用AI整合多源健康数据，发现社会劣势通过心理脆弱性影响慢性病的路径。
AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program
- 用变分自编码器提取多模态数据的潜在特征，再在潜空间做中介分析。
- 在20,804人中发现最强中介效应（NIE=0.002517），连接社会劣势与慢性病。
- 适合研究社会因素与健康关系的学者，尤其关注心理与疾病关联者。
社会劣势与多重慢性病相关，但其作用路径尚不明确。本研究基于美国全民研究计划（All of Us Research Program）数据，构建了一种融合社会经济、心理社会、临床、实验室、行为及基因组数据的AI驱动多模态中介分析框架。通过各模态专用的变分自编码器提取潜在表示，并在潜空间进行中介分析，评估社会劣势、心理社会因素与多重慢性病之间的间接关联。最终分析队列包含20,804名具有完整多模态数据的参与者。在800个暴露-中介-结果组合中，中介信号集中于少数潜在维度。最强间接效应（NIE = 0.002517）连接社会劣势维度、心理脆弱性维度与心血管代谢多重慢性病维度。心理维度表现为较差的心理健康、更高孤独感、较低社会福祉和健康素养；结局维度则关联高血压、糖尿病、高脂血症、肥胖、慢性肾病和心脏病。自助法验证了主路径的稳定性。结果表明，心理脆弱性是社会劣势与心血管代谢多重慢性病之间主导潜路径的核心中介。该框架展示了基于AI的表征学习在解析高维多模态健康数据复杂关系中的潜力。
原文摘要 · Abstract (English)
Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation analyses were subsequently performed in latent space to evaluate indirect associations between socioeconomic disadvantage, psychosocial factors, and multimorbidity. The final analytic cohort included 20,804 participants with complete multimodal data. Across 800 exposure--mediator--outcome combinations, mediation signals were concentrated within a small number of latent dimensions. The strongest indirect association linked a socioeconomic disadvantage dimension, a psychosocial vulnerability dimension, and a cardiometabolic multimorbidity dimension (NIE = 0.002517). The psychosocial dimension was characterized by poorer mental health, greater loneliness, lower social well-being, and lower health literacy, whereas the outcome dimension was associated with hypertension, diabetes, hyperlipidemia, obesity, chronic kidney disease, and heart disease. Bootstrap analyses supported the stability of the leading pathway. These findings suggest that psychosocial vulnerability was strongly represented in the dominant latent pathway linking socioeconomic disadvantage and cardiometabolic multimorbidity. More broadly, the proposed framework illustrates how AI-based representation learning can be used to investigate complex relationships across high-dimensional multimodal health data.
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