用社会因素预测阿尔茨海默病，帮西班牙裔人群早干预
Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data
- 用社会健康因素数据训练集成树模型预测认知变化
- 4年和9年认知评分预测准确率显著提升
- 为西班牙裔群体认知健康干预提供数据支持
阿尔茨海默病及相关痴呆症（AD/ADRD）是影响超过600万美国人的重大健康危机。尽管遗传因素至关重要，但最新研究显示社会健康决定因素（SDOH）对认知功能风险与进展有显著影响，如认知评分和认知衰退。本研究聚焦西班牙裔人群，该群体面临更高的AD/ADRD风险，利用墨西哥健康与老龄化研究（MHAS）及其认知评估子研究（Mex-Cog）数据，采用集成回归树模型，基于SDOH预测4年及9年后的认知评分与认知衰退情况。结果识别出关键预测性社会因素，可为多层级干预策略提供依据，助力缓解该人群的认知健康不平等。
原文摘要 · Abstract (English)
Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors play a crucial role, emerging research reveals that social determinants of health (SDOH) significantly influence both the risk and progression of cognitive functioning, such as cognitive scores and cognitive decline. This report examines how these social, environmental, and structural factors impact cognitive health trajectories, with a particular focus on Hispanic populations, who face disproportionate risk for AD/ADRD. Using data from the Mexican Health and Aging Study (MHAS) and its cognitive assessment sub study (Mex-Cog), we employed ensemble of regression trees models to predict 4-year and 9-year cognitive scores and cognitive decline based on SDOH. This approach identified key predictive SDOH factors to inform potential multilevel interventions to address cognitive health disparities in this population.
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