ArticlemedRxiv : the preprint server for health sciences2026
Development of a Biology-Informed Chemical Mixture Index for Oxidative Stress and Mortality in NHANES 2005-2010: A Survey-Weighted Quantile G-Computation Approach.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Current chemical mixture approaches are largely data-driven without considering shared biological mechanisms among mixture components, highlighting the need for biology-informed approaches. Objectives: We constructed an integrated measure of a chemical mixture's oxidative stress potential and assessed its association with mortality in the US population. Methods: The sample comprised 4,574 adults (≥20 years) from National Health and Nutrition Examination Survey (NHANES) 2005-2010. To obtain robust estimates, we performed 1,000 repeated random 50:50 splits into training and testing sets. In each training set, we used survey-weighted quantile g-computation to model serum gamma-glutamyl transferase (GGT), an oxidative stress biomarker, as a function of a 30-chemical mixture (blood metals, urinary polycyclic aromatic hydrocarbons (PAHs), pesticides, phenols/parabens, and phthalates), adjusting for sociodemographic, behavioral, and dietary factors. We then applied the fitted model from each training set to the corresponding testing set to derive the environmental risk score for oxidative stress (ERS Results: Chemicals with the largest positive weights in quantile g-computation included mono-(2-ethyl-5-hydroxyhexyl) phthalate, mono-2-ethyl-5-carboxypentyl phthalate, 2-hydroxyfluorene, methyl paraben, and benzophenone-3; chemicals with the largest negative weights included mono-(2-ethyl-5-oxohexyl) phthalate and PAH metabolites (1-hydroxynaphthalene, 3-hydroxyphenanthrene, and 3-hydroxyfluorene). The median correlation between observed and predicted GGT in the testing sets was 0.43 (2.5 Discussion: The proposed survey-weighted quantile g-computation approach may help estimate biology-informed chemical mixture effects in complex survey data, supporting the potential utility for population-generalizable environmental mixture research.
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