Epigenetic modelling of former, current and never smokers.

BACKGROUND: DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. RESULTS: We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. CONCLUSIONS: Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary.

Behavioral heterogeneity among cigarette and e-cigarette dual-users and associations with future tobacco use: Findings from the Population Assessment of Tobacco and Health Study.

Dual-users of cigarettes and e-cigarettes are commonly treated as a single group. Our study applied a more nuanced classification of this complex behavior to examine its associations with future tobacco use behaviors using data from Waves 1 and 3 of the Population Assessment of Tobacco and Health. Dual-users at Wave 1 (n = 1,665) were categorized into 4 groups based on the frequency with which they used each product (i.e., some days, daily). Analyses identified sociodemographic correlates of group membership and the prevalence of (1) completely switching to e-cigarettes and (2) quitting both products by Wave 3. Dual-users who smoked cigarettes every day and used e-cigarettes some days (69.6%) were the majority and more likely to have lower education (p < .001). Although some day smoking and daily e-cigarette use was the least common category (5.9%), these individuals were most likely to have completely switched to e-cigarettes by Wave 3 (aOR = 6.19, 95% CI = 3.91, 9.79). Dual-users who smoked and used e-cigarettes some days were most likely to have completely quit tobacco by Wave 3 (aOR = 3.98, 95% CI = 2.93, 5.40). In general, dual-users who had higher education or income were more likely to have completely switched to e-cigarettes or quit tobacco use by Wave 3. Adults who concurrently use cigarettes and e-cigarettes exhibit considerable heterogeneity in their use of these tobacco products. Dual-users that are higher on the socioeconomic gradient are more likely to engage in plausibly less harmful dual-use behaviors, which are more strongly associated with harm reduction and cessation behaviors. Future research should consider this variation to more accurately characterize the public health impact of dual-use.

Flavor Preference and Systemic Immunoglobulin Responses in E-Cigarette Users and Waterpipe and Tobacco Smokers: A Pilot Study.

Electronic cigarette (e-cigarette) use has had an exponential increase in popularity since the product was released to the public. Currently, there is a lack of human studies that assess different biomarker levels. This pilot study attempts to link e-cigarette and other tobacco product usage with clinical respiratory symptoms and immunoglobulin response. Subjects completed surveys in order to collect self-reported data on tobacco product flavor preferences. Along with this, plasma samples were collected to test for immunoglobulin G (IgG) and E (IgE) levels. Our pilot study's cohort had a 47.9% flavor preference towards fruit flavors and a 63.1% preference to more sweet flavors. E-cigarette and traditional cigarette smokers were the two subject groups to report the most clinical symptoms. E-cigarette users also had a significant increase in plasma IgE levels compared to non-tobacco users 1, and dual users had a significant increase in plasma IgG compared to non-tobacco users 2, cigarette smokers, and waterpipe smokers. Our pilot study showed that users have a preference toward fruit and more sweet flavors and that e-cigarette and dual use resulted in an augmented systemic immune response.

E-Cigarette Use by Smoking Status in Estonia, 2012-2018.

Abstract: Background: In the context of declining smoking rates in Estonia, this study aims to analyze the recent trends in e-cigarette use and its associations with smoking status and sociodemographic factors. Methods: Nationally representative data from biennial cross-sectional health surveys in 2012-2018 (n = 9988) were used to describe the prevalence of smoking and e-cigarette use by smoking status in Estonia. Multivariate logistic regression analysis was used to describe the sociodemographic patterns of e-cigarette use in three subgroups: the general population, smokers, and ex-smokers. Results: The prevalence of current smoking decreased from 45.4% in 2012 to 31.5% in 2018 among men and from 26.6% to 20.0% among women. At the same time, e-cigarette use in the general population had increased to 3.7% among men and to 1.2% among women. The increase in the prevalence of e-cigarette use was statistically significant among men in the general population, smokers, and ex-smokers, but non-significant among women. In addition to period effects, e-cigarette use was patterned by age, gender, and education. Conclusion: In 2002-2018, the e-cigarette use had increased but smoking had decreased in Estonia. A timely and targeted tobacco policy may alleviate the harm of e-cigarette use from the public health perspective.

Identifying smoker subgroups with high versus low smoking cessation attempt probability: A decision tree analysis approach.

BACKGROUND: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES). METHODS: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787). RESULTS: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES. CONCLUSIONS: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach.

Ecological momentary assessment of smoking behaviors in native and converted intermittent smokers.

BACKGROUND AND OBJECTIVES: About 22% of adult smokers in the U.S. are intermittent cigarette smokers (ITS). ITS can be further classified as native ITS who never smoked daily and converted ITS who formerly smoked daily but reduced to intermittent smoking. Ecological momentary assessment (EMA) was conducted to determine the behaviors and experiences that are associated with the decision to smoke. METHODS: The study included 24 native ITS and 36 converted ITS (N = 60) from the Pennsylvania Adult Smoking Study. A baseline questionnaire, daily log, and an EMA smoking log that assessed emotions, activities, and smoking urges was filled out with each cigarette for 1 week to capture 574 smoking sessions. RESULTS: Both groups had very low levels of cigarette dependence. Both groups were more tempted to smoke in positive or negative situations than situations associated with habituation. EMA showed that the most common emotional state during smoking sessions was positive (47%), followed by negative (32%), neutral (16%), and mixed (5%) emotions. Smokers were more likely to smoke during activities of leisure (48%) than during performative duties (29%), social (16%) or interactive occasions (7%). Converted ITS were more likely to smoke alone compared to native ITS (p < .001). DISCUSSION AND CONCLUSIONS: ITS report minimal levels of dependence when captured on traditional scales of nicotine dependence, yet experience loss of autonomy and difficulty quitting. The majority of the ITS reported positive emotions and leisure activities while smoking, and smoked during the evening. SCIENTIFIC SIGNIFICANCE: The current paper identifies environmental and behavioral factors that are associated with smoking among ITS in real time. (Am J Addict 2018;27:131-138).

