ArticleNicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco2021
Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence.
Article in Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 6 citations in OpenAlex.
- Smoking drives an epigenetic memory of aberrant hematopoiesis.medRxiv : the preprint server for health sciences · 2026Article
- Deep sequencing of candidate genes identified 14 variants associated with smoking abstinence in an ethnically diverse sample.Scientific reports · 2024Article
- Biochemical and genetic biomarkers associated with nicotine dependence in Mexican smokers.Pharmacology research & perspectives · 2023Article
- Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment.Addiction neuroscience · 2023Article
- The use of biomarkers to guide precision treatment for tobacco use.Addiction neuroscience · 2023Article
- Genetic variation in CSF2 (5q31.1) is associated with longitudinal susceptibility to pediatric malaria, severe malarial anemia, and all-cause mortality in a high-burden malaria and HIV region of Kenya.Tropical medicine and health · 2022Article
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11 authors at 7 institutions in 1 country.
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Abstract
introductionThe 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
methodsWe 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.
resultsThe 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.
conclusionsNicotine 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.
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