Department of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Center for Infectious Disease and Policy Research and Global Health and Infectious Diseases Group, Peking University, Beijing, China.
Front Immunol. 2024 May 10;15:1337208. doi: 10.3389/fimmu.2024.1337208. eCollection 2024.
To describe the lipid metabolic profile of different patients with coronavirus disease 2019 (COVID-19) and contribute new evidence on the progression and severity prediction of COVID-19.
This case-control study was conducted in Peking University Third Hospital, China. The laboratory-confirmed COVID-19 patients aged ≥18 years old and diagnosed as pneumonia from December 2022 to January 2023 were included. Serum lipids were detected. The discrimination ability was calculated with the area under the curve (AUC). A random forest (RF) model was conducted to determine the significance of different lipids.
Totally, 44 COVID-19 patients were enrolled with 16 mild and 28 severe patients. The top 5 super classes were triacylglycerols (TAG, 55.9%), phosphatidylethanolamines (PE, 10.9%), phosphatidylcholines (PC, 6.8%), diacylglycerols (DAG, 5.9%) and free fatty acids (FFA, 3.6%) among the 778 detected lipids from the serum of COVID-19 patients. Certain lipids, especially lysophosphatidylcholines (LPCs), turned to have significant correlations with certain immune/cytokine indexes. Reduced level of LPC 20:0 was observed in severe patients particularly in acute stage. The AUC of LPC 20:0 reached 0.940 in discriminating mild and severe patients and 0.807 in discriminating acute and recovery stages in the severe patients. The results of RF models also suggested the significance of LPCs in predicting the severity and progression of COVID-19.
Lipids probably have the potential to differentiate and forecast the severity, progression, and clinical outcomes of COVID-19 patients, with implications for immune/inflammatory responses. LPC 20:0 might be a potential target in predicting the progression and outcome and the treatment of COVID-19.
描述不同 2019 冠状病毒病（COVID-19）患者的脂质代谢特征，为 COVID-19 的进展和严重程度预测提供新的证据。
本病例对照研究在中国北京大学第三医院进行。纳入 2022 年 12 月至 2023 年 1 月年龄≥18 岁、临床诊断为肺炎的实验室确诊 COVID-19 患者。检测血清脂质。采用曲线下面积（AUC）计算鉴别能力。采用随机森林（RF）模型确定不同脂质的重要性。
共纳入 44 例 COVID-19 患者，其中轻症 16 例，重症 28 例。在 778 种检测到的 COVID-19 患者血清脂质中，前 5 位超类分别为三酰甘油（TAG，55.9%）、磷脂酰乙醇胺（PE，10.9%）、磷脂酰胆碱（PC，6.8%）、二酰甘油（DAG，5.9%）和游离脂肪酸（FFA，3.6%）。某些脂质，特别是溶血磷脂酰胆碱（LPCs），与某些免疫/细胞因子指标呈显著相关。在重症患者，特别是在急性期，观察到 LPC 20:0 的水平降低。LPC 20:0 区分轻症和重症患者的 AUC 为 0.940，区分重症患者急性和恢复期的 AUC 为 0.807。RF 模型的结果也表明 LPCs 在预测 COVID-19 的严重程度和进展方面具有重要意义。
脂质可能具有区分和预测 COVID-19 患者严重程度、进展和临床结局的潜力，提示其与免疫/炎症反应有关。LPC 20:0 可能是预测 COVID-19 进展和结局以及治疗的潜在靶点。
