Jorien L. Treur

Associations between smoking and caffeine consumption in two European cohorts

Abstract Aims To estimate associations between smoking initiation, smoking persistence and smoking heaviness and caffeine consumption in two population‐based samples from the Netherlands and the United Kingdom. Design Observational study employing data on self‐reported smoking behaviour and caffeine consumption. Setting Adults from the general population in the Netherlands and the United Kingdom. Participants Participants from the Netherlands Twin Register [NTR: n = 21 939, mean age 40.8, standard deviation (SD) = 16.9, 62.6% female] and the Avon Longitudinal Study of Parents and Children (ALSPAC: n = 9086, mean age 33.2, SD = 4.7, 100% female). Measurements Smoking initiation (ever versus never smoking), smoking persistence (current versus former smoking), smoking heaviness (number of cigarettes smoked) and caffeine consumption in mg per day through coffee, tea, cola and energy drinks. Findings After correction for age, gender (NTR), education and social class (ALSPAC), smoking initiation was associated with consuming on average 52.8 [95% confidence interval (CI) = 45.6–60.0; NTR] and 59.5 (95% CI = 51.8–67.2; ALSPAC) mg more caffeine per day. Smoking persistence was also associated with consuming more caffeine [+57.9 (95% CI = 45.2–70.5) and +83.2 (95% CI = 70.2–96.3) mg, respectively]. Each additional cigarette smoked per day was associated with 3.7 (95% CI = 1.9–5.5; NTR) and 8.4 (95% CI = 6.9–10.0; ALSPAC) mg higher daily caffeine consumption in current smokers. Smoking was associated positively with coffee consumption and less strongly with cola and energy drinks. For tea, associations were positive in ALSPAC and negative in NTR. Conclusions There appears to be a positive association between smoking and caffeine consumption in the Netherlands and the United Kingdom.

Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium

Objectives To investigate, using a Mendelian randomisation approach, whether heavier smoking is associated with a range of regional adiposity phenotypes, in particular those related to abdominal adiposity. Design Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730 in the CHRNA5-CHRNA3-CHRNB4 gene region) as a proxy for smoking heaviness, of the associations of smoking heaviness with a range of adiposity phenotypes. Participants 148 731 current, former and never-smokers of European ancestry aged ≥16 years from 29 studies in the consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Primary outcome measures Waist and hip circumferences, and waist-hip ratio. Results The data included up to 66 809 never-smokers, 43 009 former smokers and 38 913 current daily cigarette smokers. Among current smokers, for each extra minor allele, the geometric mean was lower for waist circumference by −0.40% (95% CI −0.57% to −0.22%), with effects on hip circumference, waist-hip ratio and body mass index (BMI) being −0.31% (95% CI −0.42% to −0.19), −0.08% (−0.19% to 0.03%) and −0.74% (−0.96% to −0.51%), respectively. In contrast, among never-smokers, these effects were higher by 0.23% (0.09% to 0.36%), 0.17% (0.08% to 0.26%), 0.07% (−0.01% to 0.15%) and 0.35% (0.18% to 0.52%), respectively. When adjusting the three central adiposity measures for BMI, the effects among current smokers changed direction and were higher by 0.14% (0.05% to 0.22%) for waist circumference, 0.02% (−0.05% to 0.08%) for hip circumference and 0.10% (0.02% to 0.19%) for waist-hip ratio, for each extra minor allele. Conclusions For a given BMI, a gene variant associated with increased cigarette consumption was associated with increased waist circumference. Smoking in an effort to control weight may lead to accumulation of central adiposity.

