| 31. |
- Peddinti, Gopal, et al.
(författare)
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Early metabolic markers identify potential targets for the prevention of type 2 diabetes
- 2017
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. Methods: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. Results: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong’s p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. Conclusions/interpretation: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
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| 32. |
- Pennells, Lisa, et al.
(författare)
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Equalization of four cardiovascular risk algorithms after systematic recalibration : individual-participant meta-analysis of 86 prospective studies
- 2019
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Ingår i: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 40:7, s. 621-
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Tidskriftsartikel (refereegranskat)abstract
- Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms.Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
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| 33. |
- Perry, John R. B., et al.
(författare)
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Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases
- 2012
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Ingår i: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m(2)) compared to obese cases (BMI >= 30 Kg/m(2)). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m(2)) or 4,123 obese cases (BMI >= 30 kg/m(2)), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4610 29, OR = 1.13 [95% CI 1.09-1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00-1.06]). A variant in HMG20A-previously identified in South Asians but not Europeans-was associated with type 2 diabetes in obese cases (P = 1.3 x 10(-8), OR= 1.11 [95% CI 1.07-1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02-1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10-1.17], P = 3.2 x 10(-14). This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05-1.08], P = 2.2 x 10(-16). This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
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| 34. |
- Rung, Johan, et al.
(författare)
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Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia
- 2009
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 41:10, s. 89-1110
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies have identified common variants that only partially explain the genetic risk for type 2 diabetes (T2D). Using genome-wide association data from 1,376 French individuals, we identified 16,360 SNPs nominally associated with T2D and studied these SNPs in an independent sample of 4,977 French individuals. We then selected the 28 best hits for replication in 7,698 Danish subjects and identified 4 SNPs showing strong association with T2D, one of which (rs2943641, P = 9.3 x 10(-12), OR = 1.19) was located adjacent to the insulin receptor substrate 1 gene (IRS1). Unlike previously reported T2D risk loci, which predominantly associate with impaired beta cell function, the C allele of rs2943641 was associated with insulin resistance and hyperinsulinemia in 14,358 French, Danish and Finnish participants from population-based cohorts; this allele was also associated with reduced basal levels of IRS1 protein and decreased insulin induction of IRS1-associated phosphatidylinositol-3-OH kinase activity in human skeletal muscle biopsies.
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| 35. |
- Rutters, Femke, et al.
(författare)
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The association between sleep duration, insulin sensitivity, and β-cell function : The EGIR-RISC study
- 2016
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Ingår i: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 0021-972X .- 1945-7197. ; 101:9, s. 3272-3280
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Tidskriftsartikel (refereegranskat)abstract
- Context: In the past decade, over 3 dozen studies reported a relationship between self-reported short sleep and disturbed glucose metabolism. A study with insulin sensitivity assessed according to the gold standard hyperinsulinemic-euglycemic clamp is, however, still missing. Objective: To evaluate the cross-sectional association of sleep duration with insulin sensitivity and β-cell function in the European group for the study of insulin resistance (EGIR-RISC) study cohort. Design, Setting, Participants, and Measures:Weused data from the baseline measurements of the European, multicentre EGIR-RISC study that included 1319 clinically healthy participants. Sleep and physical activity were measured using a single-axis accelerometer. Insulin sensitivity and β-cell function were estimated by hyperinsulinemic-euglycemic clamp and from the oral glucose insulin sensitivity index model, using an oral glucose tolerance test. Associations of sleep duration with insulin sensitivity and β-cell function were analyzed by multiple linear regression, stratified by sex. Results: In our current analysis, we included 788 participants (57% women, age 44±8 y), who had an average sleep duration of 7.3±1.5 hours. In men,weobserved an inverted U-shaped association between sleep duration categorized per hour and M/I (in μmol/min per kgFFM/nM per hour) (±- estimate [95% confidence intervals] 41 [2, 80]; P = .04 and β2-estimate -3 [-6, -0.2], P = .04) as well as a trend for the oral glucose insulin sensitivity index (in mL/min per kgFFM) (β-estimate [95% confidence intervals] 0.8 [-0.4, 2]; P = .17). In women, we observed a U-shaped association between sleep duration and β-cell function (in pmol/min perm2/mM per hour) (β-estimate-45 [-86, -3]; P = .04 and β2-estimate 3 [0.2, 6]; P = .04). Conclusions: Sleep duration is associated with insulin sensitivity and β-cell function in a sex-specific manner in clinically healthy people.
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| 36. |
- Sacerdote, Carlotta, et al.
