Orlichenko Anton, Ding Shengxian, Johns Emily, Gu Zhiling, Tian Xinyuan, Li Xiaoxuan, Zhao Yize
Department of Biostatistics, Yale School of Public Health, 300 George St., New Haven, CT 06511, USA.
Department of Biomedical Engineering, Tulane University, 201 Lindy Clairborne Boggs Center, 6823 St. Charles Avenue, New Orleans, LA 70118, USA.
medRxiv. 2025 Sep 18:2025.09.16.25335925. doi: 10.1101/2025.09.16.25335925.
Alzheimer's disease (AD) remains without effective treatment, largely due to the fact that clinical symptoms emerge only after decades of silent pathological progression. It is urgently needed to identify modifiable risk factors in earlier life stages, when preventive interventions may still be effective. Functional connectivity (FC) has emerged as a promising neuromarker for both neurodegenerative processes and behavioral traits, making it a potential bridge between early-life health profiles and late-life AD risk. In this work, we introduce a novel integrative framework that models how early-life lifestyle and physiological factors influence AD risk through their impact on brain FC. Our approach combines a modified variational autoencoder (VAE) that simulates FC changes under interventions with a predictive model that estimates AD risk based on FC patterns. This design enables training of the generative and predictive components under different datasets and populations, with FC acting as the bridge between early-life modifiable factors and late-life disease risk. Applying our framework to data from the Human Connectome Project (HCP), UK Biobank (UKB), and Alzheimer's Disease Neuroimaging Initiative (ADNI), we validate its ability to capture known risk factors, such as age and polygenic risk score, on FC-mediated AD risk. We also identify earlier-life modifiable factors including tobacco use, sleep quality, physical activity and weight/BMI that significantly influence AD risk. Notably, we observe a U-shaped relationship between blood pressure and AD risk, and highlight the brain visual and somatomotor networks as key mediators of risk through FC. Our approach provides a powerful tool for investigating the effect pathways linking early-life interventions to neurodegenerative outcomes, with broad applicability to other brain-related disorders.
阿尔茨海默病（AD）仍然没有有效的治疗方法，这主要是因为临床症状只有在数十年的无症状病理进展之后才会出现。迫切需要在生命的早期阶段识别出可改变的风险因素，此时预防性干预可能仍然有效。功能连接性（FC）已成为神经退行性过程和行为特征的一个有前景的神经标志物，使其成为早期健康状况与晚年AD风险之间的潜在桥梁。在这项工作中，我们引入了一个新颖的综合框架，该框架模拟了早期生活方式和生理因素如何通过对脑FC的影响来影响AD风险。我们的方法结合了一个经过修改的变分自编码器（VAE），它模拟干预下的FC变化，以及一个基于FC模式估计AD风险的预测模型。这种设计能够在不同的数据集和人群下训练生成性和预测性组件，FC作为早期可改变因素与晚年疾病风险之间的桥梁。将我们的框架应用于人类连接组计划（HCP）、英国生物银行（UKB）和阿尔茨海默病神经影像倡议（ADNI）的数据，我们验证了其在FC介导的AD风险上捕捉已知风险因素（如年龄和多基因风险评分）的能力。我们还识别出了早期生活中可改变的因素，包括吸烟、睡眠质量、身体活动以及体重/体重指数，这些因素会显著影响AD风险。值得注意的是，我们观察到血压与AD风险之间呈U形关系，并强调脑视觉和躯体运动网络是通过FC产生风险的关键中介。我们的方法为研究将早期生活干预与神经退行性结果联系起来的效应途径提供了一个强大的工具，对其他与脑相关的疾病具有广泛的适用性。
