1. |
- Emerging Risk Factors, Collaboration, et al.
(author)
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The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases
- 2007
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In: Eur J Epidemiol. - 0393-2990. ; 22:12, s. 839-69
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Journal article (peer-reviewed)abstract
- Many long-term prospective studies have reported on associations of cardiovascular diseases with circulating lipid markers and/or inflammatory markers. Studies have not, however, generally been designed to provide reliable estimates under different circumstances and to correct for within-person variability. The Emerging Risk Factors Collaboration has established a central database on over 1.1 million participants from 104 prospective population-based studies, in which subsets have information on lipid and inflammatory markers, other characteristics, as well as major cardiovascular morbidity and cause-specific mortality. Information on repeat measurements on relevant characteristics has been collected in approximately 340,000 participants to enable estimation of and correction for within-person variability. Re-analysis of individual data will yield up to approximately 69,000 incident fatal or nonfatal first ever major cardiovascular outcomes recorded during about 11.7 million person years at risk. The primary analyses will involve age-specific regression models in people without known baseline cardiovascular disease in relation to fatal or nonfatal first ever coronary heart disease outcomes. This initiative will characterize more precisely and in greater detail than has previously been possible the shape and strength of the age- and sex-specific associations of several lipid and inflammatory markers with incident coronary heart disease outcomes (and, secondarily, with other incident cardiovascular outcomes) under a wide range of circumstances. It will, therefore, help to determine to what extent such associations are independent from possible confounding factors and to what extent such markers (separately and in combination) provide incremental predictive value.
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3. |
- Albrechtsen, A., et al.
(author)
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Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes
- 2013
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 56:2, s. 298-310
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Journal article (peer-reviewed)abstract
- Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) > 1% with common metabolic phenotypes. The study comprised three stages. We performed medium-depth (8x) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI > 27.5 kg/m(2) and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case-control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans. Exome sequencing identified 70,182 polymorphisms with MAF > 1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 x 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 x 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 x 10(-10)). We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.
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4. |
- Lorenz, M. W., et al.
(author)
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Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration
- 2018
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In: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 13:4
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Journal article (peer-reviewed)abstract
- Aims Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk. From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C. We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.
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6. |
- Bar, N., et al.
(author)
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A reference map of potential determinants for the human serum metabolome
- 2020
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In: Nature. - : Nature Research. - 0028-0836 .- 1476-4687. ; 588:7836, s. 135-140
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Journal article (peer-reviewed)abstract
- The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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7. |
- Liao, X., et al.
(author)
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Normative values for carotid intima media thickness and its progression: Are they transferrable outside of their cohort of origin?
- 2016
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In: European Journal of Preventive Cardiology. - : Oxford University Press (OUP). - 2047-4873 .- 2047-4881. ; 23:11, s. 1165-1173
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Journal article (peer-reviewed)abstract
- Background The clinical use of carotid intima media thickness (cIMT) requires normal values, which may be subject to variation of geographical factors, ethnicity or measurement details. The influence of these factors has rarely been studied. The aim of this study was to determine whether normative cIMT values and their association with event risk are generalizable across populations. Design Meta-analysis of individual participant data. Method From 22 general population cohorts from Europe, North America and Asia we selected subjects free of cardiovascular disease. Percentiles of cIMT and cIMT progression were assessed separately for every cohort. Cox proportional hazards models for vascular events were used to estimate hazard ratios for cIMT in each cohort. The estimates were pooled across Europe, North America and Asia, with random effects meta-analysis. The influence of geography, ethnicity and ultrasound protocols on cIMT values and on the hazard ratios was examined by meta-regression. Results Geographical factors, ethnicity and the ultrasound protocol had influence neither on the percentiles of cIMT and its progression, nor on the hazard ratios of cIMT for vascular events. Heterogeneity for percentiles of cIMT and cIMT progression was too large to create meaningful normative values. Conclusions The distribution of cIMT values is too heterogeneous to define universal or regional population reference values. CIMT values vary widely between different studies regardless of ethnicity, geographic location and ultrasound protocol. Prediction of vascular events with cIMT values was more consistent across all cohorts, ethnicities and regions. © 2016 European Society of Cardiology.
