Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population

Background: The ‘exposome’ represents the accumulation of all environmental exposures across a lifetime. Topdown strategies are required to assess something this comprehensive, and could transform our understanding of how environmental factors affect human health. Metabolic profiling (metabonomics/metabolomics) defines an individual’s metabolic phenotype, which is influenced by genotype, diet, lifestyle, health and xenobiotic exposure, and could also reveal intermediate biomarkers for disease risk that reflect adaptive response to exposure. We investigated changes in metabolism in volunteers living near a point source of environmental pollution: a closed zinc smelter with associated elevated levels of environmental cadmium. Methods: High-resolution 1H NMR spectroscopy (metabonomics) was used to acquire urinary metabolic profiles from 178 human volunteers. The spectral data were subjected to multivariate and univariate analysis to identify metabolites that were correlated with lifestyle or biological factors. Urinary levels of 8-oxo-deoxyguanosine were also measured, using mass spectrometry, as a marker of systemic oxidative stress. Results: Six urinary metabolites, either associated with mitochondrial metabolism (citrate, 3-hydroxyisovalerate, 4- deoxy-erythronic acid) or one-carbon metabolism (dimethylglycine, creatinine, creatine), were associated with cadmium exposure. In particular, citrate levels retained a significant correlation to urinary cadmium and smoking status after controlling for age and sex. Oxidative stress (as determined by urinary 8-oxo-deoxyguanosine levels) was elevated in individuals with high cadmium exposure, supporting the hypothesis that heavy metal accumulation was causing mitochondrial dysfunction. Conclusions: This study shows evidence that an NMR-based metabolic profiling study in an uncontrolled human population is capable of identifying intermediate biomarkers of response to toxicants at true environmental concentrations, paving the way for exposome research. Keywords: metabonomics, cadmium, environmental health, exposome, metabolomics, molecular epidemiolog

Determinants of the urinary and serum metabolome in children from six European populations

Background Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project (http://www.projecthelix.eu). Methods Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit). Results We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum. Conclusions We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children