Multiple-index interaction models to accommodate exposure grouping in environmental mixtures.

ABSTRACT: An important goal of environmental health research is to assess risks posed by mixtures of environmental exposures. Studies in different fields often group exposures based on their shared biological features. However, such grouping information has not been widely utilized in population-based environmental mixtures analyses due to the lack of appropriate statistical tools. Inspired by data from the National Health and Nutrition Examination Survey (NHANES), we propose a semiparametric multiple-index interaction model (MIIM) to explore the impact of three groups of persistent organic pollutants (POPs) on leukocyte telomere length (LTL). MIIM effectively addresses the challenge of high dimensionality by summarizing exposures into group-level indices, while allowing for nonlinear effects and in

SUBMITTER: Lee M 

PROVIDER: S-EPMC12862247 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature