurich, Suisse
	uction

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information

	Study information Country, area reported in the article First author, publication year African Region, LMIC Mauritius: NW Tuomileht J., 1993 † United Republic of Tanzania: Dar es Salaam Swai A. B., 1993 † Eastern Mediterranean Region, LMIC Ref Age range [13] 0-19 [14] 0-19 Iran (Islamic Republic of): Fars Pishdad G. R., 2005 † [15] 0-29	T1D diagnosis criteria in adults and young adults Detect. AA/ C-Peptide Need of insulin therapy Administration insulin therapy Clinical impression 1986-1990 No Period Yes From diagnosis Yes 1982-1991 No Yes From diagnosis Yes 1990-1994 Yes (a) Yes From diagnosis Yes	Ketosis/ ketonuria NA NA Yes	Weight loss NA NA Yes	Source of information and validation of ascertainment between sources Primary Secondary Tertiary % of ascertainment Medical reports Medical statistics NA 95.0 Medical reports Hospital records NA NA Medical reports from endocrinologists Medical records NA 100	Diaz-Valencia et al. BMC Public Health (2015) 15:255
	Libyan Arab Jamahiriya:	Kadiki O. A., 1996 †	[16] 0-34	1981-1990 No	Yes	From diagnosis NA	Yes	NA	Program National Diabetes	Hospital registers	NA	95.0
	Benghazi
	Tunisia: Beja, Monastir, Gafsa	Ben Khalifa F., 1998 †	[17] 0-19	1990-1994 No	Yes	From diagnosis Yes	NA	NA	Hospital records	centers School health	NA	96.0
	European Region, LMIC
	Croatia: Zagreb Roglic G., 1995 †	55 [18] 0->	1988-1992 No	Yes	of diagnosis Within 1 week	Yes	Yes	NA	Program National Diabetes	Death certificates	association Diabetes	96.2
	Estonia: NW	Kalits I., 1990 †	50 [19] 0->	1988-1988 No	Yes	From diagnosis Yes	Yes	Yes	NA	NA	NA	NA
	Lithuania: NW	Ostrauskas R., 2011 †	[20] 15-34 1991-2008 No	Yes	of diagnosis Within 2 weeks	Yes	Yes	Yes	Program National Diabetes	endocrinologist Regional	patient Notes of	86.8
												insurance
	Lithuania: NW	Pundziute-	[21] 0-39	1991-2000 No	Yes	Within 2 weeks	Yes	Yes	NA	National Diabetes	Pediatrician and	Death	91.2
		Lycka A., 2003				of diagnosis				Program	endocrinologist	certificates
											reports
	Lithuania: NW	Ostrauskas R.,	[22] 15-39 1991-1997 No	Yes	Within 2 weeks	Yes	Yes	NA	National Diabetes	Pediatrician and	Death	91.2
		2000				of diagnosis				Program	endocrinologist	certificates
											reports
	Poland: Bialystok	Kretowski A., 2001 †	[23] 0-29	1994-1998 No	Yes	From diagnosis Yes	Yes	Yes	Internal medicine Pediatric and	registers Hospital discharge	NA	98.5
										records
	Poland:	Sobel-	[24] 0-29	1980-1999 No	Yes	From diagnosis Yes	NA	NA	Pediatric and	Others health	NA	99.0
	Province of Rzeszow	Maruniak A., 2006 †								records Internal medicine	care registers
	Poland:	Grzywa M. A.,	[25] 0-29	1980-1992 No	Yes	From diagnosis Yes	NA	NA	Pediatric and	Others health	NA	99.0
	Province of	1995								Internal medicine	care registers
	Rzeszow									records

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information (Continued)

	Poland: Warsaw Wysock M. J., 1992 † Romania: Bucharest Ionescu-Tirgoviste C., 1994 † Slovakia: NW Kyvik K O, 2004 † European Region, HIGH Austria: Upper Rami B., 2001 † Belgium: Antwerp Weets I., 2007 †	[26] 0-29 [27] 0-≥ 85 [28] 15-29 1996-1997 No 1983-1988 No 1981-1991 No [29] 0-29 1994-1996 No [30] 0-39 1989-2003 Yes	Yes Yes Yes Yes Yes	From diagnosis Yes From diagnosis Yes From diagnosis Yes From diagnosis Yes From diagnosis Yes	NA Yes NA NA NA	NA NA NA NA NA	Medical records from diabetic clinics Bucharest Diabetes Registry Pediatrician and endocrinologist reports Pediatricians and endocrinologists reports Pediatricians and endocrinologists	General practitioners and diabetologist registers NA Other health care registers Austrian Diabetes Association General practitioners and	Death certificates NA NA NA Diabetes associations	NA NA 80.0 87.0 97.0	Diaz-Valencia et al. BMC Public Health (2015) 15:255
									reports	diabetes nurses	and self-
										reports	reporting
	Belgium: Antwerp	Weets I., 2002 †	[31] 0-39	1989-2000 Yes	Yes	From diagnosis NA	NA	NA	endocrinologist Pediatrician and	practitioner and General	associations Diabetes	93
									reports	diabetes nurse	and self-
										reports	reporting
	Belgium: Antwerp	Vandewalle C., 1997 †	[32] 0-39	1989-1995 Yes	Yes	From diagnosis Yes	Yes	Yes	endocrinologist Pediatrician and	practitioner and General	associations Diabetes	85
									reports	diabetes nurse	and self-
										reports	reporting
	Bosnia and Herzegovina:	Radosevic B., 2013 †	[33] 0-18	1998-2010 No	Yes	From diagnosis Yes	NA	NA	Hospital records	prescription Insulin	NA	100
	Republic of									registers
	Srpska
	Denmark: Copenhagen	Molbak A. G., 1994 †	[34] 30-95 1973-1977 Yes (b)	Yes	From diagnosis Yes	Yes	Yes	practitioners and Hospital discharges General	of T1D Missing coding	99.0
	and									diabetologist	diagnosis in
	Frederiksborg									registers and	hospital
										death certificates	admissions
	Finland: NW	Lammi N., 2007 †	[35] 15-39 1992-1996 Yes	Yes	From diagnosis Yes	NA	NA	Program National Diabetes	registers Hospital discharge	reimbursement Drug	88.0
											registers
	France: Aquitaine,	Charkaluk M. L, 2002 †	[36] 0-19	1988-1997 No	Yes	None declared NA	NA	NA	registers Prospective	Security registers French Social	NA	96.0
	Lorraine, Basse
	Normandie,
	Haute
	Normandie

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information (Continued)

	France: Aquitaine, Lorraine, Basse Normandie, Haute Normandie Israel: NW Israel: NW Israel: NW Italy: Lombardie Garancini, P., Levy-Marchal, C., 1998 Blumenfeld O., 2014 † Sella T., 2011 Koton S., 2007 [40] 0-17 [37] 0-19 [38] 0-17 [39] 0-17 1991 † [41] 0-34 Italy: Pavia Tenconi M. T., 1995 † [42] 0-29	1988-1995 No 1997-2010 No 2000-2008 No 1997-2003 No 1981-1982 No 1988-1992 No	Yes Yes Yes Yes Yes Yes	None declared NA From diagnosis Yes None declared Yes From diagnosis Yes None declared NA From diagnosis Yes	NA NA NA NA NA NA	NA NA NA NA NA NA	Prospective registers Israel juvenile diabetes register Israel juvenile diabetes register Israel juvenile diabetes register Hospital discharge records Hospital records	French Social Security registers Israel Center for Disease Control Israel Center for Disease Control NA Hospital admission records Drug registers	NA NA NA NA NA NA	96.0 NA NA NA 85.7 100	Diaz-Valencia et al. BMC Public Health (2015) 15:255
	Italy: Sardinia	Muntoni S, 1992 †	[43] 0-29	1989-1990 No	Yes	From diagnosis Yes	NA	NA	Hospital records	association Diabetes	NA	92.8
	Italy: Sardinia (Oristano)	Frongia O., 1997 †	[44] 0-29	1993-1996 No	Yes	From diagnosis Yes	NA	NA	Hospital records	Drug registers	NA	100
	Italy: Turin	Bruno G., 2009 †	[45] 15-29 2000-2004 Yes	Yes	months of Within 6	NA	NA	NA	Hospital records	Drug registers	NA	NA
						diagnosis
	Italy: Turin	Bruno G., 2005 †	[46] 30-49 1999-2001 Yes	Yes	months of Within 6	NA	Yes	NA	Diabetes clinics	Drug registers	NA	99.0
						diagnosis
	Italy: Turin	Bruno G.,	[47] 0-29	1984-1988 No	Yes	From diagnosis NA	Yes	NA	Diabetic clinics	Hospital discharge	NA	97.0
		1993								records	records
	Luxembourg: NW	De Beaufort C. E., 1988 †	[48] 0-19	1977-1986 No	Yes	None declared NA	NA	NA	Internal medicine Pediatric and	Association Dutch Diabetes	NA	100
										records
	Malta: NW	Schranz A. G., 1989 †	[49] 0-24	1980-1987 No	Yes	of diagnosis Within 3 moths	Yes	Yes	Yes	Medical reports	records Diabetic clinic	NA	NA
	Netherlands: NW	Ruwaard D., 1994 †	[50] 0-19	1988-1990 No	Yes	None declared NA	NA	NA	Internal medicine Pediatric and	NA	NA	81.0
										records
	Norway: NW	Joner G., 1991 †	[51] 15-29 1978-1982 No	Yes	From diagnosis NA	NA	NA	endocrinologists Pediatricians and	Hospital records	NA	90.0
										reports
	Slovenia: NW	Radosevic B., 2013 †	[33] 0-18	1998-2010 No	Yes	From diagnosis Yes	NA	NA	Registry of Slovenian National	registers Insulin prescription	NA	100
										Childhood diabetes
	Spain: Badajoz	Morales-Perez F. M., 2000 †	[52] 0-29	1992-1996 No	Yes	From diagnosis Yes	Yes	NA	endocrinologists Pediatricians and	records Diabetic clinic	NA	95.0
										reports

