Christian Herder

Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and ∼2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 × 10−14), CDC123-CAMK1D (P = 1.2 × 10−10), TSPAN8-LGR5 (P = 1.1 × 10−9), THADA (P = 1.1 × 10−9), ADAMTS9 (P = 1.2 × 10−8) and NOTCH2 (P = 4.1 × 10−8) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.

Systematic identification of trans eQTLs as putative drivers of known disease associations

Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Novel biomarkers for pre-diabetes identified by metabolomics

Type 2 diabetes (T2D) can be prevented in pre‐diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre‐diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population‐based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre‐diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P‐values ranging from 2.4 × 10−4 to 2.1 × 10−13. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)‐Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D‐related genes that are associated with these three IGT‐specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Effects of coffee consumption on subclinical inflammation and other risk factors for type 2 diabetes: a clinical trial

BACKGROUND Coffee consumption is associated with a decreased risk of type 2 diabetes. Suggested mechanisms underlying the association have included attenuation of subclinical inflammation and a reduction in oxidative stress. OBJECTIVE The aim was to investigate the effects of daily coffee consumption on biomarkers of coffee intake, subclinical inflammation, oxidative stress, glucose, and lipid metabolism. DESIGN Habitual coffee drinkers (n = 47) refrained for 1 mo from coffee drinking; in the second month they consumed 4 cups of filtered coffee/d and in the third month 8 cups of filtered coffee/d (150 mL/cup). Blood samples were analyzed by gas chromatography-mass spectrometry, bead-based multiplex technology, enzyme-linked immunosorbent assay, or immunonephelometry. RESULTS Coffee consumption led to an increase in coffee-derived compounds, mainly serum caffeine, chlorogenic acid, and caffeic acid metabolites. Significant changes were also observed for serum concentrations of interleukin-18, 8-isoprostane, and adiponectin (medians: -8%, -16%, and 6%, respectively; consumption of 8 compared with 0 cups coffee/d). Serum concentrations of total cholesterol, HDL cholesterol, and apolipoprotein A-I increased significantly by 12%, 7%, and 4%, respectively, whereas the ratios of LDL to HDL cholesterol and of apolipoprotein B to apolipoprotein A-I decreased significantly by 8% and 9%, respectively (8 compared with 0 cups coffee/d). No changes were seen for markers of glucose metabolism in an oral-glucose-tolerance test. CONCLUSIONS Coffee consumption appears to have beneficial effects on subclinical inflammation and HDL cholesterol, whereas no changes in glucose metabolism were found in our study. Furthermore, many coffee-derived methylxanthines and caffeic acid metabolites appear to be useful as biomarkers of coffee intake.

Traffic-Related Air Pollution and Incident Type 2 Diabetes: Results from the SALIA Cohort Study

Background Cross-sectional and ecological studies indicate that air pollution may be a risk factor for type 2 diabetes, but prospective data are lacking. Objective We examined the association between traffic-related air pollution and incident type 2 diabetes. Design Between 1985 and 1994, cross-sectional surveys were performed in the highly industrialized Ruhr district (West Germany); a follow-up investigation was conducted in 2006 using data from the Study on the Influence of Air Pollution on Lung, Inflammation and Aging (SALIA) cohort. Participants 1,775 nondiabetic women who were 54–55 years old at baseline participated in both baseline and follow-up investigations and had complete information available. Materials and Methods Using questionnaires, we assessed 16-year incidence (1990–2006) of type 2 diabetes and information about covariates. Complement factor C3c as marker for subclinical inflammation was measured at baseline. Individual exposure to traffic-related particulate matter (PM) and nitrogen dioxide was determined at different spatial scales. Results Between 1990 and 2006, 87 (10.5%) new cases of diabetes were reported among the SALIA cohort members. The hazards for diabetes were increased by 15–42% per interquartile range of PM or traffic-related exposure. The associations persisted when different spatial scales were used to assess exposure and remained robust after adjusting for age, body mass index, socioeconomic status, and exposure to several non–traffic-related sources of air pollution. C3c was associated with PM pollution at baseline and was a strong independent predictor of incident diabetes. Exploratory analyses indicated that women with high C3c blood levels were more susceptible for PM-related excess risk of diabetes than were women with low C3c levels. Conclusions Traffic-related air pollution is associated with incident type 2 diabetes among elderly women. Subclinical inflammation may be a mechanism linking air pollution with type 2 diabetes. Relevance to clinical practice Our study identifies traffic-related air pollution as a novel and potentially modifiable risk factor of type 2 diabetes.

Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases

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/m2) compared to obese cases (BMI≥30 Kg/m2). 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/m2) or 4,123 obese cases (BMI≥30 kg/m2), 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 = 8.4×10−9, 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 = 1.3×10−8, 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 = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. 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.

