Serum proteomics identifies biomarkers for predicting non-survivors in elderly COVID-19 patients.

Abstract

In December 2022, China ceased the zero-COVID-19 policy, resulting in an increase in hospitalizations and deaths due to COVID-19, particularly among the elderly population. Predicting non-survivors aims to identify high-risk patients and enable targeted interventions to improve survival rates. Additionally, understanding factors affecting prognosis provides essential insights for further research and optimization of treatment strategies. We applied 4D-DIA mass spectrometry for serum proteome analysis and provided a comprehensive characterization of disease features in elderly patients within the Chinese population. Our study elucidated that immune disorders, lung damage, and cardiovascular disorders are predominant causes of death in these patients. Compared to clinical indices, proteomic analysis is more sensitive in tracing these disorders. We also provided a prediction panel for survival outcomes of elderly patients using levels of CXCL10, CXCL16 and IL1RA, which were validated by ELISA. These biomarkers will help improve predictive efficacy for survival outcomes in elderly patients.