Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism to date, including 7,824 adult individuals from two European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity regarding more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information regarding gene expression, heritability, overlap with known drug targets, previous association with complex disorders and inborn errors of metabolism. We further developed a database and web-based resources for data mining and results visualization. Our findings contribute to a greater understanding of the role of inherited variation in blood metabolic diversity, and identify potential new opportunities for pharmacologic development and disease understanding.

Using a nontargeted metabolomics approach of 447 fasting plasma metabolites, we searched for novel molecular markers that arise before and after hyperglycemia in a large population-based cohort of 2,204 females (115 type 2 diabetic [T2D] case subjects, 192 individuals with impaired fasting glucose [IFG], and 1,897 control subjects) from TwinsUK. Forty-two metabolites from three major fuel sources (carbohydrates, lipids, and proteins) were found to significantly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported as associated with T2D or insulin resistance. Fourteen metabolites were found to be associated with IFG. Among the metabolites identified, the branched-chain keto-acid metabolite 3-methyl-2-oxovalerate was the strongest predictive biomarker for IFG after glucose (odds ratio [OR] 1.65 [95% CI 1.39–1.95], P = 8.46 × 10−9) and was moderately heritable (h2 = 0.20). The association was replicated in an independent population (n = 720, OR 1.68 [ 1.34–2.11], P = 6.52 × 10−6) and validated in 189 twins with urine metabolomics taken at the same time as plasma (OR 1.87 [1.27–2.75], P = 1 × 10−3). Results confirm an important role for catabolism of branched-chain amino acids in T2D and IFG. In conclusion, this T2D-IFG biomarker study has surveyed the broadest panel of nontargeted metabolites to date, revealing both novel and known associated metabolites and providing potential novel targets for clinical prediction and a deeper understanding of causal mechanisms.

Background Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age.Methods Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels.Results We identified a panel of 22 metabolites which combined are strongly correlated with age (R2 = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10−157) and lung function (FEV1 beta = −0.04, SE = 0.008, P = 1.8 × 10−8 adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = −0.01, SE = 0.002, P = 1.9 × 10−6) and birthweight (beta = −0.06, SE = 0.01, P = 2.5 × 10−9). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10−6). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = −0.20, SE = 0.04, P = 2.9 × 10−8). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes.Conclusions Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.

ObjectiveTo determine the extent to which genetic and epigenetic factors contribute to variations in glycosylation of immunoglobulin G (IgG) in humans.Methods76  N-glycan traits in circulating IgG were analyzed by UPLC in 220 monozygotic and 310 dizygotic twin pairs from TwinsUK. A classical twin study design was used to derive the additive genetic, common and unique environmental components defining the variance in these traits. Epigenome-wide association analysis was performed using the Illumina 27k chip. Results51 of the 76 glycan traits studied have an additive genetic component (heritability, h 2)≥  0.5. In contrast, 12 glycan traits had a low genetic contribution (h2<0.35). We then tested for association between methylation levels and glycan levels (P<2 x10-6). Among glycan traits with low heritability probe cg08392591 maps to a CpG island 5’ from the ANKRD11 gene, a p53 activator on chromosome 16. Probe cg26991199 maps to the SRSF10 gene involved in regulation of RNA splicing and particularly in regulation of splicing of mRNA precursors upon heat shock. Among those with high heritability we found cg13782134 (mapping to the NRN1L gene) and cg16029957 mapping near the QPCT gene to be array-wide significant. The proportion of array-wide epigenetic associations was significantly larger (P<0.005) among glycans with low heritability (42%) than in those with high heritability (6.2%).ConclusionsGlycome analyses might provide a useful integration of genetic and non-genetic factors to further our understanding of the role of glycosylation in both normal physiology and disease.

Changes in an individual’s human metabolic phenotype (metabotype) over time can be indicative of disorder-related modifications. Studies covering several months to a few years have shown that metabolic profiles are often specific for an individual. This “metabolic individuality” and detected changes may contribute to personalized approaches in human health care. However, it is not clear whether such individual metabotypes persist over longer time periods. Here we investigate the conservation of metabotypes characterized by 212 different metabolites of 818 participants from the Cooperative Health Research in the Region of Augsburg; Germany population, taken within a 7-year time interval. For replication, we used paired samples from 83 non-related individuals from the TwinsUK study. Results indicated that over 40 % of all study participants could be uniquely identified after 7 years based on their metabolic profiles alone. Moreover, 95 % of the study participants showed a high degree of metabotype conservation (>70 %) whereas the remaining 5 % displayed major changes in their metabolic profiles over time. These latter individuals were likely to have undergone important biochemical changes between the two time points. We further show that metabolite conservation was positively associated with heritability (rank correlation 0.74), although there were some notable exceptions. Our results suggest that monitoring changes in metabotypes over several years can trace changes in health status and may provide indications for disease onset. Moreover, our study findings provide a general reference for metabotype conservation over longer time periods that can be used in biomarker discovery studies.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0629-y) contains supplementary material, which is available to authorized users.