Sex Differences in the Prediction of Type 2 Diabetes by Inflammatory Markers Results from the MONICA/KORA Augsburg case-cohort study, 1984–2002

OBJECTIVE—Although sex differences have been reported for associations between obesity and inflammation, the question of whether there is an effect modification by sex in the association between inflammation and type 2 diabetes has not been investigated in detail. Therefore, the aim of this study was to compare associations of markers of inflammation with type 2 diabetes risk between men and women. RESEARCH DESIGN AND METHODS—Following a case-cohort design, cases of incident type 2 diabetes were identified from 7,936 subjects aged 35–74 years at baseline who participated in the population-based Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA)/Cooperative Research in the Region of Augsburg (KORA) studies conducted between 1984 and 2002. Concentrations of C-reactive protein (CRP) and interleukin (IL)-6 were measured in 527 cases of incident type 2 diabetes (305 men and 222 women) and 1,698 noncases (889 men and 809 women). RESULTS—After adjustment for age and survey and lifestyle factors including smoking, alcohol intake, and physical activity, elevated concentrations of CRP showed a considerably stronger association with risk of type 2 diabetes in women (hazard ratio comparing tertile extremes 7.60 [95% CI 4.43–13.04]) than in men (1.84 [1.27–2.67]). The P value for the sex interaction was <0.001. Further adjustment for metabolic risk factors considerably attenuated these associations, and they became nonsignificant in men but remained significant in women. IL-6 was also more strongly associated with type 2 diabetes in women, but there was no significant sex interaction. CONCLUSIONS—Our data suggest that inflammatory processes may be of particular importance in the pathogenesis of type 2 diabetes in women.

Increased concentrations of C-reactive protein and IL-6 but not IL-18 are independently associated with incident coronary events in middle-aged men and women: results from the MONICA/KORA Augsburg case-cohort study, 1984-2002.

Objectives—We performed a prospective case–cohort study in initially healthy, middle-aged men and women from the MONICA/KORA Augsburg studies conducted between 1984 and 2002 to assess the role of IL-18 in comparison with IL-6 and CRP in the prediction of incident coronary heart disease (CHD). Methods and Results—Concentrations of IL-18 were measured in 382 case subjects with incident CHD and 1980 noncases. Mean follow-up was 11 years. Baseline concentrations of IL-18 were slightly higher in cases than in noncases (172.4 [1.0] versus 161.3 [1.0] pg/mL, respectively; P=0.114), but were clearly elevated for C-reactive protein (CRP) and IL-6 in cases compared with noncases. In multivariable analyses, accounting for classical cardiovascular risk factors and inflammatory markers, no statistically significant association was seen between increased concentrations of IL-18 and incident CHD both in men (hazard ratio [HR] and 95% confidence intervals [CIs] comparing extreme tertiles, 1.20; 95% CI, 0.85 to 1.69), and in women (HR, 1.25; 95% CI, 0.67 to 2.34). However, in this population increased concentrations of CRP and IL-6 were found to be independent predictors of future CHD events, even after multivariable adjustment. Conclusions—Elevated concentrations of CRP and IL-6, but not IL-18, were independently associated with risk of CHD in subjects from an area with moderate absolute risk.

Biomarkers for the Prediction of Type 2 Diabetes and Cardiovascular Disease

Risk prediction for type 2 diabetes (T2D) and cardiovascular disease (CVD) remains suboptimal even after the introduction of global risk assessment by various scores. This has prompted the search for additional biomarkers. A variety of blood biomarkers representing various pathophysiological pathways of insulin resistance and atherosclerosis, as well as markers of subclinical disease and genetic markers, have been investigated. This review provides an overview of studies assessing the clinical utility of various biomarkers on the basis of hypothesis‐driven selection as well as hypothesis‐free approaches from novel “‐omics” technologies. So far, the assessment of genotypes and of several candidate biomarkers from blood has resulted in only small improvements in the accuracy of prediction of CVD and T2D over and above that predicted on the basis of established risk factors. Integrated approaches, combining biomarkers from genomics, transcriptomics, proteomics, and metabolomics, as well as serial measurements of biomarkers, are required to make a complete assessment of the potential clinical usefulness of biomarkers for risk prediction of cardiometabolic disease.

Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization

Allergen-specific immunoglobulin E (present in allergic sensitization) has a central role in the pathogenesis of allergic disease. We performed the first large-scale genome-wide association study (GWAS) of allergic sensitization in 5,789 affected individuals and 10,056 controls and followed up the top SNP at each of 26 loci in 6,114 affected individuals and 9,920 controls. We increased the number of susceptibility loci with genome-wide significant association with allergic sensitization from three to ten, including SNPs in or near TLR6, C11orf30, STAT6, SLC25A46, HLA-DQB1, IL1RL1, LPP, MYC, IL2 and HLA-B. All the top SNPs were associated with allergic symptoms in an independent study. Risk-associated variants at these ten loci were estimated to account for at least 25% of allergic sensitization and allergic rhinitis. Understanding the molecular mechanisms underlying these associations may provide new insights into the etiology of allergic disease.