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8. |
- Pennells, Lisa, et al.
(author)
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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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In: European Heart Journal. - : Oxford University Press (OUP). - 0195-668X .- 1522-9645. ; 40:7, s. 621-
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Journal article (peer-reviewed)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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9. |
- den Ruijter, H. M., et al.
(author)
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Common carotid intima-media thickness does not add to Framingham risk score in individuals with diabetes mellitus: the USE-IMT initiative
- 2013
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In: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 56:7, s. 1494-1502
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Journal article (peer-reviewed)abstract
- The aim of this work was to investigate whether measurement of the mean common carotid intima-media thickness (CIMT) improves cardiovascular risk prediction in individuals with diabetes. We performed a subanalysis among 4,220 individuals with diabetes in a large ongoing individual participant data meta-analysis involving 56,194 subjects from 17 population-based cohorts worldwide. We first refitted the risk factors of the Framingham heart risk score on the individuals without previous cardiovascular disease (baseline model) and then expanded this model with the mean common CIMT (CIMT model). The absolute 10 year risk for developing a myocardial infarction or stroke was estimated from both models. In individuals with diabetes we compared discrimination and calibration of the two models. Reclassification of individuals with diabetes was based on allocation to another cardiovascular risk category when mean common CIMT was added. During a median follow-up of 8.7 years, 684 first-time cardiovascular events occurred among the population with diabetes. The C statistic was 0.67 for the Framingham model and 0.68 for the CIMT model. The absolute 10 year risk for developing a myocardial infarction or stroke was 16% in both models. There was no net reclassification improvement with the addition of mean common CIMT (1.7%; 95% CI -1.8, 3.8). There were no differences in the results between men and women. There is no improvement in risk prediction in individuals with diabetes when measurement of the mean common CIMT is added to the Framingham risk score. Therefore, this measurement is not recommended for improving individual cardiovascular risk stratification in individuals with diabetes.
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10. |
- Hassing, H. C., et al.
(author)
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SULF2 Strongly Prediposes to Fasting and Postprandial Triglycerides in Patients with Obesity and Type 2 Diabetes Mellitus
- 2014
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In: Obesity. - : Wiley. - 1930-7381 .- 1930-739X. ; 22:5, s. 1309-1316
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Journal article (peer-reviewed)abstract
- Objective: Hepatic overexpression of sulfatase-2 (SULF2), a heparan sulfate remodeling enzyme, strongly contributes to high triglyceride (TG) levels in obese, type 2 diabetic (T2DM) db/db mice. Nevertheless, data in humans are lacking. Here, the association of human hepatic SULF2 expression and SULF2 gene variants with TG metabolism in patients with obesity and/or T2DM was investigated. Methods: Liver biopsies from 121 obese subjects were analyzed for relations between hepatic SULF2 mRNA levels and plasma TG. Associations between seven SULF2 tagSNPs and TG levels were assessed in 210 obese T2DM subjects with dyslipidemia. Replication of positive findings was performed in 1,316 independent obese T2DM patients. Postprandial TRL clearance was evaluated in 29 obese T2DM subjects stratified by SULF2 genotype. Results: Liver SULF2 expression was significantly associated with fasting plasma TG (r = 0.271; P = 0.003) in obese subjects. The SULF2 rs2281279(A>G) SNP was reproducibly associated with lower fasting plasma TG levels in obese T2DM subjects (P < 0.05). Carriership of the minor G allele was associated with lower levels of postprandial plasma TG (P < 0.05) and retinyl esters levels (P < 0.001). Conclusions: These findings implicate SULF2 as potential therapeutic target in the atherogenic dyslipidemia of obesity and T2DM.
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11. |
- Wang, X., et al.
(author)
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Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study
- 2017
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In: Plos One. - : Public Library of Science (PLoS). - 1932-6203. ; 12:3
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Journal article (peer-reviewed)abstract
- Background The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation. Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group. Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women. Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.
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13. |
- Reiling, E., et al.
