Association of Plasma Vitamin D Metabolites With Incident Type 2 Diabetes: EPIC-InterAct Case-Cohort Study.

BACKGROUND: Existing evidence for the prospective association of vitamin D status with type 2 diabetes (T2D) is focused almost exclusively on circulating total 25-hydroxyvitamin D [25(OH)D] without distinction between its subtypes: nonepimeric and epimeric 25(OH)D3 stereoisomers, and 25(OH)D2, the minor component of 25(OH)D. We aimed to investigate the prospective associations of circulating levels of the sum and each of these three metabolites with incident T2D. METHODS: This analysis in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study for T2D included 9671 incident T2D cases and 13,562 subcohort members. Plasma vitamin D metabolites were quantified by liquid chromatography-mass spectrometry. We used a multivariable Prentice-weighted Cox regression to estimate hazard ratios (HRs) of T2D for each metabolite. Analyses were performed separately within country, and estimates were combined across countries using random-effects meta-analysis. RESULTS: The mean concentrations (SD) of total 25(OH)D, nonepimeric 25(OH)D3, epimeric 25(OH)D3, and 25(OH)D2 were 41.1 (17.2), 40.7 (17.3), 2.13 (1.31), and 8.16 (6.52) nmol/L, respectively. Plasma total 25(OH)D and nonepimeric 25(OH)D3 were inversely associated with incident T2D [multivariable-adjusted HR per 1 SD = 0.81 (95% CI, 0.77, 0.86) for both variables], whereas epimeric 25(OH)D3 was positively associated [per 1 SD HR = 1.16 (1.09, 1.25)]. There was no statistically significant association with T2D for 25(OH)D2 [per 1 SD HR = 0.94 (0.76, 1.18)]. CONCLUSIONS: Plasma nonepimeric 25(OH)D3 was inversely associated with incident T2D, consistent with it being the major metabolite contributing to total 25(OH)D. The positive association of the epimeric form of 25(OH)D3 with incident T2D provides novel information to assess the biological relevance of vitamin D epimerization and vitamin D subtypes in diabetes etiology.The InterAct project was funded by the European Union Framework 6 (LSHM_CT_2006_037197). Biomarker measurements for vitamin D metabolites were funded jointly by the InterAct project, the MRC Cambridge Initiative (RG71466, SJAH/004) and the EPIC-CVD project. 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. In addition, InterAct investigators acknowledge funding from the following agencies: Medical Research Council Epidemiology Unit MC_UU_12015/1 and MC_UU_12015/5, NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme (IS-BRC-1215-20014), Regional Government of Asturias and Regional Governments of Basque Country. Dr. Ivonne Sluijs is supported by the Junior Dr. Dekker grant (2015T019) from the Dutch Heart Foundation. Dr. Guy Fagherazzi is supported by the French Research Agency (Agence Nationale de la Recherche) via an “Investissement d’Avenir” grant (investment for the future grant, ANR-10-COHO-0006) and by the IDEX Paris Saclay Nutriperso Project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 701708

Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries.

AIMS/HYPOTHESIS: Gene-macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes. METHODS: We investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution. RESULTS: No interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0-51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined. CONCLUSIONS/INTERPRETATION: Genetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions

A Mendelian randomization study of circulating uric acid and type 2 diabetes

We aimed to investigate the causal effect of circulating uric acid concentrations on type 2 diabetes risk. A Mendelian randomization study was performed using a genetic score with 24 uric acid associated loci. We used data of the EPIC-InterAct case-cohort study, comprising 24,265 individuals of European ancestry from eight European countries. During a mean (SD) follow-up of 10 (4) years, 10,576 verified incident type 2 diabetes cases were ascertained. Higher uric acid associated with higher diabetes risk following adjustment for confounders, with a HR of 1.20 (95%CI: 1.11,1.30) per 59.48 µmol/L (1 mg/dL) uric acid. The genetic score raised uric acid by 17 µmol/L (95%CI: 15,18) per SD increase, and explained 4% of uric acid variation. Using the genetic score to estimate the unconfounded effect found that a 59.48 µmol/L higher uric acid concentration did not have a causal effect on diabetes (HR 1.01, 95%CI: 0.87,1.16). Including data from DIAGRAM consortium, increasing our dataset to 41,508 diabetes cases, the summary OR estimate was 0.99 (95%CI: 0.92, 1.06). In conclusion, our study does not support a causal effect of circulating uric acid on diabetes risk. Uric acid lowering therapies may therefore not be beneficial in reducing diabetes risk

