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ACKNOWLEDGEMENTS We would like to thank Keith Summerhill, Laura Wang, and Stephen Young for the measurement of plasma phospholipid fatty acids. We also want to thank all the participants in the EPIC-Norfolk study. Medical Research Council Epidemiology Unit MC_UU_12015/1 and MC_UU_12015/5; Medical Research Council Human Nutrition Research MC_UP_A090_1006; Cambridge Lipidomics Biomarker Research Initiative G0800783; NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014). Dr Ju-Sheng Zheng has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 701708. ; Peer reviewed ; Publisher PDF

This is the final version of the article. It first appeared from Public Library of Science via http://dx.doi.org/ 10.1371/journal.pmed.1002094. ; ${\bf Background:}$ Whether and how n-3 and n-6 polyunsaturated fatty acids (PUFAs) are related to type 2 diabetes (T2D) is debated. Objectively measured plasma PUFAs can help to clarify these associations. ${\bf Methods~and~Findings:}$ Plasma phospholipid PUFAs were measured by gas-chromatography among 12,132 incident T2D cases and 15,919 sub-cohort participants in EPIC-InterAct study across 8 European countries. Country-specific hazard ratios (HR) were estimated using Prentice-weighted Cox regression and pooled by random-effects meta-analysis. We also systematically reviewed published prospective studies on circulating PUFAs and T2D risk and pooled the quantitative evidence for comparison with results from EPIC-InterAct. In EPIC-InterAct, among long-chain n-3 PUFAs α-linolenic acid (ALA) was inversely associated with T2D (HR per SD 0.93; 95%CI 0.88,0.98), but eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were not significantly associated. Among n-6 PUFAs, linoleic acid (LA) (0.80; 0.77,0.83) and eicosadienoic acid (EDA) (0.89; 0.85,0.94) were inversely related, arachidonic acid (AA) was not significantly associated, while significant positive associations were observed with γ-linolenic acid (GLA), dihomo-GLA, docosatetraenoic acid (DTA) and docosapentaenoic acid (n6-DPA), with HRs between 1.13 to 1.46 per SD. These findings from EPIC-InterAct were broadly similar to comparative findings from summary estimates from up to 9 studies including between 71 to 2,499 T2D cases. Limitations included potential residual confounding and the inability to distinguish between dietary and metabolic influences on plasma phospholipid PUFAs. ${\bf Conclusions:}$ These large-scale findings suggest important inverse association of circulating plant-origin n-3 PUFA (ALA) but no convincing association of marine-derived n3 PUFAs (EPA, DHA) with T2D. Moreover they highlight that the most abundant n6-PUFA (LA) is inversely associated with T2D. The detection of associations with previously less well investigated PUFAs points to the importance of considering individual fatty acids rather than a focus on fatty acid class. ; Funding for the InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197). In addition, InterAct investigators acknowledge funding from the following sources: Medical Research Council Epidemiology Unit MC_UU_12015/1 and MC_UU_12015/5, and Medical Research Council Human Nutrition Research MC_UP_A090_1006 and Cambridge Lipidomics Biomarker Research Initiative G0800783; FLC and TJK: Cancer Research UK; JMH and MJT: Health Research Fund of the Spanish Ministry of Health; Murcia Regional Government (Nº 6236); MG: Regional Government of Navarre; -IS, DLvdA, AMWS, YTvdS: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands; Verification of diabetes cases in EPIC-NL was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation; RK: German Cancer Aid, German Ministry of Research (BMBF); KTK: Medical Research Council UK, Cancer Research UK; PMN: Swedish Research Council; KO and AT: Danish Cancer Society; JRQ: Asturias Regional Government; OR: The Västerboten County Council; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; ER: Imperial College Biomedical Research Centre.

Background: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. Methods: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27 779 individuals from eight European countries, of whom 12 403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m 2 . Calibration patterns were inconsistent for age and waist-circumference subgroups. Interpretation: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity. Funding: The European Union.

BACKGROUND: The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations. METHODS: We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups. INTERPRETATION: Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity. FUNDING: The European Union.