(author)
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Genetic association analysis of LARS2 with type 2 diabetes
- 2010
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In: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 53:1, s. 103-110
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Journal article (peer-reviewed)abstract
- LARS2 has been previously identified as a potential type 2 diabetes susceptibility gene through the low-frequency H324Q (rs71645922) variant (minor allele frequency [MAF] 3.0%). However, this association did not achieve genome-wide levels of significance. The aim of this study was to establish the true contribution of this variant and common variants in LARS2 (MAF > 5%) to type 2 diabetes risk. We combined genome-wide association data (n = 10,128) from the DIAGRAM consortium with independent data derived from a tagging single nucleotide polymorphism (SNP) approach in Dutch individuals (n = 999) and took forward two SNPs of interest to replication in up to 11,163 Dutch participants (rs17637703 and rs952621). In addition, because inspection of genome-wide association study data identified a cluster of low-frequency variants with evidence of type 2 diabetes association, we attempted replication of rs9825041 (a proxy for this group) and the previously identified H324Q variant in up to 35,715 participants of European descent. No association between the common SNPs in LARS2 and type 2 diabetes was found. Our replication studies for the two low-frequency variants, rs9825041 and H324Q, failed to confirm an association with type 2 diabetes in Dutch, Scandinavian and UK samples (OR 1.03 [95% CI 0.95-1.12], p = 0.45, n = 31,962 and OR 0.99 [0.90-1.08], p = 0.78, n = 35,715 respectively). In this study, the largest study examining the role of sequence variants in LARS2 in type 2 diabetes susceptibility, we found no evidence to support previous data indicating a role in type 2 diabetes susceptibility.
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14. |
- Allesøe, Rosa Lundbye, et al.
(author)
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
- 2023
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In: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
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Journal article (peer-reviewed)abstract
- The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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15. |
- Alssema, M, et al.
(author)
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The evaluation of screening and early detection strategies for type 2 diabetes and impaired glucose tolerance (DETECT-2) update of the Finnish diabetes risk score for prediction of incident type 2 diabetes
- 2011
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 54:5, s. 1004-1012
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Journal article (peer-reviewed)abstract
- AIMS/HYPOTHESIS: The Finnish diabetes risk questionnaire is a widely used, simple tool for identification of those at risk for drug-treated type 2 diabetes. We updated the risk questionnaire by using clinically diagnosed and screen-detected type 2 diabetes instead of drug-treated diabetes as an endpoint and by considering additional predictors. METHODS: Data from 18,301 participants in studies of the Evaluation of Screening and Early Detection Strategies for Type 2 Diabetes and Impaired Glucose Tolerance (DETECT-2) project with baseline and follow-up information on oral glucose tolerance status were included. Incidence of type 2 diabetes within 5 years was used as the outcome variable. Improvement in discrimination and classification of the logistic regression model was assessed by the area under the receiver-operating characteristic (ROC) curve and by the net reclassification improvement. Internal validation was by bootstrapping techniques. RESULTS: Of the 18,301 participants, 844 developed type 2 diabetes in a period of 5 years (4.6%). The Finnish risk score had an area under the ROC curve of 0.742 (95% CI 0.726-0.758). Re-estimation of the regression coefficients improved the area under the ROC curve to 0.766 (95% CI 0.750-0.783). Additional items such as male sex, smoking and family history of diabetes (parent, sibling or both) improved the area under the ROC curve and net reclassification. Bootstrapping showed good internal validity. CONCLUSIONS/INTERPRETATION: The predictive value of the original Finnish risk questionnaire could be improved by adding information on sex, smoking and family history of diabetes. The DETECT-2 update of the Finnish diabetes risk questionnaire is an adequate and robust predictor for future screen-detected and clinically diagnosed type 2 diabetes in Europid populations.
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16. |
- Den Ruijter, Hester M., et al.