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information (Continued)

	Spain: Canarias Islands Spain: Catalonia Abellana R., Carrillo Dominguez, A., 2000 † 2009 † Spain: Catalonia Goday A., 1992 Spain: Navarra Forga L., 2014 † Spain: Navarra Forga L., 2013 †	[53] 0-30 [54] 0-29 [55] 0-29 [56] 0-> 45 [57] 0-79	1995-1996 No 1989-1998 Yes (c) 1987-1990 No 2009-2012 Yes 2009-2011 Yes	Yes Yes Yes Yes Yes	None declared Yes From diagnosis Yes From diagnosis Yes Within 6 months of diagnosis Yes Within 6 months of Yes	NA Yes NA Yes Yes	Yes NA NA NA NA	Hospital records and Endocrinologist reports Catalan Registry of Type 1 Diabetes Catalan Registry of Type 1 Diabetes Hospital records Hospital records	Diabetes association reports and sales on blood glucose monitors Summer camps, associations, and prescription data Summer camps, patient associations, and prescription data Electronic medical records, diabetes associations Electronic medical records, diabetes	NA NA NA NA NA	90.1 90.0 90.1 98.4 98.4	Diaz-Valencia et al. BMC Public Health (2015) 15:255
						diagnosis					associations
	Sweden: NW	Dahlquist G. G., 2011 †	[58] 0-34	1983-2007 No	Yes	From diagnosis Yes	Yes	Yes	Program National Diabetes	endocrinologist Pediatricians and	NA	96.0
											reports
	Sweden: NW	Östman J.,	[59] 15-34 1983-2002 No	Yes	From diagnosis Yes	NA	NA	National Diabetes	Pediatrician and	Computer-	82
		2008								Program	endocrinologist	based patient
											reports	administrative
												register
	Sweden: NW	Pundziute-	[60] 0-34	1983-1998 No	Yes	From diagnosis Yes	Yes	Yes	National Diabetes	Pediatrician and	Computer-	91.2
		Lycka A., 2002								Program	endocrinologist	based patient
											reports	administrative
												register
	Sweden: NW	Nyström L.,	[61] 0-34	1983-1987 No	Yes	None declared NA	NA	NA	National Diabetes	Hospital admission	NA	89
		1992								Program	and discharge
											registers
	Sweden: NW	Blohme G.,	[62] 15-34 1983-1987 No	Yes	From diagnosis Yes	Yes	Yes	National Diabetes	Hospital admission	NA	NA
		1992								Program	and discharge
											registers
	Sweden: Kronoberg	Thunander M., 2008 †	[63] 0-100 1998-2001 Yes	Yes	of diagnosis Within 4 weeks	Yes	Yes	NA	screening of all Opportunistic	ophthalmology Departments of	NA	98.0
										adult patients in
										contact with the
										medical care
										system
	United Kingdom: NW	Imkampe A. K., 2011 †	[64] 0-34	1991-2008 No	Yes	of diagnosis Within 3 moths	Yes	NA	NA	Program National Diabetes	endocrinologist Pediatricians and	NA	NA
											reports
	United	Bingley P. J.,	[65] 0-21	1985-1986 No	Yes	From diagnosis Yes	NA	NA	Medical reports	Regional hospital	NA	95.0
	Kingdom:	1989								from general	records
	Oxford region									practioners and
										pediatricians

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information (Continued)

	Region of the Americas, LMIC Barbados: NW Jordan O. W., 1994 † Region of the Americas, HIGH Canada: Quebec Legault L., 2006 † United States of America: Alabama (Jefferson County) Wagenknecht L. E., 1991 † United States of America: Alabama Wagenknecht L. E.,1989	[66] 0-29 [67] 0-18 [68] 0-19 [69] 0-19	1982-1991 No 2000 No 1979-1988 No 1979-1985 No	Yes Yes Yes Yes	From diagnosis Yes None declared NA None declared NA From diagnosis Yes	NA NA NA NA	NA NA NA NA	Hospital records Departmental program: Régie des Rentes du Québec program Hospital records Hospital records	Others health care registers NA Summer camps, patient associations, and prescription data Association registers	NA NA NA NA	94.0 NA NA 95.0	Diaz-Valencia et al. BMC Public Health (2015) 15:255
	(Jefferson
	County)
	United States of America:	Vehik K., 2007 †	[70] 0-17	2000-2004 No	Yes	of diagnosis Within 2 weeks	Yes	NA	NA	endocrinologists Pediatricians and	care registers Other health	Study The SEARCH	96.5
	Colorado									reports
	United States of	Kostraba J. N.,	[71] 0-17	1978-1988 No	Yes	Within 2 weeks	Yes	NA	NA	Pediatricians and	Hospital registers	NA	93.3
	America:	1992				of diagnosis				endocrinologists
	Colorado									reports
	United States of America:	Libman I. M., 1998 †	[72] 0-19	1990-1994 No	Yes	From diagnosis Yes	NA	NA	Medical reports	practitioners and General	NA	97.7
	Pennsylvania										diabetes nurses
	(Allegheny)										reports
	United States of America: Rhode	Fishbein H. A., 1982 †	[73] 0-29	1979-1980 No	Yes	None declared NA	NA	NA	Medical reports	registers Insulin prescription	NA	NA
	Island
	United States of America: five areas §	Bell R., 2009 †	[74] 0-19	2002-2005 Yes	Yes	From diagnosis Yes	NA	NA	Medical reports	care registers Other health	Study The SEARCH	NA
	United States of America:	Allen C., 1986 †	[75] 0-29	1970-1979 No	Yes	From diagnosis Yes	NA	NA	endocrinologist Hospital discharges Pediatricians and	NA	90.0
	Wisconsin										reports
	United States of	Gorham C.,	[76] 17-34 1974-1988 No	NA*	None declared Yes	NA	NA	Hospital discharges NA	NA	NA
	America: The	1993
	United States
	Navy
	Western Pacific Region, HIGH
	Australia: New South Wales	Tran F., 2014 †	[77] 10-18 2001-2008 No	Yes	NA	Yes	NA	Yes	diabetes register Endocrine group	register National diabetes	NA	96.0

Table 1

 1 Systematic review of T1D in adults, diagnostic criteria and sources of information (Continued)

	Australia: Sydney	Sutton L., 1989 †	[78] 0-19	1984-1987 No	Yes	From diagnosis Yes	NA	NA	from general Medical reports	the area Schools in	Syringe register NA
	(Southern									practioners and
	Metropolitan									pediatricians
	Health Region)
	Japan: Osaka	Sasaki A., 1992 †	[79] 0-18	1978-1988 No	Yes	None declared Yes	Yes	NA	system Medical benefits	NA	NA	NA
	New Zealand: Canterbury	Scott, R. S., 1991 †	80 [80] 0-≥	1981-1986 No	Yes	of diagnosis Within 1 year	Yes	NA	Yes	based surveys Community-	and discharge Hospital admission	NA	95.0
										administrated	registers and
										in pharmacies	diabetologist
										where diabetic
										patients acquired
										their insulin
										supplies
	Other Regions currently non WHO
	Taiwan: NW	Lin W.-H., 2013 †	60 [81] 0-≥	1999-2010 Yes	Yes	None declared Yes	Yes	NA	Insure register National Health	of a database Random sample	NA	98.3
										and Illness	used to
										certificates	reimbursements
	US Virgin Islands: NW	Washington R. E., 2013 †	[82] 0-19	2001-2010 No	Yes	From diagnosis Yes	Yes	Yes	Medical reports	Medical providers	NA	98.7

.1.2 CONSORT PRISMA checklist SITable 14 :

 14 CONSORT PRISMA checklist

	!
	4.1Section/topic	# Checklist item		Reported on page #
	TITLE
	Title	Identify the report as a systematic review, meta-analysis, or both.		116
	ABSTRACT
	Structured summary	Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants,	116-117
		and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings;
		systematic review registration number.
	UPMC -ED 393 -2015	147	Epidemiology of Type I Diabetes:
			Global Incidence and Determinants

INTRODUCTION

Rationale

Describe the rationale for the review in the context of what is already known. 117

Objectives Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).

