A Comparison of Direct and Indirect Analytical Approaches to Measuring Total Nicotine Equivalents in Urine
Bibliographic record
Abstract
Abstract Background: Total nicotine equivalents (TNE), the sum of nicotine and metabolites in urine, is a valuable tool for evaluating nicotine exposure. Most methods for measuring TNE involve two-step enzymatic hydrolysis for indirect quantification of glucuronide metabolites. Here, we describe a rapid, low-cost direct LC/MS assay. Methods: In 139 smokers' urine samples, Bland–Altman, correlation, and regression analyses were used to investigate differences in quantification of nicotine and metabolites, TNE, and nicotine metabolite ratio (NMR) between direct and indirect LC/MS methods. DNA from a subset (n = 97 smokers) was genotyped for UGT2B10*2 and UGT2B17*2, and the known impact of these variants was evaluated using urinary ratios determined by the direct versus indirect method. Results: The direct method showed high accuracy (0%–9% bias) and precision (3%–14% coefficient of variation) with similar distribution of nicotine metabolites to literary estimates and good agreement between the direct and indirect methods for nicotine, cotinine, and 3-hydroxycotinine (ratios 0.99–1.07), but less agreement for their respective glucuronides (ratios 1.16–4.17). The direct method identified urinary 3HC+3HC-GLUC/COT as having the highest concordance with plasma NMR and provided substantially better estimations of the established genetic impact of glucuronidation variants compared with the indirect method. Conclusions: Direct quantification of nicotine and metabolites is less time-consuming and less costly, and provides accurate estimates of nicotine intake, metabolism rate, and the impact of genetic variation in smokers. Impact: Lower cost and maintenance combined with high accuracy and reproducibility make the direct method ideal for smoking biomarker, NMR, and pharmacogenomics studies. Cancer Epidemiol Biomarkers Prev; 27(8); 882–91. ©2018 AACR.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".