Genetics and Coffee: How Our DNA Shapes Our Caffeine Habits and Health


Coffee is one of the most popular drinks in the world. People love it for its taste and the energy boost it gives, thanks to caffeine. In Europe and the United States, most adults (about 60-85%) drink between 0.6 to 5.5 cups of coffee every day. Coffee has some good and bad effects on health. For example, it can help with thinking and may lower the risk of liver disease, Parkinson’s disease, heart disease, type II diabetes, and some cancers. But it can also increase the chances of other problems like substance use, certain cancers (like lung cancer), poor cholesterol levels, pregnancy issues, stomach problems, and heart problems if you drink too much.

Scientists study how our genes (the DNA we inherit from our parents) might affect how much coffee we drink and how it affects our health. Studies show that how much coffee we drink is partly due to our genes. This means that by looking at our genes, scientists can learn a lot about how coffee affects us. Researchers have found that some genes that help our bodies process caffeine are linked to how much coffee we drink. These same genes might also be linked to other health issues like liver disease, cancer, and alcohol use.

Interestingly, these links between genes and health can mean two things: either drinking coffee directly affects these health issues, or the genes have separate effects on coffee drinking and these health issues. Studies also show that coffee drinking is genetically linked to other habits like smoking, drinking alcohol, reduced brain volume, mental health issues, arthritis, sleep problems, body weight, type II diabetes, and migraines.

While coffee is the main source of caffeine for many, other sources include tea, soft drinks, and chocolate. Different parts of the world have different caffeine habits. For example, in the UK, people mostly get their caffeine from tea, while in the US, it’s mostly from coffee. This difference can affect study results. Some studies only used data from one place, like the UK, which might limit how much we can apply their results to other places with different drinking habits.

In a big study, researchers used data from 23andMe (a genetic testing company) from over 130,000 people of European ancestry in the US. They asked these people how much coffee they drink and studied their genes to see what they could find out. They also compared their results to a large study from the UK Biobank, which included data from over 334,000 people. They found that the genetic links between coffee drinking and health issues were different in the US and UK groups.

This study found that seven specific spots in our genes are linked to how much coffee we drink. Most of these spots are in genes that help process substances in our bodies. They also found that these coffee-related genes are active in the brain. Despite many studies suggesting that coffee has health benefits, this study found that the genetic links were mostly with negative health outcomes like substance use disorders and obesity. The results varied between the US and UK groups, suggesting that different populations might have different genetic links between coffee drinking and health.

Some key findings were:

  • Genes and Coffee Drinking: The study found specific genes linked to coffee drinking. These genes are also linked to how our bodies process substances.
  • Substance Use: There is a genetic link between coffee drinking and using other substances like tobacco, alcohol, and cannabis. This means people who are genetically inclined to drink more coffee might also be more likely to use these substances.
  • Body Weight: The study found a genetic link between coffee drinking and higher body weight, which is different from some studies that suggest coffee can help with weight loss.
  • Health Benefits: The study did not find strong genetic links between coffee drinking and many of the health benefits usually associated with coffee, like lower risks of certain diseases.
  • Cultural Differences: The genetic links between coffee drinking and health varied between the US and UK groups, suggesting that cultural and lifestyle differences play a role.

There are a few important things to keep in mind about this study:

  • Cause and Effect: The study doesn’t prove that coffee causes these health issues or benefits. It only shows that there are genetic links between them.
  • Different Definitions: The US study looked only at caffeinated coffee, while the UK study included decaffeinated coffee too.
  • Non-Caffeine Factors: The study didn’t look at other components of coffee besides caffeine, which might also affect health.
  • Age and Background: The people in the study were older and mostly of European descent, so the results might not apply to everyone.

Overall, this study shows that genetic links between coffee drinking and health are complex and can vary between different groups of people. While some genetic links are consistent across different groups, others can be influenced by cultural and lifestyle factors. This suggests that combining data from different groups should be done carefully.

