Introduction: Approved pharmacological treatments for smoking cessation are modestly effective, underscoring the need for improved pharmacotherapies. Glucagon-like peptide-1 receptor (GLP-1R) agonists attenuate the rewarding effects of nicotine in preclinical studies. We examined the efficacy of extended-release exenatide, a GLP-1R agonist, combined with nicotine replacement therapy (NRT, patch) for smoking cessation, craving, and withdrawal symptoms, with post-cessation body weight as a secondary outcome.Methods: Eighty-four prediabetic and/or overweight smokers were randomized (1 : 1) to once-weekly placebo or exenatide, 2 mg, subcutaneously. All participants received NRT (21 mg) and brief smoking cessation counseling. Seven-day point prevalence abstinence (expired CO level ≤5 ppm), craving, withdrawal, and post-cessation body weight were assessed following 6 weeks of treatment. A Bayesian approach for analyzing generalized linear models yielded posterior probabilities (PP) to quantify the evidence favoring hypothesized effects of treatment on the study outcomes.Results: Exenatide increased the risk for smoking abstinence compared to placebo (46.3% and 26.8%, respectively), (risk ratio [RR] = 1.70; 95% credible interval = [0.96, 3.27]; PP = 96.5%). Exenatide reduced end-of-treatment craving in the overall sample and withdrawal among abstainers. Post-cessation body weight was 5.6 pounds lower in the exenatide group compared to placebo (PP = 97.4%). Adverse events were reported in 9.5% and 2.3% of participants in the exenatide and placebo groups, respectively.Conclusions: Exenatide, in combination with the NRT improved smoking abstinence, reduced craving and withdrawal symptoms, and decreased weight gain among abstainers. Findings suggest that the GLP-1R agonist strategy is worthy of further research in larger, longer duration studies.Implications: Despite considerable progress in tobacco control, cigarette smoking remains the leading cause of preventable disease, disability, and death. In this pilot study, we showed that extended-release exenatide, a glucagon-like peptide-1 receptor agonist, added to the nicotine patch, improved abstinence and mitigated post-cessation body weight gain compared to patch alone. Further research is needed to confirm these initial positive results.

Introduction: Machine learning algorithms such as elastic net regression and backward selection provide a unique and powerful approach to model building given a set of psychosocial predictors of smoking lapse measured repeatedly via ecological momentary assessment (EMA). Understanding these predictors may aid in developing interventions for smoking lapse prevention. Methods: In a randomized-controlled smoking cessation trial, smartphone-based EMAs were collected from 92 participants following a scheduled quit date. This secondary analysis utilized elastic net-penalized cox proportional hazards regression and model approximation via backward elimination to (1) optimize a predictive model of time to first lapse and (2) simplify that model to its core constituent predictors to maximize parsimony and generalizability. Results: Elastic net proportional hazards regression selected 17 of 26 possible predictors from 2065 EMAs to model time to first lapse. The predictors with the highest magnitude regression coefficients were having consumed alcohol in the past hour, being around and interacting with a smoker, and having cigarettes easily available. This model was reduced using backward elimination, retaining five predictors and approximating to 93.9% of model fit. The retained predictors included those mentioned above as well as feeling irritable and being in areas where smoking is either discouraged or allowed (as opposed to not permitted). Conclusions: The strongest predictors of smoking lapse were environmental in nature (e.g., being in smoking-permitted areas) as opposed to internal factors such as psychological affect. Interventions may be improved by a renewed focus of interventions on these predictors. Implications: The present study demonstrated the utility of machine learning algorithms to optimize the prediction of time to smoking lapse using EMA data. The two models generated by the present analysis found that environmental factors were most strongly related to smoking lapse. The results support the use of machine learning algorithms to investigate intensive longitudinal data, and provide a foundation for the development of highly tailored, just-in-time interventions that can target on multiple antecedents of smoking lapse.

Transcranial direct current stimulation (tDCS) has demonstrated effectiveness in reducing clinical and experimental measures of pain in patients with chronic pain; however, research examining the mechanisms of action for the effects of tDCS has been lacking. The present study investigated the effect of active tDCS on measures of inflammation and stress. Older adults (aged 50–70 years) with knee osteoarthritis (OA) were randomly assigned to receive daily 20-min sessions of either tDCS (n = 20) or sham tDCS (n = 20) for 5 consecutive days. Participants provided blood samples at baseline and the end of treatment. The following measures of immune function and stress were collected: interleukin (IL)-6 and 10, tumor necrosis factor-α (TNF-α), C-reactive protein, cortisol, and β-endorphin. Generalized linear modeling evaluated each posttreatment measure as a function of tDCS group, controlling for baseline (measuring residual change, analogous to analysis of covariance). Bayesian statistical inference was used to directly quantify the probability of the effect of active tDCS. IL-6, IL-10, TNF-α, and β-endorphin demonstrated lower levels of stress and inflammation in the active tDCS group. These findings provide preliminary evidence that active (relative to sham) tDCS is associated with reduced levels of inflammation.

Heather E. Soder, Amber M. Berumen, Kira E. Gomez, Charles E. Green, Robert Suchting, Margaret C. Wardle, Jessica Vincent, Antonio L. Teixeira, Joy M. Schmitz, Scott D. Lane. Clin Psychopharmacol Neurosci 2020;18:32-40. https://doi.org/10.9758/cpn.2020.18.1.32