Metals, the Microbiome, and Kidney Health Among Adolescents in Central America

ABSTRACT Chronic kidney disease of unknown etiology (CKDu) is a deadly disease among young people in Central America, especially agricultural workers. Risk factors and presentation of this disease are distinct from that of traditional CKD; metal exposure is a hypothesized CKDu risk factor and is of concern in regions of Nicaragua due to volcanic bedrock and industrial practices. Among youth, metal exposure is known to interfere with developmental processes affecting organ and system function, contributing to deficits across the life course. Likewise, metals have been shown to affect the microbiome, which plays a role in kidney function and disease. Our work displays evidence of kidney injury in non-working young people in Nicaraguan regions with high adult prevalence of CKDu, and we hypothesize that disease processes initiate in youth before occupational exposures. Our longitudinal cohort “Jovenes-Nica Study” has enrolled over 400 youth across Nicaragua for three rounds of follow-up from 2022-2024. At each round, biological samples and a health survey are collected to assess exposures, kidney function, and overall health status of participants. In this proposal, we will apply sophisticated analytical methods to evaluate the associations between biomarkers of metal exposure and renal endpoints in youth at high risk of CKDu, while considering the urinary microbiome as a key intermediary on the exposure-disease pathway. Though understudied in comparison to other human microbiomes, the urinary microbiome has been associated with kidney health. Ms. Samantha Hall (PI) proposes the following research activities under the dedicated mentorship of experts in environmental epidemiology, biostatistics, mixtures modeling, and microbiome analysis. We will use a portable x-ray fluorescent analyzer to quantify toenail metal concentrations. Flexible mixtures models like quantile-based g-computation and Bayesian kernel machine regression will be used to explore associations between metals (arsenic, cadmium, lead, mercury) and kidney function, modeled as estimated glomerular filtration rate (eGFR) (Aim 1). Machine learning techniques, such as principal components analysis and microbial co-occurrence network models, will detail the composition and diversity indices of urinary microbiome samples in relation to metal exposure (Aim 2) and eGFR (Aim 3). The proposed training plan will expand Ms. Hall’s existing skillset through the implementation of novel methods to understand highly dimensional exposure and microbiome data. This work will address an important research gap by providing the first data to date on any association between an environmental exposure and the urinary microbiome, as well as the first analysis concerning the microbiome and renal health among youth in Central America. Our results will shed light on unexplored pathways of disease for those at high risk of CKDu with applications for early disease intervention.