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

Aug 4, 2026

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The study investigates whether factors related to metabolism, race/ethnicity, and sex are associated with Alzheimer’s disease (AD) development. Analyses were conducted on patients aged 65 and older with AD diagnoses from six University of California hospitals between January 2012 and October 2023, using matched controls without dementia. Cox proportional hazards and machine learning models assessed clinical and laboratory hazards. Results indicate higher AD risk among Hispanic/Latino and Native Hawaiian/Pacific Islander groups, while non-infectious hepatitis and alcohol abuse were significant hazards, with alcohol abuse impacting women more. Underweight increased risk, whereas overweight/obesity reduced risk.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Methods

Results

Discussion

Keywords and Highlights

UC Davis UC Davis Previously Published Works Title Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data. Permalink [https://escholarship.org/uc/item/0794q2gr](https://escholarship.org/uc/item/0794q2gr) Authors Liu, Rex Durbin-Johnson, Blythe Paciotti, Brian et al. Publication Date 2024-08-14 DOI 10.1002/alz.14101 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California Received: 8 February 2024 Revised: 4 June 2024 Accepted: 6 June 2024 DOI: 10.1002/alz.14101 RESEARCH ARTICLE Metabolic dysfunctions predict the development of Alzheimer’s disease: Statistical and machine learning analysis of EMR data Rex Liu1 Blythe Durbin-Johnson2 Brian Paciotti3 Albert T. Liu4 Alyssa Weakley5 Xin Liu1 Yu-Jui Yvonne Wan6 1 Department of Computer Science, University of California, Davis, Sacramento, California, USA 2 Department of Public Health Sciences, University of California, Davis, Sacramento, California, USA 3 Data Center of Excellence, University of California, Davis, Sacramento, California, USA 4 Department of Obstetrics/Gynecology, University of California, Davis, Sacramento, California, USA 5 Department of Neurology, University of California, Davis, Sacramento, California, USA 6 Department of Medical Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA Correspondence Yu-Jui Yvonne Wan, Department of Pathology and Laboratory Medicine, University of California, Davis Health, Room 3400B, Research Building III, 4645 2ndAve, Sacramento, CA 95817, USA. [Email: yjywan@ucdavis.edu](Email: yjywan@ucdavis.edu) Funding information California Department of Public Health; Chronic Disease Control Branch; Alzheimer’s Disease Program, Grant/Award Numbers: 18-10925, 22-10079 Abstract INTRODUCTION: The incidence ofAlzheimer’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. KEYWORDS alcohol abuse, metabolic liver disease, metabolism, non-infectious hepatitis, obesity 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. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modificatio

FAQs

What question does the study address about Alzheimer’s disease development?

Whether metabolic factors, race/ethnicity, and sex are associated with the development of Alzheimer’s disease.

How were patients and controls selected in the analyses?

The study included patients aged 65 or older with AD diagnoses from six University of California hospitals between January 2012 and October 2023, with controls matched by race/ethnicity, sex, and age and without dementia.

Which factors were identified as significant hazards for AD, and what sex difference was reported?

Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men.

Aug 4, 2026

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