Ahmmad Md Roungu, Hossain Emran, Khan Md Tareq Ferdous, Paudel Sumitra
USF-Health, College of Nursing, University of South Florida, Tampa, FL, USA.
School of Health Professions, University of Southern Mississippi, Hattiesburg, MS, USA.
J Alzheimers Dis Rep. 2025 Sep 10;9:25424823251377691. doi: 10.1177/25424823251377691. eCollection 2025 Jan-Dec.
The interactions between behavioral disturbances, chronic diseases, and Alzheimer's disease (AD) risk are not fully understood, particularly in the context of the COVID-19 pandemic.
This study aimed to identify key demographic, behavioral, and health-related predictors of AD using machine learning approaches.
We conducted a cross-sectional analysis of 3257 participants from the National Health and Aging Trends Study (NHATS) and its COVID-19 supplement. Predictors included demographic, behavioral, and chronic disease variables, with self-reported physician-diagnosed AD as the outcome. LASSO and random forest (RF) models identified significant predictors, and regression tree analysis examined interactions to estimate individual AD risk profiles and subgroups.
Stroke, diabetes, osteoporosis, depression, and sleep disturbances emerged as key predictors of AD in both LASSO and RF models. Regression tree analysis identified three risk subgroups: a high-risk subgroup with a history of stroke and diabetes, showing a 68% AD risk among females; an intermediate-risk subgroup without stroke but with osteoporosis and positive COVID-19 status, showing a 30% risk; and a low-risk subgroup without stroke or osteoporosis, with the lowest risk (∼10%). Female patients with both stroke and diabetes had significantly higher AD risk than males (68% versus 10%, p = 0.029). Among patients without stroke but with osteoporosis, COVID-19 positivity increased AD risk by 20% (30% versus 10%, p = 0.006).
Machine learning effectively delineates complex AD risk profiles, highlighting the roles of vascular and metabolic comorbidities and the modifying effects of sex, osteoporosis, and COVID-19. These insights support targeted screening and early intervention strategies to improve outcomes in older adults.
行为障碍、慢性疾病与阿尔茨海默病（AD）风险之间的相互作用尚未完全明确，尤其是在新冠疫情背景下。
本研究旨在使用机器学习方法确定AD的关键人口统计学、行为学和健康相关预测因素。
我们对来自国家健康与老龄化趋势研究（NHATS）及其新冠补充研究的3257名参与者进行了横断面分析。预测因素包括人口统计学、行为学和慢性疾病变量，以自我报告的医生诊断AD作为结局。套索（LASSO）和随机森林（RF）模型确定了显著的预测因素，回归树分析检查了相互作用以估计个体AD风险概况和亚组。
中风、糖尿病、骨质疏松症、抑郁症和睡眠障碍在LASSO和RF模型中均成为AD的关键预测因素。回归树分析确定了三个风险亚组：一个有中风和糖尿病病史的高风险亚组，女性中的AD风险为68%；一个没有中风但有骨质疏松症且新冠病毒检测呈阳性的中度风险亚组，风险为30%；以及一个没有中风或骨质疏松症的低风险亚组，风险最低（约10%）。患有中风和糖尿病的女性患者的AD风险显著高于男性（68%对10%，p = 0.029）。在没有中风但有骨质疏松症的患者中，新冠病毒检测呈阳性使AD风险增加了20%（30%对10%，p = 0.006）。
机器学习有效地描绘了复杂的AD风险概况，突出了血管和代谢合并症的作用以及性别、骨质疏松症和新冠病毒的调节作用。这些见解支持有针对性的筛查和早期干预策略，以改善老年人的预后。
