Biomarkers of Inflammation and Oxidative Stress among Adult Former Smoker, Current E-Cigarette Users—Results from Wave 1 PATH Study
Bibliographic record
Abstract
Abstract Background: Former smokers who currently use e-cigarettes have lower concentrations of biomarkers of tobacco toxicant exposure than current smokers. It is unclear whether tobacco toxicant exposure reductions may lead to health risk reductions. Methods: We compared inflammatory biomarkers (high-sensitivity C-reactive protein, IL6, fibrinogen, soluble intercellular adhesion molecule-1) and an oxidative stress marker (F2-isoprostane) among 3,712 adult participants in Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health Study by tobacco user groups: dual users of cigarettes and e-cigarettes; former smokers who currently use e-cigarettes-only; current cigarette-only smokers; former smokers who do not currently use any tobacco; and never tobacco users. We calculated geometric means (GM) and estimated adjusted GM ratios (GMR). Results: Dual users experienced greater concentration of F2-isoprostane than current cigarette-only smokers [GMR 1.09 (95% confidence interval, CI, 1.03–1.15)]. Biomarkers were similar between former smokers who currently use e-cigarettes and both former smokers who do not use any tobacco and never tobacco users, but among these groups most biomarkers were lower than those of current cigarette-only smokers. The concentration of F2-isoprostane decreased by time since smoking cessation among both exclusive e-cigarette users (Ptrend = 0.03) and former smokers who do not currently use any tobacco (Ptrend = 0.0001). Conclusions: Dual users have greater concentration of F2-isoprostane than smokers. Exclusive e-cigarette users have biomarker concentrations that are similar to those of former smokers who do not currently use tobacco, and lower than those of exclusive cigarette smokers. Impact: This study contributes to an understanding of the health effects of e-cigarettes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".