Objective: To determine if the Food Standards Agency nutrient profiling system (FSAm-NPS), which grades the nutritional quality of food products and is used to derive the Nutri-Score front-of-packet label to guide consumers towards healthier food choices, is associated with mortality. Design: Population based cohort study. Setting: European Prospective Investigation into Cancer and Nutrition (EPIC) cohort from 23 centres in 10 European countries. Participants: 521 324 adults; at recruitment, country specific and validated dietary questionnaires were used to assess their usual dietary intakes. A FSAm-NPS score was calculated for each food item per 100 g content of energy, sugars, saturated fatty acids, sodium, fibre, and protein, and of fruit, vegetables, legumes, and nuts. The FSAm-NPS dietary index was calculated for each participant as an energy weighted mean of the FSAm-NPS score of all foods consumed. The higher the score the lower the overall nutritional quality of the diet. Main outcome measure: Associations between the FSAm-NPS dietary index score and mortality, assessed using multivariable adjusted Cox proportional hazards regression models. Results: After exclusions, 501 594 adults (median follow-up 17.2 years, 8 162 730 person years) were included in the analyses. Those with a higher FSAm-NPS dietary index score (highest versus lowest fifth) showed an increased risk of all cause mortality (n=53 112 events from non-external causes; hazard ratio 1.07, 95% confidence interval 1.03 to 1.10, P<0.001 for trend) and mortality from cancer (1.08, 1.03 to 1.13, P<0.001 for trend) and diseases of the circulatory (1.04, 0.98 to 1.11, P=0.06 for trend), respiratory (1.39, 1.22 to 1.59, P<0.001), and digestive (1.22, 1.02 to 1.45, P=0.03 for trend) systems. The age standardised absolute rates for all cause mortality per 10 000 persons over 10 years were 760 (men=1237; women=563) for those in the highest fifth of the FSAm-NPS dietary index score and 661 (men=1008; women=518) for those in the lowest fifth. Conclusions: In this large multinational European cohort, consuming foods with a higher FSAm-NPS score (lower nutritional quality) was associated with a higher mortality for all causes and for cancer and diseases of the circulatory, respiratory, and digestive systems, supporting the relevance of FSAm-NPS to characterise healthier food choices in the context of public health policies (eg, the Nutri-Score) for European populations. This is important considering ongoing discussions about the potential implementation of a unique nutrition labelling system at the European Union level.

OBJECTIVE: To determine if the Food Standards Agency nutrient profiling system (FSAm-NPS), which grades the nutritional quality of food products and is used to derive the Nutri-Score front-of-packet label to guide consumers towards healthier food choices, is associated with mortality. DESIGN: Population based cohort study. SETTING: European Prospective Investigation into Cancer and Nutrition (EPIC) cohort from 23 centres in 10 European countries. PARTICIPANTS: 521 324 adults; at recruitment, country specific and validated dietary questionnaires were used to assess their usual dietary intakes. A FSAm-NPS score was calculated for each food item per 100 g content of energy, sugars, saturated fatty acids, sodium, fibre, and protein, and of fruit, vegetables, legumes, and nuts. The FSAm-NPS dietary index was calculated for each participant as an energy weighted mean of the FSAm-NPS score of all foods consumed. The higher the score the lower the overall nutritional quality of the diet. MAIN OUTCOME MEASURE: Associations between the FSAm-NPS dietary index score and mortality, assessed using multivariable adjusted Cox proportional hazards regression models. RESULTS: After exclusions, 501 594 adults (median follow-up 17.2 years, 8 162 730 person years) were included in the analyses. Those with a higher FSAm-NPS dietary index score (highest versus lowest fifth) showed an increased risk of all cause mortality (n=53 112 events from non-external causes; hazard ratio 1.07, 95% confidence interval 1.03 to 1.10, P<0.001 for trend) and mortality from cancer (1.08, 1.03 to 1.13, P<0.001 for trend) and diseases of the circulatory (1.04, 0.98 to 1.11, P=0.06 for trend), respiratory (1.39, 1.22 to 1.59, P<0.001), and digestive (1.22, 1.02 to 1.45, P=0.03 for trend) systems. The age standardised absolute rates for all cause mortality per 10 000 persons over 10 years were 760 (men=1237; women=563) for those in the highest fifth of the FSAm-NPS dietary index score and 661 (men=1008; women=518) for those in the lowest fifth. CONCLUSIONS: In this large multinational European cohort, consuming foods with a higher FSAm-NPS score (lower nutritional quality) was associated with a higher mortality for all causes and for cancer and diseases of the circulatory, respiratory, and digestive systems, supporting the relevance of FSAm-NPS to characterise healthier food choices in the context of public health policies (eg, the Nutri-Score) for European populations. This is important considering ongoing discussions about the potential implementation of a unique nutrition labelling system at the European Union level.

