Gut–lung microbial dynamics with lumacaftor/ivacaftor in children with cystic fibrosis: a prospective multicenter study
摘要
CFTR modulators such as lumacaftor/ivacaftor (LUM/IVA) may reshape microbiota-mycobiota composition in the lungs and gut. While the gut-lung axis is established in other settings, little is known about its role following modulator therapy, particularly in the 2–11 age group.
MethodsIn a prospective national multicentre study, 116 children with cystic fibrosis (2–11 years) starting LUM/IVA were followed for 12 months. Stool and sputum were collected at baseline, 3, 6 and 12 months. Bacterial and fungal communities were profiled by 16S rRNA and ITS2 sequencing; diversity, dysbiosis indices, faecal and sputum calprotectin, and gut–lung microbial networks were analysed.
ResultsLUM/IVA was associated with increased bacterial diversity and compositional shifts in gut and lung microbiota, alongside a significant reduction in faecal calprotectin. Airway mycobiota diversity remained stable. Two lung microbiome response profiles emerged: “responders” (greater bacterial diversity gain) and “non-responders” (minimal change). Baseline gut and lung composition predicted these profiles with 81% accuracy in a random-forest model. Inter-organ microbial interactions peaked at 3 months after initiation and then diverged between profiles, indicating distinct gut–lung axis remodelling.
ConclusionLUM/IVA influences gut-lung microbiota-mycobiota dynamics, with heterogeneous responses between paediatric patients. Identifying factors predictive of response is a key future challenge.
ImpactIn 116 children aged 2–11, lumacaftor/ivacaftor reshaped gut and lung microbiota and reduced fecal calprotectin over 12 months. First pediatric multicenter study integrating bacterial and fungal profiling of stool and sputum with gut–lung network analyses; identifies two distinct lung microbiome response profiles. Baseline gut and lung composition predicted the response profile with approximately 81% accuracy. Highlights a 3-month interaction peak and baseline profiling as practical markers to guide monitoring and microbiome-informed precision care.