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Predicting nicotine metabolism across ancestries using genotypes.

James W Baurley1, Andrew W Bergen2,3, Carolyn M Ervin2

  • 1BioRealm LLC, 340 S Lemon Ave, Suite 1931, 91789, Walnut, CA, USA. baurley@biorealm.ai.

BMC Genomics
|September 21, 2022
PubMed
Summary

We developed models using genetic data and basic characteristics to predict nicotine metabolism rates. This can personalize tobacco cessation treatments and assess health risks associated with tobacco use.

Keywords:
Machine learningNicotine biomarkersNicotine metabolismPolygenic risk scoreSmoking cessationStatistical learning

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Area of Science:

  • Pharmacogenomics
  • Tobacco Cessation Research
  • Metabolomics

Background:

  • Nicotine metabolism rate is crucial for tailoring tobacco cessation treatments and predicting tobacco-related disease risks.
  • Current methods for measuring nicotine metabolism (NMR) are limited to active smokers and face practical challenges.
  • Genetic variations near CYP2A6, ancestry, and environmental factors significantly influence nicotine metabolism.

Purpose of the Study:

  • To develop predictive models for nicotine metabolism using genetic markers and individual characteristics.
  • To overcome limitations of current nicotine metabolism measurement techniques.
  • To enable personalized approaches in tobacco cessation and risk assessment.

Main Methods:

  • Utilized data from four multiethnic studies including nicotine metabolites and DNA samples.
  • Developed a 263-marker panel from genome-wide association scans of NMR.
  • Applied seven machine learning techniques to train and validate prediction models on diverse datasets.

Main Results:

  • Achieved high correlations (0.69-0.97) between observed and predicted NMR using cross-validation.
  • An ensemble model demonstrated a correlation of 0.81 and generalized well across ancestries (African: 0.52, Asian: 0.61, European: 0.46).
  • Key predictors included genetic variants near CYP2A6 (e.g., rs56113850, rs11878604), age, and ancestry.

Conclusions:

  • Developed and validated an ensemble of seven models to predict NMR across ancestries using genotypes and basic demographics.
  • These models correlate with nicotine dosages and offer a promising tool for personalized tobacco cessation strategies.
  • Predicting individual nicotine metabolism can guide optimal cessation paths and inform tobacco use risk evaluations.