Smoking and caffeine consumption: A genetic analysis of their association

Smoking and caffeine consumption show a strong positive correlation, but the mechanism underlying this association is unclear. Explanations include shared genetic/environmental factors or causal effects. This study employed three methods to investigate the association between smoking and caffeine. First, bivariate genetic models were applied to data of 10 368 twins from the Netherlands Twin Register in order to estimate genetic and environmental correlations between smoking and caffeine use. Second, from the summary statistics of meta‐analyses of genome‐wide association studies on smoking and caffeine, the genetic correlation was calculated by LD‐score regression. Third, causal effects were tested using Mendelian randomization analysis in 6605 Netherlands Twin Register participants and 5714 women from the Avon Longitudinal Study of Parents and Children. Through twin modelling, a genetic correlation of r0.47 and an environmental correlation of r0.30 were estimated between current smoking (yes/no) and coffee use (high/low). Between current smoking and total caffeine use, this was r0.44 and r0.00, respectively. LD‐score regression also indicated sizeable genetic correlations between smoking and coffee use (r0.44 between smoking heaviness and cups of coffee per day, r0.28 between smoking initiation and coffee use and r0.25 between smoking persistence and coffee use). Consistent with the relatively high genetic correlations and lower environmental correlations, Mendelian randomization provided no evidence for causal effects of smoking on caffeine or vice versa. Genetic factors thus explain most of the association between smoking and caffeine consumption. These findings suggest that quitting smoking may be more difficult for heavy caffeine consumers, given their genetic susceptibility.

Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis

Abstract Background There is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases metabolism of caffeine, so this may represent a causal effect of smoking on caffeine intake. Methods We performed Mendelian randomization analyses in the UK Biobank (N = 114 029), the Norwegian HUNT study (N = 56 664) and the Copenhagen General Population Study (CGPS) (N = 78 650). We used the rs16969968 genetic variant as a proxy for smoking heaviness in all studies and rs4410790 and rs2472297 as proxies for coffee consumption in UK Biobank and CGPS. Analyses were conducted using linear regression and meta-analysed across studies. Results Each additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI: 0.03, 0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI: −0.002, 0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.16 cups per day, 95% CI: 0.11, 0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI: -0.01, 0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers or that the coffee-related variants were associated with cigarette consumption. Conclusions Higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but could also reflect a behavioural effect of smoking on coffee drinking.

Smoking and caffeine consumption

Smoking and caffeine consumption show a strong positive correlation, but the mechanism underlying this association is unclear. Explanations include shared genetic/environmental factors or causal effects. This study employed three methods to investigate the association between smoking and caffeine. First, bivariate genetic models were applied to data of 10 368 twins from the Netherlands Twin Register in order to estimate genetic and environmental correlations between smoking and caffeine use. Second, from the summary statistics of meta‐analyses of genome‐wide association studies on smoking and caffeine, the genetic correlation was calculated by LD‐score regression. Third, causal effects were tested using Mendelian randomization analysis in 6605 Netherlands Twin Register participants and 5714 women from the Avon Longitudinal Study of Parents and Children. Through twin modelling, a genetic correlation of r0.47 and an environmental correlation of r0.30 were estimated between current smoking (yes/no) and coffee use (high/low). Between current smoking and total caffeine use, this was r0.44 and r0.00, respectively. LD‐score regression also indicated sizeable genetic correlations between smoking and coffee use (r0.44 between smoking heaviness and cups of coffee per day, r0.28 between smoking initiation and coffee use and r0.25 between smoking persistence and coffee use). Consistent with the relatively high genetic correlations and lower environmental correlations, Mendelian randomization provided no evidence for causal effects of smoking on caffeine or vice versa. Genetic factors thus explain most of the association between smoking and caffeine consumption. These findings suggest that quitting smoking may be more difficult for heavy caffeine consumers, given their genetic susceptibility.

Spousal resemblance for smoking: Underlying mechanisms and effects of cohort and age

BACKGROUND In this study we ask why spouses resemble each other in smoking behaviour and assess if such resemblance depends on period of data collection or age. Spousal similarity may reflect different, not mutually exclusive, processes. These include phenotypic assortment (choice of spouse is based on phenotype) or social homogamy at the time spouses first meet, and marital interaction during the relationship. METHODS Ever and current smoking were assessed between 1991 and 2013 in surveys of the Netherlands Twin Register for 14,230 twins and 1,949 of their spouses (mean age 31.4 [SD=14.0]), and 11,536 parents of twins (53.4 [SD=8.6]). Phenotypic assortment and social homogamy were examined cross-sectionally by calculating the probability of agreement between twins and their spouses, twins and their co-twins spouse and spouses of both twins as a function of zygosity. Marital interaction was tested by investigating the association between relationship duration and spousal resemblance. RESULTS Between 1991 and 2013 smoking declined in all age groups for both genders. Spousal resemblance for ever and current smoking was higher when data were more recent. For ever smoking, a higher age of men was associated with lower spousal resemblance. Phenotypic assortment was supported for both smoking measures, but social homogamy could not be excluded. No effect of marital interaction was found. CONCLUSIONS Differences in smoking prevalence across time and age influence spousal similarity. Individuals more often choose a spouse with similar smoking behaviour (phenotypic assortment) causing higher genotypic similarity between them. Given the heritability of smoking this increases genetic risk of smoking in offspring.

