Late Breaking Abstract - Smoking and alcohol drinking increases the risk of bronchiectasis in CFTR p.Phe508del carriers
Bibliographic record
Abstract
How CFTR variants interact with environmental exposures is key to identify risk factors for non-CF pulmonary diseases. We performed Protein Quantitative Trait Loci (pQTL) analyses to identify CFTR–protein associations, and linear regression to assess differences in expression across lifestyle subgroups. In UK Biobank (n=225,281), the associations between CFTR p.Phe508del heterozygosity, smoking and/or alcohol consumption on pulmonary outcomes were assessed using cox and firth logistic regression models stratified by exposure groups. In BQC19, (n=1,254), two CFTR-associated trans pQTLs were identified: rs60387136 (linked to PNLIPRP1) and rs1343830 (linked to PRSS2, REG3G, and CPB1). In UK Biobank (n=35,989), five trans-pQTLs were identified: rs10255917 (linked to PNLIPRP1); rs994997 (linked to CPA1, PRSS2, and CTRB1); rs214152 (linked to CPA1); and rs6466619 and rs4727851 (linked to CPB1). All pancreatic-associated proteins. CFTR p.Phe508del carriers had reduced plasma levels of CPA1, CPB1 and REG3A and increased risk and odds of bronchiectasis (HR=1.38, p=0.0038; OR=1.39, p=0.005). These associations were particularly strong in never smokers (bronchiectasis: HR=1.66, p=0.0016; OR=1.69, p=0.003) and very heavy drinkers (bronchiectasis: HR=1.59, p=0.019; OR=1.62, p=0.023). Among current smokers, heterozygosity was linked to increased bronchiectasis risk (HR=1.83, p=0.042; OR=1.90, p=0.046). Kaplan–Meier analyses showed earlier onset of bronchiectasis (p=0.0043) in carriers, especially in never smokers and very heavy drinkers. We identified altered levels of pancreatic enzymes in CFTR p.Phe508del carriers and an increased risk of bronchiectasis in current smokers and very heavy drinkers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".