Files and links (1)
Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0
Journal article
Machine learning-based LASSO-Cox model for dementia prediction: the role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort
Annals of epidemiology, v 115, pp 28-36
Mar 2026
PMID: 41581843
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
We aimed to identify key midlife dementia predictors and develop a novel machine learning (ML) -enabled risk prediction model.
Using data from 9,266 Atherosclerosis Risk in Communities study participants (aged 45-64 years at baseline, 1987-1989). Incident dementia was ascertained through December 2019. A ML-based LASSO-Cox model was applied to develop the risk prediction model.
Over a 25-year mean follow-up, 2,010 participants developed dementia. The LASSO-Cox model identified 12 key predictors and achieved C-indices (95%CI) of 0.77 (0.75-0.79) in the training set (n = 6,182) and 0.78 (0.76-0.81) in the test set (n = 3,084). Predictors included age, Digit Symbol Substitution Test, apolipoprotein E ε4, HbA1c, brachial blood pressure, Factor VIII, Delayed Word Recall Test, hypertension, stroke history, C-reactive protein, white blood cell count, and apolipoprotein B. The resulting nomogram demonstrated strong discrimination (AUC 0.77-0.86) and good calibration. LASSO-Cox risk score quartiles effectively stratified participants into low, moderate, high, and very high dementia risk groups.
The findings demonstrate that the newly developed machine learning-based LASSO-Cox model provides a robust method to predict individuals at high risk of dementia.
Metrics
1 Record Views
Details
- Title
- Machine learning-based LASSO-Cox model for dementia prediction: the role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort
- Creators
- Longjian Liu (Corresponding Author) - Drexel UniversityJintong Hou - Drexel University
- Publication Details
- Annals of epidemiology, v 115, pp 28-36
- Publisher
- Elsevier
- Number of pages
- 9
- Grant note
- National Institute on Aging (NIA) , United States: R21AG082210 Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University
This study was supported by the National Institute on Aging (NIA) , United States, under grant numbers R21AG082210. Jintong Hou, a former PhD student in Biostatistics in the Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, was partially supported by a research assistantship for this study.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:001684081000001
- Scopus ID
- 2-s2.0-105029255374
- Other Identifier
- 991022155550604721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Public, Environmental & Occupational Health