ObjectiveThe aim of this review was to investigate whether e-cigarette use compared with non-use in young non-smokers is associated with subsequent cigarette smoking.Data sourcesPubMed, Embase, Web of Science, Wiley Cochrane Library databases, and the 2018 Society for Research on Nicotine and Tobacco and Society for Behavioural Medicine conference abstracts.Study selectionAll studies of young people (up to age 30 years) with a measure of e-cigarette use prior to smoking and an outcome measure of smoking where an OR could be calculated were included (excluding reviews and animal studies).Data extractionIndependent extraction was completed by multiple authors using a preprepared extraction form.Data synthesisOf 9199 results, 17 studies were included in the meta-analysis. There was strong evidence for an association between e-cigarette use among non-smokers and later smoking (OR: 4.59, 95% CI: 3.60 to 5.85) when the results were meta-analysed in a random-effects model. However, there was high heterogeneity (I2=88%).ConclusionsAlthough the association between e-cigarette use among non-smokers and subsequent smoking appears strong, the available evidence is limited by the reliance on self-report measures of smoking history without biochemical verification. None of the studies included negative controls which would provide stronger evidence for whether the association may be causal. Much of the evidence also failed to consider the nicotine content of e-liquids used by non-smokers meaning it is difficult to make conclusions about whether nicotine is the mechanism driving this association.

Background: Research suggests that acute alcohol consumption alters recognition of emotional expressions. Extending this work, we investigated the effects of alcohol on recognition of six primary expressions of emotion. Methods: We conducted two studies using a 2 × 6 experimental design with a between-subjects factor of drink (alcohol, placebo) and a within-subjects factor of emotion (anger, disgust, sadness, surprise, happiness, fear). Study one ( n = 110) was followed by a direct replication study ( n = 192). Participants completed a six alternative forced choice emotion recognition task following consumption of 0.4 g/kg alcohol or placebo. Dependent variables were recognition accuracy (i.e. hits) and false alarms. Results: There was no clear evidence of differences in recognition accuracy between groups ( ps > .58). In study one, there were more false alarms for anger in the alcohol compared to placebo group ( n = 52 and 56, respectively; t (94.6) = 2.26, p = .024, d = .44) and fewer false alarms for happiness ( t (106) = –2.42, p = .017, d = –.47). However, no clear evidence for these effects was found in study two (alcohol group n = 96, placebo group n = 93, ps > .22). When the data were combined we observed weak evidence of an effect of alcohol on false alarms of anger ( t (295) = 2.25, p = .025, d = .26). Conclusions: These studies find weak support for biased anger perception following acute alcohol consumption in social consumers, which could have implications for alcohol-related aggression. Future research should investigate the robustness of this effect, particularly in individuals high in trait aggression.

BackgroundGestational age at delivery is associated with health and social outcomes. Recently, cord blood DNA methylation data has been used to predict gestational age. The discrepancy between gestational age predicted from DNA methylation and determined by ultrasound or last menstrual period is known as gestational age acceleration. This study investigated associations of sex, socioeconomic status, parental behaviours and characteristics and birth outcomes with gestational age acceleration.ResultsUsing data from the Avon Longitudinal Study of Parents and Children (n = 863), we found that pre-pregnancy maternal overweight and obesity were associated with greater gestational age acceleration (mean difference = 1.6 days, 95% CI 0.7 to 2.6, and 2.9 days, 95% CI 1.3 to 4.4, respectively, compared with a body mass index < 25 kg/m2, p < .001). There was evidence of an association between male sex and greater gestational age acceleration. Greater gestational age acceleration was associated with higher birthweight, birth length and head circumference of the child (mean differences per week higher gestational age acceleration: birthweight 0.1 kg, 95% CI 0.1 to 0.2, p < .001; birth length 0.4 cm, 95% CI 0.2 to 0.7, p < .001; head circumference 0.2 cm, 95% CI 0.1 to − 0.4, p < .001). There was evidence of an association between gestational age acceleration and mode of delivery (assisted versus unassisted delivery, odds ratio = 0.9 per week higher gestational age acceleration, 95% CI 0.7, 1.3 (p = .05); caesarean section versus unassisted delivery, odds ratio = 0.6, 95% CI 0.4 to 0.9 per week higher gestational age acceleration (p = .05)). There was no evidence of association for other parental and perinatal characteristics.ConclusionsThe associations of higher maternal body mass index and larger birth size with greater gestational age acceleration may imply that maternal overweight and obesity is associated with more rapid development of the fetus in utero. The implications of gestational age acceleration for postnatal health warrant further investigation.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0520-1) contains supplementary material, which is available to authorized users.