Iron Metabolism and Type 2 Diabetes Incidence

OBJECTIVE: Observational studies show an association between ferritin and type 2 diabetes (T2D), suggesting a role of high iron stores in T2D development. However, ferritin is influenced by factors other than iron stores, which is less the case for other biomarkers of iron metabolism. We investigated associations of ferritin, transferrin saturation (TSAT), serum iron, and transferrin with T2D incidence to clarify the role of iron in the pathogenesis of T2D. RESEARCH DESIGN AND METHODS: The European Prospective Investigation into Cancer and Nutrition-InterAct study includes 12,403 incident T2D cases and a representative subcohort of 16,154 individuals from a European cohort with 3.99 million person-years of follow-up. We studied the prospective association of ferritin, TSAT, serum iron, and transferrin with incident T2D in 11,052 cases and a random subcohort of 15,182 individuals and assessed whether these associations differed by subgroups of the population. RESULTS: Higher levels of ferritin and transferrin were associated with a higher risk of T2D (hazard ratio [HR] [95% CI] in men and women, respectively: 1.07 [1.01-1.12] and 1.12 [1.05-1.19] per 100 μg/L higher ferritin level; 1.11 [1.00-1.24] and 1.22 [1.12-1.33] per 0.5 g/L higher transferrin level) after adjustment for age, center, BMI, physical activity, smoking status, education, hs-CRP, alanine aminotransferase, and γ-glutamyl transferase. Elevated TSAT (≥45% vs. <45%) was associated with a lower risk of T2D in women (0.68 [0.54-0.86]) but was not statistically significantly associated in men (0.90 [0.75-1.08]). Serum iron was not associated with T2D. The association of ferritin with T2D was stronger among leaner individuals (Pinteraction < 0.01). CONCLUSIONS: The pattern of association of TSAT and transferrin with T2D suggests that the underlying relationship between iron stores and T2D is more complex than the simple link suggested by the association of ferritin with T2D.We thank all EPIC participants and staff for their contribution to the study. We thank Nicola Kerrison (MRC Epidemiology Unit, Cambridge) for managing the data for the InterAct Project. We thank Dr Felix Day (MRC Epidemiology Unit, Cambridge) for assistance with figures. 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: 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); FLC: Cancer Research UK; 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; the CIBER en Epidemiología y Salud Pública (CIBERESP), Spain; Murcia Regional Government (Nº 6236); RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government. Biomarker measurements in the EPIC-InterAct subcohort were partially funded by a grant from the UK Medical Research Council and British Heart Foundation (EPIC-Heart: G0800270). Clara Podmore is funded by the Wellcome Trust (097451/Z/11/Z).This is the author accepted manuscript. The final version is available at http://care.diabetesjournals.org/content/early/2016/01/29/dc15-0257.abstract

Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independent of obesity.

