Nicotine Metabolism-informed Care for Smoking Cessation: A Pilot Precision RCT
Bibliographic record
Abstract
Introduction: Varenicline doubles cessation over nicotine replacement therapy (NRT) patch for "normal," but not "slow," nicotine metabolizers, as assessed by the nicotine metabolite ratio (NMR). Metabolism-informed care (MIC) could improve outcomes by matching normal metabolizers with non-nicotine medication (e.g., varenicline) and slow metabolizers with NRT patch. Methods: We conducted a feasibility randomized controlled trial of MIC versus guideline based care (GBC) among 81 outpatient adult daily smokers with medical comorbidity. Participants reported perceptions of MIC, underwent blood draw for NMR, and received expert cessation counseling. For MIC participants, medication selection was informed by NMR result (normal (≥0.31) vs. slow (< 0.31)). The primary outcome was MIC feasibility, reflected by attitudes toward MIC and by match rates between NMR and medication. Secondary endpoints (cessation confidence, medication use, smoking status) were assessed over 6 months to inform future studies. Results: Participants were median age 53 years, 46% female, 28% black, and ~90% endorsed MIC. Despite high varenicline prescription rates (~60%) in both arms, NMR-medication matching was higher in MIC (84%) versus GBC (58%) participants (p=0.02); unadjusted odds ratio (OR) 3.67, 95% confidence interval [1.33, 11.00; p-value=0.02]. Secondary endpoints were similar at 1, 3, and 6 months. Conclusions: MIC, an NMR-based precision approach to smoking cessation, was acceptable to 90% of smokers and improved NMR-medication match rates more than 3-fold compared to GBC, even with generally high use of varenicline. These data support the feasibility of MIC, which could maximize efficacy of smoking cessation medication while minimizing side effects and cost. Implications: Among treatment-seeking daily smokers with medical comorbidity, most viewed metabolism-informed care (MIC), guided by the nicotine metabolism ratio (NMR), favorably, and were willing to accept MIC-guided medication. Compared to GBC participants (58%), more MIC participants (84%) were prescribed NMR-matched medication (i.e., normal metabolizers received varenicline; slow metabolizers received NRT patch). MIC increased the odds of optimized matching between NMR and medication more than 3-fold over GBC. Because the number needed to treat (NNT) to help one normal metabolizer quit smoking is only 4.9 for varenicline versus 26 for patch, broad implementation of MIC will improve drug efficacy in normal metabolizers as well as minimize side effects in slow metabolizers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".