SUMMARY Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 exhibit effect sizes that are unusually high for GWAS and account for 10-60% of metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism, and Crohn’s disease. Taken together our study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21–0.44], factor 2 3.82 [2.64–5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Environmental factors such as tobacco smoking may have long-lasting effects on DNA methylation patterns, which might lead to changes in gene expression and in a broader context to the development or progression of various diseases. We conducted an epigenome-wide association study (EWAs) comparing current, former and never smokers from 1793 participants of the population-based KORA F4 panel, with replication in 479 participants from the KORA F3 panel, carried out by the 450K BeadChip with genomic DNA obtained from whole blood. We observed wide-spread differences in the degree of site-specific methylation (with p-values ranging from 9.31E-08 to 2.54E-182) as a function of tobacco smoking in each of the 22 autosomes, with the percent of variance explained by smoking ranging from 1.31 to 41.02. Depending on cessation time and pack-years, methylation levels in former smokers were found to be close to the ones seen in never smokers. In addition, methylation-specific protein binding patterns were observed for cg05575921 within AHRR, which had the highest level of detectable changes in DNA methylation associated with tobacco smoking (–24.40% methylation; p = 2.54E-182), suggesting a regulatory role for gene expression. The results of our study confirm the broad effect of tobacco smoking on the human organism, but also show that quitting tobacco smoking presumably allows regaining the DNA methylation state of never smokers.

Serum metabolite concentrations provide a direct readout of biological processes in the human body, and are associated with disorders such as cardiovascular and metabolic diseases. Here we present a genome-wide association study with 163 metabolic traits using 1809 participants from the KORA population, followed up in the TwinsUK cohort with 422 participants. In eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH, SLC16A9) the genetic variant is located in or near enzyme or solute carrier coding genes, where the associating metabolic traits match the proteins’ function. Many of these loci are located in rate limiting steps of important enzymatic reactions. Use of metabolite concentration ratios as proxies for enzymatic reaction rates reduces the variance and yields robust statistical associations with p-values between 3×10−24 and 6.5×10−179. These loci explained 5.6% to 36.3% of the observed variance. For several loci, associations with clinically relevant parameters have previously been reported.

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2×10−201), ABCG2 (p = 3.1×10−26), SLC17A1 (p = 3.0×10−14), SLC22A11 (p = 6.7×10−14), SLC22A12 (p = 2.0×10−9), SLC16A9 (p = 1.1×10−8), GCKR (p = 1.4×10−9), LRRC16A (p = 8.5×10−9), and near PDZK1 (p = 2.7×10−9). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0×10−26) and propionyl-L-carnitine (p = 5.0×10−8) concentrations, which in turn were associated with serum UA levels (p = 1.4×10−57 and p = 8.1×10−54, respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10−16 to 10−21). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.

A targeted metabolomics approach was used to identify candidate biomarkers of pre-diabetes. The relevance of the identified metabolites is further corroborated with a protein-metabolite interaction network and gene expression data.

BackgroundMetabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.Methodology/Principal Findings40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).Conclusions/SignificanceOur study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.