Enhancing the validity of CAIDE dementia risk scores with resting heart rate and machine learning: An analysis from the National Alzheimer's Coordinating Center across all races/ethnicities
Incorporating resting heart rate (RHR) into the Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) model significantly improves its predictive accuracy for dementia risk across diverse populations, offering a novel addition to dementia risk models. The application of the machine learning technique enhances dementia risk prediction by capturing complex, non-linear relationships among variables. The improved model enables more precise early identification of individuals at risk of cognitive decline, supporting preventive strategies in dementia care. Resting heart rate, a simple and non-invasive cardiovascular measure, is demonstrated to be a valuable predictor for dementia risk, making it practical for clinical application.
Authors
- Christopher Chen (ORCID: https://orcid.org/0000-0002-1047-9225)
- Panagiota Klentrou (ORCID: https://orcid.org/0000-0003-0328-5198)
- Brent E. Faught (ORCID: https://orcid.org/0000-0001-6297-4298)
- SoFong Cam Ngan (ORCID: https://orcid.org/0000-0002-1833-2746)
- Raj N. Kalaria (ORCID: https://orcid.org/0000-0001-7907-4923)
- Siu Kwan Sze (ORCID: https://orcid.org/0000-0002-5652-1687)
- Rebecca E. K. MacPherson (ORCID: https://orcid.org/0000-0002-1577-2658)
- Shakiru A. Alaka
- Mostafa Shookoni
Institutions
- Brock University (CA)
- National University Health System (SG)
- Newcastle Hospitals - Campus for Ageing and Vitality (GB)
- Newcastle University (GB)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/alz.70442
- Citations
- 4
- Primary Topic
- Heart rate and cardiovascular health
- Type
- article
- Field-Weighted Citation Impact
- 5.55
Funders
- Brock University
- National Institutes of Health
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- National Institute on Aging