Yuanjie Pang1, Roger D Peng2, Miranda R Jones3, Kevin A Francesconi4, Walter Goessler5, Barbara V Howard6, Jason G Umans7, Lyle G Best8, Eliseo Guallar9, Wendy S Post10, Joel D Kaufman11, Dhananjay Vaidya12, Ana Navas-Acien13. 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.
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 MESAparticipants. 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 SHSparticipants. 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.
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