Bayesian Profile Regression with Linear Mixed Models (Profile-LMM) applied to Longitudinal Exposome Data

Exposure to diverse non-genetic factors, known as the exposome, is a critical determinant of health outcomes. However, analyzing the exposome presents significant methodological challenges, including: high collinearity among exposures, the longitudinal nature of repeated measurements, and potential complex interactions with individual characteristics. In this paper, we address these challenges by proposing a novel statistical framework that extends Bayesian profile regression. Our method integrates profile regression, which handles collinearity by clustering exposures into latent profiles, into a linear mixed model (LMM), a framework for longitudinal data analysis. This profile-LMM approach effectively accounts for within-person variability over time while also incorporating interactions between the latent exposure clusters and individual characteristics. We validate our method using simulated data, demonstrating its ability to accurately identify model parameters and recover the true latent exposure cluster structure. Finally, we apply this approach to a large longitudinal data set from the Lifelines cohort to identify combinations of exposures that are significantly associated with diastolic blood pressure.