Summary More than 1100 abandoned mines, milling sites and waste piles from the uranium mining period are scattered across the Navajo Nation, resulting in exposures to environmental metals, including uranium. The Diné Network for Environmental Health project began in response to concerns regarding the community health effects of these environmental exposures on chronic disease. The paper presents the results of the initial Diné Network for Environmental Health survey of 1304 individuals living on the Navajo Nation. We examine the relationship between uranium mine waste exposure and kidney disease, diabetes and hypertension. These chronic diseases are found at high prevalences in the study population, present major public health risks and have been linked to metals exposures in other studies. We model the exposure–outcome relationship by using a multivariate model for the three binary responses. We implement a Bayesian multivariate t‐model, which has marginal log‐odds ratio parameter interpretations and is computationally efficient. In examining environmental exposures, appropriately adjusting for potential confounders is pivotal to obtaining policy relevant effect estimates. We use Bayesian model averaging to account for uncertainty in the functional form for confounding adjustment within a small set of measured confounders. Using this multivariate framework, we find evidence of associations between these chronic diseases and both historic mining era and legacy mining exposures.
The prevalences of cardiovascular disease (CVD) and type 2 diabetes (T2D) have increased among the Navajo Native American community in recent decades. Oxidized low-density lipoprotein (oxLDL) is a novel CVD biomarker that has never been assessed in the Navajo population. We examined the relationship of oxLDL to conventional CVD and T2D risk factors and biomarkers in a cross-sectional population of Navajo participants. This cross-sectional study included 252 participants from 20 Navajo communities from the Diné Network for Environmental Health Project. Plasma samples were tested for oxLDL levels by a sandwich enzyme-linked immunosorbent assay. Univariate and multivariate analyses were used to determine the relationship of oxLDL and oxidized- to non-oxidized lipoprotein ratios to glycated hemoglobin (HbA1c), C-reactive protein (CRP), interleukin 6 (IL6) and demographic and health variables. Type 2 diabetes, hypertension and obesity are very prevalent in this Navajo population. HbA1c, CRP, body mass index (BMI), high-density lipoprotein, and triglycerides were at levels that may increase risk for CVD and T2D. Median oxLDL level was 47 (36.8–57) U/L. Correlational analysis showed that although oxLDL alone was not associated with HbA1c, oxLDL/HDL, oxLDL/LDL and CRP were significantly associated with HbA1c and glucose. OxLDL, oxLDL/HDL and oxLDL/LDL were significantly associated with CRP. Multivariate analysis showed that triglycerides were a common and strong predictor of oxLDL, oxLDL/HDL and oxLDL/LDL. OxLDL was trended with HbA1c and glucose but did not reach significance, however, HbA1c was an independent predictor of OxLDL/HDL. CRP trended with oxLDL/HDL and was a weak predictor of oxLDL/LDL. This Navajo subset appears to have oxLDL levels comparable to subjects without evidence of CVD reported in other studies. The high prevalence of T2D, hypertension and obesity along with abnormal levels of other biomarkers including HbA1c indicate that the Navajo population has a worsening CVD risk profile.
Members of the Navajo Nation, who possess a high prevalence of cardiometabolic disease, reside near hundreds of local abandoned uranium mines (AUM), which contribute uranium, arsenic and other metals to the soil, water and air. We recently reported that hypertension is associated with mine waste exposures in this population. Inflammation is a major player in the development of numerous vascular ailments. Our previous work establishing that specific transcriptional responses of cultured endothelial cells treated with human serum can reveal relative circulating inflammatory potential in a manner responsive to pollutant exposures, providing a model to assess responses associated with exposure to these waste materials in this population. To investigate a potential link between exposures to AUM and serum inflammatory potential in affected communities, primary human coronary artery endothelial cells were treated for 4 h with serum provided by Navajo study participants (n = 145). Endothelial transcriptional responses of intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and chemokine ligand 2 (CCL2) were measured. These transcriptional responses were then linked to AUM exposure metrics, including surface area-weighted AUM proximity and estimated oral intake of metals. AUM proximity strongly predicted endothelial transcriptional responses to serum including CCL2, VCAM-1 and ICAM-1 (P < 0.0001 for each), whereas annual water intakes of arsenic and uranium did not, even after controlling for all major effect modifiers. Inflammatory potential associated with proximity to AUMs, but not oral intake of specific metals, additionally suggests a role for inhalation exposure as a contributor to cardiovascular disease.
Abstract. Because factors affecting distributional areas of species change as scale (extent and grain) changes, different environmental and biological factors must be integrated across geographic ranges at different resolutions, to understand fully the patterns and processes underlying species' ranges. We expected climate factors to be more important at coarse resolutions and biotic factors at finer resolutions. We used data on occurrence of a parasitic plant (Phoradendron californicum), restricted to parts of the Sonoran and Mojave deserts, to analyze how climate and mobility factors explain its distributional area. We developed analyses at five spatial resolutions (1, 5, 10, 20, 50 km) within the distributional area of the disperser species, and compared ecological niche models from three commonly used correlative methods with a process-based model that estimates colonization and extinction rates in a metapopulation framework. Correlative models improved when layers associated with hosts and disperser were used as predictors, in comparison with models based on climate only; however, they tended to overfit to data as more layers were added. Dispersal-related parameters were more relevant at finer resolutions (1-5 km), but importance of extinction-related parameters did not change with scale. We observed greater coincidence between correlative and process-based models when based only on dimensions of the abiotic niche (i.e., climate), but a clearer and more comprehensive mechanistic understanding was derived from the processbased algorithm.
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