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

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 interactions among exposures through these group indices. This formulation provides interpretable insights into both overall group effects and between-group interactions on the outcome, and allows for identification of key contributors within each group. MIIM can be applied to different types of health outcomes, including continuous, binary, and survival outcomes. We conducted Monte Carlo simulation studies to evaluate the performance of MIIM under various scenarios with high-dimensional and correlated exposure mixtures and illustrated its application to the NHANES data. By bridging biological insights with population-based epidemiological data, MIIM serves as a translational tool to explore the effects of environmental mixtures on health outcomes.