Fecal microbiota changes in people with cystic fibrosis after 6 months of elexacaftor/tezacaftor/ivacaftor: Findings from the promise study
Bibliographic record
Abstract
BACKGROUND: People with cystic fibrosis (PwCF) often have fecal dysbioses relative to those without CF, characterized by increased pro-inflammatory microbiota and gastrointestinal (GI) inflammation as measured by fecal calprotectin, suggesting that inflammation contributes to CF GI disease. The multicenter observational PROMISE study (NCT04038047) found that calprotectin decreased in PwCF treated with elexacaftor/tezacaftor/ivacaftor (ETI). To better understand the dynamics between fecal dysbiosis and GI inflammation, we characterized the microbiomes of fecal samples from PROMISE and the relationships with calprotectin before, 1-month post, and 6-months post ETI. METHODS: Fecal microbiota from participants ≥12 y/o were determined by shotgun metagenomic sequencing with random forest modeling and multivariate linear regression analysis to define relationships between microbiota, calprotectin, and deltaF508 genotype before and after ETI. RESULTS: We analyzed 345 samples from 124 participants. At baseline, we observed community-level differences in the fecal microbiota among participants with abnormal compared to normal calprotectin. With ETI, the relative abundances of 7 bacterial species - Escherichia coli, Staphylococcus aureus, Clostridium scindens, Enterocloster clostridioformis, Clostridium butyricum, Anaeroglobus geminatus, and Ruminococcus gnavus - decreased significantly, correlating with calprotectin decrease. We detected community-level differences in the fecal microbiota based on CFTR genotype and a distinct pattern of microbiota change in F508del homozygous compared to heterozygous participants after ETI. CONCLUSIONS: We identified 7 species for which fecal abundances decreased with ETI and correlated with calprotectin decrease, supporting a close relationship between fecal microbiota and inflammation in PwCF. Future work will define these relationships with metabolites and GI symptoms during long-term ETI therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".