Metal mixtures in urban and rural populations in the US: The Multi-Ethnic Study of Atherosclerosis and the Strong Heart Study

Abstract

Background Natural and anthropogenic sources of metal exposure differ for urban and rural residents. We searched to identify patterns of metal mixtures which could suggest common environmental sources and/or metabolic pathways of different urinary metals, and compared metal-mixtures in two population-based studies from urban/sub-urban and rural/town areas in the US: the Multi-Ethnic Study of Atherosclerosis (MESA) and the Strong Heart Study (SHS). Methods We studied a random sample of 308 White, Black, Chinese-American, and Hispanic participants in MESA (2000–2002) and 277 American Indian participants in SHS (1998–2003). We used principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) to evaluate nine urinary metals (antimony [Sb], arsenic [As], cadmium [Cd], lead [Pb], molybdenum [Mo], selenium [Se], tungsten [W], uranium [U] and zinc [Zn]). For arsenic, we used the sum of inorganic and methylated species (∑As). Results All nine urinary metals were higher in SHS compared to MESA participants. PCA and CA revealed the same patterns in SHS, suggesting 4 distinct principal components (PC) or clusters (∑As-U-W, Pb-Sb, Cd-Zn, Mo-Se). In MESA, CA showed 2 large clusters (∑As-Mo-Sb-U-W, Cd-Pb-Se-Zn), while PCA showed 4 PCs (Sb-U-W, Pb-Se-Zn, Cd-Mo, ∑As). LDA indicated that ∑As, U, W, and Zn were the most discriminant variables distinguishing MESA and SHS participants. Conclusions In SHS, the ∑As-U-W cluster and PC might reflect groundwater contamination in rural areas, and the Cd-Zn cluster and PC could reflect common sources from meat products or metabolic interactions. Among the metals assayed, ∑As, U, W and Zn differed the most between MESA and SHS, possibly reflecting disproportionate exposure from drinking water and perhaps food in rural Native communities compared to urban communities around the US.

Environmental Metals and Cardiovascular Disease in Adults: A Systematic Review Beyond Lead and Cadmium

“…Another interesting area of future research is the role of joint exposures in atherosclerosis. It is important to evaluate mixtures of metals as metals co-occur together [154, 155]. While several of the retrieved studies adjusted the regression models for other metals, statistical methods to comprehensively tackle mixtures of compounds are needed.…”

Comparative Study on Trace Element Excretions between Nonanuric and Anuric Patients Undergoing Continuous Ambulatory Peritoneal Dialysis

“…Additionally, Se and Mo can share the same transporters for sulfate in plants and there are important kind of enzymes containing both Se and Mo in bacteria [58,59]. Furthermore, a Mo-Se cluster was revealed in a well-established cohort study that aimed to identify potential common environmental sources and metabolic pathways using metal-mixtures in urine from participants [60]. From clues mentioned above, we speculated that the potential reason leading to the strong and positive association in our study might be that Se bound with Mo and formed a compound when they were discharged.…”

Co-exposure to non-persistent organic chemicals among American pre-school aged children: A pilot study

“…As suggested before (Myridakis et al, 2015, 2016), to minimize potential bias because of the relatively low creatinine concentrations in young children, we conducted PCA on the volume-based log transformed urinary biomarkers concentrations (i.e., without correction for urinary dilution). PCA identified the maximum extent of mutual correlation between biomarkers by extracting latent factors or principal components from the detected biomarkers; the coefficients defining these linear combinations, called factor loadings, are the correlation coefficients of each biomarker with that component (Pang et al, 2016). The components are orthogonal (i.e., statistically independent) by using the varimax rotation method and, therefore, biomarkers loaded (i.e., grouped) within one component are considered to be uncorrelated with biomarkers included in other components.…”