Objective: To determine if the Food Standards Agency nutrient profiling system (FSAm-NPS), which grades the nutritional quality of food products and is used to derive the Nutri-Score front-of-packet label to guide consumers towards healthier food choices, is associated with mortality. Design: Population based cohort study. Setting: European Prospective Investigation into Cancer and Nutrition (EPIC) cohort from 23 centres in 10 European countries. Participants: 521 324 adults; at recruitment, country specific and validated dietary questionnaires were used to assess their usual dietary intakes. A FSAm-NPS score was calculated for each food item per 100 g content of energy, sugars, saturated fatty acids, sodium, fibre, and protein, and of fruit, vegetables, legumes, and nuts. The FSAm-NPS dietary index was calculated for each participant as an energy weighted mean of the FSAm-NPS score of all foods consumed. The higher the score the lower the overall nutritional quality of the diet. Main outcome measure: Associations between the FSAm-NPS dietary index score and mortality, assessed using multivariable adjusted Cox proportional hazards regression models. Results: After exclusions, 501 594 adults (median follow-up 17.2 years, 8 162 730 person years) were included in the analyses. Those with a higher FSAm-NPS dietary index score (highest versus lowest fifth) showed an increased risk of all cause mortality (n=53 112 events from non-external causes; hazard ratio 1.07, 95% confidence interval 1.03 to 1.10, P<0.001 for trend) and mortality from cancer (1.08, 1.03 to 1.13, P<0.001 for trend) and diseases of the circulatory (1.04, 0.98 to 1.11, P=0.06 for trend), respiratory (1.39, 1.22 to 1.59, P<0.001), and digestive (1.22, 1.02 to 1.45, P=0.03 for trend) systems. The age standardised absolute rates for all cause mortality per 10 000 persons over 10 years were 760 (men=1237; women=563) for those in the highest fifth of the FSAm-NPS dietary index score and 661 (men=1008; women=518) for those in the lowest fifth. Conclusions: In this large multinational European cohort, consuming foods with a higher FSAm-NPS score (lower nutritional quality) was associated with a higher mortality for all causes and for cancer and diseases of the circulatory, respiratory, and digestive systems, supporting the relevance of FSAm-NPS to characterise healthier food choices in the context of public health policies (eg, the Nutri-Score) for European populations. This is important considering ongoing discussions about the potential implementation of a unique nutrition labelling system at the European Union level.

OBJECTIVE To determine if the Food Standards Agency nutrient profiling system (FSAm-NPS), which grades the nutritional quality of food products and is used to derive the Nutri-Score front-of-packet label to guide consumers towards healthier food choices, is associated with mortality. DESIGN Population based cohort study. SETTING European Prospective Investigation into Cancer and Nutrition (EPIC) cohort from 23 centres in 10 European countries. PARTICIPANTS 521 324 adults; at recruitment, country specific and validated dietary questionnaires were used to assess their usual dietary intakes. A FSAm-NPS score was calculated for each food item per 100 g content of energy, sugars, saturated fatty acids, sodium, fibre, and protein, and of fruit, vegetables, legumes, and nuts. The FSAm-NPS dietary index was calculated for each participant as an energy weighted mean of the FSAm-NPS score of all foods consumed. The higher the score the lower the overall nutritional quality of the diet. MAIN OUTCOME MEASURE Associations between the FSAm-NPS dietary index score and mortality, assessed using multivariable adjusted Cox proportional hazards regression models. RESULTS After exclusions, 501 594 adults (median follow-up 17.2 years, 8 162 730 person years) were included in the analyses. Those with a higher FSAm-NPS dietary index score (highest versus lowest fifth) showed an increased risk of all cause mortality (n=53 112 events from non-external causes; hazard ratio 1.07, 95% confidence interval 1.03 to 1.10, P(0.001 for trend) and mortality from cancer (1.08, 1.03 to 1.13, P(0.001 for trend) and diseases of the circulatory (1.04, 0.98 to 1.11, P=0.06 for trend), respiratory (1.39, 1.22 to 1.59, P(0.001), and digestive (1.22, 1.02 to 1.45, P=0.03 for trend) systems. The age standardised absolute rates for all cause mortality per 10 000 persons over 10 years were 760 (men=1237; women=563) for those in the highest fifth of the FSAm-NPS dietary index score and 661 (men=1008; women=518) for those in the lowest fifth. CONCLUSIONS In this large multinational European cohort, consuming foods with a higher FSAm-NPS score (lower nutritional quality) was associated with a higher mortality for all causes and for cancer and diseases of the circulatory, respiratory, and digestive systems, supporting the relevance of FSAm-NPS to characterise healthier food choices in the context of public health policies (eg, the Nutri-Score) for European populations. This is important considering ongoing discussions about the potential implementation of a unique nutrition labelling system at the European Union level.

