Effects of Switching to a Heat-Not-Burn Tobacco Product on Biologically Relevant Biomarkers to Assess a Candidate Modified Risk Tobacco Product: A Randomized Trial
Bibliographic record
Abstract
Abstract Background: Cigarette smoking increases the risk of chronic diseases; heating instead of burning tobacco can lower these risks, contributing to tobacco harm reduction. This study (with 984 adult American smokers) examined whether favorable changes occur in 8 co-primary endpoints (HDL-C, WBC, FEV1%pred, COHb, Total NNAL, sICAM-1, 11-DTX-B2, 8-epi-PGF2α) indicative of biological and functional effects when cigarette smokers switch to the heat-not-burn Tobacco Heating System 2.2 (THS). Additionally, these biomarkers of exposure (BoExp) were quantified: MHBMA, 3-HPMA, Total NNN, CEMA, 3-OH-B[a]P, HMPMA, Total 1-OHP, NEQ, and CO exhaled. Methods: Participants were randomized to continued smoking of their preferred cigarette brand (n = 496) or to using THS (IQOS brand; n = 488) for 6 months. THS has a maximum heating temperature of 350°C, delivering 1.21 mg nicotine/stick and 3.94 mg glycerin/stick under the Health Canada Intense smoking regimen. Results: The main outcome was a favorable change 6 months after baseline, with statistically significant improvements in 5 of 8 biomarkers of effect (HDL-C, WBC, FEV1%pred, COHb, Total NNAL) when smokers switched to THS compared with those who continued to smoke cigarettes. Likewise, BoExp were markedly reduced. Conclusions: All endpoints showed favorable changes in the same direction as with smoking cessation and improved biological effects were observed in smokers who predominantly used THS compared with continued cigarette smoking, with similar nicotine levels in both groups. Impact: Improvements in 5 of 8 biomarkers of effect are supportive of the research hypothesis, suggestive of disease risk reduction potential for smokers switching to THS instead of continuing to smoke 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".