(författare)
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Lower educational level is a predictor of incident type 2 diabetes in European countries : The EPIC-InterAct study
- 2012
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Ingår i: International Journal of Epidemiology. - : Oxford University Press (OUP). - 0300-5771 .- 1464-3685. ; 41:4, s. 1162-1173
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Tidskriftsartikel (refereegranskat)abstract
- Background Type 2 diabetes mellitus ( T2DM) is one of the most common chronic diseases worldwide. In high- income countries, low socioeconomic status seems to be related to a high incidence of T2DM, but very little is known about the intermediate factors of this relationship.Method We performed a case-cohort study in eight Western European countries nested in the EPIC study (n = 340 234, 3.99 million person-years of follow-up). A random sub-cohort of 16 835 individuals and a total of 12 403 incident cases of T2DM were identified. Crude and multivariate-adjusted hazard ratios (HR) were estimated for each country and pooled across countries using meta-analytical methods. Age-, gender- and country-specific relative indices of inequality (RII) were used as the measure of educational level and RII tertiles were analysed.Results Compared with participants with a high educational level (RII tertile 1), participants with a low educational level (RII tertile 3) had a higher risk of T2DM [HR: 1.77, 95% confidence interval (CI): 1.69-1.85; P-trend < 0.01]. The HRs adjusted for physical activity, smoking status and propensity score according to macronutrient intake were very similar to the crude HR (adjusted HR: 1.67, 95% CI: 1.52-1.83 in men; HR: 1.88, 95% CI: 1.73-2.05 in women). The HRs were attenuated only when they were further adjusted for BMI (BMI-adjusted HR: 1.36, 95% CI: 1.23-1.51 in men; HR: 1.32, 95% CI: 1.20-1.45 in women).Conclusion This study demonstrates the inequalities in the risk of T2DM in Western European countries, with an inverse relationship between educational level and risk of T2DM that is only partially explained by variations in BMI.
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| 37. |
- Saxena, Richa, et al.
(författare)
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Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:2, s. 142-148
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Tidskriftsartikel (refereegranskat)abstract
- Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).
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| 38. |
- Schmidt, Amand F., et al.
(författare)
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PCSK9 genetic variants and risk of type 2 diabetes : a mendelian randomisation study
- 2017
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Ingår i: The Lancet Diabetes and Endocrinology. - : ELSEVIER SCIENCE INC. - 2213-8587 .- 2213-8595. ; 5:2, s. 97-105
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Tidskriftsartikel (refereegranskat)abstract
- Background Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way off sets their substantial benefi ts. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely eff ects of PCSK9 inhibitors on diabetes risk. Methods In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA 1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores. Findings Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0.09 mmol/L, 95% CI 0.02 to 0.15), bodyweight (1.03 kg, 0.24 to 1.82), waist-to-hip ratio (0.006, 0.003 to 0.010), and an odds ratio for type diabetes of 1.29 (1.11 to 1.50). Based on the collected data, we did not identify associations with HbA 1c (0.03%, -0.01 to 0.08), fasting insulin (0.00%, -0.06 to 0.07), and BMI (0.11 kg/m(2), -0.09 to 0.30). Interpretation PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefi ts of PCSK9 inhibitor treatment, as was previously done for statins.
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| 39. |
- Schumann, Gunter, et al.
(författare)
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Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption
- 2011
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 108:17, s. 7119-7124
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Tidskriftsartikel (refereegranskat)abstract
- Alcohol consumption is a moderately heritable trait, but the genetic basis in humans is largely unknown, despite its clinical and societal importance. We report a genome-wide association study meta-analysis of similar to 2.5 million directly genotyped or imputed SNPs with alcohol consumption (gram per day per kilogram body weight) among 12 population-based samples of European ancestry, comprising 26,316 individuals, with replication genotyping in an additional 21,185 individuals. SNP rs6943555 in autism susceptibility candidate 2 gene (AUTS2) was associated with alcohol consumption at genome-wide significance (P = 4 x 10(-8) to P = 4 x 10(-9)). We found a genotype-specific expression of AUTS2 in 96 human prefrontal cortex samples (P = 0.026) and significant (P < 0.017) differences in expression of AUTS2 in whole-brain extracts of mice selected for differences in voluntary alcohol consumption. Downregulation of an AUTS2 homolog caused reduced alcohol sensitivity in Drosophila (P < 0.001). Our finding of a regulator of alcohol consumption adds knowledge to our understanding of genetic mechanisms influencing alcohol drinking behavior.
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| 40. |
- Scott, Robert A., et al.
(författare)
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A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease
- 2016
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Ingår i: Science Translational Medicine. - : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 8:341
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Tidskriftsartikel (refereegranskat)abstract
- Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process.
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