Connecting the dots, genome-wide association studies in substance use

partly overlapping individuals were genotyped, phenotyped and their data analyzed in genetic association studies, reflecting a huge communal effort by the substance use/addiction genetics community. These genome-wide association study (GWAS) efforts considered different stages of substance use: lifetime use (ever versus never use) was analyzed for cannabis and smoking, quantity of use (in users) was analyzed for coffee, alcohol, and smoking and age of initiation and cessation were analyzed for smoking. There are other GWA efforts and publications in the realm of addiction (see ref. 5), but here we limit ourselves to the largest meta-analyses per substance in order to maximize power. The GWA meta-analyses of substance-related traits identified many substance-specific genetic variants of moderate to small effect, which provided insight in the genetic etiology of substance use and its comorbidities. There are substantial phenotypic correlations among use of different substances, and both twin and polygenic risk prediction studies have shown that these phenotypic correlations are partly due to common genetic influences. 6, 7 Here we estimate genetic correlations (rg) between substance use-related variables based on the GWA summary statistics. These estimates of rg are based on all polygenic effects captured by single nucleotide polymorphisms. We used the recently developed linkage disequilibrium (LD) score regression method to estimate the proportion of covariance between traits that is due to single nucleotide polymorphisms, based on the expected relationship between LD and strength of association under a polygenic model. 8,9 The genetic correlation matrix revealed important information about common versus substance-specific genetic effects as well as specific patterns of cross-substance comorbidity (Figure 1). The substantial negative correlation between smoking cessation and smoking initiation reveals that the genes that predispose to initiation are negative predictors of success at cessation. Likewise, the genes that predispose individuals to smoke more cigarettes per day are negative predictors of successful cessation. Age at first cigarette is only associated with smoking initiation, not with cigarettes per day or smoking cessation. Interestingly, high genetic correlations are also observed across substance, between cannabis initiation and smoking initiation (rg=0.83, se=0.148), but also between quantity of nicotine consumption (cigarettes per day) and quantity of coffee consumed (cups per day) (rg=0.44, se=0.151), between coffee consumed and nicotine consumption (rg=0.38, se=0.16), and between alcohol consumption (alcohol per week) and cigarettes per day (rg=0.44, se=0.17). Most significant cross-substance correlations reflect genetic correlations within stage. However, both coffee per day and cigarettes per day are negatively associated with successful smoking cessation, indicating that frequent use, irrespective of substance, is genetically related to more problematic use of a different substance. The pattern of correlations observed implies a genetic model for substance use where both substance-specific and stagespecific genetic effects play a role. GWA meta-analyses of smoking, alcohol, cannabis and coffee use have shed light on the specific genetic effects for each substance. Here we show substance- and stage-specific GWAS results can be leveraged to elucidate the genetic architecture of substance use vulnerability in general. The next generation of large well-powered substance use GWA studies should systematically target all stages of use, for a broad spectrum of substances (e.g., cocaine and sugar rich foods) or addictive behavior (e.g., gambling, gaming and compulsive Internet use). Such an effort can aid in distinguishing between genes that are substance specific from genes that contribute to a specific stage of use, irrespective of substance or addictive behavior.

GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health–related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.A GWAS of lifetime cannabis use reveals new risk loci, shows that cannabis use has genetic overlap with smoking and alcohol use, and indicates that the likelihood of initiating cannabis use is causally influenced by schizophrenia.

Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (N=184,765) to date. We identified 4 independent loci containing genome-wide significant SNP associations. Gene-based tests revealed 29 genome-wide significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2–4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

Investigating causal associations between use of nicotine, alcohol, caffeine, and cannabis: A two-sample bidirectional Mendelian randomization study

BACKGROUND AND AIMS Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine and cannabis use. METHODS Two-sample MR was employed to estimate bidirectional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week) and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy. RESULTS Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these were not supported by the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing. CONCLUSIONS Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine and cannabis use.