What if a simple heartbeat measurement could help predict a person\u2019s risk of dementia?<\/p>\n
A Brock-led international research team has found that including an additional metric \u2014resting heart rate \u2014 to an established dementia risk prediction model can make its results more accurate across most racial groups, says Professor of Health Sciences Newman Sze.<\/p>\n
The Cardiovascular Risk Factors, Aging and Incidence of Dementia (CAIDE) international assessment tool uses several physiological and social measurements to evaluate a patient\u2019s vulnerability to developing dementia in the future.<\/p>\n
But the current model doesn\u2019t capture a patient\u2019s full health picture, particularly across diverse racial groups in the U.S., says Sze, the Canada Research Chair in Mechanisms of Health and Disease.<\/p>\n
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.<\/p>\n
\u201cIf the resting heart rate is too low or too fast due to heart muscle failure, there\u2019s not enough blood being pumped to the brain,\u201d says Sze. \u201cThe brain doesn\u2019t receive enough oxygen and nutrients, which leads to brain degeneration.\u201d<\/p>\n
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.<\/p>\n
Resting heart rate, or pulse rate, refers to the number of beats per minute when the body is inactive and calm.<\/p>\n
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\u2019s Coordinating Center (NACC), spanned from 2005 to 2023 and included information from interviews, physical examinations and cognitive tests.<\/p>\n