Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.

ABSTRACT

INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development. METHODS: The analyses included patients ≥ 65 years with AD diagnosis in six University of California hospitals between January 2012 and October 2023. The controls were race/ethnicity, sex, and age matched without dementia. Data analyses used the Cox proportional hazards model and machine learning (ML). RESULTS: Hispanic/Latino and Native Hawaiian/Pacific Islander, but not Black subjects, had increased AD risk compared to White subjects. Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men. While underweight increased AD risk, overweight or obesity reduced risk. ML confirmed the importance of metabolic laboratory tests in predicting AD development. DISCUSSION: The data stress the significance of metabolism in AD development and the need for racial/ethnic- and sex-specific preventive strategies. HIGHLIGHTS Hispanics/Latinos and Native Hawaiians/Pacific Islanders show increased hazards of Alzheimer's disease (AD) compared to White subjects. Underweight individuals demonstrate a significantly higher hazard ratio for AD compared to those with normal body mass index. The association between obesity and AD hazard differs among racial groups, with elderly Asian subjects showing increased risk compared to White subjects. Alcohol consumption and non-infectious hepatitis are significant hazards for AD. Machine learning approaches highlight the potential of metabolic panels for AD prediction.