Coffee Consumption Overview– 60-85% of adults in Europe and the United States consume between 0.6 to 5.5 cups of coffee daily. – Coffee intake is associated with benefits on cognitive function and lower risk of liver disease, Parkinson’s, cardiovascular disease, type II diabetes, and certain cancers.
Genetic Studies on Coffee Intake– Twin studies estimate coffee intake to be 36-56% heritable. – Over a dozen GWAS studies have examined coffee intake, identifying associations with SNPs near genes like CYP1A1 and CYP1A2, which metabolize caffeine.
Coffee Intake and Health Associations– Positive associations with cognitive function, liver disease, neurodegenerative diseases, cardiovascular disease, type II diabetes, and certain cancers. – Negative associations with substance use, certain cancers, poor lipid profile, pregnancy loss, gastrointestinal issues, and worse cardiovascular outcomes.
Genetic Correlations with Coffee Intake– Genetic correlations with other substance use, reduced gray matter volumes, psychiatric illness, osteoarthritis, sleep, body mass index (BMI), type II diabetes, and migraine have been reported.
Regional Variations in Caffeine Sources– Geographic variations in caffeine intake sources, with tea being preferred in the UK and coffee in the US. – Different genetic studies using UK-based data or combining cohorts with different beverage preferences.
Study Design and Participants– Study using data from 23andMe (N=130,153) and UK Biobank (N=334,659). – Genetic correlations, PheWAS, and LabWAS used to explore relationships between coffee intake and thousands of traits.
Key Findings– Seven loci associated with coffee intake, mostly in genes related to metabolic processes. – Genetic enrichment in brain tissues. – Positive genetic correlations with substance use and obesity-related traits.
Health Benefits and Risks of Coffee– Genetic links between coffee intake and substance use, obesity, and certain health issues. – Limited evidence for beneficial health outcomes typically associated with coffee intake.
Cultural and Cohort Influences– Differences in genetic associations between US and UK cohorts. – Cultural and lifestyle factors influencing coffee intake and its genetic associations with health.
Limitations and Considerations– Lack of causality between coffee intake and health outcomes. – Different definitions of coffee intake between studies (caffeinated vs. decaffeinated). – Age, ethnicity, and other factors affecting study generalizability.

The study …..

Coffee is a leading global food commodity that has psychoactive properties largely due to the presence of caffeine. While rates of use and daily intake vary widely by geographic region, it is estimated that approximately 60-85% of adults in Europe and the United States consume between 0.6 to 5.5 cups of coffee daily. Intake of coffee and its bioactive constituents is associated with benefits on cognitive function and a lower risk of liver disease, Parkinson’s and other neurodegenerative diseases, cardiovascular disease, type II diabetes, and certain cancers. However, coffee intake is also associated with higher risks for some adverse outcomes, including increased risk of other substance use and misuse, some cancers (e.g., lung cancer), poor lipid profile, pregnancy loss, gastrointestinal maladies, and worse cardiovascular outcomes following excessive intake. Given the widespread and regular intake of coffee across the globe, addressing the full spectrum of correlations with health and disease is an important but challenging task.

Genetic studies offer a compelling avenue to investigate the relationships between coffee intake and other complex traits. Twin studies that calculate genetic contributions to daily coffee intake estimate it to be 36-56% heritable, suggesting that coffee intake should be amenable to genetic analysis. Whereas phenotypic correlations, which depend on measuring two or more traits in the same cohort, can arise from genetic and environmental factors, genetic correlations assess genetically driven relationships using the results from genome-wide association studies (GWAS) and can therefore examine correlations between two or more traits, even if they were measured in entirely non-overlapping cohorts. In the past decade, over a dozen GWAS (N=1,207-407,072) have examined coffee intake. Several of these GWAS have found associations with single nucleotide polymorphisms (SNPs) within or near genes that metabolize caffeine, such as CYP1A1 and CYP1A2. Some of these loci are also associated with other complex traits, including liver disease, cancers, and alcohol consumption.

