Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition and Gray and White Matter Morphology

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Hypertension, diabetes, dyslipidemia, and obesity are associated with preclinical alterations in cognition and brain structure; however, this often comes from studies of comprehensive risk scores or single isolated factors. We examined associations of empirically derived cardiovascular disease risk factor domains with cognition and brain structure.


A total of 124 adults (age, 59.8 [13.1] years; 41% African American; 50% women) underwent neuropsychological and cardiovascular assessments and structural magnetic resonance imaging. Principal component analysis of nine cardiovascular disease risk factors resulted in a four-component solution representing 1, cholesterol; 2, glucose dysregulation; 3, metabolic dysregulation; and 4, blood pressure. Separate linear regression models for learning, memory, executive functioning, and attention/information processing were performed, with all components entered at once, adjusting for age, sex, and education. MRI analyses included whole-brain cortical thickness and tract-based fractional anisotropy adjusted for age and sex.


Higher blood pressure was associated with poorer learning (B = −0.19; p = .019), memory (B = −0.22; p = .005), and executive functioning performance (B = −0.14; p = .031), and lower cortical thickness within the right lateral occipital lobe. Elevated glucose dysregulation was associated with poorer attention/information processing performance (B = −0.21; p = .006) and lower fractional anisotropy in the right inferior and bilateral superior longitudinal fasciculi. Cholesterol was associated with higher cortical thickness within left caudal middle frontal cortex. Metabolic dysfunction was positively associated with right superior parietal lobe, left inferior parietal lobe, and left precuneus cortical thickness.


Cardiovascular domains were associated with distinct cognitive, gray, and white matter alterations and distinct age groups. Future longitudinal studies may assist in identifying vulnerability profiles that may be most important for individuals with multiple cardiovascular disease risk factors.

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