BackgroundThere is limited and conflicting evidence for associations between use of screen-based technology and anxiety and depression in young people. We examined associations between screen time measured at 16 years and anxiety and depression at 18.MethodsParticipants (n = 14,665; complete cases n = 1869) were from the Avon Longitudinal Study of Parents and Children, a UK-based prospective cohort study. We assessed associations between various types of screen time (watching television, using a computer, and texting, all measured via questionnaire at 16y), both on weekdays and at weekends, and anxiety and depression (measured via the Revised Clinical Interview Schedule at 18y). Using ordinal logistic regression, we adjusted for multiple confounders, particularly focussing on activities that might have been replaced by screen time (for example exercising or playing outdoors).ResultsMore time spent using a computer on weekdays was associated with a small increased risk of anxiety (OR for 1–2 h = 1.17, 95% CI: 1.01 to 1.35; OR for 3+ hours = 1.30, 95% CI: 1.10 to 1.55, both compared to < 1 h, p for linear trend = 0.003). We found a similar association between computer use at weekends and anxiety (OR for 1–2 h = 1.17, 95% CI: 0.94 to 1.46; OR for 3+ hours = 1.28, 95% CI: 1.03 to 1.48, p for linear trend = 0.03). Greater time spent using a computer on weekend days only was associated with a small increased risk in depression (OR for 1–2 h = 1.12, 95% CI: 0.93 to 1.35; OR for 3+ hours = 1.35, 95% CI: 1.10 to 1.65, p for linear trend = 0.003). Adjusting for time spent alone attenuated effects for anxiety but not depression. There was little evidence for associations with texting or watching television.ConclusionsWe found associations between increased screen time, particularly computer use, and a small increased risk of anxiety and depression. Time spent alone was found to attenuate some associations, and further research should explore this.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-6321-9) contains supplementary material, which is available to authorized users.

Objective: The aim of this review was to investigate whether e-cigarette use compared to non-use in young non-smokers is associated with subsequent cigarette smoking. Data sources: PubMed, Embase, Web of Science, Wiley Cochrane Library databases, and the 2018 Society for Research on Nicotine and Tobacco and Society for Behavioural Medicine conference abstracts. Study selection: All studies of young people (up to age 30 years) with a measure of e-cigarette use prior to smoking and an outcome measure of smoking where an odds ratio could be calculated were included (excluding reviews and animal studies). Data Extraction: Independent extraction was completed by multiple authors using a pre-prepared extraction form. Data synthesis: Of 9,199 results, 17 studies were included in the meta-analysis. There was strong evidence for an association between e-cigarette use among non-smokers and later smoking (OR 4.59, 95% CI 3.60 to 5.85) when the results were meta-analysed in a random effects model. However, there was high heterogeneity (I2 = 88%). Conclusions: Whilst the association between e-cigarette use among non-smokers and subsequent smoking appears strong, the available evidence is limited by the reliance on self-report measures of smoking history without biochemical verification. None of the studies included negative controls which would provide stronger evidence for whether the association may be causal. Much of the evidence also failed to consider the nicotine content of e-liquids used by non-smokers meaning it is difficult to make conclusions about whether nicotine is the mechanism driving this association.