This pleiotropy could suggest that these other associations are mediated by coffee intake or that these loci also influence these traits via alternative independent mechanisms. Genetic correlations have also been reported between coffee intake with other substance use, reduced gray matter volumes, psychiatric illness, osteoarthritis, sleep, body mass index (BMI), type II diabetes, and migraine. However, some genetic correlations were conducted under a priori justification (e.g., other substance use traits, sleep) and as such may fail to capture the full scope of genetic correlations between coffee and other traits.

While coffee is the primary source of caffeine for many, other common dietary sources of caffeine include tea, soft drinks, and chocolate. Consequently, when we refer to coffee intake, we mean explicit measures of coffee intake (e.g., measured as cups/day) and not caffeine intake unless otherwise specified. Intake of other caffeine sources also varies by geographic region based on beverage sales. For example, tea (rather than coffee) is the preferred source of caffeine in the United Kingdom (UK; tea vs. coffee: ~50% vs. 20%) compared to the United States (US; ~10% vs. 30%). As some genetic studies used data from the UK Biobank (UKB) only or combined cohorts across regions with different patterns of caffeinated beverage intake, this distinction may limit generalizability or introduce environmental and cultural confounds that affect the genetic associations between coffee intake and other traits.

In this study, we used survey responses from US-based 23andMe, Inc. research participants of European ancestry (N=130,153) and performed a GWAS of a single item “How many 5-ounce (cup-sized) servings of caffeinated coffee do you consume each day?”. Using genetic correlations and phenome- and laboratory-wide association studies (PheWAS, LabWAS), we explored the relationships between coffee intake and thousands of biomarkers, health features, and lifestyle traits to provide a fuller inventory of genetic correlations with coffee intake. We compared our findings from the 23andMe cohort to those from the UKB using publicly available GWAS summary statistics of coffee intake (“How many cups of coffee do you drink each day? (Include decaffeinated coffee)”, N=334,659, Although we had originally intended to perform a meta-analysis, our results revealed a lower-than-expected genetic correlation between coffee intake in the two cohorts; therefore, we instead used these datasets to explore cohort differences in coffee intake across these two distinct populations.

In this study, we contributed to the existing GWAS literature of coffee intake by analyzing a US population of 130,153 participants. We uncovered seven loci associated with coffee intake, most of which were in genes implicated in metabolic processes. Coffee-related variants were significantly enriched in the central nervous system. Despite prior evidence that coffee intake confers health benefits, we found genetic correlations mostly with adverse outcomes in both cohorts, particularly substance use disorders and obesity-related traits. Relationships with other medical, anthropologic, and psychiatric traits were inconsistent in the US and UK cohorts, suggesting that differences between populations may affect coffee intake GWAS results and its genetic relationships with other traits.

Our GWAS replicated prior associations with genes and variants implicated in coffee and caffeine intake as well as other metabolic and xenobiotic processes, including rs2472297 near CYP1A1/CYP1A2 and rs4410790 near AHR, even though our study sample was smaller compared to other GWAS. Gene-based analyses uncovered 165 candidate genes, including four genes that overlapped across all four analyses: MPI, SCAMP2, SCAMP5, and FAM219B, all of which have been implicated in a prior coffee GWAS. These overlapping genes have other associations with substance use and medical biomarkers including blood pressure, hypertension, and LDL cholesterol. We identified gene enrichment in brain tissues across the frontal cortex, putamen, and hippocampus, consistent with prior GWAS showing enrichment for SNPs associated with coffee and caffeine in the central nervous system. This is supported by brain imaging studies across cortical and subcortical areas showing morphological and functional differences between those who habitually drink coffee compared to those who do not.

