Liu Xiaoxiao, Ma Shifeng, Li Jing, Song Mingkun, Li Yun, Qi Yingyi, Liu Fei, Fang Zhongze, Zheng Rongxiu
Department of Pediatrics, Tianjin Medical University General Hospital, Tianjin 300000, China.
Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin 300000, China.
J Endocr Soc. 2025 Feb 24;9(4):bvaf032. doi: 10.1210/jendso/bvaf032. eCollection 2025 Mar 3.
This study aimed to investigate the clinical characteristics and plasma metabolites of nonalcoholic fatty liver disease (NAFLD) in obese Chinese children and to develop machine learning-based NAFLD diagnostic models.
We recruited 222 obese children aged 4 to 17 years and divided them into an obese control group and an obese NAFLD group based on liver ultrasonography. Mass spectrometry metabolomic analysis was used to measure 106 metabolites in plasma. Binary logistic regression was used to identify NAFLD-related clinical variables. NAFLD-specific metabolites were illustrated via volcano plots, cluster heatmaps, and metabolic network diagrams. Additionally, we applied 8 machine learning methods to construct 3 diagnostic models based on clinical variables, metabolites, and clinical variables combined with metabolites.
By evaluating clinical variables and plasma metabolites, we identified 16 clinical variables and 14 plasma metabolites closely associated with NAFLD. We discovered that the level of 18:0 to 22:6 phosphatidylethanolamines was positively correlated with the levels of total cholesterol, triglyceride-glucose index, and triglyceride to high-density lipoprotein cholesterol ratio, whereas the level of glycocholic acid was positively correlated with the levels of alanine aminotransferase, gamma-glutamyl transferase, insulin, and the homeostasis model assessment of insulin resistance. Additionally, we successfully developed 3 NAFLD diagnostic models that showed excellent diagnostic performance (areas under the receiver operating characteristic curves of 0.917, 0.954, and 0.957, respectively).
We identified 16 clinical variables and 14 plasma metabolites associated with NAFLD in obese Chinese children. Diagnostic models using these features showed excellent performance, indicating their potential for diagnosis.
本研究旨在调查中国肥胖儿童非酒精性脂肪性肝病（NAFLD）的临床特征和血浆代谢物，并开发基于机器学习的NAFLD诊断模型。
我们招募了222名4至17岁的肥胖儿童，并根据肝脏超声检查将他们分为肥胖对照组和肥胖NAFLD组。采用质谱代谢组学分析方法测定血浆中的106种代谢物。采用二元逻辑回归分析确定与NAFLD相关的临床变量。通过火山图、聚类热图和代谢网络图展示NAFLD特异性代谢物。此外，我们应用8种机器学习方法，基于临床变量、代谢物以及临床变量与代谢物相结合构建了3种诊断模型。
通过评估临床变量和血浆代谢物，我们确定了16个临床变量和14种血浆代谢物与NAFLD密切相关。我们发现18:0至22:6磷脂酰乙醇胺水平与总胆固醇、甘油三酯 - 葡萄糖指数以及甘油三酯与高密度脂蛋白胆固醇比值呈正相关，而甘氨胆酸水平与丙氨酸氨基转移酶、γ-谷氨酰转移酶、胰岛素水平以及胰岛素抵抗的稳态模型评估呈正相关。此外，我们成功开发了3种NAFLD诊断模型，其诊断性能优异（受试者工作特征曲线下面积分别为0.917、0.954和0.957）。
我们确定了16个与中国肥胖儿童NAFLD相关的临床变量和14种血浆代谢物。使用这些特征的诊断模型表现出优异的性能，表明它们在诊断方面具有潜力。
