Abstract 5258: Determinants of impaired lung function among never-smokers in the UK Biobank Cohort
Bibliographic record
Abstract
Abstract Introduction: The role of impaired lung function in lung cancer etiology remains contentious, especially in never-smokers who do not have primary smoking as a major risk factor, and represent an increasing proportion of lung cancers at 10-25%. Lung cancer in never-smokers (LCINS) would be the 7th most incident cancer worldwide if considered separately from smoking-related lung cancer. There has been a dearth of studies to evaluate lung function in a large cohort exclusively of never-smokers. The objective of this study was to address this gap. Methods: Based on the UK Biobank cohort, which recruited more than 500,000 individuals aged 40-69 between 2006-2010, we analyzed the association between lung function (measured as forced expiratory volume in 1-second, FEV1 and forced vital capacity, FVC) and early life factors, air pollution, and lifestyle exposures. Never-smokers were defined as those who smoked < 100 cigarettes, and impaired lung function was defined by FEV1/FVC based on GOLD criteria, FEV1 percent predicted (pp), or both (based on NICE criteria). To construct the percent predicted, reference FEV1 values were estimated using linear models in healthy never-smokers, with the predictors height, height2, age, sex, age by sex interaction, and ethnicity. Logistic regression was used to evaluate risk of impaired lung function, adjusted for all other predictors. We constructed risk-prediction models for LCINS incorporating lung function based on area under the curve (AUC) and corrected for optimism using bootstrap validation methods. Results: After excluding participants with prevalent respiratory cancers, 249,052 never-smokers were included in the analysis. The proportions of never-smokers with lung impairment based on the three criteria were: 11%, 12%, and 5%, for FEV1pp, GOLD, and NICE, respectively. Low birth weight (OR=1.32, 95% CI: 1.24-1.41) and any second-hand smoke exposure (OR=1.18, 95% CI: 1.13-1.23) increased risk of impaired FEV1 among never-smokers, as did air pollution, with PM2.5 (OR=1.83, 95% CI: 1.52-2.21) conferring the greatest increase in risk, and these associations were similar for impaired lung function based on NICE and GOLD criteria. High body mass index (OR=1.70, 95% CI: 1.62-1.77) was associated with increased risk of FEV1 impairment, but the opposite was observed with impairment defined by FEV1/FVC ratio. Risk-prediction models for LCINS incorporating lung function will also be presented. Conclusions: The association of obesity with FEV1/FVC remains paradoxical. Severe lung impairment can lead to both restrictive (FVC) and obstructive (FEV1) effects, and non-proportional impairment of these measures may help explain some of the paradoxical associations observed in this study and in the literature. Findings from this study suggest that evaluating multiple aspects of lung function may reveal a more complete picture of impairment and may inform approaches toward risk prediction. Citation Format: Matthew T. Warkentin, Rayjean J. Hung. Determinants of impaired lung function among never-smokers in the UK Biobank Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5258.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".