BACKGROUND: A high circulating concentration of interleukin 6 is associated with increased risk of coronary heart disease. Blockade of the interleukin-6 receptor (IL6R) with a monoclonal antibody (tocilizumab) licensed for treatment of rheumatoid arthritis reduces systemic and articular inflammation. However, whether IL6R blockade also reduces risk of coronary heart disease is unknown. METHODS: Applying the mendelian randomisation principle, we used single nucleotide polymorphisms (SNPs) in the gene IL6R to evaluate the likely efficacy and safety of IL6R inhibition for primary prevention of coronary heart disease. We compared genetic findings with the effects of tocilizumab reported in randomised trials in patients with rheumatoid arthritis. FINDINGS: In 40 studies including up to 133,449 individuals, an IL6R SNP (rs7529229) marking a non-synonymous IL6R variant (rs8192284; p.Asp358Ala) was associated with increased circulating log interleukin-6 concentration (increase per allele 9·45%, 95% CI 8·34-10·57) as well as reduced C-reactive protein (decrease per allele 8·35%, 95% CI 7·31-9·38) and fibrinogen concentrations (decrease per allele 0·85%, 95% CI 0·60-1·10). This pattern of effects was consistent with IL6R blockade from infusions of tocilizumab (4-8 mg/kg every 4 weeks) in patients with rheumatoid arthritis studied in randomised trials. In 25,458 coronary heart disease cases and 100,740 controls, the IL6R rs7529229 SNP was associated with a decreased odds of coronary heart disease events (per allele odds ratio 0·95, 95% CI 0·93-0·97, p=1·53×10(-5)). INTERPRETATION: On the basis of genetic evidence in human beings, IL6R signalling seems to have a causal role in development of coronary heart disease. IL6R blockade could provide a novel therapeutic approach to prevention of coronary heart disease that warrants testing in suitably powered randomised trials. Genetic studies in populations could be used more widely to help to validate and prioritise novel drug targets or to repurpose existing agents and targets for new therapeutic uses. FUNDING: UK Medical Research Council; British Heart Foundation; Rosetrees Trust; US National Heart, Lung, and Blood Institute; Du Pont Pharma; Chest, Heart and Stroke Scotland; Wellcome Trust; Coronary Thrombosis Trust; Northwick Park Institute for Medical Research; UCLH/UCL Comprehensive Medical Research Centre; US National Institute on Aging; Academy of Finland; Netherlands Organisation for Health Research and Development; SANCO; Dutch Ministry of Public Health, Welfare and Sports; World Cancer Research Fund; Agentschap NL; European Commission; Swedish Heart-Lung Foundation; Swedish Research Council; Strategic Cardiovascular Programme of the Karolinska Institutet; Stockholm County Council; US National Institute of Neurological Disorders and Stroke; MedStar Health Research Institute; GlaxoSmithKline; Dutch Kidney Foundation; US National Institutes of Health; Netherlands Interuniversity Cardiology Institute of the Netherlands; Diabetes UK; European Union Seventh Framework Programme; National Institute for Healthy Ageing; Cancer Research UK; MacArthur Foundation.