BackgroundDifferences between an individual’s estimated epigenetic gestational age (EGA) and their actual gestational age (GA) are defined as gestational age acceleration (GAA). GAA is associated with increased birthweight and birth length. Whether these associations persist through childhood is yet to be investigated.MethodsWe examined the association between GAA and trajectories of height and weight from birth to 10 years (n = 785) in a British birth cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). EGA of participants was estimated using DNA methylation data from cord blood using a recently developed prediction model. GAA of participants, measured in weeks, was calculated as the residuals from a regression model of EGA on actual GA. Analyses were performed using linear spline multilevel models and adjusted for maternal age, maternal pre-pregnancy BMI, maternal smoking during pregnancy, and maternal education.ResultsIn adjusted analyses, offspring with a one-week greater GAA were born on average 0.14 kg heavier (95% confidence interval (CI) 0.09, 0.19) and 0.55 cm taller (95% CI 0.33, 0.78) at birth. These differences in weight persisted up to approximately age 9 months but thereafter began to attenuate. From age 5 years onwards, the association between GAA and weight reversed such that GAA was associated with lower weight and this association strengthened with age (mean difference at age 10 years − 0.60 kg, 95% CI − 1.19, − 0.01). Differences in height persisted only up to age 9 months (mean difference at 9 months 0.15 cm, 95% CI − 0.09, 0.39). From age 9 months to age 10 years, offspring with a one-week greater GAA were of comparable height with those with no GAA (mean difference at age 10 years − 0.07 cm, 95% CI − 0.64, 0.50).ConclusionsGestational age acceleration is associated with increased birth weight and length and these differences persist to age 9 months. From age 5 years onwards, the association of GAA and weight reverses such that by age 10 years, greater GAA is associated with lower childhood weight. Further work is required to examine whether the weight effects of GAA strengthen through adolescence and into early adulthood.

Background Tobacco smoking and e-cigarette use are strongly associated, but it is currently unclear whether this association is causal, or due to shared factors that influence both behaviours such as a shared genetic liability. The aim of this study was to investigate whether polygenic risk scores (PRS) for smoking initiation are associated with ever use of e-cigarettes. Methods and findings Smoking initiation PRS were calculated for young adults (N = 7,859, mean age = 24 years, 51% male) of European ancestry in the Avon Longitudinal Study of Parents and Children, a prospective birth cohort study initiated in 1991. PRS were calculated using the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) summary statistics. Five thresholds ranging from 5 × 10−8 to 0.5 were used to calculate 5 PRS for each individual. Using logistic regression, we investigated the association between smoking initiation PRS and the main outcome, self-reported e-cigarette use (n = 2,894, measured between 2016 and 2017), as well as self-reported smoking initiation and 8 negative control outcomes (socioeconomic position at birth, externalising disorders in childhood, and risk-taking in young adulthood). A total of 878 young adults (30%) had ever used e-cigarettes at 24 years, and 150 (5%) were regular e-cigarette users at 24 years. We observed positive associations of similar magnitude between smoking initiation PRS (created using the p < 5 × 10−8 threshold) and both smoking initiation (odds ratio (OR) = 1.29, 95% CI 1.19 to 1.39, p < 0.001) and ever e-cigarette use (OR = 1.24, 95% CI 1.14 to 1.34, p < 0.001) by the age of 24 years, indicating that a genetic predisposition to smoking initiation is associated with an increased risk of using e-cigarettes. At lower p-value thresholds, we observed an association between smoking initiation PRS and ever e-cigarette use among never smokers. We also found evidence of associations between smoking initiation PRS and some negative control outcomes, particularly when less stringent p-value thresholds were used to create the PRS, but also at the strictest threshold (e.g., gambling, number of sexual partners, conduct disorder at 7 years, and parental socioeconomic position at birth). However, this study is limited by the relatively small sample size and potential for collider bias. Conclusions Our results indicate that there may be a shared genetic aetiology between smoking and e-cigarette use, and also with socioeconomic position, externalising disorders in childhood, and risky behaviour more generally. This indicates that there may be a common genetic vulnerability to both smoking and e-cigarette use, which may reflect a broad risk-taking phenotype.

