Key result
Structural brain changes detected via MRI enabled the statistically significant discrimination of high versus low cardiovascular risk individuals with 75% accuracy.
Why the study?
Can structural brain images predict Framingham Coronary Heart Disease Risk scores in healthy elderly individuals?
Cross-Sectional (n=164)
No
Can structural brain images predict Framingham Coronary Heart Disease Risk scores in healthy elderly individuals?
Effect estimate: 75% accuracy
p-value: p=<0.0001
Structural brain imaging combined with machine learning can predict Framingham cardiovascular risk scores in healthy elderly individuals, highlighting the impact of cardiovascular risk on brain anatomy.
MRI-based CV risk discrimination in healthy elderly is hypothesis-generating; leaves open prospective validation before any clinical use.
Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer's disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed.
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Rondina et al. (2014) conducted a cross-sectional in Cardiovascular Risk (n=164). High cardiovascular risk (Framingham Coronary Heart Disease Risk Score) vs. Low cardiovascular risk was evaluated on Classification accuracy of high versus low cardiovascular risk groups based on structural MRI patterns (75% accuracy, p=<0.0001). Structural brain changes detected via MRI enabled the statistically significant discrimination of high versus low cardiovascular risk individuals with 75% accuracy.
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