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ObjectiveType 2 diabetes arises from the interaction of physiological and lifestyle risk factors. Our objective was to develop a model for predicting the risk of T2D, which could use various amounts of background information.Research design and methodsWe trained a survival analysis model on 8483 people from three large Finnish and Spanish data sets, to predict the time until incident T2D. All studies included anthropometric data, fasting laboratory values, an oral glucose tolerance test (OGTT) and information on co-morbidities and lifestyle habits. The variables were grouped into three sets reflecting different degrees of information availability. Scenario 1 included background and anthropometric information; Scenario 2 added routine laboratory tests; Scenario 3 also added results from an OGTT. Predictive performance of these models was compared with FINDRISC and Framingham risk scores.ResultsThe three models predicted T2D risk with an average integrated area under the ROC curve equal to 0.83, 0.87 and 0.90, respectively, compared with 0.80 and 0.75 obtained using the FINDRISC and Framingham risk scores. The results were validated on two independent cohorts. Glucose values and particularly 2-h glucose during OGTT (2h-PG) had highest predictive value. Smoking, marital and professional status, waist circumference, blood pressure, age and gender were also predictive.ConclusionsOur models provide an estimation of patient’s risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.

Objective: Previously, we observed an association between family history of type 1 diabetes and development of non-insulin-dependent diabetes. The aims of this study were to assess whether type 1 diabetes susceptibility gene variants explain this association and investigate the effect of the variants on insulin secretion and presence of glutamic acid decarboxylase autoantibodies (GADA) in nondiabetic adults. Design and methods: Polymorphisms in INS (rs689), PTPN22 (rs2476601), CTLA4 (rs3087243), and the HLA-DQA1-DQB1 regions (rs2187668 and rs7454108 tagging HLA-DQ2.5 and HLA-DQ8 respectively) were genotyped in the Botnia Prospective Study (n=2764), in which initially nondiabetic participants were followed for a mean of 8.1 years. Results: The variants did not explain the association between family history of type 1 diabetes and development of non-insulin-dependent diabetes. In these nondiabetic adults, HLA-DQ and PTPN22 risk genotypes were associated with GADA (HLA-DQ2.5/HLA-DQ8 or HLA-DQ8: OR (95% CI): 1.7 (1.3-2.3), P=0.0004; PTPN22 CT/TT: OR: 1.6 (1.2-2.2), P=0.003; P values were adjusted for sex, age, BMI, and follow-up time). A higher genetic risk score was associated with lower insulin secretion (insulinogenic index: 13.27 (16.27) vs 12.69 (15.27) vs 10.98 (13.06), P=0.02) and better insulin sensitivity index (risk score of 0-1 vs 2-3 vs 4-6: 142 (111) vs 144 (118) vs 157 (127), P=0.01) at baseline and a poorer capacity to compensate for the increased insulin demand after follow-up. Conclusions: In nondiabetic adults, HLA-DQ2.5/HLA-DQ8 and PTPN22 CT/TT genotypes were associated with GADA. (Less)

ContextMitochondrial ATP production is important in the regulation of glucose-stimulated insulin secretion. Genetic factors may modulate the capacity of the β-cells to secrete insulin and thereby contribute to the risk of type 2 diabetes.ObjectiveThe aim of this study was to identify genetic loci in or adjacent to nuclear-encoded genes of the oxidative phosphorylation (OXPHOS) pathway that are associated with insulin secretion in vivo.Design and methodsTo find polymorphisms associated with glucose-stimulated insulin secretion, data from a genome-wide association study (GWAS) of 1467 non-diabetic individuals, including the Diabetes Genetic Initiative (DGI), was examined. A total of 413 single nucleotide polymorphisms with a minor allele frequency ≥0.05 located in or adjacent to 76 OXPHOS genes were included in the DGI GWAS. A more extensive population-based study of 4323 non-diabetics, the PPP-Botnia, was used as a replication cohort. Insulinogenic index during an oral glucose tolerance test was used as a surrogate marker of glucose-stimulated insulin secretion. Multivariate linear regression analyses were used to test genotype–phenotype associations.ResultsTwo common variants were identified in the DGI, where the major C-allele of rs606164, adjacent to NADH dehydrogenase (ubiquinone) 1 subunit C2 (NDUFC2), and the minor G-allele of rs1323070, adjacent to cytochrome c oxidase subunit VIIa polypeptide 2 (COX7A2), showed nominal associations with decreased glucose-stimulated insulin secretion (P=0.0009, respective P=0.003). These associations were replicated in PPP-Botnia (P=0.002 and P=0.05).ConclusionOur study shows that genetic variation near genes involved in OXPHOS may influence glucose-stimulated insulin secretion in vivo.

AimsWe investigated whether polymorphisms in candidate genes involved in lipid metabolism and type 2 diabetes are related to liver fat content.MethodsLiver fat content was measured using proton magnetic resonance spectroscopy (1H-MRS) in 302 Finns, in whom single nucleotide polymorphisms (SNPs) in acyl-CoA synthetase long-chain family member 4 (ACSL4), adiponectin receptors 1 and 2 (ADIPOR1andADIPOR2), and the three peroxisome proliferator-activated receptors (PPARA,PPARD, andPPARG) were analyzed. To validate our findings, SNPs significantly associated with liver fat content were studied in two independent cohorts and related to surrogate markers of liver fat content.ResultsIn the Finnish subjects, polymorphisms inACSL4(rs7887981),ADIPOR2(rs767870), andPPARG(rs3856806) were significantly associated with liver fat content measured with1H-MRS after adjusting for age, gender, and BMI. Anthropometric and circulating parameters were comparable between genotypes. In the first validation cohort of ∼ 600 Swedish men,ACSL4rs7887981 was related to fasting insulin and triglyceride concentrations, andADIPOR2rs767870 to serum γ glutamyltransferase concentrations after adjusting for BMI. The SNP inPPARG(rs3856806) was not significantly associated with any relevant metabolic parameter in this cohort. In the second validation cohort of ∼3000 subjects from Western Finland,ADIPOR2rs767870, but notACSL4rs7887981 was related to fasting triglyceride concentrations.ConclusionsGenetic variation, particularly in theADIPOR2gene, contributes to variation in hepatic fat accumulation in humans.