Pseudomonas aeruginosa-driven airway dysbiosis and machine learning prediction of acute exacerbations in non-cystic fibrosis bronchiectasis: a microbial-inflammatory signature approach.

ABSTRACT:

Background

While Pseudomonas aeruginosa (PA) colonization is linked to poor outcomes in bronchiectasis, emerging evidence suggests that microbial community collapse-marked by diversity loss and depletion of commensal taxa-may better reflect disease progression than pathogen load alone. This study investigates whether airway microbiota dysbiosis driven by PA colonization induces ecological fragility and evaluates the predictive utility of integrating microbial diversity indices with systemic inflammation markers to forecast 1-year acute exacerbation risk using interpretable machine learning.

Methods

Bronchoalveolar lavage fluid (BALF) samples from 23 patients (8 PA-colonized, 15 non-colonized) underwent 16 S rRNA gene sequencing. Microbial diversity and taxonomic composition

SUBMITTER: Wang WW 

PROVIDER: S-EPMC12400642 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature