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In a recent study published in the BMJ Medicine, researchers performed two sample-mendelian randomizations (MR) studies to investigate the effects of long-term exposure to higher plasma caffeine concentrations on type 2 diabetes (T2D), adiposity, and cardiovascular diseases, such as ischaemic heart disease, stroke, atrial fibrillation, to name a few.

Study: Appraisal of the causal effect of plasma caffeine on adiposity, type 2 diabetes, and cardiovascular disease: two sample mendelian randomisation study. Image Credit: ThanaponTH/Shutterstock.com

Background

Caffeine (1, omeprazole magnesium 20.6 side effects 3,7-trimethyl xanthine) in coffee and tea has thermogenic and psychostimulant effects.

Several short-term randomized controlled trials (RCTs) have found that caffeine (even in trivial amounts) helps decrease weight, fat mass, and body mass index (BMI).

Hence, it is highly likely that more caffeine consumption (via tea & coffee drinking) might also lower the risk of adipose-related diseases, such as T2D and cardiovascular disease. However, the long-term effects of caffeine intake remain elusive.

Observational studies have shown an inverse correlation between coffee consumption and T2D risk. However, observational findings on its correlation with cardiovascular diseases are inconsistent, showing positive and inverse correlations.

Thus, these studies did not reliably infer causality, potentially resulting in confounded associations.

Furthermore, other compounds in caffeinated drinks and foods make delineating the specific caffeine effects on the risk of cardiometabolic diseases challenging.

About the study

In the present study, researchers screened six population-based studies conducted among 9,876 individuals of European ancestry to identify genome-wide associations (GWA) of single nucleotide polymorphisms (SNPs) near cytochrome P450 isoform 1A2 (CYP1A2) and AHR loci.

They selected the strongest SNPs near CYP1A2 and AHR loci, i.e., rs2472297 at CYP1A2 and rs4410790 at AHR, to use as instrumental variables in this MR analysis.

The researchers assumed that genetic variants used as instrumental variables in MR analysis fulfill three assumptions, relevance assumption, independence assumption, and exclusion restriction assumption.

Genetic variations near CYP1A2, which metabolizes caffeine in the liver, and AHR, which regulates the expression of CYP1A2, are associated with plasma concentrations of caffeine.

Individuals who carry these genetic variants could help improve causal inference reliably by serving as unbiased proxy indicators.

The team obtained estimates of the correlations of the caffeine SNPs for BMI, whole body fat-free mass, and corresponding summary genetic data for T2D and cardiovascular disease subtypes and atrial fibrillation.

It helped them investigate the potential causal effects of long-term exposure to higher plasma caffeine concentrations on adiposity, T2D, and major cardiovascular diseases.

First, the authors estimated the SNP and outcome association (beta coefficient). They divided it by the estimate of the association between SNP and high plasma caffeine concentration to get the MR estimate using a standard deviation (SD) unit, representing the variation in plasma caffeine concentration per allele.

Next, they combined the MR estimates for these two SNPs per the inverse-variance weighted method. For follow-up, they conducted a two-step MR mediation analysis to investigate the extent to which BMI mediated the effects of caffeine on T2D.

Results

The authors noted an association between genetically predicted higher plasma caffeine concentrations and lower BMI (beta −0.08 SD) and whole body fat mass (beta −0.06 SD), where one SD was equal to 4.8 kg/m² in BMI and 9.5 kg in fat mass, respectively.

Per study estimates, BMI reduction mediated 43% of the effect of caffeine on T2D liability. However, for every SD increase in plasma caffeine, genetically predicted higher plasma caffeine concentrations were not associated with fat-free body mass (beta −0.01 SD), where one SD was equal to ~11.5 kg.

Furthermore, higher plasma caffeine concentrations were correlated to a lower T2D risk in FinnGen and DIAMANTE consortia, with a combined odds ratio (OR) of 0.81.

Previous observational findings could not set a clear association between genetically predicted coffee consumption and T2D in MR analyses. This MR study suggested that caffeine, at least in part, explains the inverse association between coffee consumption and T2D risk.

Though study findings for plasma caffeine might superficially appear inconsistent and contradictory to previous MR analyses, the authors expected such discrepancy because the genetic variants in the two genomic regions associated with higher plasma caffeine concentrations are also associated with lower coffee and caffeine consumption.

Moreover, the genetic method used in this study only used SNPs located in genes encoding enzymes with an established role in caffeine metabolism.

The confidence interval (CI) for the cardiovascular outcomes in this study was 95%, suggesting that any protective effect of plasma caffeine concentrations on ischaemic heart disease and atrial fibrillation is unlikely to be larger than 15% and 12% and more harmful than 1% and 5%, respectively.

The magnitude of the association was stronger in this MR analysis than in previous MR studies, which might be related to the use of different instruments and data sources for BMI and T2D.

Conclusion

This MR study found robust evidence to support causal associations of higher plasma caffeine concentrations with lower adiposity and risk of T2D.

Yet, more clinical studies are warranted to investigate the translational potential of these findings toward reducing the burden of metabolic disease. RCTs on caffeine consumption and chronic diseases are costly and tedious to implement; subsequently, barely a few have been published.

However, more RCTs are warranted to assess whether non-caloric caffeinated beverages could help reduce the risk of obesity and T2D.

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.