We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterize their association with intermediate phenotypes, and to investigate their role in type 2 diabetes (T2D) risk among normal-weight, overweight, and obese individuals. We investigated the association of genetic scores with euglycemic-hyperinsulinemic clamp- and oral glucose tolerance test-based measures of insulin resistance and secretion and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight, and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs per allele [95% CI], -0.03 [-0.04, -0.01]; P = 0.004). This score was associated with lower BMI (-0.01 [-0.01, -0.0]; P = 0.02) and gluteofemoral fat mass (-0.03 [-0.05, -0.02; P = 1.4 × 10(-6)) and with higher alanine transaminase (0.02 [0.01, 0.03]; P = 0.002) and γ-glutamyl transferase (0.02 [0.01, 0.03]; P = 0.001). While the secretion score had a stronger association with T2D in leaner individuals (Pinteraction = 0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI or waist strata (Pinteraction > 0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.The MRC-Ely Study was funded by the Medical Research Council (MC_U106179471) and Diabetes UK. We are grateful to all the volunteers, and to the staff of St. Mary’s Street Surgery, Ely and the study team. The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. DBS and RKS are funded by the Wellcome Trust, the U.K. NIHR Cambridge Biomedical Research Centre and the MRC Centre for Obesity and Related Metabolic Disease. Genotyping in ULSAM was performed by the SNP&SEQ Technology Platform in Uppsala (www.genotyping.se), which is supported by Uppsala University, Uppsala University Hospital, Science for Life Laboratory - Uppsala and the Swedish Research Council (Contracts 80576801 and 70374401). The RISC Study was supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. The RISC Study Project Management Board: B Balkau, F Bonnet, SW Coppack, JM Dekker, E Ferrannini, A Golay, A Mari, A Natali, J Petrie, M Walker. We thank all EPIC participants and staff for their contribution to the study. We thank the lab team at the MRC Epidemiology Unit for sample management and Nicola Kerrison of the MRC Epidemiology Unit for data management. Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197).In addition, EPIC-InterAct investigators acknowledge funding from the following agencies: PWF: Swedish Research Council, Novo Nordisk, Swedish Diabetes Association, Swedish Heart-Lung Foundation; LCG: Swedish Research Council; NS: Health Research Fund (FIS) of the Spanish Ministry of Health; Murcia Regional Government (Nº 6236); LA: We thank the participants of the Spanish EPIC cohort for their contribution to the study as well as to the team of trained nurses who participated in the recruitment; RK: German Cancer Aid, German Ministry of Research (BMBF); TJK: Cancer Research UK; PMN: Swedish Research Council; KO: Danish Cancer Society; SP: Compagnia di San Paolo; JRQ: Asturias Regional Government; OR: The Västerboten County Council; 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; RT: AIRE-ONLUS Ragusa, AVIS-Ragusa, Sicilian Regional Government; IS: Verification of diabetes cases was additionally funded by NL Agency grant IGE05012 and an Incentive Grant from the Board of the UMC Utrecht; IB: Wellcome Trust grant 098051 and United Kingdom NIHR Cambridge Biomedical Research Centre; MIM: InterAct, Wellcome Trust (083270/Z/07/Z), MRC (G0601261); ER: Imperial College Biomedical Research.This is the author accepted manuscript. The final version is available from the American Diabetes Association via http://dx.doi.org/10.2337/db14-031

Association of Plasma Phospholipid n-3 and n-6 Polyunsaturated Fatty Acids with Type 2 Diabetes: The EPIC-InterAct Case-Cohort Study.

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

Cycling and sports, but not walking, are associated with 10-year cardiovascular disease incidence: The MORGEN Study

Background: Physical activity is inversely related to cardiovascular diseases. However, the type of activities that contribute most to these beneficial effects remain unclear. For this reason, we investigated self-reported leisure time physical activities in relation to fatal/nonfatal cardiovascular disease incidence. Design: The Dutch Monitoring Project on Risk Factors for Chronic Diseases Study, carried out between 1993 and 1997, is a prospective cohort study of over 23000 men and women aged 20–65 years from the general Dutch population. Methods: From 1994 till 1997 physical activity was assessed with a questionnaire in 7451 men and 8991 women who were followed for an average of 9.8 years. Cox proportional hazards models were used adjusting for age, sex, other physical activities, smoking, alcohol consumption, and educational level. Results: Almost the entire study population (97%) was engaged in walking, about 75% in regular cycling, and about half the population in sports or gardening. Cycling [hazard ratio (HR): 0.82, 95% confidence interval (CI): 0.71–0.95] and sports (HR: 0.74, 95% CI: 0.64–0.87) were both inversely related to cardiovascular disease incidence, whereas walking and gardening were not. For sports (P<0.001), but not for cycling (P=0.06), we found a dose-response relationship with respect to cardiovascular disease incidence. Engaging in both cycling and sports resulted in an even greater risk reduction (HR: 0.64, 95% CI: 0.52–0.77). Conclusion: In this relatively active population, types of activities of at least moderate intensity, such as cycling and sports were associated with lower CVD incidence, whereas activities of lower intensity, such as walking and gardening, were not

Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries.

AIMS/HYPOTHESIS: Gene-macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes. METHODS: We investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution. RESULTS: No interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0-51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined. CONCLUSIONS/INTERPRETATION: Genetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions