Seeing what is behind the smokescreen: A systematic review of methodological aspects of smoking interaction studies over the last three decades and implications for future clinical trials

Genetic susceptibility to caffeine intake and metabolism: a systematic review

BACKGROUND

Coffee and tea consumption account for most caffeine intake and 2-3 billion cups are taken daily around the world. Caffeine dependence is a widespread but under recognized problem.

OBJECTIVES

To conduct a systematic review on the genetic susceptibility factors affecting caffeine metabolism and caffeine reward and their association with caffeine intake.

METHODOLOGY

We conducted PubMed and Embase searches using the terms "caffeine", "reward", "gene", "polymorphism", "addiction", "dependence" and "habit" from inception till 2024. The demographics, genetic and clinical data from included studies were extracted and analyzed. Only case-control studies on habitual caffeine drinkers with at least 100 in each arm were included.

RESULTS

A total of 2552 studies were screened and 26 studies involving 1,851,428 individuals were included. Several genes that were involved with caffeine metabolism such as CYP1A2, ADORA2A, AHR, POR, ABCG2, CYP2A6, PDSS2 and HECTD4 rs2074356 (A allele specific to East Asians and monomorphic in Europeans, Africans and Americans) were associated with habitual caffeine consumption with effect size difference of 3% to 32% in number of cups of caffeinated drink per day per effect allele. In addition, ALDH2 was linked to the Japanese population. Genes associated with caffeine reward included BDNF, SLC6A4, GCKR, MLXIPL and dopaminergic genes such as DRD2 and DAT1 which had around 2-5% effect size difference in number of cups of caffeinated drink for each allele per day.

CONCLUSION

Several genes that were involved in caffeine metabolism and reward were associated with up to 30% effect size difference in number of cups of caffeinated drink per day, and some associations were specific to certain ethnicities. Identification of at-risk caffeine dependence individuals can lead to early diagnosis and stratification of at-risk vulnerable individuals such as pregnant women and children, and can potentially lead to development of drug targets for dependence to caffeine.

Phenotype-Genotype Correlation Applying a Cocktail Approach and an Exome Chip Analysis Reveals Further Variants Contributing to Variation of Drug Metabolism

Although great progress has been made in the fine-tuning of diplotypes, there is still a need to further improve the predictability of individual phenotypes of pharmacogenetically relevant enzymes. The aim of this study was to analyze the additional contribution of sex and variants identified by exome chip analysis to the metabolic ratio of five probe drugs. A cocktail study applying dextromethorphan, losartan, omeprazole, midazolam, and caffeine was conducted on 200 healthy volunteers. CYP2D6, 2C9, 2C19, 3A4/5, and 1A2 genotypes were analyzed and correlated with metabolic ratios. In addition, an exome chip analysis was performed. These SNPs correlating with metabolic ratios were confirmed by individual genotyping. The contribution of various factors to metabolic ratios was assessed by multiple regression analysis. Genotypically predicted phenotypes defined by CPIC discriminated very well the log metabolic ratios with the exception of caffeine. There were minor sex differences in the activity of CYP2C9, 2C19, 1A2, and CYP3A4/5. For dextromethorphan (CYP2D6), IP6K2 (rs61740999) and TCF20 (rs5758651) affected metabolic ratios, but only IP6K2 remained significant after multiple regression analysis. For losartan (CYP2C9), FBXW12 (rs17080138), ZNF703 (rs79707182), and SLC17A4 (rs11754288) together with CYP diplotypes, and sex explained 50% of interindividual variability. For omeprazole (CYP2C19), no significant influence of CYP2C:TG haplotypes was observed, but CYP2C19 rs12777823 improved the predictability. The comprehensive genetic analysis and inclusion of sex in a multiple regression model significantly improved the explanation of variability of metabolic ratios, resulting in further improvement of algorithms for the prediction of individual phenotypes of drug-metabolizing enzymes.