Exploring Potential for a Personalized Medicine Approach to Smoking Cessation With an American Indian Tribe
Bibliographic record
Abstract
INTRODUCTION: A potential precision medicine approach to smoking cessation is tailoring pharmacotherapy to a biomarker known as the nicotine metabolite ratio (NMR). Little is known about the potential impact and acceptability of this approach for American Indian (AI) persons. AIMS AND METHODS: Tribal-academic collaboration was formed and during 2019-2020 AI adults who smoke(N = 54) were recruited to (1) examine correlations between NMR, dependence, and smoking exposure; (2) assess the extent to which pharmacotherapy preference aligned with NMR-informed recommendations; (3) explore acceptability of NMR-informed pharmacotherapy selection. Participants provided samples for assessment of salivary NMR and urinary total nicotine equivalents (TNE) and completed a questionnaire that assessed cigarettes per day (CPD), Fagerstrom Test for Cigarette Dependence (FTCD), pharmacotherapy preference, and perceptions of NMR-informed pharmacotherapy selection. RESULTS: Significant positive correlations were observed between NMR and FTCD (r = 0.29;p = .0383) and its abbreviated version Heaviness of Smoking Index (HIS) (r = 0.28;p =.0426). Post-hoc analyses suggest that relationships between dependence and NMR were driven by time to first cigarette. Nonsignificant, but directionally consistent, relationships were observed between NMR and CPD (r = 0.21; p =0.1436) and TNE (r = 0.24;p = .2906). Most participants preferred nicotine replacement therapy (71%) over varenicline (29%) and preference for pharmacotherapy matched NMR-based recommendations in 54% of participants. NMR-informed pharmacotherapy selection was supported by 62% of participants. CONCLUSION: In a sample of AI adults who smoke, NMR was related to cigarette dependence and about one-half of participants' pharmacotherapy preference matched their NMR-informed recommendation. There was lower acceptability of NMR-informed approach in this sample of AI adults than prior studies among white or black/African American people who smoke. IMPLICATIONS: Relationships between NMR, dependence, and self-preference for pharmacotherapy suggest that NMR-informed pharmacotherapy selection may have potential for enhancing smoking quitting success in this Tribe. Lower acceptability of NMR-informed pharmacotherapy in this Tribe suggests that this approach may not be equitably utilized. Future work could include identifying community-driven solutions to mitigate precision medicine concerns.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".