Suchergebnisse

BACKGROUND: Low circulating concentrations of 25-hydroxyvitamin D (25[OH]D), a marker of vitamin D status, are associated with an increased risk of type 2 diabetes, but whether this association is causal remains unclear. We aimed to estimate the unconfounded, causal association between 25(OH)D concentration and risk of type 2 diabetes using a mendelian randomisation approach. METHODS: Using several data sources from populations of European descent, including type 2 diabetes cases and non-cases, we did a mendelian randomisation analysis using single nucleotide polymorphisms (SNPs) within or near four genes related to 25(OH)D synthesis and metabolism: DHCR7 (related to vitamin D synthesis), CYP2R1 (hepatic 25-hydroxylation), DBP (also known as GC; transport), and CYP24A1 (catabolism). We assessed each SNP for an association with circulating 25(OH)D concentration (5449 non-cases; two studies), risk of type 2 diabetes (28 144 cases, 76 344 non-cases; five studies), and glycaemic traits (concentrations of fasting glucose, 2-h glucose, fasting insulin, and HbA1c; 46 368 non-cases; study consortium). We combined these associations in a likelihood-based mendelian randomisation analysis to estimate the causal association of 25(OH)D concentration with type 2 diabetes and the glycaemic traits, and compared them with that from a meta-analysis of data from observational studies (8492 cases, 89 698 non-cases; 22 studies) that assessed the association between 25(OH)D concentration and type 2 diabetes. FINDINGS: All four SNPs were associated with 25(OH)D concentrations (p0·25). INTERPRETATION: The association between 25(OH)D concentration and type 2 diabetes is unlikely to be causal. Efforts to increase 25(OH)D concentrations might not reduce the risk of type 2 diabetes as would be expected on the basis of observational evidence. These findings warrant further investigations to identify causal factors that might increase 25(OH)D concentration and also reduce the risk of type 2 diabetes. FUNDING: UK Medical Research Council Epidemiology Unit and European Union Sixth Framework Programme. ; This is the final published version. It is available from Elsevier in The Lancet Diabetes & Endocrinology here: http://www.sciencedirect.com/science/article/pii/S2213858714701846.

$\textbf{BACKGROUND}$ Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field. $\textbf{METHODS AND FINDINGS}$ We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia. $\textbf{CONCLUSIONS}$ This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context. ; This study was supported by the Medical Research Council UK (grant reference no. MC_UU_12015/1), http://gtr.rcuk.ac.uk/projects?ref=MC_UU_12015/1; Netherlands Organization for Scientific Research (NWO project number 825.13.004), http://www.nwo.nl/en/research-and-results/research-projects/i/85/10585.html; Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), http://www.emif.eu/about. GSK provided support in the form of salaries for DW, DJN, AS. Pfizer provided support in the form of salary to JMB.

BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes. ; This work was supported by grants from the German Federal Ministry of Education and Research (BMBF), by BMBF Grant No. 01ZX1313C (project e:Athero-MED) and Grant No. 03IS2061B (project Gani_Med). Moreover, the research leading to these results has received funding from the European Union's Seventh Framework Programme [FP7-Health-F5-2012] under grant agreement No. 305280 (MIMOmics) and from the European Research Council (starting grant "LatentCauses"). KS is supported by Biomedical Research Program funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. The KORA Augsburg studies were financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany and supported by grants from the German Federal Ministry of Education and Research (BMBF). Analyses in the EPIC-Norfolk study were supported by funding from the Medical Research Council (MC_PC_13048 and MC_UU_12015/1).

