Protective effect of club cell secretory protein (CC-16) on COPD risk and progression: a Mendelian randomisation study
Bibliographic record
Abstract
Background The anti-inflammatory pneumoprotein club cell secretory protein-16 (CC-16) is associated with the clinical expression of chronic obstructive pulmonary disease (COPD). We aimed to determine if there is a causal effect of serum CC-16 level on the risk of having COPD and/or its progression using Mendelian randomisation (MR) analysis. Methods We performed a genome-wide association meta-analysis for serum CC-16 in two COPD cohorts (Lung Health Study (LHS), n=3850 and ECLIPSE, n=1702). We then used the CC-16-associated single-nucleotide polymorphisms (SNPs) as instrumental variables in MR analysis to identify a causal effect of serum CC-16 on ‘COPD risk’ (ie, case status in the International COPD Genetics Consortium/UK-Biobank dataset; n=35 735 COPD cases, n=222 076 controls) and ‘COPD progression’ (ie, annual change in forced expiratory volume in 1 s in LHS and ECLIPSE). We also determined the associations between SNPs associated with CC-16 and gene expression using n=1111 lung tissue samples from the Lung Expression Quantitative Trait Locus Study. Results We identified seven SNPs independently associated (p<5×10 –8 ) with serum CC-16 levels; six of these were novel. MR analysis suggested a protective causal effect of increased serum CC-16 on COPD risk (MR estimate (SE) −0.11 (0.04), p=0.008) and progression (LHS only, MR estimate (SE) 7.40 (3.28), p=0.02). Five of the SNPs were also associated with gene expression in lung tissue (at false discovery rate <0.1) of several genes, including the CC-16-encoding gene SCGB1A1 . Conclusion We have identified several novel genetic variants associated with serum CC-16 level in COPD cohorts. These genetic associations suggest a potential causal effect of serum CC-16 on the risk of having COPD and its progression, the biological basis of which warrants further investigation.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".