Impact of CFTR modulation with Ivacaftor on Gut Microbiota and Intestinal Inflammation
Bibliographic record
Abstract
Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Next to progressive airway disease, CF is also associated with intestinal inflammation and dysbiosis. Ivacaftor, a CFTR potentiator, has improved pulmonary and nutritional status but its effects on the intestinal microbiota and inflammation are unclear. Hence, we assessed the changes on the intestinal microbial communities (16S rRNA variable 3 gene region) and inflammatory markers (calprotectin and M2-pyruvate kinase [M2-PK]) in 16 CF individuals (8 children and 8 adults) before and after (median 6.1 months) ivacaftor. Stool calprotectin significantly decreased following ivacaftor (median [IQR]: 154.4 [102.1-284.2] vs. 87.5 [19.5-190.2] mg/kg, P = 0.03). There was a significant increase in Akkermansia with ivacaftor. Increased abundance of Akkermansia was associated with normal stool M2-PK concentrations, and decreased abundances of Enterobacteriaceae correlated with decreased stool calprotectin concentrations. In summary, changes in the gut microbiome and decrease in intestinal inflammation was associated with Ivacaftor treatment among individuals with CF carrying at least one gating CFTR mutation. Thus, CFTR-modifying therapy may adequately improve the aberrant pathophysiology and milieu of the CF gut to favor a more healthy microbiota, which in turn reduces intestinal inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".