Publisher's version (útgefin grein). ; Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3, 514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 × 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2, 159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 × 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 × 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension. ; The following authors declare commercial private and/or governmental affiliations: Bruce M. Psaty (BMP) serves on the DSMB of a clinical trial funded by Zoll Lifecor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Barbara V. Howard (BVH) has a contract from National Heart, Lung, and Blood Institute (NHLBI). Brenda W.J.H. Penninx (BWJHP) has received research funding (non-related to the work reported here) from Jansen Research and Boehringer Ingelheim. Mike A. Nalls (MAN) is supported by a consulting contract between Data Tecnica International LLC and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA. MAN also consults for Illumina Inc., the Michael J. Fox Foundation, and the University of California Healthcare. MAN also has commercial affiliation with Data Tecnica International, Glen Echo, MD, USA. Mark J. Caulfield (MJC) has commercial affiliation and is Chief Scientist for Genomics England, a UK government company. Oscar H Franco (OHF) is supported by grants from Metagenics (on women's health and epigenetics) and from Nestlé (on child health). Peter S. Sever (PSS) is financial supported from several pharmaceutical companies which manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. Paul W. Franks (PWF) has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects. Fimlab LTD provided support in the form of salaries for author Terho Lehtimäki (TL) but did not have any additional role in the study design to publish, or preparation of the manuscript. Gen‐info Ltd provided support in the form of salaries for author Ozren Polašek (OP) but did not have any additional role in the study design to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. There are no patents, products in development, or marked products to declare. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer Reviewed

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape. ; Funding: Funding for this study was provided by the Aarne Koskelo Foundation; the Aase and Ejner Danielsens Foundation; the Academy of Finland (40758, 41071, 77299, 102318, 104781, 117787, 117844, 118590, 120315, 121584, 123885, 124243, 124282, 126925, 129269, 129293, 129378, 130326, 134309, 134791, 136895, 139635, 211497, 263836, 263924, 1114194, 24300796); the Agency for Health Care Policy Research (HS06516); the Agency for Science, Technology and Research of Singapore (A*STAR); the Ahokas Foundation; the ALF/LUA research grant in Gothenburg; the ALK-Abello A/S (Horsholm, Denmark), Timber Merchant Vilhelm Bangs Foundation, MEKOS Laboratories Denmark; the Althingi (the Icelandic Parliament); the American Heart Association (AHA; 13POST16500011); the ANR ("Agence Nationale de la 359 Recherche"); the Ark (NHMRC Enabling Facility); the Arthritis Research UK (19542, 18030); the AstraZeneca; the Augustinus Foundation; the Australian National Health and Medical Research Council (NHMRC; 241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485, 613672, 613601 and 1011506); the Australian Research Council (ARC; DP0770096 and DP1093502); the Becket Foundation; the bi-national BMBF/ANR funded project CARDomics (01KU0908A); the Biobanking and Biomolecular Resources Research Infrastructure (BBMRINL; 184.021.007, CP 32); the Biocentrum Helsinki; the Boehringer Ingelheim Foundation; the British Heart Foundation (RG/10/12/28456, SP/04/ 002); the Canadian Institutes for Health Reseaerch (FRCN-CCT-83028); the Cancer Research UK (C490/A10124, C490/A10119); the Center for Medical Systems Biology (CMSB; NWO Genomics); the Centers for Disease Control and Prevention and Association of Schools of Public Health (1734, S043, S3486); the Centre of Excellence Baden-Wurttemberg Metabolic Disorders; the Chief Scientist Office of the Scottish Government; the Clinical Research Facility at Guys & St Thomas NHS Foundation Trust; the Contrat de Projets Etat-Region (CPER); the Croatian Science Council (Grant no. 8875); the CVON (GENIUS); the Danish Agency