Predictive Markers of the Course of Covid-19 Based on Biomarker Serum Amyloid A (SAA) and Genetic Markers

Overview

This study focuses on identifying genetic and biochemical markers (biomarkers) that could help predict the course of COVID-19. The investigators aim to analyze samples taken from the nasopharynx (nose and throat), saliva, and blood of patients with acute respiratory tract infections who come to the Outpatient Clinic of the Department of Respiratory Diseases at the Thomayer University Hospital. The main objectives include measuring the expression of the SAA1 gene (using quantitative RT-PCR) and conducting broader gene expression analysis (using RNA sequencing) in nasopharyngeal cells. At the same time, the investigators will examine the patients' DNA to identify rare genetic variants or common polymorphisms that may influence their immune response to the virus. The goal is to relate the values of SAA expression with clinical and radiological status and outcome of the patients. The investigators hypothesize, that SAA may predict the severity of the disease, need for hospitalizastion and outcome of the patients.

This cross-sectional study evaluated nasal SAA1 expression as a biomarker for early detection and severity assessment of respiratory tract infections, particularly in COVID-19 patients. A total of 369 participants presenting with infectious or non-infectious respiratory symptoms to the outpatient clinic were prospectively recruited between March 6, 2021, and June 26, 2023. Each patient underwent a single nasopharyngeal swab, which was used both for RT-qPCR testing of SARS-CoV-2 RNA and for SAA1 expression analysis, ensuring methodological consistency and direct comparability of results. Additionally, all participants underwent a chest X-ray, a comprehensive clinical examination, and routine blood and biochemistry testing. RNA Extraction and SAA1 mRNA Quantification: Nasal swabs were collected and stored under RNase-free conditions for RNA extraction. The relative expression of SAA1 was determined using real-time quantitative polymerase chain reaction (RT-qPCR).