One of the most striking observations of this study is the breadth and magnitude of positive associations between coffee intake and substance use. It is widely believed that the use of one substance heightens the risk for the use of other substances and that there are common genetic risk factors for any substance use; coffee, which is not generally considered a drug of misuse, does not appear to be exempt from this. We identified positive genetic correlations between coffee intake and other substances (i.e., tobacco, alcohol, cannabis, and opioid use), as well as relevant personality traits like externalizing behavior. The genetics of coffee intake aligned with substance consumption phenotypes, corroborating prior GWAS and twin studies, but not with substance misuse. This is perhaps unsurprising because the phenotype probed by the 23andMe and UKB cohorts focuses on quantity rather than clinically defined dependence. We and others previously demonstrated that the genetic architectures of other substance intake versus problematic use are unique, and this is likely also true for coffee.

We found consistent positive genetic correlations with BMI and obesity in both 23andMe and the UKB. This is in contrast to meta-analyses of randomized control trials and epidemiological studies that found unclear effects by any coffee or decaffeinated coffee intake on waist circumference and BMI-defined obesity, and a modest inverse relationship between coffee intake and BMI. Results for these studies are highly heterogeneous, likely due to interindividual variability in the inclusion of sugary coffee additives, cultivation, roasting, and brewing conditions affecting its chemical makeup, and other habits surrounding coffee intake (e.g., concurrent food intake or appetite suppression by nicotine if smoking concurrently). This contentious relationship may also be explained by the amount of coffee intake, as greater coffee intake seems to attenuate the genetic associations with BMI and obesity, possibly due to the appetite suppressant effects of caffeine. Alongside accounting for other dietary intake, detailed accounting of coffee preparation, and consumptive habits formed with coffee intake, future subgroup analyses may help explain discrepant associations between the genetics and prevalence of coffee intake with BMI-related traits.

We did not recapitulate the beneficial phenotypic relationships between coffee intake and a variety of health outcomes that are generally reported by health association studies, perhaps because our study focused on the genetic relationship between coffee intake and other medical outcomes, or because our study focused on coffee intake and not caffeine intake. At the genetic level, we find no evidence of a common genetic background that could explain the beneficial effects of coffee on 29 cancers, Alzheimer’s disease/dementia/cognitive impairments, Parkinson’s disease, diabetes, cirrhosis, most cardiovascular conditions, or gout. In fact, some of these associations (e.g., cardiovascular traits and type II diabetes) were positive in the 23andMe cohort but showed no significant associations in the UKB cohort. Similarly, phenome-wide analysis did not support prior cancerous, metabolic, cardiovascular, or neurological health advantages of coffee intake. Although this may seem discrepant to phenotypic associations that generally report health benefits of coffee intake, recent meta-analysis of over 100 phenotypic studies on coffee intake health outcomes suggest high levels of heterogeneity across cohorts, especially across geographically separated populations.

We found many opposing relationships with the genetics of coffee intake between 23andMe and UKB. For example, genetic correlations with pain, psychiatric illnesses, and gastrointestinal traits were positively genetically correlated with coffee intake in 23andMe, but these associations were negative in the UKB. Inversely, the UKB analysis revealed that coffee intake was positively genetically correlated with cognitive traits, such as executive function and intelligence, corroborating prior evidence, yet genetic correlations with these two traits were negative in 23andMe. Multiple PheWAS associations were also discordant. When polygenic scores (PGS) were derived from 23andMe, we observed heightened odds between genetic liability for coffee intake and respiratory illnesses, ischemic heart disease, infection, and alcohol-related disorders. Higher odds for musculoskeletal and sleep conditions were mostly associated with coffee PGS generated from the UKB. Only 11 out of the 42 phenotypes associated with coffee intake PGS showed negative associations, and none of these purported health “benefits” were consistently observed in both cohorts. Whereas the coffee intake PGS from 23andMe was associated with lower odds for ear conditions, skin neoplasms, allergic rhinitis, and tonsillitis, the PGS of coffee intake from the UKB was associated with a lower risk of anxiety disorders. Also of note is that the number of positive genetic correlations and PGS associations between coffee intake and these other traits was greater when analyzed using data from the 23andMe cohort versus from the UKB, and the strength of these associations was usually stronger. Partially consistent with this, one meta-analysis of mortality found an inverse relationship between coffee intake and all-cause mortality in European but not US studies.

