The pharmacogenomics of inhaled corticosteroids and lung function decline in COPD
Bibliographic record
Abstract
Inhaled corticosteroids (ICS) are widely prescribed for patients with chronic obstructive pulmonary disease (COPD), yet have variable outcomes and adverse reactions, which may be genetically determined. The primary aim of the study was to identify the genetic determinants for forced expiratory volume in 1 s (FEV1) changes related to ICS therapy. In the Lung Health Study (LHS)-2, 1116 COPD patients were randomised to the ICS triamcinolone acetonide (n=559) or placebo (n=557) with spirometry performed every 6 months for 3 years. We performed a pharmacogenomic genome-wide association study for the genotype-by-ICS treatment effect on 3 years of FEV1 changes (estimated as slope) in 802 genotyped LHS-2 participants. Replication was performed in 199 COPD patients randomised to the ICS, fluticasone or placebo. A total of five loci showed genotype-by-ICS interaction at p<5×10−6; of these, single nucleotide polymorphism (SNP) rs111720447 on chromosome 7 was replicated (discovery p=4.8×10−6, replication p=5.9×10−5) with the same direction of interaction effect. ENCODE (Encyclopedia of DNA Elements) data revealed that in glucocorticoid-treated (dexamethasone) A549 alveolar cell line, glucocorticoid receptor binding sites were located near SNP rs111720447. In stratified analyses of LHS-2, genotype at SNP rs111720447 was significantly associated with rate of FEV1 decline in patients taking ICS (C allele β 56.36 mL·year−1, 95% CI 29.96–82.76 mL·year−1) and in patients who were assigned to placebo, although the relationship was weaker and in the opposite direction to that in the ICS group (C allele β −27.57 mL·year−1, 95% CI −53.27– −1.87 mL·year−1). The study uncovered genetic factors associated with FEV1 changes related to ICS in COPD patients, which may provide new insight on the potential biology of steroid responsiveness in COPD.
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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) | 0.001 | 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 | 0.000 |
| 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".