2026-10-12T21:10:18Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33971194 2026-09-23T05:53:42Z category_393 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_An interpretable machine-learning model identifies obesity-related metabolic complications in children attending a tertiary obesity clinic.zip Tingting Li (10553) Nan Nan (589273) Wenjie Wei (777256) Ying Zhu (90057) Jiayue Li (5819225) Lingzhi Zhang (1290495) Bingwu Wang (2038567) Zhenqiang Da (25094605) Jiaxin Chai (24530792) Cheng Wang (102692) Xingwen Xie (15364183) Foetal Development and Medicine body composition childhood obesity LASSO machine learning metabolic complications risk prediction <p>Childhood obesity-related metabolic complications are common in Northwest China, yet region-specific evidence and risk-stratification tools are lacking. In a single-visit cross-sectional study of 131 children (3–18 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.</p> 2026-09-23T05:53:42Z Dataset Dataset 10.3389/fped.2026.1969179.s002 https://figshare.com/articles/dataset/Supplementary_file_1_An_interpretable_machine-learning_model_identifies_obesity-related_metabolic_complications_in_children_attending_a_tertiary_obesity_clinic_zip/33971194 CC BY 4.0