{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T01:03:35Z","timestamp":1790298215782,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)\u2010based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, the body composition of 25 patients aged \u226450\u00a0years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI\u2010based software tool (Visage Imaging). All patients underwent electrocardiography\u2010triggered CT of the aorta twice within 2\u00a0years for suspected progression of aortic disease, suspected dissection, and\/or pre\u2010operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2\u00a0mm. Patients meeting this definition were assigned to the \u2018progressive aortic enlargement\u2019 group (proAE group) and patients with stable diameters to the \u2018stable aortic enlargement\u2019 group (staAE group). Statistical analysis was performed using the Mann\u2013Whitney <jats:italic>U<\/jats:italic> test. Two possible body composition predictors of aortic enlargement\u2014skeletal muscle density (SMD) and psoas muscle index (PMI)\u2014were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>There were 13 patients in the proAE group and 12 patients in the staAE group. AI\u2010based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0\u00a0\u00b1\u00a08.6 HU vs. staAE group: 39.0\u00a0\u00b1\u00a015.0 HU; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8\u00a0\u00b1\u00a02.3 vs. staAE group: 5.6\u00a0\u00b1\u00a01.3; <jats:italic>P<\/jats:italic>\u00a0=\u00a00.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.05) and PMI (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.04).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Artificial intelligence\u2010based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow\u2010up intervals and the need for aortic repair.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.12731","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T12:00:50Z","timestamp":1623931250000},"page":"993-999","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Artificial intelligence\u2010based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4960-5652","authenticated-orcid":false,"given":"Nick Lasse","family":"Beetz","sequence":"first","affiliation":[{"name":"Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu 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of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin, Department of Internal Medicine \u2013 Cardiology  Berlin Germany"},{"name":"DZHK (German Centre for Cardiovascular Research), partner site Berlin  Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petra","family":"Gehle","sequence":"additional","affiliation":[{"name":"Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin, Department of Internal Medicine \u2013 Cardiology  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uli","family":"Fehrenbach","sequence":"additional","affiliation":[{"name":"Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin, Department of Radiology  Augustenburger Platz 1 13353 Berlin 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