Urinary Tobacco and Nicotine Exposure Biomarkers as Predictors of Transitions between Cigarette and e-Cigarette Use...

Urinary tobacco and nicotine exposure biomarkers may be predictive of subsequent transitions in product use.

We used data from an observational study of 371 adults who smoked cigarettes daily, some of whom also used e-cigarettes, and who were followed every 2 months for up to 2 years (Wisconsin, United States, 2015-2019). Using a multistate transition model, we assessed continuous associations between transition propensities and urinary tobacco biomarker concentrations, namely 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol) (NNAL) and NE-2 (cotinine + trans-3'-hydroxycotinine), the nicotine metabolite ratio (NMR; trans-3'-hydroxycotinine:cotinine), and NNAL:NE-2, measured every 4 months.

The biomarkers were generally more predictive of transitions from dual use, but not from cigarette-only use, than self-reported product use was. Propensity to stop smoking cigarettes decreased with increasing concentrations of NNAL and NE-2, for both participants who smoked only cigarettes and those who used both cigarettes and e-cigarettes. For example, we estimated that, at 20 pg NNAL per mg creatinine, 30.2% (95% CI = 14.6%, 47.0%) of those who only smoke cigarettes and 26.6% (95% CI = 11.3%, 42.5%) who dual use would transition to non-current cigarette use and e-cigarette use in 1 year, while at the 200 pg/ng, we estimate these probabilities to be 3.2% (95% CI = 1.7%, 5.8%) and 3.9% (95% CI = 1.9%, 8.5%), respectively. The ratio NNAL:NE-2 was predictive of transitions from dual use to cigarette-only (higher ratio) or e-cigarette-only (lower ratio) use.

Urinary tobacco biomarkers were non-linearly associated with transitions in tobacco product use and may guide the development of clinical interventions to promote harm-reducing product use transitions.

This work may inform the development of clinical interventions to promote harm-reducing product use transitions by using urine testing that more sensitively reflects product exposure than self-reported product use. Additionally, regulations restricting nicotine concentration in cigarettes or e-cigarettes may affect individuals' biomarker levels. By understanding how different biomarker levels are differentially associated with transition propensities, we can help to contribute to impact analysis of potential nicotine limiting regulations.