دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.
المؤلفون: Liu R; Department of Computer Science, University of California, Davis, Sacramento, California, USA., Durbin-Johnson B; Department of Public Health Sciences, University of California, Davis, Sacramento, California, USA., Paciotti B; Data Center of Excellence, University of California, Davis, Sacramento, California, USA., Liu AT; Department of Obstetrics/Gynecology, University of California, Davis, Sacramento, California, USA., Weakley A; Department of Neurology, University of California, Davis, Sacramento, California, USA., Liu X; Department of Computer Science, University of California, Davis, Sacramento, California, USA., Wan YY; Department of Medical Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA.
المصدر: Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2024 Aug 14. Date of Electronic Publication: 2024 Aug 14.
Publication Model: Ahead of Print
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE
أسماء مطبوعة: Publication: 2020- : Hoboken, NJ : John Wiley & Sons, Ltd.
Original Publication: Orlando, FL : Elsevier, Inc.
مستخلص: 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.
(© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
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معلومات مُعتمدة: California Department of Public Health; Chronic Disease Control Branch; 18-10925 Alzheimer's Disease Program; 22-10079 Alzheimer's Disease Program
فهرسة مساهمة: Keywords: alcohol abuse; metabolic liver disease; metabolism; non‐infectious hepatitis; obesity
تواريخ الأحداث: Date Created: 20240814 Latest Revision: 20240814
رمز التحديث: 20240814
DOI: 10.1002/alz.14101
PMID: 39140368
قاعدة البيانات: MEDLINE
الوصف
تدمد:1552-5279
DOI:10.1002/alz.14101