e-Cigarettes, Smoking Cessation, and Weight Change: Retrospective Secondary Analysis of the Evaluating the Efficacy of e-Cigarette Use for Smoking Cessation Trial

Abstract

Background: While smoking cessation has been linked to substantial weight gain, the potential influence of e-cigarettes on weight changes among individuals who use these devices to quit smoking is not fully understood.

Objective: This study aims to reanalyze data from the Evaluating the Efficacy of e-Cigarette Use for Smoking Cessation (E3) trial to assess the causal effects of e-cigarette use on change in body weight.

Methods: This is a secondary analysis of the E3 trial in which participants were randomized into 3 groups: nicotine e-cigarettes plus counseling, nonnicotine e-cigarettes plus counseling, and counseling alone. With adjustment for baseline variables and the follow-up smoking abstinence status, weight changes were compared between the groups from baseline to 12 weeks' follow-up. Intention-to-treat and as-treated analyses were conducted using doubly robust estimation. Further causal analysis used 2 different propensity scoring methods to estimate causal regression curves for 4 smoking-related continuous variables. We evaluated 5 different subsets of data for each method. Selection bias was addressed, and missing data were imputed by the machine learning method extreme gradient boosting (XGBoost).

Results: A total of 257 individuals with measured weight at week 12 (mean age: 52, SD 12 y; women: n=122, 47.5%) were included. Across the 3 treatment groups, of the 257 participants, 204 (79.4%) who continued to smoke had, on average, largely unchanged weight at 12 weeks, with comparable mean weight gain ranging from -0.24 kg to 0.33 kg, while 53 (20.6%) smoking-abstinent participants gained weight, with a mean weight gain ranging from 2.05 kg to 2.70 kg. After adjustment, our analyses showed that the 2 e-cigarette arms exhibited a mean gain of 0.56 kg versus the counseling alone arm. The causal regression curves analysis also showed no strong evidence supporting a causal relationship between weight gain and the 3 e-cigarette-related variables. e-Cigarettes have small and variable causal effects on weight gain associated with smoking cessation.

Conclusions: In the E3 trial, e-cigarettes seemed to have minimal effects on mitigating the weight gain observed in individuals who smoke and subsequently quit at 3 months. However, given the modest sample size and the potential underuse of e-cigarettes among those randomized to the e-cigarette treatment arms, these results need to be replicated in large, adequately powered trials.

Trial registration: ClinicalTrials.gov NCT02417467; https://www.clinicaltrials.gov/study/NCT02417467.

Keywords: e-cigarettes; nicotine; smoking cessation; vaping; weight change; weight gain.

Figure 1

(A) Mean weight gain from baseline to week 12, shown by smoking abstinence at week 12 (quit smoking / returned to smoking) separately. Each error band is constructed using a 95% confidence interval. (B) Individual weight gain from baseline to week 12, shown by smoking abstinence at week 12 (quit smoking / returned to smoking) separately.

Figure 2

Pairwise comparisons with 95% confidence intervals between the three arms from a DRE model for intention-to-treat (A) and as-treated (B) analysis. In both models, censoring selection bias is adjusted. Treatment selection bias is also adjusted in the as-treated analysis. In (A) the intervention variable is the randomized arms; in (B) the intervention variable is the actual treatment arms (See Supplement table for the details of the intervention definitions). Note* Baseline variables used in the IPW analysis include: age, gender, baseline weight, height, education, average cigarettes per day smoked in the past 10 years, years smoked, Fagerström score, BDI score, Other smoker at home, high cholesterol, history depression, HBP, respiratory problems, history heart disease, diabetes, alcohol use, smoking abstinence at week 12.

Figure 3

Results of As-Treated Causal Regression Analysis. Causal regression curves with bootstrap confidence limits are shown in a 3 x 4 grid, corresponding to five different subsets of data and four different causal covariates (log(1+x) transformed). For x-axis, 0 = log(0+1) represents the level of 0 on the original scale; similarly, 2, 4 and 6 represent the level of 6, 54, and 402 respectively on the original scale. The red and blue curves correspond to IPW with MSM and GPS causal inference methods, respectively. The columns correspond to (i) Conventional cigarettes per week; (ii) E-cigarette puffs used per week; (iii) Used e-liquid cartridges returned; (iv) Unused e-liquid cartridges returned. The analyses were performed for five subsets of data separately, as labeled on the right Y-axis, where participants are from one or two of the randomized treatment groups, with rows in the grid corresponding to (i) nicotine e-cigarettes plus counseling; (ii) nicotine e-cigarettes plus counseling and nonnicotine e-cigarettes plus counseling; (iii) nonnicotine e-cigarettes plus counseling. Note * Any of the curves in Figure 3 can be used to roughly answer “what-if” questions by finding a value of interest on the x-axis and projecting through the curve to the y-axis. GPS: generalized propensity score; MSM: marginal structural model.