for Science, Technology and Innovation; the Danish Centre for Health Technology Assessment, Novo Nordisk Inc.; the Danish Council for Independent Research (DFF 1333-00124); the Danish Diabetes Association; Danish Heart Foundation; the Danish Medical Research Council; the Danish Ministry of Internal Affairs and Health; the Danish National Research Foundation; the Danish Pharmaceutical Association; Danish Pharmacists Fund; the Danish Research Council; the Deutsche Forschungsgemeinschaft; the Diabetes Hilfs-und Forschungsfonds Deutschland (DHFD); the Dr. Robert Pfleger-Stiftung; the Dresden University of Technology Funding Grant, Med Drive; the Dutch Brain Foundation; the Dutch Diabetes Research Foundation; the Dutch Economic Structure Enhancing Fund (FES); the Dutch Kidney Foundation; the Dutch Ministry for Health, Welfare and Sports; the Dutch Ministry of Economic Affairs; the Dutch Ministry of Education, Culture and Science; the Egmont Foundation; the Else Kraner-Fresenius Stiftung (2012_A147, P48/08//A11/08); the Emil Aaltonen Foundation; the Erasmus Medical Center and Erasmus University, Rotterdam; the Estonian Ministry of Science and Education (SF0180142s08); the European Commission (223004, 2004310, DGXII, FP6-EUROSPAN, FP6-EXGENESIS, FP6-LSHG-CT2006-018947, FP6-LSHG-CT-2006-01947, FP6-LSHM- CT-2004-503485, FP6-LSHM-CT-2006037593, FP6-LSHM-CT-2007-037273, FP7-201379, FP7-201668, FP7-279143, FP7-305739, FP7313010, FP7-ENGAGE-HEALTH-F4-2007-201413, FP7-EurHEALTHAgeing-277849, FP7-HEALTH-F42007-201550, HEALTH-2011.2.4.2-2-EU-MASCARA, HEALTH-F2-2008-201865-GEFOS, HEALTH-F7305507 HOMAGE, LSHM-CT-2006-037593, QLG1CT-2001-01252, QLG1-CT-2002-00896, QLG2-CT2002-01254); the European Regional Development Fund (ERDF) and the Wissenschaftsoffensive TMO; the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN; 3.2.0304.11-0312); the European Research Council (ERC; 2011-StG-280559-SEPI, 2011-294713-EPLORE, 230374); the European Science Foundation (ESF; EU/QLRT-2001-01254); the EuroSTRESS project FP-006; the Finlands Slottery Machine Association; the Finnish Centre for Pensions (ETK); the Finnish Cultural Foundation; the Finnish Diabetes Association; the Finnish Diabetes Research Foundation; the Finnish Foundation for Cardiovascular Research; the Finnish Foundation for Pediatric Research; the Finnish Funding Agency for Technology and Innovation (40058/07); the Finnish Medical Society; the Finnish Ministry of Education and Culture (627; 2004-2011); the Finnish Ministry of Health and Social Affairs (5254); the Finnish National Public Health Institute (current National Institute for Health and Welfare); the Finnish Special Governmental Subsidy for Health Sciences; the Finska Lakaresallskapet, Signe and Ane Gyllenberg Foundation; the Flemish League against Cancer, ITEA2 (project Care4Me); the Folkhalsan Research Foundation; the Fonds voor Wetenschappelijk Onderzoek (FWO) Vlaanderen; the Foundation for Life and Health in Finland; the Foundation for Strategic Research (SSF) and the Stockholm County Council (560283); the G. Ph. Verhagen Foundation; the Gene-diet Interactions in Obesity' project (GENDINOB); the Genetic Association Information Network (GAIN); the GENEVA Coordinating Center (U01 HG 004446); the GenomEUtwin (EU/QLRT2001-01254; QLG2-CT-2002-01254); the German Bundesministerium fuer Forschung und Technology (01 AK 803 A-H, 01 IG 07015 G); the German Diabetes Association; the German Ministry of Cultural Affairs; the German Federal Ministry of Education and Research (BMBF; 03IS2061A, 03ZIK012, 01ZZ9603, 01ZZ0103, 01ZZ0403); the German National Genome Research Network (NGFN-2 and NGFN-plus); the German Research Council (SFB1052 "Obesity mechanisms"); the Great Wine Estates of the Margaret River region of Western Australia; the Greek General Secretary of Research and Technology research grant (PENED 2003); the Gyllenberg Foundation; the Health Care Centers in Vasa, Narpes and Korsholm; the Health Fund of the Danish Health Insurance Societies; the Helmholtz Zentrum Munchen-German Research Center for Environmental Health; the Helsinki University Central Hospital special government funds (EVO #TYH7215, #TKK2012005, #TYH2012209); the Hjartavernd (the Icelandic Heart Association); the Ib Henriksen Foundation; the Illinois Department of Public Health, and the