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OBJECTIVES: To examine the association between environmental exposure to takeaway food outlets, takeaway food consumption, and body weight, while accounting for home, work place, and commuting route environments. DESIGN: Population based, cross sectional study, using data on individual participants' diet and weight, and objective metrics of food environment exposure. PARTICIPANTS: Working adults participating in the Fenland Study, Cambridgeshire, UK (n = 5442, aged 29-62 years), who provided home and work addresses and commuting preferences. Takeaway food outlet exposure was derived using data from local authorities for individual environmental domains (at home, at work, and along commuting routes (the shortest route between home and work)), and for exposure in all three domains combined. Exposure was divided into quarters (Q); Q1 being the least exposed and Q4 being the most exposed. MAIN OUTCOME MEASURES: Self reported consumption of takeaway type food (g/day; pizza, burgers, fried foods, and chips) using food frequency questionnaires, measured body mass index, and cut-offs for body mass index as defined by the World Health Organization. RESULTS: In multiple linear regression models, exposure to takeaway food outlets was positively associated with consumption of takeaway food. Among domains at home, at work, and along commuting routes, associations were strongest in work environments (Q4 v Q1; β coefficient = 5.3 g/day, 95% confidence interval 1.6 to 8.7; P<0.05), with evidence of a dose-response effect. Associations between exposure in all three domains combined and consumption were greater in magnitude across quarters of exposure (Q4 v Q1; 5.7 g/day, 2.6 to 8.8; P<0.001), with evidence of a dose-response effect. Combined exposure was especially strongly associated with increased body mass index (Q4 v Q1; body mass index 1.21, 0.68 to 1.74; P<0.001) and odds of obesity (Q4 v Q1; odds ratio 1.80, 1.28 to 2.53; P<0.05). There was no evidence of effect modification by sex. CONCLUSIONS: Exposure to takeaway food outlets in home, work, and commuting environments combined was associated with marginally higher consumption of takeaway food, greater body mass index, and greater odds of obesity. Government strategies to promote healthier diets through planning restrictions for takeaway food could be most effective if focused around the workplace.

Objective To estimate the potential population impact of different screening strategies for identifying and treating people at high risk of cardiovascular disease, including strategies using routine data for cardiovascular risk stratification, in light of the UK government's recommended national strategy to screen all adults aged 40-74 for cardiovascular risk.

Vitamin D deficiency and physical inactivity have been associated with bone loss and fractures, but their combined effect has scarcely been studied either in younger or older adults. Therefore, we aimed to assess the associations between physical activity, age and 25-hydroxyvitamin D (25(OH)D) status separately and in combination with the incidence of fracture risk in the EPIC-Norfolk cohort study. Baseline (1993-1998) self-reported physical activity and serum 25(OH)D concentrations at follow-up (1998-2000) were collected in 14,624 men and women (aged 42-82 y between 1998 and 2000). Fracture incidence was ascertained up to March 2015. Cox proportional hazard model was used to determine HRs of fractures by plasma 25(OH)D (90 nmol/L), age (65 y) and physical activity (inactive and active) categories, by follow-up time per 20 nmol/L increase in serum 25(OH)D and to explore age-25(OH)D and physical activity-25(OH)D interactions. The age-, sex-, and month-adjusted HRs (95% CIs) for all fractures (1183 fractures) by increasing vitamin D category were not significantly different. With additional adjustment for body mass index, smoking status, alcohol intake, supplement use and history of fractures, the fracture risk was 29% lower in those participants with 50 to 70 nmol/L compared with those in the lowest quintile (<30 nmol/L). Physical inactivity based on a single baseline assessment was not associated with fracture risk. Vitamin D status appeared inversely related to fractures in middle aged adults. In older adults, the relationship between vitamin D status and fracture risk was observed to be J-shaped. Clinical and public health practice in vitamin D supplementation could partially explain these findings, although definitive conclusions are difficult due to potential changes in exposure status over the long follow up period. ; This work was supported by Medical Research Council (MRC) - MKS/S16 (RG19715) / Sponsor Funding Ref: G9502233; Cancer Research UK (CRUK) - MKS/R07 (RG14230) / Sponsor Funding Ref: SP2024/0201; and Cancer Research UK (CRUK) - MKS/T21 (RG23772) / Sponsor Funding Ref: SP2024/0204. CJA received a Grant FPU13/00421 from the Government of Spain "Ministerio de Educación, Cultura y Deporte".

