Huang Xiu, Yi Kun, Jia Lin, Li Yinmei, He Hui, Ma Can, Fang Xiao
Department of Paediatrics, Nanchong City Jialing District People's Hospital (Jialing Branch of Nanchong Central Hospital), Nanchong, China.
School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China.
Transl Pediatr. 2025 Mar 31;14(3):452-462. doi: 10.21037/tp-2024-502. Epub 2025 Mar 26.
Insulin resistance (IR) is a precursor to metabolic disorders like type 2 diabetes and hypertension in children and adolescents. Early detection of IR is critical to prevent severe metabolic complications. IR is influenced by factors such as diet, inflammation, and genetics. However, existing studies often focus on limited populations and overlook dietary factors. This study aimed to evaluate the use of machine learning (ML) models for early IR prediction in children and adolescents, emphasizing accuracy.
We used physical examination data of children and adolescents aged 6-17 years from the China Health and Nutrition Survey (CHNS) database as the training set and collected routine physical examination data from children and adolescents aged 6-17 years admitted to Nanchong Central Hospital and the Nanchong City Jialing District People's Hospital in Sichuan Province from January 2019 to October 2024 for validation. IR was assessed using the Homeostatic Model Assessment for IR (HOMA-IR) score, with a cutoff of >3.0 indicating IR. Potential predictors included demographic details, lifestyle habits, and blood test results. We conducted univariate logistic regression (LR) analysis to select variables with statistical significance and then constructed and compared the back propagation neural network (BPNN), exhaustive Chi-squared automatic interaction detector (E-CHAID), support vector machine (SVM), and LR models.
The training sample included 827 children and adolescents (281 with IR and 546 without IR), while the test sample included 207 participants. The SVM model demonstrated superior predictive accuracy (91.90% in training and 90.34% in test set) compared to the E-CHAID (77.75% in training and 72.95% in test set), BPNN (75.94% in training and 70.05% in test set), and LR models (76.18% in training and 71.01% in test set). Sensitivity, specificity, Youden's index, and area under the curve (AUC) values also favored the SVM model in both training and test samples.
Compared with the E-CHAID, BPNN, and LR models, the SVM model exhibited superior predictive ability for IR in children and adolescents based on physical examination data that include dietary factors. These findings suggest that the SVM model could serve as a valuable tool for early clinical prediction of IR, potentially aiding in the prevention of type 2 diabetes mellitus (T2DM) and associated metabolic complications. Further research is needed to validate these results in larger and more diverse populations.
胰岛素抵抗（IR）是儿童和青少年代谢紊乱（如2型糖尿病和高血压）的先兆。早期检测IR对于预防严重的代谢并发症至关重要。IR受饮食、炎症和遗传等因素影响。然而，现有研究往往聚焦于有限人群，忽视了饮食因素。本研究旨在评估机器学习（ML）模型在儿童和青少年早期IR预测中的应用，重点关注准确性。
我们将来自中国健康与营养调查（CHNS）数据库的6至17岁儿童和青少年的体格检查数据用作训练集，并收集了2019年1月至2024年10月期间在四川省南充市中心医院和南充市嘉陵区人民医院就诊的6至17岁儿童和青少年的常规体格检查数据进行验证。使用IR的稳态模型评估（HOMA-IR）评分评估IR，临界值>3.0表示存在IR。潜在预测因素包括人口统计学细节、生活习惯和血液检测结果。我们进行单因素逻辑回归（LR）分析以选择具有统计学意义的变量，然后构建并比较反向传播神经网络（BPNN）、穷尽卡方自动交互检测器（E-CHAID）、支持向量机（SVM）和LR模型。
训练样本包括827名儿童和青少年（281名有IR，546名无IR），而测试样本包括207名参与者。与E-CHAID（训练集为77.75%，测试集为72.95%）、BPNN（训练集为75.94%，测试集为70.05%）和LR模型（训练集为76.18%，测试集为71.01%）相比，SVM模型表现出更高的预测准确性（训练集为91.90%，测试集为90.34%）。在训练和测试样本中，敏感性、特异性、约登指数和曲线下面积（AUC）值也都有利于SVM模型。
与E-CHAID、BPNN和LR模型相比，基于包含饮食因素的体格检查数据，SVM模型在儿童和青少年IR预测方面表现出卓越的预测能力。这些发现表明，SVM模型可作为IR早期临床预测的有价值工具，可能有助于预防2型糖尿病（T2DM）及相关代谢并发症。需要进一步研究以在更大、更多样化的人群中验证这些结果。
