A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals.

BACKGROUND: In Europeans, 45 genetic risk variants for coronary artery disease (CAD) have been identified in genome-wide association studies. We constructed a genetic risk score (GRS) of these variants to estimate the effect on incidence and clinical predictability of myocardial infarction (MI) and CAD. METHODS: Genotype was available from 6041 Danes. An unweighted GRS was constructed by making a summated score of the 45 known genetic CAD risk variants. Registries provided information (mean follow-up = 11.6 years) on CAD (n = 374) and MI (n = 124) events. Cox proportional hazard estimates with age as time scale was adjusted for sex, BMI, type 2 diabetes mellitus and smoking status. Analyses were also stratified either by sex or median age (below or above 45 years of age). We estimated GRS contribution to MI prediction by assessing net reclassification index (NRI) and integrated discrimination improvement (IDI) added to the European SCORE for 10-year MI risk prediction. RESULTS: The GRS associated significantly with risk of incident MI (allele-dependent hazard ratio (95%CI): 1.06 (1.02-1.11), p = 0.01) but not with CAD (p = 0.39). Stratification revealed association of GRS with MI in men (1.06 (1.01-1.12), p = 0.02) and in individuals above the median of 45.11 years of age (1.06 (1.00-1.12), p = 0.03). There was no interaction between GRS and gender (p = 0.90) or age (p = 0.83). The GRS improved neither NRI nor IDI. CONCLUSION: The GRS of 45 GWAS identified risk variants increase the risk of MI in a Danish cohort. The GRS did not improve NRI or IDI beyond the performance of conventional European SCORE risk factors.

The effect of GWAS identified BMI loci on changes in body weight among middle-aged Danes during a five-year period.

OBJECTIVE: Genome-wide association studies have identified genetic variants associating with BMI, however, it is un-clarified whether the same variants also influence body weight fluctuations. METHODS: Among 3,982 adult individuals that attended both a baseline and a five-year follow-up examination in the Danish Inter99 intervention study, a genetic risk score (GRS) was constructed based on 30 BMI variants to address whether it is associated with body weight changes. Moreover, it was examined whether the effect of lifestyle changes was modulated by the GRS. RESULTS: The GRS associated strongly with baseline body weight, with a per risk allele increase of 0.45 (0.33-0.58) kg (P = 2.7 × 10(-12) ), corresponding to a body weight difference of 3.41 (2.21-4.60) kg comparing the highest (≥ 30 risk alleles) and lowest (≤ 26 risk alleles) risk allele tertile. No association was observed with changes in body weight during the five years. Changes in lifestyle, including physical activity, diet and smoking habits associated strongly with body weight changes, however, no interactions with the GRS was observed. CONCLUSION: The GRS associated with body weight cross-sectionally, but not with changes over a five-year period. Body weight changes were influenced by lifestyle changes, however, independently of the GRS.

The frequent UCP2 -866G>A polymorphism protects against insulin resistance and is associated with obesity: a study of obesity and related metabolic traits among 17 636 Danes.

CONTEXT: Uncoupling protein 2 (UCP2) is involved in regulating ATP synthesis, generation of reactive oxygen species and glucose-stimulated insulin secretion in ß-cells. Polymorphisms in UCP2 may be associated with obesity and type 2 diabetes mellitus. OBJECTIVE: To determine the influence of a functional UCP2 promoter polymorphism (-866G>A, rs659366) on obesity, type 2 diabetes and intermediary metabolic traits. Furthermore, to include these and previously published data in a meta-analysis of this variant with respect to its impact on obesity and type 2 diabetes. DESIGN: We genotyped UCP2 rs659366 in a total of 17 636 Danish individuals and established case-control studies of obese and non-obese subjects and of type 2 diabetic and glucose-tolerant subjects. Meta-analyses were made in own data set and in publicly available data sets. Quantitative traits relevant for obesity and type 2 diabetes were analysed within separate study populations. RESULTS: We found no consistent associations between the UCP2 -866G-allele and obesity or type 2 diabetes. Yet, a meta-analysis of data from 12 984 subjects showed an association with obesity (GA vs GG odds ratio (OR) (95% confidence interval (CI)): 0.894(0.826-0.968) P=0.00562, and AA vs GG OR(95% CI): 0.892(0.800-0.996), P=0.0415. Moreover, a meta-analysis for type 2 diabetes of 15 107 individuals showed no association. The -866G-allele was associated with elevated fasting serum insulin levels (P=0.002) and HOMA insulin resistance index (P=0.0007). Insulin sensitivity measured during intravenous glucose tolerance test in young Caucasian subjects (n=377) was decreased in carriers of the GG genotype (P=0.05). CONCLUSIONS: The UCP2 -866G-allele is associated with decreased insulin sensitivity in Danish subjects and is associated with obesity in a combined meta-analysis.

Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes.

AIMS/HYPOTHESIS: 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. METHODS: The study comprised three stages. We performed medium-depth (8×) 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. RESULTS: 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 × 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10(-10)). CONCLUSIONS/INTERPRETATION: 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.

Association studies of novel obesity-related gene variants with quantitative metabolic phenotypes in a population-based sample of 6,039 Danish individuals.

AIMS/HYPOTHESIS: Genome-wide association studies have identified novel WHR and BMI susceptibility loci. The aim of this study was to elucidate if any of these loci had an effect on quantitative measures of glucose homeostasis, including estimates of insulin release and insulin sensitivity in an epidemiological setting. METHODS: By applying an additive genetic model, 14 WHR-associated gene variants and 18 BMI-associated variants were investigated for their relationships with glucose-related metabolic traits in treatment-naive individuals from the population-based Inter99 study sample (n = 6,039). RESULTS: Of the variants associated with BMI, the QPCTL rs2287019 C allele was associated with an increased insulinogenic index of 7.4% per risk allele (p = 4.0 × 10⁻7) and increased disposition index of 5.6% (p = 6.4 × 10⁻5). The LRP1B rs2890652 C allele was associated with insulin resistance, showing a 3.3% increase (p = 0.0011) using the HOMA-insulin resistance (HOMA-IR) index and a 2.2% reduction (p = 0.0014) with the Matsuda index. Of the variants associated with WHR, LYPLAL1/SLC30A10 rs4846567 G allele carriers showed a 5.2% lower HOMA-IR (p = 0.00086) in women, indicating improved insulin sensitivity. Female carriers of the VEGFA rs6905288 A allele were insulin resistant, with a 3.7% increase in HOMA-IR (p = 0.00036) and 4.0% decrease in Matsuda index (p = 2 × 10⁻4). CONCLUSIONS: Our correlative findings from analysing single-locus data suggest that some variation in validated BMI and WHR loci are associated with either increased or decreased insulin sensitivity and thereby potentially with metabolically healthy or metabolically unhealthy subsets of obesity. The results call for testing in larger study samples and for further physiological exploration of the possible metabolic implications of these loci.