Impact of Elexacaftor–Tezacaftor–Ivacaftor on Muscle Composition in Cystic Fibrosis: An AI-Assisted Chest CT-Based Body Composition Analysis
Bibliographic record
Abstract
Background: This study aimed to investigate longitudinal changes in muscle mass, quality, and composition (sarcopenia and myosteatosis) in adult people with cystic fibrosis (pwCF) using artificial intelligence (AI)-assisted body composition analysis (BCA) with chest computed tomography (CT) at the T12 level and to examine the influence of CFTR modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI). Methods: A retrospective observational study was conducted on 102 adult pwCF (42 females (41%), mean age 33.9 ± 11.1 years) who underwent routine chest CT scans with a minimum of six months between scans. PwCF were categorized into ETI and no ETI groups. AI-assisted BCA was performed on chest CT images at the T12 level to measure skeletal muscle area (SMA), inter- and intramuscular adipose tissue (IMAT), and low-attenuation muscle area (LAMA). IMAT/SMA ratio and height- and weight-related skeletal muscle indices (SMI) were calculated. Results: The ETI group showed a significant increase in SMA over time (p < 0.001), whereas the IMAT, LAMA, and IMAT/SMA ratio increased in both groups (all p < 0.05). SMI showed alterations only in the ETI group, with an increase in SMA/m2 (p < 0.001) and a decrease in SMA/kg (p = 0.003) and SMA/BMI (p = 0.006). Sex-specific analysis showed that SMA and myosteatosis increased regardless of sex (all p < 0.05). Weight-adjusted SMI decreased only in females receiving ETI therapy (p < 0.05). Conclusions: Adult pwCF, particularly those undergoing ETI therapy, experience significant changes in body composition, including increased muscle mass and myosteatosis. Trends in the development of sarcopenic obesity have been observed, particularly in female pwCF. These findings emphasize the importance of comprehensive body composition assessments and targeted interventions in pwCF treated with ETI to optimize muscle mass and quality while managing adipose tissue accumulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".