Enhancing dementia risk prediction with heart rate and machine learning in the Canadian Longitudinal Study on Aging

Topic: Risk, prevention and care in population studies

BackgroundAccurate and accessible risk assessment tools are essential for effective dementia management. The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) model is the widely used tool to assess mid-life dementia risk.ObjectiveTo determine whether adding resting heart rate (RHR), a simple, readily measurable, non-invasive vital sign, improves dementia risk prediction within the CAIDE model using machine learning methods.MethodsData from 27,768 participants of comprehensive cohort in the Canadian Longitudinal Study on Aging were analyzed to predict 3-year dementia risk. Predictive models were developed using random forest and support vector machine algorithms. Performance was assessed using key metrics, including area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Matthew's correlation coefficient (MCC), and Brier score. Internal cross-validation was used to ensure model robustness.ResultsAmong the 18,013 participants with complete data for analysis, 516 (2.86%) exhibited dementia. Incorporating RHR into the CAIDE model led to a significant improvement in predictive accuracy. Random forest models with RHR achieved an AUC of 0.67 and an MCC of 0.32 in training data, compared to 0.65 and 0.29 in the test data. Similarly, support vector machines demonstrated a 2-3% increase in both AUC and MCC with the inclusion of RHR.ConclusionsIncorporating RHR modestly but significantly improves the predictive performance of the CAIDE model using machine learning methods. This approach may support earlier identification of at-risk individuals using non-invasive, routinely available data, representing a step toward scalable and practical dementia risk screening in clinical and community settings.

Key findings

• Incorporating RHR into the CAIDE model led to a significant improvement in predictive accuracy.
• Random forest models with RHR achieved an AUC of 0.67 and an MCC of 0.32 in training data, compared to 0.65 and 0.29 in the test data.
• Similarly, support vector machines demonstrated a 2-3% increase in both AUC and MCC with the inclusion of RHR.ConclusionsIncorporating RHR modestly but significantly improves the predictive performance of the CAIDE model using machine learning methods.

Why it matters

Adding resting heart rate to an existing dementia risk model modestly but significantly improved how well the model predicted 3-year dementia risk in a large Canadian cohort. This is a risk prediction tool, not a treatment, cure, or prevention of Alzheimer's disease.

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Study details

• Study designlongitudinal study
• Participants27,768
• Effect size2.86%
• Evidence strengthModerate

Read the original

• DOI: 10.1177/13872877251390562
• PubMed: 41160465
• Original source →
• Free full text: Journal of Alzheimer s Disease →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{alaka2025-41160465,
  author  = {Alaka, Shakiru A and Ngan, SoFong Cam and MacPherson, Rebecca E. K. and Pickett, William and Chen, Christopher and Sze, Siu Kwan},
  title   = {{Enhancing dementia risk prediction with heart rate and machine learning in the Canadian Longitudinal Study on Aging}},
  journal = {Journal of Alzheimer s Disease},
  year    = {2025},
  doi     = {10.1177/13872877251390562},
  pmid    = {41160465},
  url     = {https://doi.org/10.1177/13872877251390562}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

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• 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 Alzheimer s & Dementia · 2025-08-01 · usefulness score 43/100 · Disparities

Later studies that cite this one

1 study in the alzhub library lists this one in its references.

• Race-Specific Dementia Risk Prediction Using 2024 Lancet Commission Risk Factors and Resting Heart Rate: A Survival Analysis of 55,004 NACC Participants medRxiv (preprint) · 2026-08-25 · usefulness score 16/100 · Disparities

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