Chen Zigui, Fung Erik, Wong Chun-Kwok, Ling Lowell, Lui Grace, Lai Christopher K C, Ng Rita W Y, Sze Ryan K H, Ho Wendy C S, Hui David S C, Chan Paul K S
Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR 999077, China.
Cardiovascular Science Center and Division of Cardiology, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.
Metabolites. 2024 Jul 9;14(7):380. doi: 10.3390/metabo14070380.
This prospective study in Hong Kong aimed at identifying prognostic metabolomic and immunologic biomarkers for Coronavirus Disease 2019 (COVID-19). We examined 327 patients, mean age 55 (19-89) years, in whom 33.6% were infected with Omicron and 66.4% were infected with earlier variants. The effect size of disease severity on metabolome outweighed others including age, gender, peak C-reactive protein (CRP), vitamin D and peak viral levels. Sixty-five metabolites demonstrated strong associations and the majority (54, 83.1%) were downregulated in severe disease (z score: -3.30 to -8.61). Ten cytokines/chemokines demonstrated strong associations ( < 0.001), and all were upregulated in severe disease. Multiple pairs of metabolomic/immunologic biomarkers showed significant correlations. Fourteen metabolites had the area under the receiver operating characteristic curve (AUC) > 0.8, suggesting a high predictive value. Three metabolites carried high sensitivity for severe disease: triglycerides in medium high-density lipoprotein (MHDL) (sensitivity: 0.94), free cholesterol-to-total lipids ratio in very small very-low-density lipoprotein (VLDL) (0.93), cholesteryl esters-to-total lipids ratio in chylomicrons and extremely large VLDL (0.92);whereas metabolites with the highest specificity were creatinine (specificity: 0.94), phospholipids in large VLDL (0.94) and triglycerides-to-total lipids ratio in large VLDL (0.93). Five cytokines/chemokines, namely, interleukin (IL)-6, IL-18, IL-10, macrophage inflammatory protein (MIP)-1b and tumour necrosis factor (TNF)-a, had AUC > 0.8. In conclusion, we demonstrated a tight interaction and prognostic potential of metabolomic and immunologic biomarkers enabling an outcome-based patient stratification.
这项在香港开展的前瞻性研究旨在确定2019冠状病毒病(COVID-19)的预后代谢组学和免疫学生物标志物。我们对327名患者进行了检查,他们的平均年龄为55岁(19至89岁),其中33.6%感染了奥密克戎毒株,66.4%感染了早期变体。疾病严重程度对代谢组的效应大小超过了其他因素,包括年龄、性别、C反应蛋白(CRP)峰值、维生素D和病毒载量峰值。65种代谢物显示出强烈关联,其中大多数(54种,83.1%)在重症疾病中表达下调(z值:-3.30至-8.61)。10种细胞因子/趋化因子显示出强烈关联(<0.001),且在重症疾病中均上调。多对代谢组学/免疫学生物标志物显示出显著相关性。14种代谢物的受试者工作特征曲线下面积(AUC)>0.8,表明具有较高的预测价值。三种代谢物对重症疾病具有高敏感性:中高密度脂蛋白(MHDL)中的甘油三酯(敏感性:0.94)、极小极低密度脂蛋白(VLDL)中的游离胆固醇与总脂质比率(0.93)、乳糜微粒和极大VLDL中的胆固醇酯与总脂质比率(0.92);而特异性最高的代谢物是肌酐(特异性:0.94)、大VLDL中的磷脂(0.94)和大VLDL中的甘油三酯与总脂质比率(0.93)。五种细胞因子/趋化因子,即白细胞介素(IL)-6、IL-18、IL-10、巨噬细胞炎性蛋白(MIP)-1b和肿瘤坏死因子(TNF)-α,AUC>0.8。总之,我们证明了代谢组学和免疫学生物标志物之间存在紧密的相互作用和预后潜力,能够实现基于结果的患者分层。