代谢特征和基于尿道有机酸的氨酸缺乏症诊断模型
Peiyao Wang1, Peichun Chen2, Xinjie Yang1
1Department of Genetics and Metabolism, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Clinical and translational medicine
|September 15, 2025
概括
这项研究开发了一种机器学习模型,以准确诊断由素缺乏症 (NICCD) 引起的新生儿肝内胆固醇症,将其与其他代谢障碍区分开来. 该模型使用尿道有机酸概况来提高NICCD的诊断准确性.
科学领域:
- 生物化学和新陈代谢学
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的机器学习
背景情况:
- 由素缺乏症 (NICCD) 引起的新生儿肝内胆固醇症可能被误诊,原因是尿路代谢物形状与非特异性代谢异常 (NAG) 重叠.
- 在NICCD和NAG中,高水平的4-xyphenyllactic酸 (4-HPLA) 和4-xyphenylpyruvic酸 (4-HPPA) 是常见的,使差异诊断复杂化.
研究的目的:
- 在NICCD患者中描述尿有机酸概况.
- 使用机器学习开发一个诊断模型,区分NICCD和NAG.
- 为准确的NICCD诊断确定关键的尿路生物标志物.
主要方法:
- 从105名NICCD患者,144名健康对照和298名NAG个体的尿道有机酸的回顾性分析,使用气色谱-质谱.
- 使用五种机器学习方法开发和验证诊断模型,包括随机森林,数据分为培训,测试和外部验证.
- 通过三步特征选择策略来识别生物标志物.
主要成果:
- 与健康对照组相比,NICCD患者表现出显著的代谢差异,富含铁素,阿斯巴酸,铁酸,脂酸和TCA循环通路.
- 确定了9种强大的尿路生物标志物,包括4-HPLA,4-HPPA,银醇和酸,用于区分NICCD和NAG,其诊断性能高 (AUC>0.8).
- 随机森林模型实现了卓越的分类准确度,灵敏度和特异性,导致在线临床计算器的开发.
结论:
- NICCD的特点是能量和氨基酸通路的代谢变化.
- 一个可解释的机器学习模型有效地区分NICCD和NAG,基于尿道有机酸概况.
- 开发的模型和在线计算器为改善NICCD诊断在临床环境中提供了宝贵的工具.
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