117

METHODS

Protocol and registration

Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number.

.1.4 Geographic repartition, and reported adult T1D incidences found in the SR SITable 16 :

 16 Geographic repartition, and reported adult T1D incidences found in the SR

	!
	4.1Country, Area	First Author,	Ref	Age	Period	0-14	15-19	20-24	25-29	30-34	35-39	40-44	45-49	50-59	60-69	70-79 80-100
		publication year		range
	African Region, LMIC
	Mauritius: NW	Tuomilehto J., 1993	(332)	0-19	1986-1990	2.1	1.1		---		---	---	---	---	---	---	---	---	---
		†
	Country, Area reported in the article United Republic of Tanzania: Swai A. B., 1993 † Dar es Salaam Eastern Mediterranean Region, LMIC	First Author, publication year (311) 0-19	Mean TID incidence group of 1982-1991 0.9	Period of 3.4 study	---	Ref.	---	---	---	---	---	---	---	---	---
					age 0-14
	Iran (Islamic Republic of): Fars Pishdad G. R., 2005	(248)	0-29	years 1990-1994	3.7	4.7		3.4	3.3	---	---	---	---	---	---	---	---
	Australia: New South Wales	†	Diamond, 2006	14.5		1990-1993		(318)
	Slovakia: NW Libyan Arab Jamahiriya:	Kadiki O. A., 1996	Diamond, 2006 (153) 0-34	8.90 1981-1990	8.1	1990-1999 16.9	7.0	(318) 10.4	12.4	---	---	---	---	---	---	---
	Denmark: Copenhagen and Frederiksborg Benghazi †	Eurodiab, 2000	16.00		1989-1994		(90)
	Finland: NW Tunisia: Beja, Monastir, Gafsa Ben Khalifa F.,	Diamond, 2006 (32) 0-19	40.90 1990-1994	6.7	1990-1999 7.5	---	(318)	---	---	---	---	---	---	---	---	---
	Italy: Turin	1998 †	Bruno G, 2009	11.86		1984-2004		(46)
	Italy: Turin Norway: NW European Reion, LMIC		Bruno G, 2009 Joner G, 1989	14.8 20.22		2000-2004 1973-1982		(46) (150)
	Lithuania: NW United States: Wisconsin Croatia: Zagreb Estonia: NW	Roglic G., 1995 † Kalits I., 1990 †	Patterson CC, 2012 Allen C, 1986 (268) 0->55 1988-1992 14.20 18.10 (157) 0->50 1988-1988	7.4 10.5	2004-2008 1970-1979 9.6 39.9	9.6 9.2	(243) 6.5 (13) 9.2	6.5 11.1	5.4 11.1	5.4 12.3	4.1 12.3	4.5 3.4	------	------	------
	NW: Nation-wide Lithuania: NW	† Ostrauskas R., 2011	(235)	15-34 1991-2008	14.2*	7.4		7.1	8.9	9.8		---	---	---	---	---	---
	Lithuania: NW	Pundziute-Lycka	(254)	0-39	1991-2000	8.4 (a) 11.1 (b)		7.8	7.8	7.8	7.8	---	---	---	---	---	---
		A., 2003
	Lithuania: NW	Ostrauskas R., 2000	(234)	15-39 1991-1997	---	6.9		6.9	5.5	8.8	7.8	---	---	---	---	---	---
	Poland: Bialystok	Kretowski A., 2001	(172)	0-29	1994-1998	7.3	6.0		6.0	6.0	-
		†
	UPMC -ED 393 -2015					150								Epidemiology of Type I Diabetes:
														Global Incidence and Determinants

Other Regions currently non-WHO

	New Zealand: Canterbury	Scott, R. S., 1991 †	(281)	0-≥80 1981-1986	12.2 16.9 (b)	8.1	8.1	10.6	10.6	11.8	11.8	13.1	18.6	21.7	---
	Taiwan: NW	Lin W. -H., 2013 †	(192)	0-≥60 1999-2010	5.1	3.68	2.23	0.98	0.73

Table 17 :

 17 T1D incidences by sex in young adults and adults found in the systematic review

	Country, Area	First Author,			Age	Inc.	Inc.	Male/Fe
		publication year	Ref	Period	range	Men	Fem	m ratio
	African Region, LMIC
	Mauritius: NW	Tuomilehto J., 1993 †	(332)	1986-1990	15-19	0.4	1.7	0.24
	Eastern Mediterranean Region,
	LMIC
	Iran (Islamic Republic of): Fars	Pishdad G. R., 2005 †	(248)	1990-1994	15-29	2.7	4.0	0.68
	Libyan Arab Jamahiriya: Benghazi	Kadiki O. A., 1996 †	(153)	1981-1990	15-34	14.2	9.0	1.58
	Tunisia: Beja, Monastir, Gafsa	Ben Khalifa F., 1998 †	(32)	1990-1994	15-19	6.2	8.8	0.70
	European Region, LMIC
	Estonia, NW	Kalits I., 1990 †	(157)	1988-1988	15-39	18.2	21.9	0.83
	Lithuania, NW	Ostrauskas R., 2011 †	(235)	1991-2008	15-34	10.4	6.1	1.71
		Pundziute-Lycka A.,
	Lithuania, NW	2003	(254)	1991-2000	20-39	10.2	5.4	1.89
	Lithuania, NW	Ostrauskas R., 2000	(234)	1991-1997	15-39	9.7	5.7	1.70
	Poland: Province of Rzeszow	Sobel-Maruniak A., 2006 (296)	1980-1999	15-29	6.8	4.7	1.45
	Poland: Province of Rzeszow	Grzywa M. A., 1995 †	(123)	1980-1992	15-29	6.0	5.1	1.16
	Poland: Warsaw	Wysocki M. J., 1992 †	(366)	1983-1988	15-29	6.5	4.4	1.48
	Slovakia: NW	Kyvik K. O., 2004 †	(176)	1996-1997	25-29	5.8	3.9	1.49
	European Region, HIGH
	Austria: Upper	Rami B., 2001 †	(259)	1994-1996	25-30	8.2	4.4	1.86
	Belgium: Antwerp	Weets I., 2002 †	(352)	1989-2000	15-39	7.0	10.6	0.66
	Belgium: Antwerp	Vandewalle C., 1997 †	(341)	1989-1995	15-39	11.0	6.9	1.59
	Bosnia and Herzegovina: Republic of
	Srpska	Radosevic B., 2013 †	(256)	1998-2010	15-18	6.3	3.9	1.62
	Denmark: Copenhagen and
	Frederiksborg	Molbak A. G., 1994 †	(215)	1973-1977	>30	9.1	7.5	1.21
	Finland: NW	Lammi N., 2007 †	(177)	1992-1996	15-39	20.1	11.8	1.70
		Blumenfeld O., 2014 †
	Israel: NW	(a)	(39)	1997-2010	15-17	11.0	7.4	1.49
	Israel: NW	Koton S., 2007	(171)	1997-2003	15-17	10.6	7.1	1.49
	Italy: Lombardie	Garancini P., 1991 †	(108)	1981-1982	15-34	4.9	2.9	1.69
	Italy: Pavia	Tenconi M. T., 1995 †	(317)	1988-1992	15-29	6.9	6.9	1.00
	Italy: Sardinia	Muntoni S., 1992 †	(220)	1989-1990	15-29	25.3	12.1	2.09
	Italy: Sardinia (Oristano)	Frongia O., 1997 †	(102)	1993-1996	15-29	21.7	22.3	0.97
	Italy: Turin	Bruno G, 2009 †	(46)	2000-2004	15-29	8.2	5.9	1.39
	Italy: Turin	Bruno G, 2005 †	(47)	1999-2001	30-39	9.2	5.4	1.70
	Italy: Turin	Bruno G., 1993	(45)	1984-1988	15-29	7.4	4.2	1.75
	Malta: NW	Schranz A. G., 1989 †	(280)	1980-1987	15-24	13.3	12.2	1.09
	Netherlands: NW	Ruwaard D., 1994 †	(271)	1988-1990	15-19	16.6	13.3	1.25
	Norway: NW	Joner G., 1991 †	(149)	1978-1982	15-29	19.0	15.0	1.27
	Slovenia: NW	Radosevic B., 2013 †	(256)	1998-2010	15-18	8.4	6.6	1.27
		Morales-Perez F. M.,
	Spain: Badajoz	2000 †	(218)	1992-1996	15-29	10.2	7.3	1.40