PURPOSE: The study aimed to systematically ascertain male sex chromosome abnormalities, 47,XXY (Klinefelter syndrome [KS]) and 47,XYY, and characterize their risks of adverse health outcomes. METHODS: We analyzed genotyping array or exome sequence data in 207,067 men of European ancestry aged 40 to 70 years from the UK Biobank and related these to extensive routine health record data. RESULTS: Only 49 of 213 (23%) of men whom we identified with KS and only 1 of 143 (0.7%) with 47,XYY had a diagnosis of abnormal karyotype on their medical records or self-report. We observed expected associations for KS with reproductive dysfunction (late puberty: risk ratio [RR] = 2.7; childlessness: RR = 4.2; testosterone concentration: RR = -3.8 nmol/L, all P < 2 × 10-8), whereas XYY men appeared to have normal reproductive function. Despite this difference, we identified several higher disease risks shared across both KS and 47,XYY, including type 2 diabetes (RR = 3.0 and 2.6, respectively), venous thrombosis (RR = 6.4 and 7.4, respectively), pulmonary embolism (RR = 3.3 and 3.7, respectively), and chronic obstructive pulmonary disease (RR = 4.4 and 4.6, respectively) (all P < 7 × 10-6). CONCLUSION: KS and 47,XYY were mostly unrecognized but conferred substantially higher risks for metabolic, vascular, and respiratory diseases, which were only partially explained by higher levels of body mass index, deprivation, and smoking. ; Y.Z., E.J.G., M.P., M.K., R.Y.J., H.L.A., F.R.D., C.L., J.R.B.P. and K.K.O. are supported by the Medical Research Council (Unit programs: MC_UU_00006/1 and MC_UU_00006/2). M.K. is supported by a Gates Fellowship. A.R.W. and J.T. hold Academy of Medical Sciences (AMS) Springboard awards [SBF004\1079 and SBF006\1134], which are supported by the AMS, the Wellcome Trust, the Government Department of Business, Energy and Industrial strategy, the British Heart Foundation and Diabetes UK. R.N.B. and A.M. are supported by the Medical Research Council (MR/T00200X/1). K.S.R. is supported by Cancer Research ...

BACKGROUND: Insulinemia and type 2 diabetes (T2D) have been associated with endometrial cancer risk in numerous observational studies. However, the causality of these associations is uncertain. Here we use a Mendelian randomization (MR) approach to assess whether insulinemia and T2D are causally associated with endometrial cancer. METHODS: We used single nucleotide polymorphisms (SNPs) associated with T2D (49 variants), fasting glucose (36 variants), fasting insulin (18 variants), early insulin secretion (17 variants), and body mass index (BMI) (32 variants) as instrumental variables in MR analyses. We calculated MR estimates for each risk factor with endometrial cancer using an inverse-variance weighted method with SNP-endometrial cancer associations from 1287 case patients and 8273 control participants. RESULTS: Genetically predicted higher fasting insulin levels were associated with greater risk of endometrial cancer (odds ratio [OR] per standard deviation = 2.34, 95% confidence internal [CI] = 1.06 to 5.14, P = .03). Consistently, genetically predicted higher 30-minute postchallenge insulin levels were also associated with endometrial cancer risk (OR = 1.40, 95% CI = 1.12 to 1.76, P = .003). We observed no associations between genetic risk of type 2 diabetes (OR = 0.91, 95% CI = 0.79 to 1.04, P = .16) or higher fasting glucose (OR = 1.00, 95% CI = 0.67 to 1.50, P = .99) and endometrial cancer. In contrast, endometrial cancer risk was higher in individuals with genetically predicted higher BMI (OR = 3.86, 95% CI = 2.24 to 6.64, P = 1.2x10(-6)). CONCLUSION: This study provides evidence to support a causal association of higher insulin levels, independently of BMI, with endometrial cancer risk. ; This study was supported by MRC grant MC_UU_12015/1 and by the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n° 115372 (contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies). ANECS recruitment was supported by project grants from the National Health and Medical Research Council of Australia (ID#339435), The Cancer Council Queensland (ID#4196615) and Cancer Council Tasmania (ID#403031 and ID#457636). SEARCH recruitment was funded by a programme grant from Cancer Research UK [C490/A10124]. Case genotyping was supported by the National Health and Medical Research Council (ID#552402). Control data was generated by the Wellcome Trust Case Control Consortium (WTCCC), and a full list of the investigators who contributed to the generation of the data is available from the WTCCC website. We acknowledge use of DNA from the British 1958 Birth Cohort collection, funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. Funding for this project was provided by the Wellcome Trust under award 085475. Recruitment of the QIMR controls was supported by the National Health and Medical Research Council of Australia (NHMRC). The University of Newcastle, the Gladys M Brawn Senior Research Fellowship scheme, The Vincent Fairfax Family Foundation, the Hunter Medical Research Institute and the Hunter Area Pathology Service all contributed towards the costs of establishing the Hunter Community Study. K.T.N. was supported by the Gates Cambridge Trust. R.K.S. is supported by the Wellcome Trust (grant number WT098498). A.B.S. is supported by the National Health and Medical Research Council (NHMRC) Fellowship Scheme. D.F.E. is a Principal Research Fellow of Cancer Research UK. A.M.D is supported by the Joseph Mitchell Trust. ; This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/jnci/djv178

