A model including CD15, ACE2 and age efficiently predicts COVID-19 severity

Area of Science:

• Immunology
• Virology
• Biomarker Discovery

Background:

• COVID-19 presents diverse clinical outcomes, necessitating early identification of severe cases.
• Angiotensin-converting enzyme 2 (ACE2) and Transmembrane serine protease 2 (TMPRSS2) are crucial for viral entry and have anti-inflammatory roles.
• CD15 and CD45 are key immune response proteins implicated in SARS-CoV-2 infection.

Purpose of the Study:

• To analyze the expression of ACE2, TMPRSS2, CD15, and CD45 as potential biomarkers for predicting COVID-19 severity.
• To assess the correlation between these biomarkers and clinical symptom severity in a patient cohort.
• To develop and validate a predictive model for COVID-19 severity using these biomarkers.

Main Methods:

• Quantitative Polymerase Chain Reaction (qPCR) and flow cytometry were used to measure biomarker expression.
• Mixed-effects linear regression models and Receiver Operating Characteristic (ROC) curves were employed for analysis.
• A cohort of 216 patients with mild (111) and severe (105) COVID-19 disease was studied.

Main Results:

• Severe COVID-19 cases showed significantly lower surface expression of CD15 and ACE2.
• Aging was strongly associated with increased disease severity.
• The developed predictive model demonstrated high performance (AUC=0.91), with 92.9% specificity and 79.3% sensitivity.

Conclusions:

• Combined analysis of CD15 and ACE2, alongside age, can serve as effective biomarkers for predicting COVID-19 severity.
• This predictive model can significantly improve COVID-19 management through early risk identification.
• Enhanced patient stratification will optimize treatment strategies and resource distribution during the pandemic.