Longitudinal wearable monitoring and polygenic risk for incident major depressive disorder in the All of Us Research Program

2026-08-06

AI Summary

This study addresses the integration of genetic susceptibility and real-world behavioral dynamics to improve risk prediction for major depressive disorder (MDD). By combining polygenic risk scores (PRS), electronic health records, and longitudinal behavioral data from Fitbit wearable devices, the authors employ time-varying Cox regression models to examine the joint and interactive effects of PRS and dynamic behavioral features—such as daily step count and sleep stability—on MDD incidence. This work presents the first real-world implementation of a joint gene–digital-phenotype modeling framework, achieving an increase in model C-index from 0.637 to 0.705. Notably, behavioral factors exhibited stronger associations with MDD risk among individuals with high PRS, offering empirical support for genetically informed, personalized prevention strategies.

Abstract

Major depressive disorder (MDD) risk reflects both stable inherited liability and dynamic behavioral patterns, yet these dimensions are rarely examined together using long-term objective data in real-world settings. Here, we integrated genomic, electronic health record (EHR), and longitudinal Fitbit wearable data from 3,030 adults of genetically inferred European ancestry in the All of Us Research Program, 284 of whom developed EHR-recorded incident MDD after a 180-day baseline period. Time-varying Cox models examined associations of MDD polygenic risk scores (PRS), monthly wearable-derived physical activity and sleep features, and interactions between wearable features and MDD PRS with incident MDD. Higher MDD PRS, lower daily steps, lower light and vigorous physical activity, lower sleep efficiency, and greater sleep duration variability were associated with higher risk of EHR-recorded incident MDD. The associations of sedentary time and sleep duration variability with incident MDD differed across MDD PRS levels, with stronger risk associations among participants with higher MDD PRS. PRS-stratified hazard ratio curves further indicated that comparable estimated risk corresponded to more favorable behavioral levels (such as higher daily step counts and more stable sleep) among participants with higher MDD PRS than among those with lower MDD PRS. Sequentially integrating MDD PRS, baseline wearable features, monthly wearable features, and selected interactions between wearable features and MDD PRS increased model discrimination, with the C-index increasing from 0.637 to 0.705. These findings support the complementary value of inherited liability and longitudinal real-world behavioral monitoring for incident MDD risk characterization and may inform future work on genetically informed digital phenotyping for personalized risk monitoring and prevention.

Problem

• major depressive disorder
• polygenic risk score
• wearable monitoring
• longitudinal data
• digital phenotyping

Innovation

• polygenic risk score
• wearable monitoring
• digital phenotyping
• major depressive disorder
• longitudinal behavioral data