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 profiles 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.