BACKGROUND AND AIMS: Lipoprotein(a) (Lp[a]) is a strong genetic risk factor for cardiovascular disease (CVD). The American Heart Association has prioritised seven cardiovascular health metrics to reduce the burden of CVD: body mass index, healthy diet, physical activity, smoking status, blood pressure, diabetes and cholesterol levels (together also known as ideal cardiovascular health). Our objective was to determine if individuals with high Lp(a) levels could derive cardiovascular benefits if characterized by ideal cardiovascular health. METHODS: A total of 14,051 participants of the EPIC-Norfolk study were stratified according to the cardiovascular health score (based on the number of health metrics with an ideal, intermediate or poor status). Of them, 1732 had a CVD event during a mean follow-up of 11.5 years. Cox proportional hazards models were used to describe the association between the cardiovascular health score and Lp(a) level or genotype (as estimated by the rs10455872 variant) with the risk of CVD. RESULTS: We observed little or no differences in serum Lp(a) levels across the seven cardiovascular health metric categories. Among participants with high serum Lp(a) levels ≥50 mg/dl), those in the highest (i.e. healthiest) cardiovascular health score category (10-14) had an adjusted hazard ratio for cardiovascular disease of 0.33 (95% CI = 0.17-0.63, p = 0.001) compared to participants in the lowest (i.e. unhealthiest) cardiovascular health score category(0-4). Similar results were obtained when we replaced Lp(a) with rs10455872. CONCLUSIONS: Although Lp(a) levels are only slightly influenced by cardiovascular health metrics, an ideal cardiovascular health could substantially reduce CVD risk associated with high Lp(a) levels or genotype. ; EPIC-Norfolk is supported by program grants from the Medical Research Council UK and Cancer Research UK and with additional support from the European Union, Stroke Association, British Heart Foundation, and Research into Ageing. RV is supported by a grant from the European Union [TransCard: FP7-603091-2]. BJA holds a junior scholar award from the Fonds de recherche du Québec: Santé (FRQS).

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.

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

Elevated intraocular pressure (IOP) is an important risk factor for glaucoma. Mechanisms involved in its homeostasis are not well understood, but associations between metabolic factors and IOP have been reported. To investigate the relationship between levels of circulating metabolites and IOP, we performed a metabolome-wide association using a machine learning algorithm, and then employing Mendelian Randomization models to further explore the strength and directionality of effect of the metabolites on IOP. We show that O-methylascorbate, a circulating Vitamin C metabolite, has a significant IOP-lowering effect, consistent with previous knowledge of the anti-hypertensive and anti-oxidative role of ascorbate compounds. These results enhance understanding of IOP control and may potentially benefit future IOP treatment and reduce vision loss from glaucoma. ; Acknowledgements & Author Contributions EPIC-Norfolk infrastructure and core functions are supported by grants from the Medical Research Council (G1000143) and Cancer Research UK (C864/A14136). The clinic for the third health examination was funded by Research into Ageing (262). Genotyping was funded by the Medical Research Council (MC_PC_13048). We thank all staff from the MRC Epidemiology laboratory team for the preparation and quality control of DNA samples. Mr Khawaja is supported by a Moorfields Eye Charity fellowship. Professor Foster has received additional support from the Richard Desmond Charitable Trust (via Fight for Sight) and the Department for Health through the award made by the National Institute for Health Research to Moorfields Eye Hospital and the UCL Institute of Ophthalmology for a specialist Biomedical Research Centre for Ophthalmology. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR) – funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St. Thomas' NHS Foundation Trust in partnership with King's College London. CH and PH acknowledge the support from the TFC Frost Charitable Trust.

