tNGS-based detection of respiratory pathogens in a single center: associations with age, gender, season, and co-infections.

Background: Respiratory tract infections represent a significant global health challenge. Conventional diagnostic methods frequently fail to detect complex infections or novel pathogens. This study employed Targeted Next-Generation Sequencing to achieve an unbiased and comprehensive identification of respiratory pathogens, as well as to conduct analysis of pathogen distribution across age, gender and seasons. Methods: We conducted a retrospective analysis of clinical samples, including throat swabs, sputum, and bronchoalveolar lavage fluid, obtained from symptomatic patients. The analysis utilized targeted next-generation sequencing in conjunction with bioinformatics. Statistical assessments were performed to evaluate associations with age, gender, season, and co-infections, primarily employing Chi-square tests. Results: A high pathogen detection rate of 97.08% was achieved among 20059 individuals. Bacteria were the most frequently detected pathogens, accounting for 49.62%, followed by viruses at 43.31%, and special pathogens at 7.07%. Significant age-related differences in pathogen profiles were observed. Although no overall gender effect was detected, variations specific to certain pathogens were noted. Clear seasonal trends emerged for key pathogens. Co-infections were highly prevalent, with bacterial-viral combinations being the most common, affecting 49.03% of patients, which exceeded the rate of bacterial infections alone at 15.69%. Conclusion: Targeted next-generation sequencing serves as a robust tool for elucidating the intricate spectrum and epidemiology of respiratory pathogens. This study underscores significant associations with patient age, seasonal variations, and the prevalence of co-infections, providing essential insights for targeted clinical and public health interventions in response to respiratory tract infections.