Relations of Artificial Intelligence Vascular Age With Cardiometabolic Disease Progression: The Framingham Heart Study

Abstract

BACKGROUND: Aortic stiffening increases pulsatile power transmission into the microvasculature, resulting in blunted microvascular reactivity, which can impair postprandial nutrient processing and glucose homeostasis. Cardiometabolic disease (CMD) contributes to aortic stiffening and risk for major cardiovascular disease events. Our goal was to assess potential bidirectional relations of aortic stiffness with CMD progression. METHODS: Relations of artificial intelligence vascular age (AI-VA), a measure of aortic stiffness, with new-onset hypertension, diabetes, obesity, and lipid abnormalities were assessed in 7188 Framingham Heart Study (FHS) participants by using Cox proportional hazards regression. Short-term bidirectional relations of AI-VA with key continuous cardiometabolic measures were assessed by using autoregressive cross-lagged panel models at 2 visits 6 years apart. Progression to CMD and cardiovascular disease states was visualized using multistate transition analysis. RESULTS: In models adjusted for standard vascular risk factors, accelerated AI-VA was associated with elevated risk for incident hypertension (hazard ratio [HR], 1.44 [95% CI, 1.24-1.61]), diabetes (HR 1.29 [95% CI, 1.07-1.56]), lipid abnormalities (HR 1.13 [95% CI, 1.00-1.28]), and obesity (HR 1.23 [95% CI, 1.06-1.42]). Autoregressive cross-lagged panel models demonstrated bidirectional relations between AI-VA and fasting blood glucose, triglycerides, and mean arterial pressure consistent with a self-reinforcing cycle of CMD progression. Multistate transition analysis demonstrated that most cardiovascular disease events (70%) occurred in participants who had developed at least 2 CMD states. CONCLUSIONS: In our community-based sample with repeated measures, AI-VA, a measure of aortic stiffness, had bidirectional relations with continuous cardiometabolic measures and incident CMD, highlighting the potential utility of AI-VA as a novel screening tool for CMD risk.