BackgroundNicotine preloading means using nicotine replacement therapy prior to a quit date while smoking normally. The aim is to reduce the drive to smoke, thereby reducing cravings for smoking after quit day, which are the main cause of early relapse. A prior systematic review showed inconclusive and heterogeneous evidence that preloading was effective and little evidence of the mechanism of action, with no cost-effectiveness data.ObjectivesTo assess (1) the effectiveness, safety and tolerability of nicotine preloading in a routine NHS setting relative to usual care, (2) the mechanisms of the action of preloading and (3) the cost-effectiveness of preloading.DesignOpen-label randomised controlled trial with examination of mediation and a cost-effectiveness analysis.SettingNHS smoking cessation clinics.ParticipantsPeople seeking help to stop smoking.InterventionsNicotine preloading comprised wearing a 21 mg/24 hour nicotine patch for 4 weeks prior to quit date. In addition, minimal behavioural support was provided to explain the intervention rationale and to support adherence. In the comparator group, participants received equivalent behavioural support. Randomisation was stratified by centre and concealed from investigators.Main outcome measuresThe primary outcome was 6-month prolonged abstinence assessed using the Russell Standard. The secondary outcomes were 4-week and 12-month abstinence. Adverse events (AEs) were assessed from baseline to 1 week after quit day. In a planned analysis, we adjusted for the use of varenicline (Champix®; Pfizer Inc., New York, NY, USA) as post-cessation medication. Cost-effectiveness analysis took a health-service perspective. The within-trial analysis assessed health-service costs during the 13 months of trial enrolment relative to the previous 6 months comparing trial arms. The base case was based on multiple imputation for missing cost data. We modelled long-term health outcomes of smoking-related diseases using the European-study on Quantifying Utility of Investment in Protection from Tobacco (EQUIPT) model.ResultsIn total, 1792 people were eligible and were enrolled in the study, with 893 randomised to the control group and 899 randomised to the intervention group. In the intervention group, 49 (5.5%) people discontinued preloading prematurely and most others used it daily. The primary outcome, biochemically validated 6-month abstinence, was achieved by 157 (17.5%) people in the intervention group and 129 (14.4%) people in the control group, a difference of 3.02 percentage points [95% confidence interval (CI) –0.37 to 6.41 percentage points; odds ratio (OR) 1.25, 95% CI 0.97 to 1.62;p = 0.081]. Adjusted for use of post-quit day varenicline, the OR was 1.34 (95% CI 1.03 to 1.73;p = 0.028). Secondary abstinence outcomes were similar. The OR for the occurrence of serious AEs was 1.12 (95% CI 0.42 to 3.03). Moderate-severity nausea occurred in an additional 4% of the preloading group compared with the control group. There was evidence that reduced urges to smoke and reduced smoke inhalation mediated the effect of preloading on abstinence. The incremental cost-effectiveness ratio at the 6-month follow-up for preloading relative to control was £710 (95% CI –£13,674 to £23,205), but preloading was dominant at 12 months and in the long term, with an 80% probability that it is cost saving.LimitationsThe open-label design could partially account for the mediation results. Outcome assessment could not be blinded but was biochemically verified.ConclusionsUse of nicotine-patch preloading for 4 weeks prior to attempting to stop smoking can increase the proportion of people who stop successfully, but its benefit is undermined because it reduces the use of varenicline after preloading. If this latter effect could be overcome, then nicotine preloading appears to improve health and reduce health-service costs in the long term. Future work should determine how to ensure that people using nicotine preloading opt to use varenicline as cessation medication.Trial registrationCurrent Controlled Trials ISRCTN33031001.FundingThis project was funded by the NIHR Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 22, No. 41. See the NIHR Journals Library website for further project information.