Abstract
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.
Highlights
Cardiovascular risk factors (CVRF) such as hypertension, diabetes, dyslipidemia, and smoking, are highly prevalent in the elderly population and have significant impact on cognitive performance
Studies using functional imaging methods such as positron emission tomography (PET) or functional magnetic resonance imaging (MRI) (Minoshima et al, 1997; Chetelat et al, 2003; Jagust, 2006; Kawachi et al, 2006; Mosconi et al, 2007; Marchand et al, 2011) have reported brain activity deficits correlated with cardiovascular risk
Given that the Framingham score takes into account age and gender, we tried an alternative approach, considering all subjects without matching
Summary
Cardiovascular risk factors (CVRF) such as hypertension, diabetes, dyslipidemia, and smoking, are highly prevalent in the elderly population and have significant impact on cognitive performance. Studies using functional imaging methods such as positron emission tomography (PET) or functional MRI (fMRI) (Minoshima et al, 1997; Chetelat et al, 2003; Jagust, 2006; Kawachi et al, 2006; Mosconi et al, 2007; Marchand et al, 2011) have reported brain activity deficits correlated with cardiovascular risk These neuroimaging alterations often involve brain regions known to be implicated in the pathophysiology of Alzheimer’s disease (AD), such as the hippocampal region, posterior cingulate gyrus, temporal, and parietal cortices (Korf et al, 2004; Seshadri et al, 2004; Riello et al, 2005; Chen et al, 2006; Viswanathan et al, 2009; Neufang et al, 2011, 2013; Rasgon et al, 2011).
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