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

Giratallah, Haidy; Chenoweth, Meghan J.; Addo, Newton; Ahluwalia, Jasjit S.; Cox, Lisa Sanderson; Lerman, Caryn; George, Tony P.; Benowitz, Neal L.; Tyndale, Rachel F.

doi:10.1016/j.drugalcdep.2021.108708

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…For example, in the METS, participants were administered fixed doses of labeled nicotine and cotinine followed by biospecimen collection at regular intervals, with the 6 hour collection being used to assess NMR. However, this timing does not allow the metabolite ratio to reach a steady state, and thus the NMR is under estimated relative to NMR measured based on steady state kinetics as in the MEC [ 8 ]. In addition, non-smokers will have slightly reduced NMR estimates compared to smokers, as cigarette smoking has been associated with increases in the NMR [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

“…This may account for the lower correlation between the predicted urinary total 3HC/free COT and the laboratory-based NMR in the METS. Additionally, the HSS NMR measure included total cotinine, with individual variation in the cotinine glucuronidation ratio, as this NMR measure contains glucuronidated cotinine in the denominator [ 8 , 46 ]. Specific adjustment for cotinine glucuronidation ratio was shown to substantially improve prediction of plasma NMR using the urinary total 3HC to total COT ratio [ 3 , 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the HSS NMR measure included total cotinine, with individual variation in the cotinine glucuronidation ratio, as this NMR measure contains glucuronidated cotinine in the denominator [ 8 , 46 ]. Specific adjustment for cotinine glucuronidation ratio was shown to substantially improve prediction of plasma NMR using the urinary total 3HC to total COT ratio [ 3 , 8 ]. These differences however provided a unique opportunity for validation since while the NMR measures do differ between some of the cohorts studied here, we do know these NMRs are correlated [ 8 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

“…Specific adjustment for cotinine glucuronidation ratio was shown to substantially improve prediction of plasma NMR using the urinary total 3HC to total COT ratio [ 3 , 8 ]. These differences however provided a unique opportunity for validation since while the NMR measures do differ between some of the cohorts studied here, we do know these NMRs are correlated [ 8 , 46 ]. As such, in our analysis, we trained on the largest and most diverse cohort (MEC), and were able to validate the NMR models in the other three studies (METS, CENIC, and HSS).…”

Section: Discussionmentioning

confidence: 99%

“…While measurements of nicotine metabolites have good reproducibility within these biofluids [ 7 ], differences in metabolite measurements (e.g. total or free 3HC and COT) make results difficult to compare and interpret across studies [ 8 ]. Biochemical measurement of the NMR also requires biofluids to be collected relatively soon after the intake of nicotine, which is impractical in former smokers or if a smoker is an occasional user of tobacco.…”

Section: Introductionmentioning

confidence: 99%

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Predicting nicotine metabolism across ancestries using genotypes

et al. 2022

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Background There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3’-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight). Results We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry. Conclusions We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting nicotine metabolism across ancestries using genotypes

et al. 2022

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PharmVar GeneFocus: CYP2A6

Langlois,

Chenoweth,

Twesigomwe

et al. 2024

Clin Pharma and Therapeutics

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The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the human CYP2A gene locus containing the highly polymorphic CYP2A6 gene. CYP2A6 plays a role in the metabolism of nicotine and various drugs. Thus, genetic variation can substantially contribute to the function of this enzyme and associated efficacy and safety. This GeneFocus provides an overview of the clinical significance of CYP2A6, including its genetic variation and function. We also highlight and discuss caveats in the identification and characterization of allelic variation of this complex pharmacogene, a prerequisite for accurate genotype determination and prediction of phenotype status.

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