Background: Epidemiological evidence indicates that diets rich in plant foods are associated with a lower risk of ischaemic heart disease (IHD), but there is sparse information on fruit and vegetable subtypes and sources of dietary fibre. This study examined the associations of major plant foods, their subtypes and dietary fibre with risk of IHD in EPIC. Methods: We conducted a prospective analysis of 490,311 men and women in ten European countries without a history of myocardial infarction or stroke at recruitment (12.6 years of follow-up, n cases=8504). Dietary intake was assessed using validated questionnaires, calibrated with 24-hour recalls. Multivariable Cox regressions were used to estimate hazard ratios (HR) of IHD. Results: There was a lower risk of IHD with a higher intake of fruit and vegetables combined (HR per 200 g/day higher intake 0.94, 95% CI:0.90-0.99, P-trend=0.009), and total fruits (per 100g/day 0.97, 0.95-1.00, P-trend=0.021). There was no evidence for a reduced risk for fruit subtypes, except for bananas. Risk was lower with higher intakes of nuts and seeds (per 10g/day 0.90, 0.82-0.98, P-trend=0.020), total fibre (per 10g/day 0.95, 0.85-0.98, P-trend=0.015), fruit and vegetable fibre (per 4g/day 0.95, 0.91-0.99, P-trend=0.022), and fruit fibre (per 2 g/day 0.97, 0.95-1.00, P-trend=0.045). No associations were observed between vegetables, vegetables subtypes, legumes, cereals and IHD risk. Conclusions: In this large prospective study, we found some small inverse associations between plant foods and IHD risk, with fruit and vegetables combined being the most strongly inversely associated with risk. Whether these small associations are causal remains unclear. ; Analyses supported by the UK Medical Research Council (MR/M012190/1), Cancer Research UK (C8221/A19170 and 570/A16491), and the Wellcome Trust (Our Planet Our Health, Livestock Environment and People 205212/Z/16/Z). APC is supported by a Cancer Research UK Population Research Fellowship (C60192/A28516) and by the World Cancer Research Fund (WCRF UK), as part of the Word Cancer Research Fund International grant programme (2019/1953). EPIC-CVD has been supported by the European Union Framework 7 (HEALTH-F2-2012-279233), the European Research Council (268834), the UK Medical Research Council (G0800270 and MR/L003120/1), the British Heart Foundation (SP/09/002 and RG/08/014 and RG13/13/30194), and the UK National Institute of Health Research. The InterAct project was funded by the EU FP6 programme (grant number LSHM_CT_2006_037197), and provided the biomarker data in the subcohort that was used in the current study. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia (no. 6236) and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), UK Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford, MC_UU_12015/1 (NJW), MC_UU_12015/5 (NGF), and MC_UU_12015/520, and NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014) to the MRC Epidemiology Unit Cambridge (NJW, NGF). JD holds a British Heart Foundation Chair and an NIHR Senior Investigator Award. KEB holds a Girdlers' New Zealand Health Research Council Fellowship.

For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta-analysis has emerged as the method of choice to combine results from multiple studies. Many meta-analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta-analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two-stage meta-analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta-analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype-specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type-I error rate, and our approach is more powerful than inverse variance weighted meta-analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose-associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates. ; Generation Scotland: Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorate CZD/16/6 and the Scottish Funding Council HR03006. Genotyping of the GS:SFHS samples was carried out by the Genetics ...

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

At the time of writing, it is unclear how the COVID-19 pandemic will play out in rapidly urbanising regions of the world. In these regions, the realities of large overcrowded informal settlements, a high burden of infectious and non-communicable diseases, as well as malnutrition and precarity of livelihoods, have raised added concerns about the potential impact of the COVID-19 pandemic in these contexts. COVID-19 infection control measures have been shown to have some effects in slowing down the progress of the pandemic, effectively buying time to prepare the healthcare system. However, there has been less of a focus on the indirect impacts of these measures on health behaviours and the consequent health risks, particularly in the most vulnerable. In this current debate piece, focusing on two of the four risk factors that contribute to >80% of the NCD burden, we consider the possible ways that the restrictions put in place to control the pandemic, have the potential to impact on dietary and physical activity behaviours and their determinants. By considering mitigation responses implemented by governments in several LMIC cities, we identify key lessons that highlight the potential of economic, political, food and built environment sectors, mobilised during the pandemic, to retain health as a priority beyond the context of pandemic response. Such whole-of society approaches are feasible and necessary to support equitable healthy eating and active living required to address other epidemics and to lower the baseline need for healthcare in the long term.

$\textbf{BACKGROUND}$: 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.