Translational Genomics Research Institute; the INTERREG IV Oberrhein Program (Project A28); the Interuniversity Cardiology Institute of the Netherlands (ICIN; 09.001); the Italian Ministry of Health "targeted project" (ICS110.1/RF97.71); the Italian National Centre of Research InterOmics PB05_ SP3; the John D and Catherine T MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health; the Johns Hopkins University Center for Inherited Disease Research (CIDR); the Joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania; the Juho Vainio Foundation; the Juselius Foundation (Helsinki, Finland); the Juvenile Diabetes Research Foundation International (JDRF); the KfH Stiftung Praventivmedizin e. V.; the Knut and Alice Wallenberg Foundation; the Kuopio University Hospital; the Leenaards Foundation; the Leiden University Medical Center; the Liv och Halsa; the Local Government Pensions Institution (KEVA); the Lokaal Gezondheids Overleg (LOGO) Leuven and Hageland; the LudwigMaximilians- Universitat, as part of LMUinnovativ; the Lundberg Foundation; the March of Dimes Birth Defects Foundation; the Medical Research Council (G0601966; G0700931; G0000934; G0500539; G0600705; G1002319; G0701863; PrevMetSyn/SALVE; MC_ U106179471; MC_ UU_ 12019/1); the MRC centre for Causal Analyses in Translational Epidemiology (MRC CAiTE); the MRC Centre for Obesity and Related Metabolic Diseases; the MRC Human Genetics Unit; the Medical Research Council of Canada; the Mid-Atlantic Nutrition and Obesity Research Center (P30 DK072488); the Ministry of the Flemish Community, Brussels, Belgium (G. 0881.13 and G. 0880. 13); the MIUR-CNR Italian Flagship Project; the Montreal Heart Institute Foundation; the Munich Center of Health Sciences (MC Health); the Municipal Health Care Center and Hospital in Jakobstad; the Narpes Health Care Foundation; the National Alliance for Research on Schizophrenia and Depression (NARSAD); the National Cancer Institute (CA047988); the National Center for Advancing Translational Sciences (UL1TR000124); the National Center for Research Resources (U54RR020278); the National Heart, Lung and Blood Institute (NHLBI, 1RL1MH083268-01, 5R01HL087679-02, HHSN268200800007C, HHSN268201200036C, HL043851, HL080467, HL087647, HL36310, HL45670, N01HC25195, N01HC55015, N01HC55016, N01HC55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N02HL64278, R01HL086694, R01HL087641, R01HL087652, R01HL087676, R01HL59367, R01HL103612, R01HL105756, R01HL120393, U01HL080295); the National Human Genome Research Institute (NHGRI, U01HG004402); the National Institute for Health and Welfare (THL); the National Institute for Health Research (NIHR, RP-PG-0407-10371); the National Institute of Allergy and Infectious Diseases (NIAID); the National Institute of Child Health and Human Development (NICHD); the National Institute of Diabetes and Digestive and Kidney Disease (NIDDKDRC, 1R01DK8925601, DK063491, R01DK089256, P30 DK072488); the National Institute of Food and Agriculture (2007-35205-17883); the National Institute of Neurological Disorders and Stroke (NINDS); the National Institute on Aging (NIA; 263-MA-410953, 263-MD-821336, 263-MD-9164, AG023629, AG13196, NO1AG12109, P30AG10161, R01AG15819, R01AG17917, R01AG023629, R01AG30146); the National Institute of Arthritis and Musculoskeletal and Skin Diseases (5-P60-AR30701, 5-P60-AR49465-03); the National Institutes of Health (NIH; 1R01DK8925601, 1RC2MH089951, 1RC2MH089995, 1Z01HG000024, 2T32 HL 00705536, 5R01DK075681, 5R01MH63706: 02, AA014041, AA07535, AA10248, AA13320, AA13321, AA13326, AG028555, AG08724, AG04563, AG10175, AG08861, DA12854, DK046200, DK091718, F32AR059469, HG002651, HHSN268200625226C, HHSN268200782096C, HL084729, MH081802, N01AG12100, N01HG65403, R01AG011101, R01AG030146, R01D0042157-01A, R01DK062370, R01DK072193, R01DK093757, R01DK075787, R01DK075787, R01HL71981, R01MH59565, R01MH59566, R01MH59571, R01MH59586, R01MH59587, R01MH59588, R01MH60870, R01MH60879, R01MH61675, R01MH67257, R01MH81800, R01NS45012, U01066134, U01CA098233, U01DK062418, U01GM074518, U01HG004423, U01HG004436, U01HG004438, U01HL072515-06, U01HL105198, U01HL84756, U01MH79469, U01MH79470, U01NS069208-01, UL1RR025005); the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust; the NIHR Cambridge Biomedical research Centre; the Netherlands Heart Foundation (2001 D 032); the Netherlands Organisation for Scientific Research (NWO; Geestkracht program grant 10-000-1002; 050-060-810; 100-001-004; 175.010.2003.005; 175.010.2005.011; 175.010.2007. 