By Gazette Staff
August 13th, 2025
BURLINGTON, ON
What if a simple heartbeat measurement could help predict a person’s risk of dementia?
A Brock-led international research team has found that including an additional metric — resting heart rate — to an established dementia risk prediction model can make its results more accurate across most racial groups, says Professor of Health Sciences Newman Sze.
The Cardiovascular Risk Factors, Aging and Incidence of Dementia (CAIDE) international assessment tool uses several physiological and social measurements to evaluate a patient’s vulnerability to developing dementia in the future.
But the current model doesn’t capture a patient’s full health picture, particularly across diverse racial groups in the U.S., says Sze, the Canada Research Chair in Mechanisms of Health and Disease.
After obesity and hypertension, Sze says resting heart rate is one of the most important risk factors for dementia, a feature not captured in the current model.
“If the resting heart rate is too low or too fast due to heart muscle failure, there’s not enough blood being pumped to the brain,” says Sze. “The brain doesn’t receive enough oxygen and nutrients, which leads to brain degeneration.”
Sze and his eight-member research team tested the impacts of including resting heart rate (RHR) in the CAIDE model to see if adding that measurement would improve the model as a whole and increase equitable access to dementia prediction.
Resting heart rate, or pulse rate, refers to the number of beats per minute when the body is inactive and calm.
The research team analyzed data from 44,467 U.S. participants aged 18 and older, including those aged 65 and above. The data, collected by the National Alzheimer’s Coordinating Center (NACC), spanned from 2005 to 2023 and included information from interviews, physical examinations and cognitive tests.
The team divided participants in the NACC database into self-reported racial groups: two American Indigenous populations, Asian, Black African, Hispanic and White.
The team ran each group through the current CAIDE model, which is comprised of age, sex, body mass index, hypercholesteremia, level of education and hypertension measurements.
They then repeated the procedure with a CAIDE-RHR model that included resting heart rate.
“This adjustment significantly improved dementia risk prediction across most racial groups, offering a more inclusive and accessible way to identify at-risk individuals,” says Sze.
As resting heart rate is easy to measure, more people can be screened and monitored, which makes the model more inclusive, says the study’s lead author, PhD student Shakiru Alaka.
He says other researchers have previously attempted to improve the CAIDE model’s accuracy by introducing expensive and time-consuming lab analysis to detect dementia biomarkers in blood samples.
But this addition may reduce access for multi-racial, underserved populations, especially in the U.S., says Alaka.
“In contrast, resting heart rate can be measured with a simple blood pressure cuff or by placing fingers on the wrist — methods that are quick, non-invasive and widely available, even in underserved community settings,” he says.
The team found the CAIDE-RHR model significantly improved the accuracy of dementia risk prediction for all racial groups in the study except the American Indigenous populations, although Sze says the low number of participants may have affected the model’s accuracy for that group.
“This finding highlights the important connection between heart health and brain health,” says Sze. “It’s a step toward addressing systemic gaps in how we assess dementia risk across diverse populations.”
Newman Sze and PhD student Shakiru Alaka checking data at Brock University
Although the study was conducted with U.S. participants, the findings have important implications for Canada, where dementia-related mortality has increased by 59 per cent over the past 10 years, says Sze.
“The CAIDE-RHR model offers a low-cost, non-invasive tool that could be integrated into routine care, including in rural and underserved communities, to help identify those at risk earlier and more equitably,” he says.
The study, “Enhancing the Validity of CAIDE Dementia Risk Scores with Heart Rate and Machine Learning: An Analysis from National Alzheimer Coordinating Centre Across All Races/Ethnicity,” was published Friday, Aug. 8 in Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association.
In addition to Sze and Alaka, the research team includes Brock University Professor of Health Sciences Brent Faught, Distinguished Professor of Kinesiology Panagiota Klentrou, Associate Professor of Health Sciences Rebecca MacPherson, Assistant Professor of Health Sciences Mostafa Shokoohi, Research Associate So-Fong Cam Ngan and researchers from the U.K.’s Newcastle University and the National University Health System in Singapore.
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