Nicotine metabolite ratio: Comparison of the three urinary versions to the plasma version and nicotine clearance in three clinical studies

Drug Alcohol Depend. 2021 Apr 16;223:108708. doi: 10.1016/j.drugalcdep.2021.108708. Online ahead of print.


BACKGROUND: Variation in CYP2A6 activity influences tobacco smoking behaviors and smoking-related health outcomes. Plasma Nicotine Metabolite Ratio (NMR) is a robust phenotypic biomarker of CYP2A6 activity and nicotine clearance. In urine, the NMR has been calculated as a ratio of free trans-3′-hydroxycotinine to free cotinine (NMRF/F), total trans-3′-hydroxycotinine to free cotinine (NMRT/F), or total trans-3′-hydroxycotinine to total cotinine (NMRT/T). We evaluated these three urinary NMR versions relative to plasma NMR and nicotine clearance and elucidated mechanisms of discrepancies among them.

METHODS: Baseline plasma and urine biomarker data were available from two smoking cessation clinical trials and one nicotine pharmacokinetic study (total N = 768). NMRs were compared using Pearson correlations, linear regressions and ANOVA analyses. UGT2B10 and UGT2B17 were genotyped.

RESULTS: Urinary NMRT/F was the most highly related to plasma NMR (R2 = 0.70, P <2.2e-16) followed by NMRF/F (R2 = 0.68, P <2.2e-16), while NMRT/T was less strongly related (R2 = 0.60, P <2.2e-16); consistent across study, ethnicity, sex, heaviness of smoking, and analyte analysis. Controlling for cotinine glucuronidation, as a phenotype or UGT2B10 genotype, corrected the NMRT/T discordance with plasma NMR (Panova<0.001). Similar findings were obtained for relationships of nicotine clearance with plasma NMR > urinary NMRT/F > NMRF/F > NMRT/T (R2 = 0.41 > 0.37 > 0.35 > 0.25 respectively).

CONCLUSION: Urinary NMRT/F followed by NMRF/F are the best urinary alternatives to plasma NMR or nicotine clearance. NMRT/T has the least utility as it is influenced substantially by variation in cotinine glucuronidation.

IMPACT: This work highlighted the variation in urinary NMRs, and identified mechanisms for disparities among them, which facilitates their use in predicting smoking-related outcomes.

PMID:33873029 | DOI:10.1016/j.drugalcdep.2021.108708