All of Us研究项目中用于首发重度抑郁症的纵向可穿戴监测与多基因风险
Longitudinal wearable monitoring and polygenic risk for incident major depressive disorder in the All of Us Research Program
AI总结：
本研究整合All of Us项目的基因组、EHR及Fitbit数据，发现MDD PRS与纵向可穿戴行为特征可互补预测首发MDD，提升模型区分度，为个性化风险监测提供依据。
AI中文摘要：
重度抑郁症（MDD）的风险既反映稳定的遗传易感性，也反映动态的行为模式，但在真实世界场景中，很少有研究使用长期客观数据同时考察这两个维度。本研究整合了All of Us研究项目中3030名经遗传推断为欧洲血统的成年人的基因组数据、电子健康记录（EHR）数据以及纵向Fitbit可穿戴设备数据，其中284名受试者在180天基线期后出现EHR记录的首发MDD。研究采用时变Cox模型，考察了MDD多基因风险评分（PRS）、每月可穿戴设备衍生的身体活动与睡眠特征，以及可穿戴特征与MDD PRS的交互作用与首发MDD的关联。结果显示，更高的MDD PRS、更低的每日步数、更低的轻度与剧烈身体活动量、更低的睡眠效率以及更大的睡眠时长变异性，均与EHR记录的首发MDD风险升高相关。 sedentary时间和睡眠时长变异性与首发MDD的关联在不同MDD PRS水平上存在差异，在MDD PRS较高的受试者中，这些因素的风险关联更强。按PRS分层的风险比曲线进一步表明，与MDD PRS较低的受试者相比，MDD PRS较高的受试者在达到相当的估计风险时，可对应更优的行为水平（如更高的每日步数和更稳定的睡眠）。依次整合MDD PRS、基线可穿戴特征、每月可穿戴特征以及选定的可穿戴特征与MDD PRS的交互作用，提升了模型的区分度，C指数从0.637升至0.705。这些发现支持遗传易感性与纵向真实世界行为监测在首发MDD风险表征中的互补价值，或可为未来基于基因的数字表型研究提供参考，以实现个性化风险监测与预防。
英文摘要：
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.
