{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:23:30Z","timestamp":1766157810189,"version":"3.48.0"},"reference-count":88,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"24","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2025,12,16]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">Relations of artificial intelligence vascular age (AI\u2010VA), a measure of aortic stiffness, with new\u2010onset hypertension, diabetes, obesity, and lipid abnormalities were assessed in 7188 Framingham Heart Study (FHS) participants by using Cox proportional hazards regression. Short\u2010term bidirectional relations of AI\u2010VA with key continuous cardiometabolic measures were assessed by using autoregressive cross\u2010lagged panel models at 2 visits 6\u2009years apart. Progression to CMD and cardiovascular disease states was visualized using multistate transition analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">In models adjusted for standard vascular risk factors, accelerated AI\u2010VA was associated with elevated risk for incident hypertension (hazard ratio [HR], 1.44 [95% CI, 1.24\u20131.61]), diabetes (HR 1.29 [95% CI, 1.07\u20131.56]), lipid abnormalities (HR 1.13 [95% CI, 1.00\u20131.28]), and obesity (HR 1.23 [95% CI, 1.06\u20131.42]). Autoregressive cross\u2010lagged panel models demonstrated bidirectional relations between AI\u2010VA and fasting blood glucose, triglycerides, and mean arterial pressure consistent with a self\u2010reinforcing 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">In our community\u2010based sample with repeated measures, AI\u2010VA, a measure of aortic stiffness, had bidirectional relations with continuous cardiometabolic measures and incident CMD, highlighting the potential utility of AI\u2010VA as a novel screening tool for CMD risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.044872","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T10:49:58Z","timestamp":1765363798000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Relations of Artificial Intelligence Vascular Age 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