Clinical characteristics and microbial signatures in the lower airways of diabetic and nondiabetic patients with pneumonia.

BACKGROUND

The microbial signatures in diabetes with pneumonia and the risk factors of severe pneumonia (SP) in diabetic patients are not clear. Our study explored microbial signatures and the association between clinical characteristics and SP then constructed a risk model to find effective biomarkers for predicting pneumonia severity.

METHODS

Our study was conducted among 273 patients with pneumonia diagnosed and treated in our hospital from January 2018 to May 2021. Bronchoalveolar lavage fluid (BALF) samples and clinical data were collected. Metagenomic sequencing was applied after extracting the DNA from samples. Appropriate statistical methods were used to compare the microbial signatures and clinical characteristics in patients with or without diabetes mellitus (DM).

RESULTS

In total, sixty-one pneumonia patients with diabetes and 212 pneumonia patients without diabetes were included. Sixty-six differential microorganisms were found to be associated with SP in diabetic patients. Some microbes correlated with clinical indicators of SP. The prediction model for SP was established and the receiver operating characteristic (ROC) curve demonstrated its accuracy, with the sensitivity and specificity of 0.82 and 0.91, respectively.

CONCLUSIONS

Some microorganisms affect the severity of pneumonia. We identified the microbial signatures in the lower airways and the association between clinical characteristics and SP. The predictive model was more accurate in predicting SP by combining microbiological indicators and clinical characteristics, which might be beneficial to the early identification and management of patients with SP.