Brock's study advances prediction of dementia susceptibility
In a groundbreaking study led by Professor Newman Sze at Brock University, the Cognitive Epidemiology research group has developed a new dementia risk prediction model that could have significant implications for addressing systemic gaps in assessing dementia risk across diverse populations. The study, titled 'Enhancing the Validity of CAIDE Dementia Risk Scores with Heart Rate and Machine Learning,' was published in Alzheimer's & Dementia: The Journal of the Alzheimer's Association. The research team aimed to improve the accuracy of the established CAIDE model by incorporating resting heart rate. The CAIDE model, which includes age, sex, body mass index, hypercholesteremia, level of education, and hypertension measurements, was applied to a diverse group of participants. The team divided participants in the National Alzheimer's Coordinating Center (NACC) database into self-reported racial groups: two American Indigenous populations, Asian, Black African, Hispanic, and White. The data, collected from 2005 to 2023, spanned interviews, physical examinations, and cognitive tests. First, the team ran each group through the current CAIDE model. Then, they repeated the procedure with a CAIDE-RHR model that includes resting heart rate. The addition of resting heart rate to the CAIDE model significantly improved the accuracy of dementia risk prediction for all racial groups in the study except the American Indigenous populations. Resting heart rate, or pulse rate, refers to the number of beats per minute when the body is inactive and calm. It can be measured with a simple blood pressure cuff or by placing fingers on the wrist, making it quick, non-invasive, and widely available. This low-cost, non-invasive tool could be integrated into routine care, including in rural and underserved communities. However, the low number of participants may have affected the model's accuracy for the American Indigenous populations. The study's findings have important implications for addressing systemic gaps in how we assess dementia risk across diverse populations. The addition of resting heart rate to the CAIDE model may reduce access for multi-racial, underserved populations, especially in the U.S. The study's findings are particularly relevant in Canada, where dementia-related mortality has increased by 59 per cent over the past 10 years. The CAIDE-RHR model offers a promising tool for early detection and prevention efforts, which are crucial for managing the growing burden of dementia.
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