Other Regions currently non-WHO

	Country, Area	First Author,			Age	Inc.	Inc.	Male/Fe
		publication year	Ref	Period	range	Men	Fem	m ratio
		De Beaufort C. E., 1988
	Luxembourg: NW	† (d)	(75)	1977-1986	NA	NA	NA	NA
	Region of the Americas, HIGH
	Canada: Quebec	Legault L., 2006 †	(184)	2000	NA	NA	NA	NA
	United States of America: Alabama	Wagenknecht L. E., 1991
	(Jefferson County)	† (d)	(350)	1979-1988	NA	NA	NA	NA
	United States of America:
	Pennsylvania (Allegheny)	Libman I. M., 1998 † (d) (191)	1990-1994	NA	NA	NA	NA
		Fishbein H. A., 1982 †
	United States of America: Rhode Island	(d)	(96)	1979-1980	NA	NA	NA	NA
	Region of the Americas, LMIC
	Barbados: NW	Jordan O. W., 1994 † (d) (152)	1982-1991	NA	NA	NA	NA
	Western Pacific Region, HIGH
	Australia: New South Wales	Tran F., 2014 †	(326)	2001-2008	NA	NA	NA	NA
	Australia: Sydney (Southern
	Metropolitan Heath Region)	Sutton L., 1989 †	(308)	1984-1987	NA	NA	NA	NA
	Other Regions currently non-WHO
		Washington R. E., 2013
	US Virgin Islands: NW	†	(351)	2001-2010	NA	NA	NA	NA
	Taiwan: NW	Lin W. -H., 2013 †	(192)	1999-2010	15-29	3.2	4.1	0.77
	No available information
	African Region, LMIC
	United Republic of Tanzania: Dar es
	Salaam	Swai A. B., 1993 †	(311)	1982-1991	NA	NA	NA	NA
	European Region, LMIC
	Croatia: Zagreb	Roglic G., 1995 †	(268)	1988-1992	NA	NA	NA	NA
	Poland: Bialystok	Kretowski A., 2001 †	(172)	1994-1998	NA	NA	NA	NA
		Ionescu-Tirgoviste C.,
	Romania: Bucharest	1994 †	(143)	1981-1991	NA	NA	NA	NA
	European Region, HIGH
	Belgium: Antwerp	Weets I., 2007 †	(353)	1989-2003	NA	NA	NA	NA
	France: Aquitaine, Lorraine, Basse
	Normandie, Haute Normandie	Charkaluk M. L., 2002 † (59)	1988-1997	NA	NA	NA	NA
	France: Aquitaine, Lorraine, Haute-
	Normandie, and Basse-Normandie.	Levy-Marchal C., 1998	(187)	1988-1995	NA	NA	NA	NA
	Israel: NW	Sella T., 2010 (d)	(284)	2000-2008	NA	NA	NA	NA
	UPMC -ED 393 -2015	156		Epidemiology of Type I Diabetes:
				Global Incidence and Determinants

Male-to-female ratios >1 are highlighted. Ref: Reference.

Table 19 -

 19 Checklist). The registration number in PROSPERO, the International Prospective Register of Systematic

	Reviews	is:	CRD42012002369	(available	on
	http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002369).	We
	searched MEDLINE, Web of Science and Google Scholar for all relevant original papers
	published in English between 1975 and 2014 including the reviews Diamond (318) and

Table 18 :

 18 Incidence rates of T1D among children aged 0-14 years retrieved from 89 countries during the years 1975-2014 by main geographical areas

		Incident	Person-years at risk (n)	Mean Incidence per
		cases (n)		100.000 person-years
				(95%CI)
	Africa	1,372	30,084,343	5.9 (4.0-7.8)
	Asia	12,169	934,989,765	4.0 (3.1-4.8)
	Europe	147,220	1 048,075,945	15.6 (14.9-16.4)
	Finland	6,048	15,302,876	43.9 (35.8-51.9)
	Latin America and the Caribbean	5,961	152,894,342	7.2 (6.1-8.3)
	Northern America	13,106	82,974,910	16.6 (15.4-17.8)
	Oceania	12,914	78,585,224	14.7 (13.0-16.4)
	All	192,741	2,327,604,529	13.7 (13.1-14.2)

Table 20 :

 20 List of selected countries and areas reporting incidence of T1D in 0-14 year-olds included in the APC model

	Continent/Country	Area	First Author, publication	Ref	Period	Mean	SD	Population	Number	Asc.
			year			T1D Inc.		at risk	of cases	%
	AFRICA
	Algeria	Oran	Diamond, 2006	(318) 1990-1999	8.60	NA	2441279		NA
		Alexandria,
	Egypt	Damanhour	Arab M, 1992	(18)	1992-1992	8.00	NA	602535		NA
	Ethiopia	Gondar	Alemu S, 2009	(12)	1995-2008	0.33	NA	1727076		NA
	Ethiopia	Jimma	Alemu S, 2009	(12)	2002-2008	0.33	NA	5623928		NA
	Libya	Benghazi	Kadiki O A, 2002	(154) 1991-2000	8.53 6.19	5860200		100
	Libya	Benghazi	Kadiki O A, 1998	(155) 1991-1995	9.27 8.26	1564100		100
	Mauritius	NW	Toumilehto J, 1993	(332) 1986-1990	2.07 0.96	1530250		95
	Nigeria	Anambra	Afoke AO, 1992	(7)	1990-1990	1.96	NA	612245		NA
	Sudan	Khartoum	Elamin A, 1992	(88)	1987-1990	7.93 1.72	4100000		95
		Beja, Monastir,
	Tunisia	Gafsa	Ben Khalifa F, 1997	(32)	1990-1994	6.69 4.25	1713430		96
	Tanzania (United
	Rep.)	Dar es Salaam Swai A B, 1993	(311) 1982-1991	0.92 1.13	4309300		NA
	ASIA
		23 local
	China	centers	Li X H, 2000	(189) 1988-1996	0.60 0.47 142560000		NA
	China	22 centers	Yang Z, 2005	(371) 1988-1994	0.47 0.05 123929940		93
	China	Harbin	Zhang H, 2008	(375) 1990-2000	0.74 0.49	14147694		98
	China	North middle	Yang Z, 1998	(372) 1985-1994	0.58 0.19	65338202		94
	China	Shanghai	Fu H, 1994	(103) 1980-1991	0.71 0.50	10221570		85
	China	Shanghai	Shen S X , 1996	(291) 1989-1993	0.84 0.25	7008320		85
	China	South middle	Yang Z, 1998	(372) 1985-1994	0.42 0.23	60126801		94
			Amirkhanashvili K,
	Georgia	NW	2000	(17)	1998-1999	3.30 1.68	5285667		NA
	Hong Kong SAR	Hong Kong	Huen K F, 2000	(139) 1984-1996	1.37 0.42	16185143		100
	Hong Kong SAR	Hong Kong	Wong G W K , 1993	(365) 1986-1990	1.93 1.03	1120030		94
	India	Madras	Ramachandran A, 1996 (258) 1991-1994	11.00 1.70	1671170		90
	Iran (Islamic
	Republic of)	Fars	Pishdad G R, 2005	(248) 1991-1996	3.68 1.45	9564000		100
	Israel	NW	Koton S, 2007	(171) 1997-2003	10.38 5.09	12580033		NA
	Israel	NW	Laron Z V, 1985	(181) 1975-1980	4.20 2.74	7421790		95
	Israel	NW	Shamis I , 1997	(289) 1965-1993	5.03 3.47	1992501		95
	Japan	Hokkaido	Kawasaki E, 2006	(163) 1973-1996	1.89 1.22	3461452		NA
	Japan	Hokkaido	Matsuura N, 1998	(207) 1973-1992	1.54 0.66	15909018		100
			Japan IDDM
			Epidemiology Study
	Japan	Kagoshima	Group, 1993	(145) 1980-1989	1.77 0.30	533623		95
	Japan	NW	Kawasaki E, 2006	(163) 1998-2001	2.35 0.70	73539756		NA
	Japan	NW	Kida K, 2000	(165) 1986-1990	1.46 0.75 103448508		95
	Japan	Osaka	Sasaki A, 1992	(275) 1978-1988	1.89 1.12	5987276		NA
			Japan IDDM
			Epidemiology Study
	Japan	Tokyo	Group, 1993	(145) 1985-1989	1.62 0.43	11613492		95
	Jordan	NW	Ajlouni K, 1999	(9)	1992-1996	3.33 2.10	8467045		95
	Korea (Dem.
	People's Rep.)	Seoul	Diamond, 2006	(318) 1990-1991	1.10	NA	2369578		NA
	Kuwait	Farwania	Abdul-Rasoul M, 2007 (4)	1995-1999	21.08 12.98	336745		98
	Kuwait	NW	Shaltout A A, 2002	(288) 1992-1997	20.72 8.43	1811204		93
	Oman	NW	Soliman A T, 1996	(297) 1993-1995	2.59 1.35	610175		96
	Pakistan	Karachi	Staines A, 1997	(303) 1989-1993	1.03 0.59	34281426		NA

Table 21 :

 21 Comparison environmental correlations and incidence of T1D by countries, adjusted using am APC model and crudes

	Category/
	Cod.

