Early-Onset Chronic Obstructive Pulmonary Disease Is Associated with Female Sex, Maternal Factors, and African American Race in the COPDGene Study
Bibliographic record
Abstract
RATIONALE: The characterization of young adults who develop late-onset diseases may augment the detection of novel genes and promote new pathogenic insights. METHODS: We analyzed data from 2,500 individuals of African and European ancestry in the COPDGene Study. Subjects with severe, early-onset chronic obstructive pulmonary disease (COPD) (n=70, age < 55 yr, FEV1 < 50% predicted) were compared with older subjects with COPD (n =306, age >64 yr, FEV1 <50% predicted). MEASUREMENTS AND MAIN RESULTS: Subjects with severe, early-onset COPD were predominantly females (66%), P =0.0004. Proportionally,early-onset COPD was seen in 42% (25 of 59) of African Americans versus 14% (45 of 317) of non-Hispanic whites, P <0.0001. Other risk factors included current smoking (56 vs. 17%, P < 0.0001) and self-report of asthma (39 vs. 25%, P =0.008). Maternal smoking (70 vs. 44%, P=0.0001) and maternal COPD (23 vs.12%, P=0.03) were reported more commonly in subjects with early-onset COPD. Multivariable regression analysis found association with African American race, odds ratio (OR), 7.5 (95% confidence interval [CI], 2.3–24; P ¼=0.0007); maternal COPD, OR, 4.7 (95% CI,1.3–17; P=0.02); female sex, OR, 3.1 (95% CI, 1.1–8.7; P=0.03); and each pack-year of smoking, OR, 0.98 (95% CI, 0.96–1.0; P ¼ 0.03). CONCLUSIONS: These observations support the hypothesis that severe, early-onset COPD is prevalent in females and is influenced by maternal factors. Future genetic studies should evaluate (1) gene-by-sex interactions to address sex-specific genetic contributions and (2) gene-by-race interactions.
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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 | 0.001 | 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) | 0.001 | 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".