Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.

Aims/hypothesisThe DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT.MethodsPrediabetic participants (target sample size 2,200–2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires.Conclusions/interpretationDIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-014-3216-x) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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Aims-Small, short studies suggest metformin influences the glucagon-like peptide (GLP)-1 axis in individuals with and without type 2 diabetes (T2DM). In the Carotid Atherosclerosis: Metformin for insulin ResistAnce (CAMERA) trial (NCT00723307) we investigated whether this effect is sustained and related to changes in glycaemia or weight. In the cross-sectional DIabetes Contributors: DP had the idea for and designed the analysis, analysed and interpreted data, wrote the first draft and revised later drafts of the article. AD analysed the DIRECT data and contributed to the manuscript. ERP, PWF, AJ and MW participated in the design and sample collection of the DIRECT diabetes progression study, interpreted the data and revised the manuscript. PW coordinated the laboratory work for GLP-1 and leptin measurements, interpreted data and revised the article. CS analysed leptin, analysed and interpreted the data and revised the article. RRH interpreted the data and revised the article. NS had the idea for and designed the analysis, interpreted data, revised the article, and supervised the analysis. DIRECT collaborators are listed in Supplementary Table 3.Conflicts of interest: DP, PW, CS, AJ, THH, JD, and RK report no conflicts of interest. RRH has received research support from Amylin, Bayer, Merck, and Novartis; participated in advisory boards for Amylin, Lilly, Merck, Novartis, and Novo Nordisk; and received compensation for lectures from Bayer, Lilly, Merck, and Merck Serono. ERP has received lecture fees from Eli Lilly, Novo Nordisk, Astra Zeneca and Sanofi. NS has consulted for Eli Lilly, Bristol-Myers Squibb, Merck Sharp & Dohme, AstraZeneca, Sanofi, and Boehringer Ingelheim; and received research support from Merck. PWF has received consulting honoraria from Sanofi Aventis and Eli Lilly Inc, and research support from Novo Nordisk. REsearCh on patient straTification (DIRECT) study, we investigated basal and post-meal GLP-1 levels in diabetic patients. Europe PMC Funders GroupMaterials and Methods-CAMERA was a double-blinded randomized placebo-controlled trial of metformin in 173 participants without diabetes. Using six-monthly fasted total GLP-1 levels over 18 months, we evaluated metformin's effect on total GLP-1 with repeated-measures and ANCOVA analyses. In DIRECT, we examined active and total fasting and 60-minute post-meal GLP-1 levels in 775 patients recently diagnosed with T2DM treated with metformin or diet, using Student's T-tests and linear regression. but not post-meal incremental GLP-1. These changes were independent of potential confounders including age, sex, adiposity and HbA1c. Results-InConclusions-In non-diabetic individuals, metformin increases total GLP-1 in a sustained manner and independently of changes in weight or glycaemia. Metformin-treated diabetic patients also have higher fasted GLP-1 independent of weight and glycaemia.

Aims/hypothesis Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). Methods From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study ( n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6–24 months previously ( n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. Results Using ADA 2011 glycaemic categories, 33% ( n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% ( n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m 2 ; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants’ clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% ( n = 517) were treated by lifestyle modification and 34% ( n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m 2 ; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants’ clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. Conclusions/interpretation The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes. Electronic supplementary mater...

Genome-Wide Association Study (GWAS) Higher Blood pressure Arthritides Neuropsychiatric conditions Malignancies Lower Anaemias Lipidaemias Ischaemic heart disease Genetically higher central obesity Highlights Variants in HFE and TMPRSS6 are associated with higher liver iron. There is genetic evidence that higher central obesity causes higher liver iron. Liver iron variants are not organ specific and associate with multiple diseases.

Highlights d Soft clustering based on 32 phenotypes identified 4 quantitative archetypes d These reflect different patterns of dysfunction across T2D etiological processes d The four archetypes are different in disease progression, GRSs, and omics signals d Some patients are dominated by one archetype, but many have etiological combinations

Recent genome-wide meta-analyses identified 157 loci associated with cross-sectional lipid traits. Here we tested whether these loci associate (singly and in trait-specific genetic risk scores [GRS]) with longitudinal changes in total cholesterol (TC) and triglyceride (TG) levels in a population-based prospective cohort from Northern Sweden (the GLACIER Study). We sought replication in a southern Swedish cohort (the MDC Study; N = 2,943). GLACIER Study participants (N = 6,064) were genotyped with the MetaboChip array. Up to 3,495 participants had 10-yr follow-up data available in the GLACIER Study. The TC- and TG-specific GRSs were strongly associated with change in lipid levels (β = 0.02 mmol/l per effect allele per decade follow-up, P = 2.0×10−11 for TC; β = 0.02 mmol/l per effect allele per decade follow-up, P = 5.0×10−5 for TG). In individual SNP analysis, one TC locus, apolipoprotein E (APOE) rs4420638 (β = 0.12 mmol/l per effect allele per decade follow-up, P = 2.0×10−5), and two TG loci, tribbles pseudokinase 1 (TRIB1) rs2954029 (β = 0.09 mmol/l per effect allele per decade follow-up, P = 5.1×10−4) and apolipoprotein A-I (APOA1) rs6589564 (β = 0.31 mmol/l per effect allele per decade follow-up, P = 1.4×10−8), remained significantly associated with longitudinal changes for the respective traits after correction for multiple testing. An additional 12 loci were nominally associated with TC or TG changes. In replication analyses, the APOE rs4420638, TRIB1 rs2954029, and APOA1 rs6589564 associations were confirmed (P≤0.001). In summary, trait-specific GRSs are robustly associated with 10-yr changes in lipid levels and three individual SNPs were strongly associated with 10-yr changes in lipid levels.