Variable Intercept of incidence of T1D in the APC model Incidence of T1D in the review

  (81)

		!
	Category/
		Cod.
				Countries with available info. (n of 89)	Spearman Correlation Estimate (rho) p-value	Mean	SD	Countri es with availabl e info. (n of 80)	Spearman Correlation Estimate (rho) p-value	Mean	SD	Reference
		Agriculture
		CA_1	Agricultural land (% of land area)	86	0.03	8.19E-01	37	21	80	0.07 5.29E-01	37	WB
		CA_2	Arable land (% of land area)	86	0.00	9.82E-01	17	14	80	0.10 3.96E-01	17	WB
		CA_3	Cereal production (metric tons)	82	-0.03	7.94E-01	25999771 77463855	79	-0.03 8.02E-01	27600000	WB
	Climate and environment	CA_4 CA_5 CA_6 CA_7 Environment Cereal yield (kg per hectare) Fertilizer consumption (kilograms per hectare of arable land) Forest area (% of land area) Permanent cropland (% of land area) CE_8 Annual precipitation by country: values in millimeters. (Mean 1961-1999) CE_9 Annual temperatures by country: values in degrees Celsius. (Mean 1961-1999) CE_10 CO2 emissions (metric tons per capita)	82 80 86 85 80 80 85	0.30 0.11 -0.08 -0.32 -0.19 -0.33 0.62	5.92E-03 3.23E-01 4.41E-01 3.00E-03 9.44E-02 2.48E-03 3.62E-10	4291 219 30 2 885 14 7	2077 472 20 4 601 9 6	78 76 80 79 77 77 80	0.33 2.77E-03 0.02 8.63E-01 -0.06 5.85E-01 -0.36 1.08E-03 -0.16 1.71E-01 -0.44 5.45E-05 0.61 1.38E-09	4419 233 29 2 846 14 7	WB WB WB WB WB WB WB
		CE_11	Latitude	89	0.49	1.31E-06	29	25	80	0.56 5.31E-08	30	Google Maps
		CE_12	Longitude	89	-0.12	2.60E-01	12	61	80	-0.14 2.15E-01	15	Google Maps
		CE_13	Methane emissions (kt of CO2 equivalent)	77	-0.08	5.01E-01	79231	215420	75	-0.18 1.32E-01	81100	WB
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		!
	Category/ Cod.		Countries with available info. (n of 89)	Spearman Correlation Estimate (rho) p-value	Mean	SD	Countri es with availabl e info. (n of 80)	Spearman Correlation (rho) p-value Estimate	Mean	SD	Reference
		CE_14	Nitrous oxide emissions (thousand metric tons of CO2 equivalent)	77	0.48 -8.16E-02	29914	77690	75	-0.15 1.94E-01	30500	79200	WB
		CE_15	Nitrous oxide emissions in industrial and energy processes (% of total nitrous oxide emissions)	77	0.29	1.08E-02	18	12	75	0.32 5.69E-03	18	12	WB
		CE_16	Outdoor air pollution (Annual PM10 [ug/m3])	81	-0.37	6.53E-04	52	39	77	-0.42 1.38E-04	52	40	WHO
		CE_17	PM10, country level (micrograms per cubic meter)	81	-0.29	9.53E-03	32	26	76	-0.32 5.25E-03	33	27	WB
		CE_18	UV radiation (J/mt2, 2004)	82	-0.56	4.44E-08	3254	1432	80	-0.63 6.68E-10	3153$	1395$	WHO
		Demography
		DD_19	Adolescent fertility rate (births per 1,000 women ages 15-19)	84	-0.35	1.07E-03	28	27	80	-0.47 1.06E-05	27	26	WB
		DD_20	Female mean age of childbearing (years)	85	0.36	8.47E-04	29	2	80	0.44 3.62E-05	29	2	UN
		DD_21	Fertility rate, total (births per woman)	84	-0.13	2.48E-01	2	1	80	-0.25 2.65E-02	2	1	WB
		DD_22	Life expectancy at birth, female (years)	84	0.52	4.43E-07	78	6	80	0.58 1.94E-08	79	6	WB
		DD_23	Life expectancy at birth, male (years)	84	0.57	1.69E-08	73	6	80	0.60 3.53E-09	73	6	WB
	Demography	DD_24 DD_25 DD_26 DD_27 DD_28	Life expectancy at birth, total (years) Population ages 0-14 (% of total) Population 15-64 years of age (% of total) Population ages 65 and above (% of total) Population growth (annual %)	84 85 85 85 87	0.55 -0.30 0.00 0.44 -0.19	6.25E-08 5.11E-03 9.90E-01 3.13E-05 8.52E-02	76 21 67 12 1	6 8 5 6 1	80 80 80 80 80	0.60 2.92E-09 -0.42 1.10E-04 0.07 5.29E-01 0.51 1.52E-06 -0.20 8.05E-02	76 21 67 12 1	6 8 5 6 2	WB WB WB WB WB
		DD_29	Population, female (% of total)	85	0.08	4.77E-01	50	4	80	0.14 2.15E-01	50	4	WB
		DD_30	Probability of dying between birth and the age of 40 years (both sexes combined) (deaths under age 40 per 1,000 live births)	85	-0.56	2.16E-08	54	55	80	-0.65 9.91E-11	52	48	UN
		DD_31	Rural population (% of total population)	87	-0.45	1.51E-05	31	21	80	-0.41 1.46E-04	30	20	WB
		DD_32	Total population 0-14 years (both sexes combined)	85	-0.25	2.23E-02	14825	48326	80	-0.32 4.16E-03	15200	49900	UN
		DD_33	Urban population (% of total population)	87	0.45	1.50E-05	69	21	80	0.41 1.45E-04	70	20	WB
		Country Economy
	Economic factors	EC_33 EC_34 EC_35 Development GDP growth (annual %) GDP per capita (constant 2005 US$) GDP per capita, PPP (constant 2005 international $) ED_36 Adjusted savings: education expenditure (% of GNI) ED_37 Improved sanitation facilities (% of population with access) ED_38 Improved water source (% of population with access)	85 85 83 80 84 83	-0.48 0.68 0.65 0.49 0.66 0.66	3.08E-06 1.04E-12 3.04E-11 4.43E-06 1.29E-11 7.52E-12	3 17207 19687 5 88 93	4 17402 14333 2 21 13	79 79 78 75 78 79	-0.47 1.07E-05 0.72 9.05E-14 0.71 5.24E-13 0.45 5.56E-05 0.67 2.08E-11 0.67 1.60E-11	3 17800 20100 5 88 94	4 17900 14500 2 21 13	WB WB WB WB WB WB
		ED_39	Energy use (kg of oil equivalent per capita)	84	0.68	9.28E-13	3151	2963	79	0.69 1.22E-12	3303	3045	WB
		ED_40	Energy use (kg of oil equivalent) per $1,000 GDP (constant	82	-0.06	5.94E-01	181	114	77	-0.14 2.13E-01	184	114	WB
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		!
	Category/ Cod.		Countries with available info. (n of 89)	Spearman Correlation Estimate (rho) p-value	Mean	SD	Countri es with availabl e info. (n of 80)	Spearman Correlation (rho) p-value Estimate	Mean	SD	Reference
			2005 PPP)
		ED_41	Mobile cellular subscriptions (per 100 people)	86	0.32	2.41E-03	116	39	80	0.44 4.93E-05	113		WB
		ED_42	Population using solid fuels (Total population: urban + Rural)	84	-0.52	3.87E-07	13	24	80	-0.50 2.68E-06	11		WHO
		Health Economy
		EH_43	External resources for health (% of total expenditure on health)	83	-0.59	5.25E-09	2	7	79	-0.62 7.62E-10	2		WB
		EH_44	Health expenditure per capita (current US$)	83	0.71	6.12E-14	1838	2150	79	0.73 1.62E-14	1942		WB
		EH_45	Health expenditure per capita, PPP (constant 2005 international $)	83	0.72	2.27E-14	1843	1685	79	0.75 2.32E-15	1924		WB
		Risk Factors for Non-Communicable Diseases
		HR_46	Mean BMI (kg/m2) (age-standardized estimate) Female +20	84	0.15	1.66E-01	26	2	80	0.11 3.32E-01	26		WHO
		HR_47	Mean BMI (kg/m2) (age-standardized estimate) Male +20	84	0.59	4.47E-09	26	2	80	0.63 2.58E-10	26		WHO
		HR_48	Mean systolic blood pressure in mm Hg (age-standardized estimate) Female +25	84	-0.24	2.56E-02	125	4	80	-0.18 1.19E-01	125		WHO
		HR_49	Mean systolic blood pressure in mm Hg (age-standardized estimate) Male +25	84	0.09	4.38E-01	131	4	80	0.19 8.41E-02	131		WHO
		HR_50	Mean Total Cholesterol in mmol/l (age-standardized estimate) Female+25	84	0.60	1.94E-09	5	0	80	0.62 1.25E-09	5		WHO
	Health conditions	HR_51 HR_52 HR_53 HR_54	Mean Total Cholesterol in mmol/l (age-standardized estimate) Male +25 Obesity: % of defined population with a BMI ≥ 30 kg/m2 (age-standardized estimate) Both Sexes +20 Overweight: % population with a BMI ≥ 25 kg/m2 (age-standardized estimate) Both Sexes +20 Preterm birth rate (per 100 live births)	84 84 84 82	0.65 0.31 0.37 -0.24	3.43E-11 3.88E-03 6.16E-04 3.00E-02	5 21 53 9	0 8 14 2	80 80 80 79	0.70 5.18E-13 0.33 3.06E-03 0.37 7.49E-04 -0.27 1.44E-02	5 21 53 8		WHO WHO WHO WHO
		HR_55	Prevalence of undernourishment (% of population)	79	-0.58	1.74E-08	8	8	75	-0.59 2.41E-08	8		WB
		HR_56	Total alcohol in litres of pure alcohol per capita consumption, estimation for 2008, Both Sexes +15	83	0.34	1.83E-03	9	5	79	0.44 4.38E-05	9		WHO
		Infectious Diseases and Immunization
		HI_57	Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%)	84	0.17	1.24E-01	93	9	80	0.24 3.00E-02	94		WHO
		HI_58	Haemophilus influenzae type B vaccine (Hib3) immunization coverage among 1-year-olds (%)	84	0.38	3.40E-04	82	30	80	0.48 5.20E-06	82		WHO
		HI_59	Hepatitis B (HepB3) immunization coverage among 1-year-olds (%)	84	-0.16	1.55E-01	78	33	80	-0.18 1.07E-01	78		WHO
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	!
		Countries with available info. (n of 89) This page was intentionally left blank. Spearman Correlation Estimate (rho) p-value	Mean	SD	Countri availabl e info. (n of 80) es with	(rho) Estimate Correlation p-value Spearman	Mean	SD	Reference
	HI_60	Incidence of tuberculosis (per 100,000 people)		87	-0.60	1.15E-09	48	70	80	-0.61 1.91E-09	49	WB
	HI_61	Measles -number of reported cases (standardized using population 0-14 years, per 100,000 people)	82	0.09	4.17E-01	1873	12117	80	0.05 6.91E-01	1754	WHO
	HI_62	Measles (MCV) immunization coverage among 1-year-olds (%)	84	-0.01	9.47E-01	93	9	80	0.02 8.28E-01	93	WHO
	HI_63	Measles immunization coverage among children ages 12-23 months (%)	83	-0.06	6.08E-01	93	8	79	-0.03 7.70E-01	93	WB
	HI_64	Pertussis -number of reported cases (standardized using population 0-14 years, per 100,000 people)	77	0.12	2.94E-01	2085	15988	76	0.08 4.91E-01	2113	WHO
	HI_65	Polio (Pol3) immunization coverage among 1-year-olds (%)	84	0.16	1.48E-01	93	8	80	0.23 4.46E-02	94	WHO
	Deaths from Non-Communicable Diseases
	HD_66	Cancers, deaths per 100,000 (age-standardized estimate) Female	84	0.12	2.79E-01	96	17	80	0.22 4.60E-02	94	WHO
	HD_67	Cancers, deaths per 100,000 (age-standardized estimate) Male	84	0.21	5.41E-02	147	44	80	0.28 1.26E-02	146	WHO
	HD_68	Cardiovascular diseases and diabetes, deaths per 100,000 (age-standardized estimate) Female	84	-0.51	8.98E-07	239	126	80	-0.50 2.77E-06	230	WHO
	HD_69	Cardiovascular diseases and diabetes, deaths per 100,000 (age-standardized estimate) Male	84	-0.41	9.45E-05	330	166	80	-0.39 3.10E-04	326	WHO
	HD_70	Chronic respiratory diseases, deaths per 100,000 (age-standardized estimate) Female	84	-0.25	2.08E-02	23	21	80	-0.34 1.99E-03	23	WHO
	HD_71	Chronic respiratory diseases, deaths per 100,000 (age-standardized estimate) Male	84	-0.27	1.15E-02	42	31	80	-0.37 7.89E-04	42	WHO
	HD_72	NCD deaths under age 60 (% of all non communicable diseases deaths) Both Sexes	84	-0.54	1.41E-07	21	11	80	-0.58 1.31E-08	21	WHO
		Probability of dying between exact ages 30 and 70 from any of
	HD_73	cardiovascular disease, cancer, diabetes, or chronic respiratory
		disease (%)		84	-0.45	1.76E-05	19	6	80	-0.44 3.98E-05	19	WHO
	Infant and Maternal Mortality Rates
	HM_75	Infant mortality rate (probability of dying between birth and age 1 per 1000 live births) Both sexes	84	-0.62	2.40E-10	12	14	80	-0.66 2.01E-11	11	WHO
	HM_76	Maternal mortality ratio (modeled estimate, per 100,000 live births)	82	-0.57	2.57E-08	61	124	80	-0.62 1.12E-09	56	WB
	HM_77	Mortality rate, under-5 (per 1,000 live births) (probability of dying by age 5 per 1000 live births)	84	-0.63	1.08E-10	15	20	80	-0.67 7.57E-12	14	WB
	UPMC -ED 393 -2015	200		Epidemiology of Type I Diabetes: Global Incidence and Determinants

Table 22 :

 22 Final Stepwise MLR model

	Coefficients of HLA alleles	Estimate	Std. Error	p -Value
	(Intercept)	6.81	2.48	0.0110
	DRB1*03:01	-8.90	24.97	0.7245
	DRB1*04:01	115.62	25.79	0.0001
	DRB1*04:05	-36.55	37.15	0.3346
	DRB1*08:01	92.62	28.41	0.0032

per'100.000' Allele'Frequency' DRB1*08:01' 6.1.7.2 Susceptible/protective HLA alleles related with T1D SI Figure 44:

  Major genetic determinants alleles at HLA-DR for susceptibility of T1D

					DRB1*03:01'								DRB1*04:01'
												70$				Sp.$Cor$=$0.55,$p,value$=$0.0001$$$
																$
												60$
	Incidence'of'T1D'Per'100.000'										Incidence'of'T1D'per'100.000'	10$ 20$ 30$ 40$ 50$
												0$
		00$	0.02$	0.04$	0.06$	0.08$	0.10$	0.12$	0.14$	0.16$	0.18$	0.00$		0.05$	0.10$	0.15$	0.20$		0.25$
						Allele'Frequency'								Allele'Frequency'
					DRB1*04:02'								DRB1*04:04'
		70$						Sp.$Cor$=$)0.33,$p)value$=$0.0635$$ $	70$					Sp.$Cor$=$0.26,$p-value$=$0.1024$
		60$										60$
	Incidence'of'T1D'per'100.000'	20$ 30$ 40$ 50$									Incidence'of'T1D'per'100.000'	20$ 30$ 40$ 50$
		10$										10$
		0$ 0.00$	0.01$	0.02$	0.03$	0.04$	0.05$	0.06$	0.07$	0.08$	0.09$	0$ 0.00$	0.01$	0.02$	0.03$	0.04$	0.05$	0.06$	0.07$	0.08$
						Allele'Frequency'								Allele'Frequency'
					DRB1*04:05'
		70$					Sp.$Cor$=$)0.41,$p)value$=$0.0093$$ $		70$					$ Sp.$Cor$=$0.42,$p-value$=$0.0127$
		60$										60$
	Incidence'of'T1D'per'100.000'	20$ 30$ 40$ 50$									Incidence'of'T1D'	20$ 30$ 40$ 50$
		10$										10$
		0$ 0.00$	0.02$	0.04$	0.06$	0.08$	0.10$	0.12$	0.14$	0.16$	0.18$	0$ 0.00$		0.05$	0.10$	0.15$	0.20$		0.25$
						Allele'Frequency'

Red dot: Finland.