$\textbf{BACKGROUND}$: Higher circulating levels of the branched-chain amino acids (BCAAs; i.e., isoleucine, leucine, and valine) are strongly associated with higher type 2 diabetes risk, but it is not known whether this association is causal. We undertook large-scale human genetic analyses to address this question. $\textbf{METHODS AND FINDINGS}$: Genome-wide studies of BCAA levels in 16,596 individuals revealed five genomic regions associated at genome-wide levels of significance (p < 5 × 10-8). The strongest signal was 21 kb upstream of the PPM1K gene (beta in standard deviations [SDs] of leucine per allele = 0.08, p = 3.9 × 10-25), encoding an activator of the mitochondrial branched-chain alpha-ketoacid dehydrogenase (BCKD) responsible for the rate-limiting step in BCAA catabolism. In another analysis, in up to 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of 1 SD in amino acid level was associated with an odds ratio for type 2 diabetes of 1.44 (95% CI 1.26-1.65, p = 9.5 × 10-8) for isoleucine, 1.85 (95% CI 1.41-2.42, p = 7.3 × 10-6) for leucine, and 1.54 (95% CI 1.28-1.84, p = 4.2 × 10-6) for valine. Estimates were highly consistent with those from prospective observational studies of the association between BCAA levels and incident type 2 diabetes in a meta-analysis of 1,992 cases and 4,319 non-cases. Metabolome-wide association analyses of BCAA-raising alleles revealed high specificity to the BCAA pathway and an accumulation of metabolites upstream of branched-chain alpha-ketoacid oxidation, consistent with reduced BCKD activity. Limitations of this study are that, while the association of genetic variants appeared highly specific, the possibility of pleiotropic associations cannot be entirely excluded. Similar to other complex phenotypes, genetic scores used in the study captured a limited proportion of the heritability in BCAA levels. Therefore, it is possible that only some of the mechanisms that increase BCAA levels or affect BCAA metabolism are implicated in type 2 diabetes. $\textbf{CONCLUSIONS}$: Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes. ; MRC Epidemiology Unit, Fenland study, EPIC-InterAct study, EPIC-Norfolk case-cohort study funding: this study was funded by the United Kingdom's Medical Research Council through grants MC_UU_12015/1, MC_UU_12015/5, MC_PC_13046, MC_PC_13048 and MR/L00002/1. We acknowledge support from the National Institute for Health Research Biomedical Research Centre. The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement number 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. EPIC-InterAct Study funding: funding for the InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197). MRC Human Nutrition Research funding: This research was supported by the Medical Research Council (MC_UP_A090_1006) and Cambridge Lipidomics Biomarker Research Initiative (G0800783). The SABRE study was funded at baseline by the UK Medical Research Council, Diabetes UK and the British Heart Foundation and at follow-up by a programme grant from the Wellcome Trust (WT082464) and British Heart Foundation (SP/07/001/23603); Diabetes UK funded the metabolomics analyses (13/0004774). RJOS, EN, JRZ and AK received funding from the Swedish Research Council, Stockholm County Council, Novo Nordisk Foundation and Diabetes Wellness. DBS is supported by the Wellcome Trust grant number 107064. MIM is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. IB is supported by the Wellcome Trust grant WT098051.

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (P$_v$), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (P$_m$). Correlations between P$_v$ and P$_m$ were stronger for SNPs with established marginal effects (Spearman's ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When P$_v$ and P$_m$ were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the P$_v$ distribution (P$_{binomial}$ <0.05). SNPs from the top 1% of the P$_m$ distribution for BMI had more significant Pv values (P$_{Mann-Whitney}$ = 1.46×10$^{-5}$), and the odds ratio of SNPs with nominally significant (<0.05) P$_m$ and P$_v$ was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (P$_{int}$<0.05) were enriched with nominally significant P$_v$ values (P$_{binomial}$ = 8.63×10$^{-9}$ and 8.52×10$^{-7}$ for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them. ; This research was undertaken as part of a research program supported by the European Commission (CoG-2015_681742_NASCENT), Swedish Research Council (Distinguished Young Researchers Award in Medicine), Swedish HeartLung Foundation, and the Novo Nordisk Foundation, all grants to PWF. DS is supported by the Swedish Research Council International Postdoc Fellowship (4.1-2016-00416). TVV is supported by the Novo Nordisk Foundation Postdoctoral Fellowship within Endocrinology/ Metabolism at International Elite Research Environments via NNF16OC0020698. TWW was supported by the grants "Bundesministerium fur Bildung und Forschung": BMBF-01ER1206, BMBF- 01ER1507. APM is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant WT098017). LAC acknowledges funding for the Framingham Heart Study: This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195 and Contract No. HHSN268201500001I) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. This research was partially supported by grant R01-DK089256 from the National Institute of Diabetes and Digestive and Kidney Diseases (MPIs: I.B. Borecki, LAC, K. North). TOK was supported by the Danish Council for Independent Research (DFF—1333-00124) and Sapere Aude program grant (DFF—1331-00730B). RM would like to acknowledge the High Performance Computing Center of University of Tartu. EGCUT was supported by EU H2020 grants 692145, 676550, 654248, 692065, Estonian Research Council Grant IUT20-60, and PerMed I NIASC, EIT—Health and European Union through the European Regional Development Fund (Project No, 2014-2020.4.01.15-0012 GENTRANSMED).

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date. Objective: We aimed to identify existing evidence for genemacronutrient interactions and T2D and to examine the reported interactions in a large-scale study. Design: We systematically reviewed studies reporting genemacronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution. Results: Thirteen observational studies met the eligibility criteria (n , 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n–3 (v-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7–like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction , 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates. Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions. ; 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 agencies: 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. IS, JWJB and YTvdS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht (The Netherlands; HBBdM, AMWS and DLvdA: Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); German Federal Ministry of Education and Research (BMBF) and the State of Brandenburg to the German Center for Diabetes Research (DZD); FLC: Cancer Research UK C8221/A19170 and C570/A16491 and Medical Research Council MR/M012190/1; PWF: Swedish Research Council, Novo Nordisk, Swedish Heart Lung Foundation, Swedish Diabetes Association; JH, KO and AT: Danish Cancer Society; RK: Deutsche Krebshilfe; SP: Associazione Italiana per la Ricerca sul Cancro; JRQ: Asturias Regional Government; MT: Health Research Fund (FIS) of the Spanish Ministry of Health; Navarre Regional Government; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Murcia Regional Government (Nº 6236); RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; Red Temática de Investigación Cooperativa en Cáncer of the Instituto de Salud Carlos III (ISCIII RTICC RD12/0036/0018), cofounded by FEDER funds/European Regional Development Fund (ERDF); German Cancer Aid, German Ministry of Research (BMBF); Compagnia di San Paolo; Imperial College Biomedical Research Centre.

Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF~0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 × 10(-3)), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies. ; This work was supported by the Wellcome Trust (ISF, IB) (098497/Z/12/Z; WT098051), Medical Research Council (ISF, SOR) (MRC_MC_UU_12012/5), NIHR Cambridge Biomedical Research Centre (ISF, IB, SOR), Bernard Wolfe Health Neuroscience Endowment (ISF), European Research Council (ISF) and NIH grant DK064265 (GLM), the European Community's Seventh Framework Programme (FP7/2007–2013) project Beta-JUDO n°279153 (ISF, AK). This study comprises one arm of the UK10K Consortium (WT091310). The UK Household Longitudinal Study is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. AH and JH were funded by the German Ministry for Education and Research (National Genome Research Net-Plus 01GS0820), the German Research Foundation (DFG; HI865/2-1), the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements n°245009 and n°262055. CHD case ascertainment and validation, genotyping, and clinical chemistry assays in EPICCVD were supported by grants awarded to the University of Cambridge from the EU Framework Programme 7 (HEALTH-F2-2012-279233), the UK Medical Research Council (G0800270) and British Heart Foundation (SP/09/002), the European Research Council (268834), the UK National Institute for Health Research Cambridge Biomedical Research Centre, Merck and Pfizer. JD is supported by a British Heart Foundation Professorship, NIHR Senior Investigator, European Research Council Senior Investigator. Leipzig LIFE Child as part of the Leipzig Childhood Obesity cohort was funded by the European Union, by the European Regional Development Fund (ERFD) by means of the Free State of Saxony within the framework of the excellence initiative.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. ; This work has been sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III and Beatriu de Pinós fellowship from the Agency for Management of University and Research Grants (AGAUR). Sílvia Bonàs was FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya). This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. This study also makes use of data generated by the UK10K Consortium, derived from samples from UK10K COHORT IMPUTATION (EGAS00001000713). A full list of the investigators who contributed to the generation of the data is available in www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. We acknowledge PRACE for awarding us to access MareNostrum supercomputer, based in Spain at Barcelona. The technical support group, particularly Pablo Ródenas and Jorge Rodríguez, from the Barcelona Supercomputing Center is gratefully acknowledged. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 667191. Mercè Planas-Fèlix is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. Work from Irene Miguel-Escalada, Ignasi Moran, Goutham Atla, and Jorge Ferrer was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the Wellcome Trust (WT101033), Ministerio de Economía y Competitividad (BFU2014-54284-R) and Horizon 2020 (667191). Irene Miguel-Escalada has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement No 658145. We acknowledge Prof. Giulio Cossu (Institute of Inflammation and Repair, University of Manchester) for providing the muscle myoblast cell line. We also acknowledge the InterAct and SIGMA Type 2 Diabetes Consortia for access to the data to replicate the rs146662075 variant. A full list of the investigators of the SIGMA Type 2 Diabetes and the InterAct consortia is provided in Supplementary Notes 3 and 4. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This research has been conducted using the UK Biobank Resource (application number 16803). We also acknowledge Bianca C. Porneala, MS for his technical assistance in the collection and curation of the genotype and phenotype data from Partners Biobank. We also thank Marcin von Grotthuss for their support for uploading the summary statistics data to the Type 2 Diabetes Genetic Portal (AMP-T2D portal). Finally, we thank all the Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. ; Peer Reviewed ; Postprint (published version)

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. ; This work has been sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III and Beatriu de Pinós fellowship from the Agency for Management of University and Research Grants (AGAUR). Sílvia Bonàs was FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya). This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. This study also makes use of data generated by the UK10K Consortium, derived from samples from UK10K COHORT IMPUTATION (EGAS00001000713). A full list of the investigators who contributed to the generation of the data is available in www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. We acknowledge PRACE for awarding us to access MareNostrum supercomputer, based in Spain at Barcelona. The technical support group, particularly Pablo Ródenas and Jorge Rodríguez, from the Barcelona Supercomputing Center is gratefully acknowledged. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 667191. Mercè Planas-Fèlix is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. Work from Irene Miguel-Escalada, Ignasi Moran, Goutham Atla, and Jorge Ferrer was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the Wellcome Trust (WT101033), Ministerio de Economía y Competitividad (BFU2014-54284-R) and Horizon 2020 (667191). Irene Miguel-Escalada has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement No 658145. We acknowledge Prof. Giulio Cossu (Institute of Inflammation and Repair, University of Manchester) for providing the muscle myoblast cell line. We also acknowledge the InterAct and SIGMA Type 2 Diabetes Consortia for access to the data to replicate the rs146662075 variant. A full list of the investigators of the SIGMA Type 2 Diabetes and the InterAct consortia is provided in Supplementary Notes 3 and 4. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This research has been conducted using the UK Biobank Resource (application number 16803). We also acknowledge Bianca C. Porneala, MS for his technical assistance in the collection and curation of the genotype and phenotype data from Partners Biobank. We also thank Marcin von Grotthuss for their support for uploading the summary statistics data to the Type 2 Diabetes Genetic Portal (AMP-T2D portal). Finally, we thank all the Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. ; Peer Reviewed ; Postprint (published version)

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches. ; This work has been sponsored by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government. This work was supported by an EFSD/Lilly research fellowship. Josep M. Mercader was supported by Sara Borrell Fellowship from the Instituto Carlos III and Beatriu de Pinós fellowship from the Agency for Management of University and Research Grants (AGAUR). Sílvia Bonàs was FI-DGR Fellowship from FI-DGR 2013 from Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya). This study makes use of data generated by the WTCCC. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. This study also makes use of data generated by the UK10K Consortium, derived from samples from UK10K COHORT IMPUTATION (EGAS00001000713). A full list of the investigators who contributed to the generation of the data is available in www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310. We acknowledge PRACE for awarding us to access MareNostrum supercomputer, based in Spain at Barcelona. The technical support group, particularly Pablo Ródenas and Jorge Rodríguez, from the Barcelona Supercomputing Center is gratefully acknowledged. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 667191. Mercè Planas-Fèlix is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. Work from Irene Miguel-Escalada, Ignasi Moran, Goutham Atla, and Jorge Ferrer was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the Wellcome Trust (WT101033), Ministerio de Economía y Competitividad (BFU2014-54284-R) and Horizon 2020 (667191). Irene Miguel-Escalada has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska–Curie grant agreement No 658145. We acknowledge Prof. Giulio Cossu (Institute of Inflammation and Repair, University of Manchester) for providing the muscle myoblast cell line. We also acknowledge the InterAct and SIGMA Type 2 Diabetes Consortia for access to the data to replicate the rs146662075 variant. A full list of the investigators of the SIGMA Type 2 Diabetes and the InterAct consortia is provided in Supplementary Notes 3 and 4. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This research has been conducted using the UK Biobank Resource (application number 16803). We also acknowledge Bianca C. Porneala, MS for his technical assistance in the collection and curation of the genotype and phenotype data from Partners Biobank. We also thank Marcin von Grotthuss for their support for uploading the summary statistics data to the Type 2 Diabetes Genetic Portal (AMP-T2D portal). Finally, we thank all the Computational Genomics group at the BSC for their helpful discussions and valuable comments on the manuscript. ; Peer reviewed

Dietary flavanols and flavonols, flavonoid subclasses, have been recently associated with a lower risk of type 2 diabetes (T2D) in Europe. Even within the same subclass, flavonoids may differ considerably in bioavailability and bioactivity. We aimed to examine the association between individual flavanol and flavonol intakes and risk of developing T2D across European countries. The European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study was conducted in 8 European countries across 26 study centers with 340,234 participants contributing 3.99 million person-years of follow-up, among whom 12,403 incident T2D cases were ascertained and a center-stratified subcohort of 16,154 individuals was defined. We estimated flavonoid intake at baseline from validated dietary questionnaires using a database developed from Phenol-Explorer and USDA databases. We used country-specific Prentice-weighted Cox regression models and random-effects meta-analysis methods to estimate HRs. Among the flavanol subclass, we observed significant inverse trends between intakes of all individual flavan-3-ol monomers and risk of T2D in multivariable models (all P-trend < 0.05). We also observed significant trends for the intakes of proanthocyanidin dimers (HR for the highest vs. the lowest quintile: 0.81; 95% CI: 0.71, 0.92; P-trend = 0.003) and trimers (HR: 0.91; 95% CI: 0.80, 1.04; P-trend = 0.07) but not for proanthocyanidins with a greater polymerization degree. Among the flavonol subclass, myricetin (HR: 0.77; 95% CI: 0.64, 0.93; P-trend = 0.001) was associated with a lower incidence of T2D. This large and heterogeneous European study showed inverse associations between all individual flavan-3-ol monomers, proanthocyanidins with a low polymerization degree, and the flavonol myricetin and incident T2D. These results suggest that individual flavonoids have different roles in the etiology of T2D. ; The EPIC-InterAct Study was supported by the European Union (Integrated Project LSHM-CT-2006-037197 in the ...

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. METHODS AND FINDINGS: Plasma phospholipid PUFAs were measured by gas chromatography among 12,132 incident T2D cases and 15,919 subcohort participants in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study across eight European countries. Country-specific hazard ratios (HRs) 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 standard deviation [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; 95% CI 0.77-0.83) and eicosadienoic acid (EDA) (0.89; 95% CI 0.85-0.94) were inversely related, and 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 nine 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. CONCLUSIONS: These large-scale findings suggest an important inverse association of circulating plant-origin n-3 PUFA (ALA) but no convincing association of marine-derived n3 PUFAs (EPA and 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 focusing 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.

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.