Nicotine Metabolite Ratio (3-Hydroxycotinine/Cotinine) in Plasma and Urine by Different Analytical Methods and Laboratories: Implications for Clinical Implementation
Bibliographic record
Abstract
BACKGROUND: The highly genetically variable enzyme CYP2A6 metabolizes nicotine to cotinine (COT) and COT to trans-3'-hydroxycotinine (3HC). The nicotine metabolite ratio (NMR, 3HC/COT) is commonly used as a biomarker of CYP2A6 enzymatic activity, rate of nicotine metabolism, and total nicotine clearance; NMR is associated with numerous smoking phenotypes, including smoking cessation. Our objective was to investigate the impact of different measurement methods, at different sites, on plasma and urinary NMR measures from ad libitum smokers. METHODS: Plasma (n = 35) and urine (n = 35) samples were sent to eight different laboratories, which used similar and different methods of COT and 3HC measurements to derive the NMR. We used Bland-Altman analysis to assess agreement, and Pearson correlations to evaluate associations, between NMR measured by different methods. RESULTS: Measures of plasma NMR were in strong agreement between methods according to Bland-Altman analysis (ratios, 0.82-1.16) and were highly correlated (all Pearson r > 0.96, P < 0.0001). Measures of urinary NMR were in relatively weaker agreement (ratios 0.62-1.71) and less strongly correlated (Pearson r values of 0.66-0.98, P < 0.0001) between different methods. Plasma and urinary COT and 3HC concentrations, while weaker than NMR, also showed good agreement in plasma, which was better than that in urine, as was observed for NMR. CONCLUSIONS: Plasma is a very reliable biologic source for the determination of NMR, robust to differences in these analytical protocols or assessment site. IMPACT: Together this indicates a reduced need for differential interpretation of plasma NMR results based on the approach used, allowing for direct comparison of different studies.
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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 | 0.397 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".