发表机构
Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于All of Us研究计划数据，开发AI驱动的多模态中介框架，通过变分自编码器推导潜在表征，发现心理社会脆弱性是连接社会经济劣势与心脏代谢共病的关键中介通路，为探究健康相关复杂关系提供了新方法。
AI 中文摘要
社会劣势与共病存在关联，但社会状况与疾病负担之间的关联通路仍知之甚少。我们开发了一种人工智能驱动的多模态中介框架，该框架整合了来自All of Us研究计划的社会经济、心理社会、临床、实验室、行为及基因组数据。采用模态特定的变分自编码器来推导各数据域的潜在表征，随后在潜在空间中开展中介分析，以评估社会经济劣势、心理社会因素与共病之间的间接关联。最终分析队列包含20804名拥有完整多模态数据的参与者。在800种暴露-中介-结局组合中，中介信号集中于少数潜在维度。最强的间接关联涉及一个社会经济劣势维度、一个心理社会脆弱性维度与一个心脏代谢共病维度（自然间接效应NIE=0.002517）。心理社会维度的特征为心理健康状况较差、孤独感更强、社会幸福感较低以及健康素养较低，而结局维度与高血压、糖尿病、高脂血症、肥胖、慢性肾病及心脏病相关。自助抽样分析证实了该主要通路的稳定性。这些发现表明，心理社会脆弱性在连接社会经济劣势与心脏代谢共病的主导潜在通路中得到了显著体现。更广泛而言，所提出的框架阐明了基于人工智能的表征学习如何用于探究高维多模态健康数据之间的复杂关系。
英文摘要
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.
Comments25 pages, 4 figures
