{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T21:22:50Z","timestamp":1770067370897,"version":"3.49.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (<jats:italic>n<\/jats:italic>\u2009=\u2009133), HTN (<jats:italic>n<\/jats:italic>\u2009=\u2009442), T2DM (HTN+) (<jats:italic>n<\/jats:italic>\u2009=\u2009256), and control (<jats:italic>n<\/jats:italic>\u2009=\u2009320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25\u2009mm<jats:sup>3<\/jats:sup> in the T2DM group and 5.23\u2009mm<jats:sup>3<\/jats:sup> in the T2DM (HTN+) group, with no significant difference (<jats:italic>p<\/jats:italic>\u2009&amp;gt;\u20090.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (<jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT \u22640.75 compared to the T2DM group (13.8%). FFR-CT \u22640.75 correlated with CAD-RADS 2.0 (OR\u2009=\u20097.986, 95% CI\u2009=\u20095.466\u201311.667, cutoff\u2009=\u20093, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001) and noncalcified plaque volume (OR\u2009=\u20091.006, 95% CI\u2009=\u20091.003\u20131.009, cutoff\u2009=\u200929.65\u2009mm<jats:sup>3<\/jats:sup>, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001). HRPs were associated with HbA1c levels (OR\u2009=\u20091.631, 95% CI\u2009=\u20091.387\u20131.918).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2024.1446640","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T05:10:56Z","timestamp":1729660256000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography"],"prefix":"10.3389","volume":"7","author":[{"given":"Yan","family":"Xi","sequence":"first","affiliation":[]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Shu","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1161\/HYPERTENSIONAHA.118.11769","article-title":"Macrovasculature and microvasculature at the crossroads between type 2 diabetes mellitus and hypertension","volume":"73","author":"Climie","year":"2019","journal-title":"Hypertension"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jcct.2020.03.005","article-title":"Association of high-risk coronary atherosclerosis at CCTA with clinical and circulating biomarkers: insight from CAPIRE study","volume":"15","author":"Conte","year":"2021","journal-title":"J. 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