Liu Jiani, Zhang Xin, Li Wei, Bigambo Francis Manyori, Wang Dandan, Wang Xu, Teng Beibei
School of Public Health, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
Department of Pneumology, Children's Hospital of Nanjing Medical University, Nanjing, China.
BMC Endocr Disord. 2025 May 12;25(1):129. doi: 10.1186/s12902-025-01936-x.
Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity because of the complex etiology of the disorder. Hence, this study aims to employ machine learning techniques to develop an interpretable predictive model for normal-variant short stature and to explore how growth environments influence its development.
We conducted a case‒control study including 100 patients with normal-variant short stature who were age-matched with 200 normal controls from the Endocrinology Department of Nanjing Children's Hospital from April to September 2021. Parental surveys were conducted to gather information on the children involved. We assessed 33 readily accessible medical characteristics and utilized conditional logistic regression to explore how growth environments influence the onset of normal-variant short stature. Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. The Shapley additive explanation (SHAP) method was subsequently employed to prioritize factor importance and refine the final model.
In the multivariate logistic regression analysis, children's weight (OR = 0.92, 95% CI: 0.86, 0.99), maternal height (OR = 0.79, 95% CI: 0.72, 0.87), paternal height (OR = 0.83, 95% CI: 0.75, 0.91), sufficient nighttime sleep duration (OR = 0.48, 95% CI: 0.26, 0.89), and outdoor activity time exceeding three hours (OR = 0.02, 95% CI: 0.00, 0.66) were identified as protective factors for normal-variant short stature. This study revealed that parental height, caregiver education, and children's weight significantly influenced the prediction of normal-variant short stature risk, and both the random forest model and gradient boosting machine model exhibited the best discriminatory ability among the 9 machine learning models.
This study revealed a close correlation between environmental growth factors and the occurrence of normal-variant short stature, particularly anthropometric characteristics. The random forest model and gradient boosting machine model performed exceptionally well, demonstrating their potential for clinical applications. These findings provide theoretical support for clinical identification and preventive measures for short stature.
身材矮小是一种常见的儿科内分泌疾病，早期发现和预测对于改善治疗效果至关重要。然而，由于该疾病病因复杂，现有的诊断标准往往缺乏必要的敏感性和特异性。因此，本研究旨在运用机器学习技术开发一种可解释的正常变异型身材矮小预测模型，并探讨生长环境如何影响其发展。
我们进行了一项病例对照研究，纳入了100例正常变异型身材矮小患者，他们与2021年4月至9月来自南京儿童医院内分泌科的200名正常对照者年龄匹配。通过对家长进行调查，收集有关所涉及儿童的信息。我们评估了33项易于获取的医学特征，并利用条件逻辑回归来探讨生长环境如何影响正常变异型身材矮小的发病。此外，我们评估了9种机器学习算法的性能，以确定最佳模型。随后采用夏普利加法解释（SHAP）方法对因素重要性进行排序并优化最终模型。
在多变量逻辑回归分析中，儿童体重（OR = 0.92，95%CI：0.86，0.99）、母亲身高（OR = 0.79，95%CI：0.72，0.87）、父亲身高（OR = 0.83，95%CI：0.75，0.91）、充足的夜间睡眠时间（OR = 0.48，95%CI：0.26，0.89）以及户外活动时间超过三小时（OR = 0.02，95%CI：0.00，0.66）被确定为正常变异型身材矮小的保护因素。本研究表明，父母身高、照顾者教育程度和儿童体重显著影响正常变异型身材矮小风险的预测，并且随机森林模型和梯度提升机模型在9种机器学习模型中表现出最佳的区分能力。
本研究揭示了环境生长因素与正常变异型身材矮小发生之间的密切相关性，尤其是人体测量特征。随机森林模型和梯度提升机模型表现出色，显示出它们在临床应用中的潜力。这些发现为身材矮小的临床识别和预防措施提供了理论支持。
