标题： 基于纵向暴露组数据的贝叶斯轮廓回归与线性混合模型（轮廓-LMM） 显示英文标题

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

摘要： 暴露于各种非遗传因素，即暴露组，是健康结果的关键决定因素。 然而，分析暴露组面临重大的方法学挑战，包括：暴露之间的高度共线性、重复测量的纵向性质以及与个体特征的潜在复杂相互作用。 在本文中，我们通过提出一种新的统计框架来解决这些挑战，该框架扩展了贝叶斯轮廓回归。 我们的方法将轮廓回归整合到线性混合模型（LMM）中，轮廓回归通过将暴露聚类为潜在轮廓来处理共线性，LMM是一种用于纵向数据分析的框架。 这种轮廓-LMM方法能够有效考虑随时间变化的个体内部变异，同时结合潜在暴露聚类与个体特征之间的交互作用。 我们使用模拟数据验证了我们的方法，证明了其准确识别模型参数和恢复真实潜在暴露聚类结构的能力。 最后，我们将这种方法应用于来自Lifelines队列的大规模纵向数据集，以识别与舒张压显著相关的暴露组合。 显示英文摘要

摘要： 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.