Inferring Synergistic and Antagonistic Interactions in Mixtures of Exposures

TL;DR

This paper introduces a Bayesian framework called SAID for detecting and characterizing synergistic and antagonistic interactions in chemical mixtures, improving interpretability over existing methods.

Contribution

The paper proposes a novel Bayesian approach that decomposes response surfaces to explicitly identify and select synergistic and antagonistic interactions in mixtures.

Findings

SAID effectively detects interactions in simulations.

Application to NHANES data demonstrates practical utility.

Method outperforms existing approaches in accuracy and interpretability.

Abstract

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as synergistic interaction, while chemicals that inhibit each other's effects have antagonistic interactions. Current approaches for assessing the health effects of chemical mixtures do not explicitly consider synergy or antagonism in the modeling, instead focusing on either parametric or unconstrained nonparametric dose response surface modeling. The parametric case can be too inflexible, while nonparametric methods face a curse of dimensionality that leads to overly wiggly and uninterpretable surface estimates. We propose a Bayesian approach that decomposes the response surface into additive main effects and pairwise interaction effects, and then detects synergistic and antagonistic interactions. Variable selection decisions for each interaction component are also provided. This Synergistic Antagonistic Interaction Detection (SAID) framework is evaluated relative to existing approaches using simulation experiments and an application to data from NHANES.