An atlas of metallome and metabolome interactions and associations with incident diabetes in the Strong Heart Family Study

BACKGROUND

Chronic exposure to certain metals plays a role in disease development. Integrating untargeted metabolomics with urinary metallome data may contribute to better understanding the pathophysiology of diseases and complex molecular interactions related to environmental metal exposures. To discover novel associations between urinary metal biomarkers and metabolism networks, we conducted an integrative metallome-metabolome analysis using a panel of urinary metals and untargeted blood metabolomic data from the Strong Heart Family Study (SHFS).

METHODS

The SHFS is a prospective family-based cohort study comprised of American Indian men and women recruited in 2001-2003. This nested case-control analysis of 145 participants of which 50 developed incident diabetes at follow up in 2006-2009, included participants with urinary metal and untargeted metabolomic data. Concentrations of 8 creatinine-adjusted urine metals/metalloids [antimony (Sb), cadmium (Cd), lead (Pb), molybdenum (Mo), selenium (Se), tungsten (W), uranium (U) and zinc (Zn)], and 4 arsenic species [inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (AsB)] were measured. Global metabolomics was performed on plasma samples using high-resolution Orbitrap mass spectrometry. We performed an integrative network analysis using xMWAS and a metabolic pathway analysis using Mummichog.

RESULTS

8,810 metabolic features and 12 metal species were included in the integrative network analysis. Most metal species were associated with distinct subsets of metabolites, forming single-metal-multiple-metabolite clusters (|r|>0.28, p-value < 0.001). DMA (clustering with W), iAs (clustering with U), together with Mo and Se showed modest interactions through associations with common metabolites. Pathway enrichment analysis of associated metabolites (|r|>0.17, p-value < 0.1) showed effects in amino acid metabolism (AsB, Sb, Se and U), fatty acid and lipid metabolism (iAs, Mo, W, Sb, Pb, Cd and Zn). In stratified analyses among participants who went on to develop diabetes, iAs and U clustered together through shared metabolites, and both were associated with the phosphatidylinositol phosphate metabolism pathway; metals were also associated with metabolites in energy metabolism (iAs, MMA, DMA, U, W) and xenobiotic degradation and metabolism (DMA, Pb) pathways.

CONCLUSION

In this integrative analysis of multiple metals and untargeted metabolomics, results show common associations with fatty acid, energy and amino acid metabolism pathways. Results for individual metabolite associations differed for different metals, indicating that larger populations will be needed to confirm the metal-metal interactions detected here, such as the strong interaction of uranium and inorganic arsenic. Understanding the biochemical networks underlying metabolic homeostasis and their association with exposure to multiple metals may help identify novel biomarkers, pathways of disease, potential signatures of environmental metal exposure.

Metal-mixtures in toenails of children living near an active industrial facility in Los Angeles County, California

BACKGROUND

Children residing in communities near metalworking industries are vulnerable to multiple toxic metal exposures. Understanding biomarkers of exposure to multiple toxic metals is important to characterize cumulative burden and to distinguish potential exposure sources in such environmental justice neighborhoods impacted by industrial operations. Exposure to metal mixtures has not been well-characterized among children residing in the United States, and is understudied in communities of color.

METHODS

In this study we used toenail clippings, a noninvasive biomarker, to assess exposure to arsenic (As), cadmium (Cd), mercury (Hg), manganese (Mn), lead (Pb), antimony (Sb), selenium (Se), and vanadium (V). We used nonnegative matrix factorization (NMF) to identify "source" signatures and patterns of exposure among predominantly working class Latinx children residing near an industrial corridor in Southeast Los Angeles County. Additionally, we investigated the association between participant demographic, spatial, and dietary characteristics with identified metal signatures.

RESULTS

Through NMF, we identified three groupings (source factors) for the metal concentrations in children's toenails. A grouping composed of Sb, Pb, As, and Cd, was identified as a potential industrial source factor, reflective of known airborne elemental emissions in the industrial corridor. We further identified a manganese source factor primarily composed of Mn, and a potential dietary source factor driven by Se and Hg. We observed differences in the industrial source factor by age of participants, while the dietary source factor varied by neighborhood.

CONCLUSION

Utilizing an unsupervised dimension reduction technique (NMF), we identified a "source signature" of contamination in toenail samples from children living near metalworking industry. Investigating patterns and sources of exposures in cumulatively burdened communities is necessary to identify appropriate public health interventions.