006; 261-98-710; 40-0056-98-9032; 400-05-717; 452-04-314; 452-06-004; 480-01-006; 480-04-004; 480-05-003; 480-07-001; 481-08-013; 60-60600-97-118; 904-61-090; 904-61-193; 911-03012; 985-10-002; Addiction-31160008; GB-MW 94038- 011; SPI 56-464-14192); the Netherlands Organization for the Health Research and Development (ZonMw; 91111025); the Nordic Center of Excellence in Disease Genetics; the Nordic Centre of Excellence on Systems biology in controlled dietary interventions and cohort studies, SYSDIET (070014); the Northern Netherlands Collaboration of Provinces (SNN); the Novo Nordisk Foundation; the Office of Research and Development, Medical Research Service, and the Baltimore Geriatrics Research, Education, and Clinical Center of the Department of Veterans Affairs; the Ollqvist Foundation; the Paavo Nurmi Foundation; the Pahlssons Foundation; the Paivikki and Sakari Sohlberg Foundation; the Perklen Foundation; the Republic of Croatia Ministry of Science, Education and Sports research (108-1080315-0302); the Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Foundation of Copenhagen County; the Research Institute for Diseases in the Elderly (014-93-015; RIDE2); the Reynold's Foundation; the Rotterdam Oncologic Thoracic Study Group, Erasmus Trust Fund, Foundation against Cancer; the Royal Swedish Academy of Science; the Russian Foundation for Basic Research (NWO-RFBR 047.017.043); the Rutgers University Cell and DNA Repository cooperative agreement (NIMH U24 MH068457-06); the Samfundet Folkhalsan; the Sigrid Juselius Foundation; the Social Insurance Institution of Finland, Kuopio, Tampere and Turku University Hospital Medical Funds (9M048, 9N035); the Social Ministry of the Federal State of Mecklenburg-West Pomerania; the Societe Francophone du 358 Diabste (SFD); the South Tyrolean Sparkasse Foundation; the Stichting Nationale Computerfaciliteiten (National Computing Facilities Foundation, NCF); the Strategic Cardiovascular Programme of Karolinska Institutet and the Stockholm County Council (560183); the Susan G. Komen Breast Cancer Foundation; the Swedish Cancer Society; the Swedish Cultural Foundation in Finland; the Swedish Diabetes Association; the Swedish Diabetes Foundation (grant no. 2013-024); the Swedish Foundation for Strategic Research (SSF; ICA08-0047); the Swedish HeartLung Foundation (20120197); the Swedish Medical Research Council (K2007-66X-20270-01-3, 20121397); the Swedish Ministry for Higher Education; the Swedish Research Council (8691, M-2005-1112, 2009-2298); the Swedish Society for Medical Research; the Swiss National Science Foundation (31003A-143914, 3200B0105993, 3200B0-118308, 33CSCO-122661, 33CS30-139468, 33CS30148401); SystemsX. ch (51RTP0_151019); the Tampere Tuberculosis Foundation; the TEKES (70103/06, 40058/07); the The Paul Michael Donovan Charitable Foundation; the Torsten and Ragnar Sderberg Foundation; the Umea Medical Research Foundation; the United Kingdom NIHR Cambridge Biomedical Research Centre; the Universities and Research of the Autonomous Province of Bolzano, South Tyrol; the University Hospital of Regensburg (ReForM A, ReForM C); the University Hospital Oulu, Biocenter, University of Oulu, Finland (75617); the University Medical Center Groningen; the University of Groningen; the University of Maryland General Clinical Research Center (M01RR16500, AG000219); the University of Tartu (SP1GVARENG); the University of Tromso, Norwegian Research Council (185764); the Vasterbottens Intervention Programme; the Velux Foundation; the VU University Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA); the Wellcome Trust (064890, 068545/Z/02, 076113/B/04/Z, 077016/Z/05/Z, 079895, 084723/Z/08/Z, 086596/Z/ 08/Z, 088869/B/09/Z, 089062, 090532, 098017, 098051, 098381); the Western Australian DNA Bank (NHMRC Enabling Facility); the Yrjo Jahnsson Foundation (56358); and the Zorg Onderzoek Nederland-Medische Wetenschappen, KWF Kankerbestrijding, Stichting Centraal Fonds Reserves van voormalig Vrijwillige Ziekenfondsverzekeringen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. More details of acknowledgements can be found in S2 Text.