Table 24 :

 24 Final Stepwise MLR model

	Model	Coefficients	Estimate	CI 2.5%	CI 97.5%	Std. Error	t-valeu	Pr(>|t|)
	Res. standard error:	(Intercept)	6.810	1.700	11.921	2.481	2.745	0.0110
	5.272 on 25 DF;	DRB1*03:01	-8.900	-60.334	42.529	24.972	-0.356	0.7245
	Adjusted R2: 0.6108; F-statistic: 12.38 on 4	DRB1*04:01	115.620	62.493	168.739	25.794	4.482	0.0001
	and 25 DF, p-value: 1.097e-05	DRB1*04:05	-36.550	-113.053	39.962	37.148	-0.984	0.3346
		DRB1*08:01	92.620	34.104	151.137	28.412	3.260	0.0032
		SI

Table 25 :

 25 Analysis of variance (Anova)

	Coefficients	Df	Sum Sq	Mean Sq	F-value	Pr(>F)
	DRB1*03:01	1	235.110	235.110	8.460	0.0075
	DRB1*04:01	1	790.830	790.830	28.456	1.57 x 10 -4
	DRB1*04:05	1	54.650	54.650	1.967	0.1731
	DRB1*08:01	1	295.330	295.330	10.627	0.0032
	Residuals	25	694.770	27.790
			SI

Table 26 :

 26 Residuals

	Min	1Q	Median	3Q	Max
	-9.729	-2.523	-0.557	2.558	14.572

Table 27 :

 27 Partial correlation for DRB1*03.01

	!!	!!	Partial'correlations'for'DRB1.03.01'	Sp.'cor'	r	2 '	t'	p!
	!	!	'	'	'	'	'
	!!	XY!	Incidence'*'DRB1.03.01'	0.459'	0.211' ''		0.001'
	!	!	!	!	!	!	!
	Z1! XZ1!	Incidence!*!UV!radiation!	/0.709!	0.503! !!	!!
	!!	YZ1!	DRB1.03.01!*!UV!radiation!	/0.301!	0.091! !!	!!
	!!	XY.Z1!	Incidence'*'DRB1.03.01'*'UV'radiation'	0.365'	0.133'	2.86'	0.006'
	!	!	!	!	!	!	!
	Z2! XZ2!	Incidence!*!Mobile!cellular!subscriptions!!	0.409!	0.167! !!	!!
	!!	YZ2!	DRB1.03.01!*!Mobile!cellular!subscriptions!!	0.245!	0.060! !!	!!
	!!	XY.Z2!	Incidence'*'DRB1.03.01'*'Mobile'cellular'subscriptions''	0.406'	0.164'	3.23'	0.002'
	!	!	!	!	!	!	!
	Z3! XZ3!	Incidence!*!Health!expenditure!per!capita!!	0.765!	0.585! !!	!!
	!!	YZ3!	DRB1.03.01!*!Health!expenditure!per!capita!!	0.391!	0.153! !!	!!
	!!	XY.Z3!	Incidence'*'DRB1.03.01'*'Health'expenditure'per'capita''	0.270'	0.073'	2.04'	0.046'
	!	!	!	!	!	!	!
	Z4! XZ4!	Incidence!*!Hepatitis!B!immunization!!	/0.184!	0.034! !!	!!
	!!	YZ4!	DRB1.03.01!*!Hepatitis!B!immunization!	0.024!	0.001! !!	!!
	!!	XY.Z4!	Incidence'*'DRB1.03.01'*'Hepatitis'B'immunization'	0.472'	0.222'	3.89'	0.0003'
	!	!	!	!	!	!	!
	Z5! XZ5!	Incidence!*!Mean!BMI!Male!	0.587!	0.345! !!	!!
	!!	YZ5!	DRB1.03.01!*!Mean!BMI!Male!	0.297!	0.880! !!	!!
	!!	XY.Z5!	Incidence'*'DRB1.03.01'*'Mean'BMI'Male'	0.368'	0.136'	2.88'	0.006'
			SI

Table 28 :

 28 Partial correlation for DRB1*04.01

	!!	!!	Partial'correlations'for'DRB1.04.01'	Sp.'cor'	r	2 '	t'	p!
	!	!	'	'	'	'	'
	!!	XY!	Incidence'*'DRB1.04.01'	0.549'	0.301' ''		0.0001'
	!	!	!	!	!	!	!
	Z1! XZ1!	Incidence!*!UV!radiation!	/0.709!	0.503! !!	!!
	!!	YZ1!	DRB1.04.01!*!UV!radiation!	/0.668!	0.446! !!	!!
	!!	XY.Z1!	Incidence'*'DRB1.04.01'*'UV'radiation'	0.144'	0.224'	1.06'	0.294'
	!	!	!	!	!	!	!
	Z2! XZ2!	Incidence!*!Mobile!cellular!subscriptions!!	0.409!	0.167! !!	!!
	!!	YZ2!	DRB1.04.01!*!Mobile!cellular!subscriptions!!	0.355!	0.126! !!	!!
	!!	XY.Z2

! Incidence'*'DRB1.04.01'*'Mobile'cellular'subscriptions'' 0.473' 0.224' 3.91' 0.0003'

	!	!	!	!	!	!	!
	Z3! XZ3!	Incidence!*!Health!expenditure!per!capita!!	0.765!		0.585! !!	!!
	!!	YZ3!	DRB1.04.01!*!Health!expenditure!per!capita!!	0.578!		0.334! !!	!!
	!!	XY.Z3

! Incidence'*'DRB1.04.01'*'Health'expenditure'per'capita'' 0.203' 0.042' 1.51' 0.137'

			SI
	!	!	!		!	!	!	!
	Z4! XZ4!	Incidence!*!Hepatitis!B!immunization!!		/0.184!	0.034! !!	!!
	!!	YZ4!	DRB1.04.01!*!Hepatitis!B!immunization!		/0.351!	0.123! !!	!!
	!!	XY.Z4!	Incidence'*'DRB1.04.01'*'Hepatitis'B'immunization'		0.526'	0.277'	4.51'	<0.0001'
	!	!	!		!	!	!	!
	Z5! XZ5!	Incidence!*!Mean!BMI!Male!		0.587!	0.345! !!	!!
	!!	YZ5!	DRB1.04.01!*!Mean!BMI!Male!		0.392!	0.154! !!	!!
	!!	XY.Z5!	Incidence'*'DRB1.04.01'*'Mean'BMI'Male'		0.428'	0.183'	3.45'	0.001'
	UPMC -ED 393 -2015	223	Epidemiology of Type I Diabetes: Global Incidence and Determinants

Table 29 :

 29 Partial correlation for DRB1*04.05

	!!	!!	Partial'correlations'for'DRB1.04.05'	Sp.'cor'	r	2 '	t'	p!
	!	!	'	'	'	'	'
	!!	XY!	Incidence'*'DRB1.04.05'	L0.406'	0.165' ''		0.0093'
	!	!	!	!	!	!	!
	Z1! XZ1!	Incidence!*!UV!radiation!	/0.709!	0.503! !!	!!
	!!	YZ1!	DRB1.04.05!*!UV!radiation!	0.386!	0.149! !!	!!
	!!	XY.Z1!	Incidence'*'DRB1.04.05'*'UV'radiation'	L0.203'	0.041'	L1.51'	0.137'
	!	!	!	!	!	!	!
	Z2! XZ2!	Incidence!*!Mobile!cellular!subscriptions!!	0.409!	0.167! !!	!!
	!!	YZ2!	DRB1.04.05!*!Mobile!cellular!subscriptions!!	/0.140!	!0.020! !!	!!
	!!	XY.Z2!	Incidence'*'DRB1.04.05'*'Mobile'cellular'subscriptions''	L0.386'	0.149'	L3.05'	0.004'
	!	!	!	!	!	!	!
	Z3! XZ3!	Incidence!*!Health!expenditure!per!capita!!	0.765!	0.585! !!	!!
	!!	YZ3!	DRB1.04.05!*!Health!expenditure!per!capita!!	/0.399!	0.159! !!	!!
	!!	XY.Z3!	Incidence'*'DRB1.04.05'*'Health'expenditure'per'capita''	L0.171'	0.029'	L1.26'	0.213'
	!	!	!	!	!	!	!
	Z4! XZ4!	Incidence!*!Hepatitis!B!immunization!!	/0.184!	0.034! !!	!!
	!!	YZ4!	DRB1.04.05!*!Hepatitis!B!immunization!	0.320!	0.102! !!	!!
	!!	XY.Z4!	Incidence'*'DRB1.04.05'*'Hepatitis'B'immunization'	L0.373'	0.139'	L2.92'	0.005'
	!	!	!	!	!	!	!
	Z5! XZ5!	Incidence!*!Mean!BMI!Male!	0.587!	0.345! !!	!!
	!!	YZ5!	DRB1.04.05!*!Mean!BMI!Male!	/0.330!	0.109! !!	!!
	!!	XY.Z5!	Incidence'*'DRB1.04.05'*'Mean'BMI'Male'	L0.278'	0.077'	L2.11'	0.040'
			SI

Table 30 :

 30 Partial correlation for DRB1*08.01

	!!	!!	Partial'correlations'for'DRB1.08.01'	Sp.'cor'	r	2 '	t'	p!
	!	!	'	'	'	'	'
	!!	XY!	Incidence'*'DRB1.08.01'	0.417'	0.174' ''		0.0127'
	!	!	!	!	!	!	!
	Z1! XZ1!	Incidence!*!UV!radiation!	/0.709!	0.503! !!	!!
	!!	YZ1!	DRB1.08.01!*!UV!radiation!	/0.501!	0.251! !!	!!
	!!	XY.Z1!	Incidence'*'DRB1.08.01'*'UV'radiation'	0.101'	0.010'	0.74'	0.463'
	!	!	!	!	!	!	!
	Z2! XZ2!	Incidence!*!Mobile!cellular!subscriptions!!	0.409!	0.167! !!	!!
	!!	YZ2!	DRB1.08.01!*!Mobile!cellular!subscriptions!!	0.264!	0.070! !!	!!
	!!	XY.Z2

! Incidence'*'DRB1.08.01'*'Mobile'cellular'subscriptions'' 0.351' 0.123' 2.73' 0.009'

	!	!	!	!	!	!	!
	Z3! XZ3!	Incidence!*!Health!expenditure!per!capita!!	0.765!		0.585! !!	!!
	!!	YZ3!	DRB1.08.01!*!Health!expenditure!per!capita!!	0.367!		0.135! !!	!!
	!!	XY.Z3

! Incidence'*'DRB1.08.01'*'Health'expenditure'per'capita'' 0.227' 0.052' 1.70' 0.095'

	!	!	!	!	!	!	!
	Z4! XZ4!	Incidence!*!Hepatitis!B!immunization!!	/0.184!	0.034! !!	!!
	!!	YZ4!	DRB1.08.01!*!Hepatitis!B!immunization!	/0.300!	0.090! !!	!!
	!!	XY.Z4!	Incidence'*'DRB1.08.01'*'Hepatitis'B'immunization'	0.386'	0.149'	3.04'	0.004'
	!	!	!	!	!	!	!
	Z5! XZ5!	Incidence!*!Mean!BMI!Male!	0.587!	0.345! !!	!!
	!!	YZ5!	DRB1.08.01!*!Mean!BMI!Male!	0.088!	0.008! !!	!!
	!!	XY.Z5!	Incidence'*'DRB1.08.01'*'Mean'BMI'Male'	0.453'	0.205'	3.70'	0.001'

8.3.2 Step 2. Query # 1 using the main MeSH terms retrieved from selected articles

	Query #1	No. Ref. retrieved	Observations
	("diabetes mellitus, type 1/epidemiology"[MAJR] OR "type 1	9817	Query #1 included
	diabetes"[TIAB]) AND (("incidence"[MeSH Major Topic] OR		the main MeSH
	incidence[Title/Abstract]) AND age[Title/Abstract] OR "age		terms retrieved in
	distribution"[MeSH Terms] OR "age factors"[MeSH Terms] OR "age		the step 1.

8.3.4 Step 4. New query including selected MeSH terms retrieved from irrelevant papers

	Query #2	No. Ref. retrieved	Observations
	("diabetes mellitus, type 1/epidemiology"[MAJR] OR "type 1	4230	Query #2 is equal to
	diabetes"[TIAB]) AND (("incidence"[MeSH Major Topic] OR		query #1, except for the
	incidence[Title/Abstract]) AND age[Title/Abstract] OR "age		inclusion of the terms:
	distribution"[MeSH Terms] OR "age factors"[MeSH Terms] OR "age		("Diabetic
	groups"[MeSH Terms] AND "humans"[MeSH Terms]) AND (Journal		Neuropathies"[MeSH
	Article[ptyp] AND English[lang] AND ("1990/1/1"[PDAT] :		Terms] OR "Diabetic
	"2011/11/28"[PDAT])) NOT ("Diabetic Neuropathies"[MeSH Terms]		Nephropathies"[MeSH
	OR "Diabetic Nephropathies"[MeSH Terms] OR		Terms] OR
	"Autoantibodies"[MeSH Terms] OR "Blood Glucose"[MeSH Terms]		"Autoantibodies"[MeSH
	OR "Cholesterol"[MeSH Terms] OR "Antigens"[MeSH Terms] OR		Terms] OR "Blood
	"histocompatibility antigens class ii"[MeSH Terms] OR		Glucose"[MeSH Terms]

6

"age groups"[MeSH Terms] included: Adolescent, Adult, Aged +, Middle Aged, Young Adult, Child, Child, Preschool, Infant, Infant, Newborn +.

Disponible sur http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002369

Meeting assistants: Pr. Alain-Jacques Valleron, Pr. Pierre-Ives Boëlle, Sophie Valtat, Sofia Meurisse, Nga Mai, Pascal Championnat, Flavian Quintus, Thomas Obadia, Sthephany Gomme, Segolene Charaudeau. Place: Hopital Saint-Antoine.

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PLOS ONE | DOI:10.1371/journal.pone.0118298February 23, 2015

N 956

PROSPERO registration number: CRD42012002369.Available on: http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42012002369

Acknowledgments

This work was carried out at the Unit of « Epigénétique et Environnement », INSERM U1169 in the Kremlin-Bicêtre, France, during the years 2011-2015. I wish to thank

Acknowledgments

We thank Anne-Lise Haenni for critical reading and reviewing the English of this manuscript and Nga Mai for her recommendations during the statistical analyses.

Acknowledgements

We thank Anne-Lise Haenni of the Institut Jacques Monod, CNRS -Paris-Diderot University, for critically reading and reviewing the English of this manuscript.

Funding

This study was supported by grants from the Programme Hospitalier de Recherche Clinique, and from Colciencias, the Administrative Department of Science, Technology and Innovation for Colombia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability Statement: All relevant data are within the paper and its Supporting Information files.

Availability of supporting data

All the supporting data are included as additional files.

Supporting Information

Additional files

Additional file 1: Search equation used for the bibliographic analysis.

Additional file 2: PRISMA checklist.

Additional file 3: List of selected papers reporting incidence of T1D in 0-14 year-olds in 9 countries.

Additional file 4: Table S1. Geographic repartition, and reported adult T1D incidences found in the systematic review. Incidence was per 100.000 persons per year. T1D: Type 1 Diabetes. NW: Nation-wide study. HIGH, LMIC: High, Low-Medium Income Level. Highlighted: reports of the systematic review using the autoantibodies/C-peptide as diagnosis (See figure on previous page.) Figure 3 Geographical correlation of T1D incidence between individuals aged 0-14 years and adults. Studies using autoantibodies/C-Peptide for T1D case definition are identified by Red diamonds. The corresponding countries are shown as: BE1: Belgium (2007) [30]; BE2: Belgium (2002) [31]; BE3: Belgium (1997) [32]; DK: Denmark [34]; ES1: Spain, Catalonia [54]; ES2: Spain, Navarra (2014) [56]; ES3: Spain, Navarra (2013) [57]; FI: Finland [35]; IR: Iran (Islamic Republic of) [15]; IT: Italy [45,46]; SE: Sweden [63], TW: Taiwan [81]; US: United States of America [74]. Sp. Cor: Spearman correlation. The five areas were Ohio (8 counties), Washington State (5 counties), South Carolina, Colorado and California; the table presents the mean incidence calculated, retrieved from 5 populations: African American, Asian Pacific Islander, Navajo, Hispanic and non-Hispanic young.

Additional file 5: Table S2. T1D incidences by sex in young adults and adults found in the Systematic Review. Male-to-Female ratios >1 are highlighted. Ref: Reference. First author and publication year in reports of the systematic review using the autoantibodies/C-peptide as diagnosis criteria are highlighted. Inc: incidence per 100.000 persons per year. NW: Nation-wide study. HIGH, LMIC: High, Low-Medium Income Level. ( †) Studies used for analyses. ( §) The five areas were Ohio (8 counties), Washington State (5 counties), South Carolina, Colorado and California; the table presents the mean incidence calculated retrieved from 5 populations: African American, Asian Pacific Islander, Navajo, Hispanic and non-Hispanic young. Incidence was calculated as the mean of retrieved information: (a) in Jews and other non-Arabs and Arabs; (b) in White and Black populations; (c) in Non-Hispanic Whites and Hispanic Whites. (d) Study giving the total incidence by sex, not by age classes.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

PAD-V conducted the data collection and analyses. PAD-V, PB and AJV, contributed to the writing of the manuscript. All authors read and approved the final manuscript.

212

T1D is a polygenetic disorder where the susceptibility for T1D follows a recessive pattern and incomplete penetrance in which the MHC plays a crucial role in pathogenesis (182). A previous study, in which only (HLA)-DQ loci were explored, suggest that the variation of the T1D risk for European populations is explained by variations in the distribution of DQ genotypes, that confer a high susceptibility of T1D in the general population (269).

We aimed to describe the relationship between certain HLA haplotypes and T1D incidence by geographical regions; however, the limited available global information did not allow us to reach this goal. Only a description of the currently available information for the respective susceptible and protective HLA haplotypes mentioned above is possible. Nonetheless, the limited data retrieved, revealed that some susceptible and protective haplotypes in one population could confer susceptibility or may constitute a certain protection in other populations, a phenomenon also described in a study that assessed the effect of Asian-specific HLA haplotypes (162). The second finding was that among all the alleles studied, DRB1*04:01 and DRB1*08:01 were significant predictors of the incidence of T1D in the ensemble of the 56 countries for which we could establish a correlation with the information of the frequency of the HLA allele. This finding suggests that these alleles are determinants of T1D risk, as was suggested by the Type 1 Diabetes Genetics Consortium Families who showed that the risk of T1D is conferred predominantly by DRB1 alleles (89). 23 andSI Table 24-