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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Toxicology

Background:

  • Quantifying health effects of environmental exposures is crucial.
  • Bayesian kernel machine regression (BKMR) handles complex exposure associations but struggles with large numbers of exposures due to low power and interpretation challenges.
  • Exposomic analyses involve numerous environmental factors, exacerbating BKMR's limitations.

Purpose of the Study:

  • To propose a flexible statistical framework unifying additive and kernel machine regression models.
  • To enhance power and simplify interpretation in environmental exposure mixture analyses.
  • To enable separate prior specification for additive and non-additive effects and facilitate inference on interactions.

Main Methods:

  • Developed a unified framework for analyzing additive and non-additive effects of environmental exposures.
  • Extended the approach to multiple index models, including kernel machine-distributed lag models.
  • Applied the method to a subcohort of the Human Early Life Exposome (HELIX) study with 65 mixture components.

Main Results:

  • The proposed method offers increased statistical power and improved interpretability compared to traditional BKMR, especially when interactions are minimal.
  • The framework allows for distinct prior specifications for additive and non-additive effects.
  • Statistical inference on non-additive interactions is enabled.

Conclusions:

  • The novel framework effectively addresses the limitations of existing methods for analyzing complex environmental exposure mixtures.
  • It provides a more powerful and interpretable approach for environmental epidemiology and exposome research.
  • The method is applicable to real-world datasets, as demonstrated in the HELIX study.