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A deep contrast clustering approach was used to cluster and predict diabetic complications. The clustering approach was further validated using data from elderly patients with diabetes (<jats:italic>n<\/jats:italic>\u00a0=\u00a0397) from one medical examination cohort of 20,000 people followed up yearly from 2014 to date in Beijing Jiuhua Hospital.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The patients were clustered into 6 categories by analysing 20 indicators. Cluster 1\u2014Heavy smoking and a high cardiovascular disease (CVD) risk; Cluster 2\u2014High alcohol consumption, high aminotransferase levels, the highest risk of stroke complications, and a high fatty liver disease (FLD) risk; Cluster 3\u2014High blood lipid levels and a risk of FLD and stroke complications; Cluster 4\u2014Good health indicators and a low risk of FLD, stroke, and CVD complications; Cluster 5\u2014Older age, higher uric acid concentration and creatinine level, and the highest risk of CVD complications; Cluster 6\u2014Large waist circumference, high BMI, high blood pressure, and the highest risk of FLD complications. The gene for nonalcoholic fatty liver disease in cluster 2 had the highest risk coefficient. This was consistent with cluster 2, which had a higher FLD prevalence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A new clustering method was developed from two large Chinese cohorts of older patients with diabetes, which may effectively predict complications by clustering into different categories.<\/jats:p><\/jats:sec>","DOI":"10.1002\/dmrr.70000","type":"journal-article","created":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T12:10:17Z","timestamp":1729685417000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Contrast Clustering Analysis to Distinguish Diabetic Complications in Elderly Chinese Patients"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-2793","authenticated-orcid":false,"given":"Weihao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Endocrinology Beijing Hospital National Centre of 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