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

Yuanjie Pang; Roger D Peng; Miranda R Jones; Kevin A Francesconi; Walter Goessler; Barbara V Howard; Jason G Umans; Lyle G Best; Eliseo Guallar; Wendy S Post; Joel D Kaufman; Dhananjay Vaidya; Ana Navas-Acien

doi:10.1016/j.envres.2016.02.032

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

Environ Res. 2016 May:147:356-64. doi: 10.1016/j.envres.2016.02.032. Epub 2016 Mar 2.

Authors

Affiliations

¹ Departments of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA. Electronic address: yuanjie.p@gmail.com.
² Departments of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA. Electronic address: rdpeng@jhu.edu.
³ Departments of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA. Electronic address: mjone132@jhu.edu.
⁴ Institute of Chemistry -Analytical Chemistry, University of Graz, Graz 8010, Austria. Electronic address: kevin.francesconi@uni-graz.at.
⁵ Institute of Chemistry -Analytical Chemistry, University of Graz, Graz 8010, Austria. Electronic address: walter.goessler@uni-graz.at.
⁶ MedStar Health Research Institute, Hyattsville, MD 20782, USA; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC 20057, USA. Electronic address: Barbara.V.Howard@Medstar.net.
⁷ MedStar Health Research Institute, Hyattsville, MD 20782, USA; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC 20057, USA. Electronic address: jason.umans@gmail.com.
⁸ Missouri Breaks Industries Research, Inc., Timber Lake, SD 57656, USA. Electronic address: lbest@restel.com.
⁹ Departments of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. Electronic address: eguallar@jhu.edu.
¹⁰ Departments of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. Electronic address: wpost@jhmi.edu.
¹¹ Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA 98195, USA. Electronic address: joelk@u.washington.edu.
¹² Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. Electronic address: dvaidya@jhmi.edu.
¹³ Departments of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA. Electronic address: anavasa1@jhu.edu.

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.

Keywords: Biomarker; Exposure sources; Metals; Statistical methods; Urine.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Aged
Aged, 80 and over
Arsenic / urine*
Cluster Analysis
Cohort Studies
Environmental Exposure / analysis*
Humans
Indians, North American / statistics & numerical data
Middle Aged
Principal Component Analysis
Rural Population / statistics & numerical data
Tungsten / urine*
United States
Uranium / urine*
Urban Population / statistics & numerical data

Substances

Uranium
Arsenic
Tungsten

Abstract

Publication types

MeSH terms

Substances

Grants and funding