(author)
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Common Carotid Intima-Media Thickness Measurements in Cardiovascular Risk Prediction A Meta-analysis
- 2012
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In: JAMA: The Journal of the American Medical Association. - : American Medical Association (AMA). - 1538-3598. ; 308:8, s. 796-803
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Journal article (peer-reviewed)abstract
- Context The evidence that measurement of the common carotid intima-media thickness (CIMT) improves the risk scores in prediction of the absolute risk of cardiovascular events is inconsistent. Objective To determine whether common CIMT has added value in 10-year risk prediction of first-time myocardial infarctions or strokes, above that of the Framingham Risk Score. Data Sources Relevant studies were identified through literature searches of databases (PubMed from 1950 to June 2012 and EMBASE from 1980 to June 2012) and expert opinion. Study Selection Studies were included if participants were drawn from the general population, common CIMT was measured at baseline, and individuals were followed up for first-time myocardial infarction or stroke. Data Extraction Individual data were combined into 1 data set and an individual participant data meta-analysis was performed on individuals without existing cardiovascular disease. Results We included 14 population-based cohorts contributing data for 45 828 individuals. During a median follow-up of 11 years, 4007 first-time myocardial infarctions or strokes occurred. We first refitted the risk factors of the Framingham Risk Score and then extended the model with common CIMT measurements to estimate the absolute 10-year risks to develop a first-time myocardial infarction or stroke in both models. The C statistic of both models was similar (0.757; 95% CI, 0.749-0.764; and 0.759; 95% CI, 0.752-0.766). The net reclassification improvement with the addition of common CIMT was small (0.8%; 95% CI, 0.1%-1.6%). In those at intermediate risk, the net reclassification improvement was 3.6% in all individuals (95% CI, 2.7%-4.6%) and no differences between men and women. Conclusion The addition of common CIMT measurements to the Framingham Risk Score was associated with small improvement in 10-year risk prediction of first-time myocardial infarction or stroke, but this improvement is unlikely to be of clinical importance. JAMA. 2012;308(8):796-803 www.jama.com
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17. |
- Obura, M., et al.
(author)
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Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time : An IMI-DIRECT study
- 2021
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In: Diabetic Medicine. - : Wiley. - 0742-3071 .- 1464-5491. ; 38:2
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Journal article (peer-reviewed)abstract
- Aim: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. Methods: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. Results: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1–4. Participants in Subgroups 2–4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (β = 0.36, 95% CI 0.13–0.58), Subgroup 3 (β = 0.30; 95% CI 0.10–0.50) and Subgroup 2 (β = 0.18; 95% CI 0.04–0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. Conclusions: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.
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18. |
- Reiling, Erwin, et al.
(author)
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The Association of Mitochondrial Content with Prevalent and Incident Type 2 Diabetes.
- 2010
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In: The Journal of clinical endocrinology and metabolism. - : The Endocrine Society. - 1945-7197 .- 0021-972X. ; 95, s. 1909-1915
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Journal article (peer-reviewed)abstract
- Context: It has been shown that mitochondrial DNA (mtDNA) content is associated with type 2 diabetes (T2D) and related traits. However, empirical data, often based on small samples, did not confirm this observation in all studies. Therefore, the role of mtDNA content in T2D remains elusive. Objective: In this study, we assessed the heritability of mtDNA content in buccal cells and analyzed the association of mtDNA content in blood with prevalent and incident T2D. Design and Setting: mtDNA content from cells from buccal and blood samples was assessed using a real-time PCR-based assay. Heritability of mtDNA content was estimated in 391 twins from the Netherlands Twin Register. The association with prevalent T2D was tested in a case control study from The Netherlands (n = 329). Incident T2D was analyzed using prospective samples from Finland (n = 444) and The Netherlands (n = 238). Main Outcome Measures: We measured the heritability of mtDNA content and the association of mtDNA content in blood with prevalent and incident T2D. Results: A heritability of mtDNA content of 35% (19-48%) was estimated in the twin families. We did not observe evidence of an association between mtDNA content and prevalent or incident T2D and related traits. Furthermore, we observed a decline in mtDNA content with increasing age that was male specific (P = 0.001). Conclusion: In this study, we show that mtDNA content has a heritability of 35% in Dutch twins. There is no association between mtDNA content in blood and prevalent or incident T2D and related traits in our study samples.
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19. |
- Wood, A. R., et al.
(author)
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A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants
- 2017
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In: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 66:8, s. 2296-2309
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Journal article (peer-reviewed)abstract
- Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose-raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose-raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide-based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.
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20. |
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