Revised and extended serum cotinine cut-offs to classify smokers and non-smokers.

PURPOSE: To revise and extend the previously published serum cotinine cut offs to classify smokers and non-smokers for US adolescents and adults. MATERIALS AND METHODS: Cross-sectional data (N = 10171) from National Health and Nutrition Examination Survey for 2011-2014 were used to compute serum cotinine cut-offs to classify smokers and non-smokers for US adults aged ≥20 years and 2007-2014 (N = 4583) data were used to compute serum cotinine cut-offs for US adolescents aged 12-19 years. RESULTS: Specificities and sensitivities for the cut-offs among adults were ≥95% and ≥75% among adolescents. For adults, serum cotinine cut-offs in ng/mL to classify smokers from non-smokers were 3.3 for the total population, 4.13 for males, 2.99 for females, 4.03 for non-Hispanic whites, 8.85 for non-Hispanic blacks, 0.377 for Mexican Americans, 1.72 for other Hispanics and 1.41 for non-Hispanic Asians. For adolescents, serum cotinine cut-offs in ng/mL to classify smokers from non-smokers were 0.765 for the total population, 1.1 for males, 0.408 for females, 1.2 for non-Hispanic whites, 1.98 for non-Hispanic blacks, 0.215 for Mexican Americans and 0.321 for other Hispanics. CONCLUSIONS: Serum cotinine cut-offs to distinguish smokers from non-smokers for US adults and adolescents were developed.

Types of Smokers Who Seek Smoking Cessation Treatment According to Psychopathology.

Psychopathology and psychological distress have been shown to be related to poor smoking cessation outcomes and abstinence maintenance. Thus, it is important to identify individuals with high levels of psychopathology before undergoing smoking cessation treatment in order to increase their likelihood of success. OBJECTIVE: The primary aim of the present study was to analyze whether we could classify smokers by using self-reported measures of psychopathology. In addition, a secondary aim was to examine if there were significant differences among the groups of smokers regarding sociodemographic information, nicotine dependence, and cessation rates at the end of treatment and at 6- and 12-month follow-ups. METHODS: Participants were 281 smokers seeking smoking cessation treatment. Participants were classified into different smoking groups by using a 2-step cluster analysis based on baseline scores on the Restructured Clinical (RC) scales of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF), Beck Depression Inventory-II (BDI-II), and State-Trait Anxiety Inventory (STAI). RESULTS: Smokers were classified into 3 groups according to levels of psychopathology: Low (n = 158), Intermediate (n = 78), and High (n = 45). Smokers in the High Group were more likely to present higher levels of psychopathology and to continue smoking at the end of treatment when compared with the two other clusters. In addition, smokers classified in this group were more likely to be nicotine dependent and from a low social class. CONCLUSIONS: A subgroup of smokers can be easily identified through self-report measures of psychopathology. Furthermore, these individuals were more likely to continue smoking at the end of treatment. This suggests that this group with high levels of psychopathology might benefit from future interventions that are more intensive or cessation treatments targeted to their specific characteristics.

Subgroups Among Smokers in Preparation: A Cluster Analysis Using the I-Change Model.

BACKGROUND: Investigating potential sub-stages of change could provide important information that could be used to improve the tailoring of smoking cessation interventions to individual smokers' profiles. Smokers in the preparation stage may be most interesting, as they are most likely to participate in smoking cessation interventions. OBJECTIVE: To examine whether Dutch adult smokers in the preparation stage of change, i.e. motivated to quit smoking within one month, can be organized into subgroups. METHODS: Data from 753 smokers who participated in an effectiveness trial of a web-based, computer-tailored smoking cessation programme were subjected to secondary analysis. Cluster analyses were based on respondents' baseline responses to items on pros and cons of quitting and quitting self-efficacy. Chi-squared tests and ANOVA were used to compare the baseline characteristics of the resulting clusters. Logistic and multinomial regression were used for longitudinal comparisons of clusters with respect to smoking abstinence and stage transition at six-week and six-month follow-ups. RESULTS: Four clusters were identified; Classic, Unprepared, Progressing and Disengaged Preparers. Cross-sectional and longitudinal analyses validated these clusters: they differed with respect to the clustering variables, gender, cigarette dependence and educational level. Disengaged Preparers were less likely than Progressing Preparers to report smoking abstinence at six months (OR = 0.28; p < .05). CONCLUSIONS: These results suggest that smoking cessation interventions tailored to the preparation stage of change, i.e. the set of cognitions usually present in preparers, are only appropriate for the subgroup we defined as Classic Preparers. The other clusters might need different interventions as they display different cognition sets.