Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Cardiovascular dysfunction induced by combined exposure to nicotine inhalation and high-fat diet

Smoking and high-fat diet (HFD) consumption are two modifiable risk factors for cardiovascular (CV) diseases, and individuals who are overweight or obese due to unhealthy diet are more likely to use tobacco products. In this study, we aim to investigate the combined effects of nicotine (the addictive component of all tobacco products) and HFD on CV health, which are poorly understood. C57BL/6N male mice were placed on either HFD (60 kcal% fat) or regular diet (22 kcal% fat) and exposed to air or nicotine vapor for 10-12 wk. CV function was monitored by echocardiography and radiotelemetry, with left ventricular (LV) catheterization and aortic ring vasoreactivity assays performed at end point. Mice on HFD exhibited increased heart rate and impaired parasympathetic tone, whereas nicotine exposure increased sympathetic vascular tone as evidenced by increased blood pressure (BP) response to ganglionic blockade. Although neither nicotine nor HFD alone or in combination significantly altered BP, nicotine exposure disrupted circadian BP regulation with reduced BP dipping. LV catheterization revealed that combined exposure to nicotine and HFD led to LV diastolic dysfunction with increased LV end-diastolic pressure (LVEDP). Moreover, combined exposure resulted in increased inhibitory phosphorylation of endothelial nitric oxide synthase and greater impairment of endothelium-dependent vasodilation. Finally, a small cohort of C57BL/6N females with combined exposure exhibited similar increases in LVEDP, indicating that both sexes are susceptible to the combined effect of nicotine and HFD. In summary, combined exposure to nicotine and HFD leads to greater CV harm, including both additive and new-onset CV dysfunction.NEW & NOTEWORTHY Nicotine product usage and high-fat diet consumption are two modifiable risk factors for cardiovascular diseases. Here, we demonstrate that in mice, combined exposure to inhaled nicotine and high-fat diet results in unique cardiovascular consequences compared with either treatment alone, including left ventricular diastolic dysfunction, dysregulation of blood pressure, autonomic dysfunction, and greater impairment of endothelium-dependent vasorelaxation. These findings indicate that individuals who consume both nicotine products and high-fat diet have distinctive cardiovascular risks.

Adult Electronic Nicotine Delivery System Use in the USA: a Scoping Review Through a Health Equity Lens

Purpose of Review

The objective of this scoping review is to use a health equity lens to understand the extent and type of evidence that exists about the use of electronic nicotine delivery systems (ENDS) based on socioecological understandings of health influences (i.e., the US National Institute of Minority Health and Health Disparities' (NIMHD) Research Framework). The research question guiding the review was as follows: What is the range and scope of research that exists to help characterize health disparities related to ENDS use?

Recent Findings

Ninety-eight articles published between 2019 and 2022 often examined racial/ethnic differences in ENDS use; however, other demographic characteristics, domains of influence across the life course, and levels of influence beyond individuals were rarely considered. As most studies were derived from large-scale, cross-sectional US national surveys, few longitudinal studies or intervention trials were published.

Summary

The use of the NIMHD framework to analyze recent literature helped identify research patterns and gaps that may be important to recognize for optimizing population health strategies to advance health equity. Future research on non-individual level factors influencing ENDS, interventions to effectively use ENDS for combustible cigarette cessation, and subpopulations susceptible to dual use may enhance existing science. Monitoring research using the NIMHD research framework can help researchers and policy makers to identify and when appropriate, prioritize support for overlooked but important research questions.

Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach

The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.

Cross-Sectional Associations of Self-Reported Social/Emotional Support and Life Satisfaction with Smoking and Vaping Status in Adults

This study aimed to examine the cross-sectional association of self-reported social/emotional support and life satisfaction with smoking/vaping status in US adults. The study included 47,163 adult participants who self-reported social/emotional support, life satisfaction, and smoking/vaping status in the 2016 and 2017 BRFSS national survey data. We used multivariable weighted logistic regression models to measure the cross-sectional association of self-reported social/emotional support and life satisfaction with smoking/vaping status. Compared to never users, dual users and exclusive smokers were more likely to have low life satisfaction, with an adjusted odds ratio (aOR) = 1.770 (95% confidence interval [CI]: 1.135, 2.760) and an aOR = 1.452 (95% CI: 1.121, 1.880) respectively, especially for the age group 18-34. Exclusive cigarette smokers were more likely to have low life satisfaction compared to ex-smokers (aOR = 1.416, 95% CI: 1.095, 1.831). Exclusive cigarette smokers were more likely to have low social/emotional support (aOR = 1.193, 95% CI: 1.030, 1.381) than never users, especially those aged 65 and above. In addition, exclusive cigarette smokers were more likely to have low social/emotional support than ex-smokers, with an aOR = 1.279 (95% CI: 1.097, 1.492), which is more pronounced among the age group 18-34, as well as 65 and above. Our results suggest that life satisfaction and social/emotional support may play important roles in smoking and vaping, which should be incorporated into behavioral interventions to reduce tobacco use.

Building risk prediction models for daily use of marijuana using machine learning techniques

Identifying the characteristics of adults with recent marijuana use is limited by standard statistical methods and requires a unique approach. The objective of this study is to evaluate the efficiency of machine learning models in predicting daily marijuana use and identify factors associated with daily use among adults. The study analyzed pooled data from the 2016-2019 Behavioral Risk Factor Surveillance System (BRFSS) Survey in 2020. Prediction models were developed using four machine learning algorithms, including Logistic Regression, Decision Tree, and Random Forest with Gini function, and Naïve Bayes. Respondents were randomly divided into training and testing samples. The performance of all the models was compared using accuracy, AUC, precision, and recall. The study included 253,569 respondents, of whom 10,182 (5.9 %) reported daily marijuana use in the last 30 days. Of daily marijuana use, 53.4 % were young adults (age 18-34 years), 34.3 % female, 56.1 % non-Hispanic White, 15.2 % were college graduates, and 67.3 % were employed. Random Forest was the best performing model with AUC 0.97, followed by a Decision tree (AUC 0.95). The most important factors for daily marijuana use were the current use of e-cigarette and combustible cigarette use, male gender, unmarried, poor mental health, depression, cognitive decline, abnormal sleep pattern, and high-risk behavior. Data mining methods were useful in the discovery of behavior health-risk knowledge and to visualize the significance of predicting modeling from a multidimensional behavioral health survey.

E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18-1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.