ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Translational and Clinical Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1642083
This article is part of the Research TopicUtilizing Real-World Evidence for Better Endocrine Health ManagementView all articles
Development and validation of a nomogram model for prediction of dyslipidemia in children with Wilson disease: a retrospective analysis
Provisionally accepted
- 1The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
- 2Anhui University of Chinese Medicine Key Laboratory of Xin'an Medicine of the Ministry of Education, Hefei, China
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Background: Wilson disease (WD), an inherited copper metabolism disorder, is linked to hepatic injury from copper accumulation-induced dyslipidemia. Children with WD have a high incidence of dyslipidemia, yet personalized risk assessment tools are lacking. This study established a predictive nomogram to provide foundational evidence for early detection in this population.In this retrospective cohort study, clinical data from 913 children with WD were retrospectively collected at the First Affiliated Hospital of Anhui University of Chinese Medicine (November 2018-February 2025). The cohort was stratified by age group and dyslipidemic status using stratified random sampling, resulting in a division into a training set (70%, n = 641) and a validation set (30%, n = 272). Independent risk factors were identified using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analyses. The nomogram prediction model was constructed and validated internally. The model's discriminatory efficacy was evaluated using Receiver Operating Characteristic (ROC) curves with the area under the curve (AUC), while its calibration performance was assessed using calibration curves and the Hosmer-Lemeshow test. Furthermore, the clinical utility of the model was examined through decision curve analysis (DCA) and clinical impact curves (CIC).The prevalence of dyslipidemia was 68.24%. The nomogram incorporated six significant clinical variables: age group (≥ 10 years vs. < 10 years), alanine aminotransferase (ALT), gammaglutamyl transpeptidase (GGT), homocysteine (Hcy), superoxide dismutase (SOD), and platelet count (PLT). The prediction model demonstrated good discrimination (AUC: 0.810 in the training set, 0.831 in the validation set), excellent calibration (Hosmer-Lemeshow p > 0.280), and significant clinical utility.Children with WD exhibit a high incidence of dyslipidemia. The nomogram prediction model based on these six variables effectively predicts dyslipidemic risk in pediatric WD patients, enabling early identification and clinical risk stratification.
Keywords: Wilson disease, Children, Dyslipidemia, nomogram, prediction, Risk factors
Received: 06 Jun 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Hua, Xuan, Sun, Song, Yang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Han Wang, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
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