{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:12:14Z","timestamp":1792156334092,"version":"4.4.0"},"reference-count":40,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS\u20102023\u201000231864"],"award-info":[{"award-number":["RS\u20102023\u201000231864"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Computed tomography (CT) body compositions reflect age\u2010related metabolic derangements. We aimed to develop a multi\u2010outcome deep learning model using CT multi\u2010level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long\u2010term mortality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The derivation set (<jats:italic>n<\/jats:italic>\u00a0=\u00a0516; 75% train set, 25% internal test set) was constructed using age\u2010 and sex\u2010stratified random sampling from two community\u2010based cohorts. Data from participants in the individual health assessment programme (<jats:italic>n<\/jats:italic>\u00a0=\u00a0380) were used as the external test set 1. Semi\u2010automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi\u2010layer perceptron (MLP)\u2010based multi\u2010label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary\u2010level institution (<jats:italic>n<\/jats:italic>\u00a0=\u00a010\u00a0141).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The mean ages of the derivation and external sets were 62.8 and 59.7\u00a0years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9\u00a0kg\/m<jats:sup>2<\/jats:sup>). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi\u2010level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT\u2010parameter\u2010based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5\u00a0years, women 79%; median follow\u2010up 4.9\u00a0years), a total of 907 individuals (8.9%) died during follow\u2010up. Among model\u2010predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS\u00a0+\u00a0sarcopenia (aHR 1.65), osteoporosis\u00a0+\u00a0sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A CT body composition\u2010based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community\u2010dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long\u2010term mortality, independent of covariates.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jcsm.13487","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T03:29:16Z","timestamp":1713842956000},"page":"1418-1429","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults"],"prefix":"10.1002","volume":"15","author":[{"given":"Sang Wouk","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Endocrine Research Institute Severance Hospital, Yonsei University College of Medicine  Seoul South Korea"},{"name":"Institue for Innovation in Digital Healthcare (IIDH) Yonsei University Health System  Seoul South 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