Zhou Manqi, Tang Alice S, Zhang Hao, Xu Zhenxing, Ke Alison M C, Su Chang, Huang Yu, Mantyh William G, Jaffee Michael S, Rankin Katherine P, DeKosky Steven T, Zhou Jiayu, Guo Yi, Bian Jiang, Sirota Marina, Wang Fei
Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA 94143, USA.
J Biomed Inform. 2025 May;165:104820. doi: 10.1016/j.jbi.2025.104820. Epub 2025 Apr 1.
Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records.
The framework, dynaPhenoM, extracts coherent clinical topics across patient visits and employs a time-aware latent class analysis to characterize subphenotypes. We validated dynaPhenoM using three patient databases with a total of 3952 AD patients across the United States, demonstrating its effectiveness in revealing mild cognitive impairment (MCI) progression to AD.
Our study identified five subphenotypes associated with distinct organ systems for disease progression from MCI to AD, including common subtypes across cohorts-respiratory, musculoskeletal, cardiovascular, and endocrine/metabolic-as well as a cohort-specific digestive subtype.
Our study unravels the complexity and heterogeneity of the progression from MCI to AD. These findings highlight disease progression heterogeneity and can inform both diagnostic and therapeutic strategies, thereby advancing precision medicine for Alzheimer's disease.
识别具有临床意义的疾病进展亚表型能够增进对疾病异质性和潜在病理生理学的理解。在本研究中，我们提出了一个机器学习框架，用于基于纵向真实世界患者记录识别阿尔茨海默病进展的亚表型。
该框架dynaPhenoM提取患者就诊期间连贯的临床主题，并采用时间感知潜在类别分析来表征亚表型。我们使用三个患者数据库对dynaPhenoM进行了验证，这些数据库共有来自美国的3952名AD患者，证明了其在揭示轻度认知障碍（MCI）向AD进展方面的有效性。
我们的研究确定了从MCI到AD的疾病进展中与不同器官系统相关的五种亚表型，包括各队列共有的亚型——呼吸、肌肉骨骼、心血管和内分泌/代谢——以及特定队列的消化亚型。
我们的研究揭示了从MCI到AD进展的复杂性和异质性。这些发现突出了疾病进展的异质性，并可为诊断和治疗策略提供信息，从而推动阿尔茨海默病的精准医学发展。
