在人类COVID-19感染期间的代谢概况:使用机器学习识别发生,严重程度和结果的潜在生物标志物的识别
Gamalat A Elgedawy1, Mohamed Samir2, Naglaa S Elabd3
1Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
PloS one
|May 30, 2024
概括
COVID-19 感染会改变宿主代谢物,不同的代谢特征因疾病严重程度而异. 这些发现为2019年冠状病毒病 (COVID-19) 提供了新的诊断和治疗策略.
科学领域:
- 生物化学 生物化学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 导致COVID-19,这是一个具有不同临床严重性的全球健康危机.
- 代谢干扰与COVID-19有关,但特定阶段的代谢物变化及其在患者分层中的作用仍然不清楚.
研究的目的:
- 在COVID-19感染期间表征特定阶段的宿主代谢物变化.
- 根据COVID-19的严重程度来确定患者分层的代谢特征.
主要方法:
- 在295名受试者中对50种代谢物和17种血液参数进行了有针对性的代谢学分析 (对照组,非严重,严重,严重的COVID-19).
- 机器学习和生物统计学应用于人口,临床和代谢数据,用于预测建模.
主要成果:
- 不严重和严重的COVID-19显示了最显著的代谢物变化;关键阶段的变化较小.
- 关键代谢物小组确定了非严重,严重,危急和已故患者的预测因素.
- 氨酸,马洛尼尔甲基马洛尼尔苏奇尼尔卡尼丁和tauroursodeoxycholic酸成为感染发病和进展的重要生物标志物.
结论:
- 独特的代谢学特征标志着COVID-19感染的每个阶段.
- 代谢物概况具有开发COVID-19新型诊断和治疗方法的巨大潜力.
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