Metabolomic signature and prediction of incident lung cancer from air pollution exposure in a national cohort: Unraveling the link and underlying role
Bibliographic record
Abstract
Air pollution exposure has been identified as a pathogenic factor of lung cancer, whereas the metabolic profile disturbance involved and its underlying role remain unclear while attract much attention. Metabolomic profiling in plasma was conducted among 205,974 participants in the UK Biobank. Particulate matter (PM) with aerodynamic diameter ≤ 10 μm (PM 10 ), PM 2.5 , PM 2.5–10 , nitrogen dioxide (NO 2 ), and nitrogen oxides (NO x ) were assessed by land-use regression models. Mediation roles of metabolic features involved in air pollution and incident lung cancer, and performance of the lung cancer prediction model incorporating crucial metabolite features identified by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression, were evaluated. During a median follow-up period of 13.1 years, 1,536 incident lung cancer cases were recorded. Among the 143 metabolite features, 66 overlapped in PM 2.5 , NO 2 , or NO x exposure-associated incident lung cancer after multivariate adjustment (false discovery rate P < 0.05). The highest mediation proportions were observed for Albumin (percentage mediated: 4.02 %), Phospholipids in Medium Very-Low-Density Lipoproteins (M-VLDL) (6.38 %), and M-VLDL (6.42 %) in incident lung cancer from PM 2.5 , NO 2 , and NO x exposure, respectively. LASSO and multivariate Cox regression identified 15 metabolite features associated with lung cancer, and inclusion of these metabolite features significantly improved the prediction of lung cancer (C statistic: 0.851; Net reclassification improvement index: 0.144; Integrated discrimination improvement index: 0.005). Disturbance and mediation role of circulating metabolic features in air pollution exposure and incident lung cancer were identified, and metabolite profiling may well improve early prediction of lung cancer. • Metabolic features associated with air pollution exposure were identified. • Metabolic disturbance was associated with lung cancer risk. • Mediation role of metabolites in air pollution and lung cancer was unveiled. • Metabolite profiling may well improve early prediction of lung cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".