Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada.
Department of Computer Science, McGill University, Montréal, QC, Canada.
Sci Rep. 2023 Apr 17;13(1):6236. doi: 10.1038/s41598-023-31850-y.
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
预测 COVID-19 的严重程度具有挑战性，且其涉及的生物学途径尚未完全阐明。为了研究这一问题，我们在两个独立队列中共计 986 人检测了 4701 种循环人类蛋白丰度。然后，我们利用包括蛋白丰度和临床风险因素在内的预测模型来预测 417 名受试者的 COVID-19 严重程度，并在 569 名独立受试者的队列中对这些模型进行了测试。对于严重的 COVID-19，在测试队列中，包含年龄和性别的基线模型提供了 65%的接收者操作特征曲线（AUC）。从 4701 种独特的蛋白丰度中选择 92 种蛋白，将 AUC 提高到训练队列中的 88%，在测试队列中保持相对稳定，为 86%，表明具有良好的泛化能力。从不同 COVID-19 严重程度中选择的蛋白富含细胞因子和细胞因子受体，但超过一半的富集途径与免疫无关。总的来说，这些发现表明，在疾病进展的早期阶段测量的循环蛋白是 COVID-19 严重程度的合理准确预测因子。需要进一步的研究来了解如何将蛋白测量纳入临床护理。
