Key result
Higher AI-derived vascular age is linked to worse cerebral small vessel disease markers.
Why the study?
Does higher artificial intelligence-based vascular age associate with worse brain structure and neuropsychological function in adults without dementia or stroke?
Cross-Sectional (n=3,001)
No
Does higher artificial intelligence-based vascular age associate with worse brain structure and neuropsychological function in adults without dementia or stroke?
Effect estimate: β 0.09 per SD (95% CI 0.04-0.14)
p-value: p=<0.001
AI-estimated vascular age from peripheral pressure waveforms may serve as a noninvasive marker for subclinical cerebral small vessel disease and cognitive decline risk.
May flag subclinical cerebral injury risk; hypothesis-generating for AI vascular age as noninvasive stratifier.
BACKGROUND: Accelerated vascular aging, assessed as artificial intelligence–based vascular age (AIVA), is associated with small vessel disease that may impact brain structure and neuropsychological function. METHODS: In a cross-section of Framingham Heart Study participants, AIVA was estimated using a validated convolutional neural network trained to predict carotid-femoral pulse wave velocity from a normalized pressure waveform. Brain structure was assessed using magnetic resonance imaging with diffusion tensor imaging, and neuropsychological function was assessed using a standardized test battery. Analyses included magnetic resonance imaging (N=2313) and neuropsychological (N=3001) samples. We used multivariable linear and logistic regression to relate AIVA to brain structural and neuropsychological functional measures. RESULTS: The mean±SD age across participants was 62±11 years; 56% were women. In multivariable models, higher AIVA was associated with worse markers of cerebral small vessel disease (mean white matter free water: β [per SD], 0.09 [95% CI, 0.04–0.14]; P <0.001; peak width of skeletonized mean diffusivity: β , 0.05 [95% CI, 0.00–0.10]; P =0.045; white matter hyperintensity volume: β , 0.08 [95% CI, 0.03–0.12]; P <0.001) and higher odds of cerebrovascular injury (presence of extensive white matter hyperintensities: odds ratio [per SD], 1.22 [95% CI, 1.02–1.47]; P =0.03; presence of brain infarcts: odds ratio, 1.44 [95% CI, 1.02–2.04]; P =0.04). In addition, higher AIVA was associated with worse performance on Trails B-A ( β , −0.05 [95% CI, −0.10 to −0.01]; P =0.03), similarities ( β , −0.06 [95% CI, −0.11 to −0.01]; P =0.02), and global cognition ( β , − 0.07 [95% CI, −0.12 to −0.03]; P =0.002) and higher odds of prevalent depressive symptoms (odds ratio, 1.24 [95% CI, 1.08–1.41]; P =0.002) and high Center for Epidemiologic Studies Depression Scale score (odds ratio, 1.20 [95% CI, 1.00–1.44]; P =0.047). Vascular brain injury markers partially mediated the associations of AIVA with global cognition score and presence of depressive symptoms. CONCLUSIONS: Peripheral pressure waveform AIVA may be a novel, noninvasive indicator of subclinical vascular brain injury and neuropsychological function.
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Cooper et al. (2026) conducted a cross-sectional in Subclinical vascular brain injury and cognitive function (n=3,001). Artificial intelligence-based vascular age (AIVA) vs. Lower AIVA was evaluated on Mean white matter free water (β 0.09 per SD, 95% CI 0.04-0.14, p=<0.001). Higher artificial intelligence-based vascular age was associated with worse markers of cerebral small vessel disease, including increased mean white matter free water (β 0.09 per SD, p<0.001).
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