李宗夷教授研究團隊發表研究成果於Frontiers in Microbiology
連結網址：https://pubmed.ncbi.nlm.nih.gov/41395471/
Introduction: Systemic glucocorticoids (SG) are administered to quell hyper-inflammation in severe community acquired pneumonia (SCAP), yet trials report inconsistent efficacy and no mechanistic explanation.
Methods: We enrolled 200 ventilated SCAP patients, whom received hydrocortisone within 48 h of ICU admission, and generated longitudinal lower-airway microbiome profiles by 16S rRNA amplicon and metagenomic sequencing on ICU Days 1, 3 and 7. Compositional data were integrated with clinical variables through a fully reproducible bioinformatics analysis workflow.
Results: Baseline community structures did not differ between SG and control cohorts, but by Day 7 survivors exhibited enrichment of Actinobacteria and Gammaproteobacteria whereas non-survivors accumulated Alphaproteobacteria and Campylobacteria. A random-forest model restricted to Bacilli and Alphaproteobacteria achieved AUROC = 0.89 (sensitivity 0.83, specificity 0.81) on a patient-held-out test set, significantly outperforming conventional severity indices like APACHE II, SOFA and mNUTRIC scores.
Discussion: Collectively, our results demonstrate that SG therapy imposes reproducible ecological pressures on the lung microbiome and that a two-feature microbial fingerprint can forecast treatment success with single-sample resolution. These findings show that SG therapy actively reshapes the respiratory ecosystem and that lightweight microbiome-aware machine learning can stratify treatment response, offering a tractable path toward precision corticosteroid stewardship.
Keywords: gut-lung axis; lung microbiome; machine learning; severe community-acquired pneumonia (SCAP); systemic glucocorticoids response.