Our study shows that cultural, cohort, or geographic influences could affect the inferred genetic architecture of coffee intake and its associations with other health and lifestyle outcomes. Geographic regions may have an observable influence on GWAS results. We observed no significant differences in subtle geographic differences on coffee intake correlations using location data available in the UKB, suggesting cultural differences may contribute more to the cohort variations we report here. There is considerable variation in how or with whom one may consume coffee that could be subject to cultural influence. Caffeinated beverage sales, for instance, suggest that coffee and carbonated caffeinated beverages are more preferred in the US than the UK, whereas tea is the preferred source of caffeine in the UK and may modify coffee intake. Higher levels of coffee intake or caffeine from high-caloric beverages in the US cohort may partially explain the higher number and magnitude of negative health associations observed in the 23andMe analysis. Even across coffee beverage subtypes, the concentration of caffeine, other coffee chemical constituents, and manufacturing byproducts (e.g., plastics and metals from packaging) varies and thus may be important parameters in health associations. A recent investigation revealed the volume of ground or instant coffee is important to the potential health effects of its intake; instant coffee (~60 mg of caffeine per cup) is more commonplace in the UK whereas fresh brewed coffee (~85 mg of caffeine per cup) is preferred in the US. Cultural differences in coffee intake could help explain the divergent patterns of health and lifestyle associations between UK and US participants, though the relative contributions of culture, geography, and their interactions to these differences will need further exploration.

There are multiple caveats to consider when interpreting our findings. Firstly, our study does not address causality between coffee intake and other health and lifestyle traits. Mendelian randomization (MR) studies have attempted to address the exposure-outcome relationships between two traits by using genetic instruments (i.e., SNPs identified by GWAS) as proxies for exposure and associating them with an outcome of interest. For example, MR using genetic markers associated with coffee intake suggests that coffee consumption has no causal effect on obesity and endocrine disorders, despite observational studies suggesting protective effects of coffee. Similarly, MR studies of coffee and other substance use (e.g., tobacco, alcohol, cannabis) are also contentious, with evidence that inconsistencies may be driven by gene-cohort confounds such as those we found in this study. Secondly, the phenotype examined by 23andMe was exclusively caffeinated coffee intake, with one cup defined as 5 ounces, whereas the UKB also included decaffeinated coffee and did not explicitly define the volume of one cup. The caffeine content within coffee was also not directly measured. However, secondary analysis using summary statistics of estimated caffeine intake from any coffee subtype in the UKB yielded remarkably similar patterns of genetic correlations as those derived from our GWAS of cups of coffee consumed. This analysis presumably mitigated the relative contribution of decaffeinated coffee (3 mg of caffeine per cup versus 60 to 85 mg per cup of caffeinated coffee) to the revealed genetic associations, so we do not believe the cohort discrepancies are driven by the inclusion of decaffeinated coffee drinkers in the UKB. Another consideration is the possible health effects of non-caffeine coffee components, which are comparatively under-investigated, such as other coffee bean phytochemicals and drink additives. Furthermore, while it is unlikely that the discrepancies in genetic associations are driven by age, which is similar between cohorts (approximately 53 years old in 23andMe versus 57 years old in UKB), these cohorts skew older than the population average. They are also of above-average socioeconomic status and are of European descent, limiting the generalizability of our findings to a larger population. Some studies also show sex-dependent differences in coffee and caffeine metabolism and health associations with intake, which was not examined in our study.

Overall, we present striking differences in genetic associations of coffee intake across two large cohorts of European ancestry. While some genetic signals replicate across diverse cohorts, such as our GWAS findings and the associations between coffee intake with substance use and obesity traits, other associations may be obscured by cohort or cultural differences related to the phenotype in question. Our study provides a cautionary perspective on combining large cohort datasets gathered from unique geo-cultural populations.

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