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In a single-visit cross-sectional study of 131 children (3\u201318 years) with overweight or obesity attending a Gansu maternal and child-care hospital, obesity-related metabolic complications (ORMC) were present in 31.3% (dyslipidaemia 16.0%, hyperuricaemia 9.2%, elevated blood pressure 7.6%). An interpretable model combining LASSO-selected adiposity measures (body mass index, neck circumference, body fat percentage) with penalised logistic regression achieved moderate, internally cross-validated discrimination (AUC 0.72, 95% CI 0.62-0.82) that awaits external validation, comparable to a random forest (0.73). Screen time was the strongest modifiable behavioural correlate. Routine metabolic screening and simple body-composition measures should be embedded in paediatric obesity clinics, with screen-time reduction as a counselling priority.<\/jats:p>","DOI":"10.3389\/fped.2026.1969179","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:54:01Z","timestamp":1790142841000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable machine-learning model identifies obesity-related metabolic complications in children attending a tertiary obesity clinic"],"prefix":"10.3389","volume":"14","author":[{"given":"Tingting","family":"Li","sequence":"first","affiliation":[{"name":"Department of Child Health Care, Gansu Province Maternity and Child Health Hospital","place":["Lanzhou, Gansu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Nan","sequence":"additional","affiliation":[{"name":"Department of Child Health 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