{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T20:06:47Z","timestamp":1777061207743,"version":"3.51.4"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Gastro and Hepatol"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Aims<\/jats:title><jats:p>Both fibrosis status and body weight are important for assessing prognosis in nonalcoholic fatty liver disease (NAFLD). The aim of this study was to identify population clusters for specific clinical outcomes based on fibrosis\u20104 (FIB\u20104) index and body mass index (BMI) using an unsupervised machine learning method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a multicenter study of 1335 biopsy\u2010proven NAFLD patients from Japan. Using the Gaussian mixture model to divide the cohort into clusters based on FIB\u20104 index and BMI, we investigated prognosis for these clusters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The cohort consisted of 223 cases (16.0%) with advanced fibrosis (F3\u20134) as assessed from liver biopsy. Median values of BMI and FIB\u20104 index were 27.3\u00a0kg\/m<jats:sup>2<\/jats:sup> and 1.67. The patients were divided into four clusters by Bayesian information criterion, and all\u2010cause mortality was highest in cluster d, followed by cluster b (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.001). Regarding the characteristics of each cluster, clusters d and b presented a high FIB\u20104 index (median 5.23 and 2.23), cluster a presented the lowest FIB\u20104 index (median 0.78), and cluster c was associated with moderate FIB\u20104 level (median 1.30) and highest BMI (median 34.3\u00a0kg\/m<jats:sup>2<\/jats:sup>). Clusters a and c had lower mortality rates than clusters b and d. However, all\u2010cause of death in clusters a and c was unrelated to liver disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our clustering approach found that the FIB\u20104 index is an important predictor of mortality in NAFLD patients regardless of BMI. Additionally, non\u2010liver\u2010related diseases were identified as the causes of death in NAFLD patients with low FIB\u20104 index.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jgh.16326","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T21:18:22Z","timestamp":1692652702000},"page":"1832-1839","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of clinical phenotypes associated with poor prognosis in patients with nonalcoholic fatty liver disease via unsupervised machine learning"],"prefix":"10.1111","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2594-0854","authenticated-orcid":false,"given":"Takanori","family":"Ito","sequence":"first","affiliation":[{"name":"Department of Gastroenterology and Hepatology Nagoya University Graduate School of Medicine  Nagoya 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