Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence

Abstract

Introduction The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers. Aims and Methods We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups. Results The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score. Conclusions Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence. Implications We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

Smoking, race, ancestry and prospective abstinence

“…Confirmation of the African American TRHD of reduced abstinence using both the social construct of race and genomic ancestry, and the novel association of the predicted uNMR with abstinence, supports future analyses of cessation trials of sufficient sample size to observe and dissect disparities by ancestry 45 , models of nicotine metabolism and consumption 25,46 , socioeconomic factors 6 , and treatments. Such combined biopsychosocial cessation research would support reenvisioning of interventions in the context of the public health crises in the United States 47 .…”

Biochemical and genetic biomarkers associated with nicotine dependence in Mexican smokers

“…Muhammad‐Kah et al, found weak to moderate associations between urinary TNE in 24 h urines and nicotine dependence scores 38 . Bergen et al, reported correlations between urinary NMR and TNE with FTND score in the Multiethnic Cohort Study 39 . Overmeire et al found correlations between urinary levels of nicotine, cotinine, and 3HC with FTND score, especially with time before the first cigarette after waking 40 .…”

“…Nicotine-N′-oxide formation accounts for 3%-5% of total urinary nicotine metabolites in humans. [33][34][35][36][37][38][39][40][41][42][43] Nicotine is oxidized to nicotine-N′-oxide by FMO1, FMO2, and FMO3, 44 which are expressed both hepatically and in extrahepatic tissues including brain. 45 In rats, nicotine N′-oxide has been found to be the second most abundant nicotine metabolite in blood and brain.…”

The use of biomarkers to guide precision treatment for tobacco use

“…GWAS analyses included age, sex, BMI, principal components of genetic variation, genomic control values or relatedness matrices in GWAS analyses, and then performed inverse variance meta-analyses. NMR prediction models have selected variants from GWAS analysis [ 109 , 115 – 118 ], CYP2A6 pharmacogenetic alleles [ 116 , 117 ], and used weights from GWAS regression [ 109 , 116 , 117 ] and penalized regression and/or machine learning analyses for prediction [ 115 , 118 ]. Genomic NMR models have been externally validated with measured NMR values [ 116 – 118 ], smoking related behavior [ 109 , 115 ], and abstinence [ 116 , 117 ].…”