Exploring the genetic links: Alcohol, smoking, coffee intake, and arthritis risk

In a recent study published in Nutrients, a group of researchers investigated the genetic relationship between alcohol consumption, smoking, coffee intake, and the risk of arthritis through Mendelian randomization studies.

Study: The Causal Association between Alcohol, Smoking, Coffee Consumption, and the Risk of Arthritis: A Meta-Analysis of Mendelian Randomization Studies. Image Credit: Szasz-Fabian Jozsef/Shutterstock.com

Background 

Arthritis, including osteoarthritis (OA) and rheumatoid arthritis (RA), affects millions, causing joint pain and damage. OA causes deterioration of cartilage and bones, affecting more than 500 million people with pain and walking difficulties.

RA is an autoimmune disease that leads to joint inflammation and muscular pains, affecting about one percent of the population with enhanced probabilities for severe infections and heart diseases. Lifestyle factors such as smoking, alcohol, and coffee intake are linked to arthritis, but ethical constraints limit research.

Mendelian Randomization (MR) overcomes these limitations by using genetic variants to study these links. However, varying qualities and results in MR studies call for more comprehensive research to clarify these associations.

About the study 

In the present study, researchers conducted a MR analysis using data from several Genome-Wide Association Studies (GWAS). They used a dataset based on the study with 1.2 million study subjects for data on alcohol intake and cigarette smoking.

In contrast, datasets on alcohol intake frequency and coffee consumption were obtained from the UK Biobank, encompassing hundreds of thousands of participants.

The research also included large numbers of both patient and control subjects collected from the meta-analysis of OA and RA and also from the FinnGen project.

The selection of instrumental variables (IVs) was done carefully, focusing on single nucleotide polymorphisms (SNPs) significantly associated with each exposure.

SNPs with linkage disequilibrium effects were excluded, and those associated with confounders or outcomes were carefully screened out using the PhenoScanner database. The selected IVs all had F statistics greater than 10, ensuring reduced bias in the MR analysis.

Statistical methods included the inverse-variance weighted (IVW) method for primary analysis and the weighted median method for secondary analyses, both designed to provide accurate estimates.

Sensitivity analyses were performed to validate the causal relationships, using methods like MR-Egger and MR-PRESSO to account for horizontal pleiotropy and other biases.

The research followed the PRISMA-P guidelines and included studies selected based on specific criteria related to MR, OA, RA, and lifestyle factors such as alcohol, smoking, and coffee consumption.

Exclusion criteria were set to maintain the focus and quality of the review. Two trained researchers independently conducted data extraction and quality assessment, following established guidelines for MR studies.

Finally, meta-analyses were carried out using Stata 16.0. Odds ratios and confidence intervals were calculated to determine the causal effect of lifestyle factors on arthritis, considering various body parts and arthritis subtypes to understand the overall impact.

Study results 

The study assessed the genetic causality between lifestyle factors—specifically alcohol intake, smoking, and coffee consumption—and arthritis, employing MR methods and a meta-analysis of past MR studies. The meta-analysis incorporated 11 studies focusing on osteoarthritis (OA) and rheumatoid arthritis (RA).

Regarding alcohol intake, the analysis, which included results from two studies and ten MR analyses, found a non-significant positive association with arthritis overall (Odds Ratio [OR]: 1.02, 95% Confidence Interval [CI]: 0.94–1.11).

Subgroup analyses for OA and RA also showed non-significant associations. A sensitivity analysis using the weighted median method confirmed the lack of significant genetic causality between alcohol intake and arthritis.

In contrast, the study found a significant positive genetic causal relationship between smoking behavior and arthritis.

The meta-analysis, using outcomes from the inverse variance weighted method, indicated a positive association between smoking and arthritis (OR: 1.44, 95% CI: 1.27–1.64). Subgroup analyses suggested similar associations for both OA and RA. Sensitivity analyses using the weighted median method supported these findings.

The study also revealed a positive genetic causal association between coffee consumption and arthritis, with an overall OR of 1.02 (95% CI: 1.01–1.03). Subgroup analyses indicated stronger associations for RA compared to OA. Again, sensitivity analyses using the weighted median method validated these results.

Conclusion

Overall, the study's findings demonstrated significant positive genetic causality between both smoking and coffee consumption and arthritis (both OA and RA), while the evidence for a genetic causal relationship between alcohol intake and arthritis was insufficient.

The quality of the included studies was assessed as high, and the risk of bias was considered low, underpinning the robustness of the data and the conclusions drawn from this study. 

These results have potential clinical implications, suggesting that individuals susceptible to osteoarthritis and patients should consider reducing smoking and coffee consumption.

However, the study did not find a genetic causal relationship between alcohol intake and arthritis and, therefore, does not support the notion that alcohol might be beneficial for arthritis. The findings also offer directions for further research into the pathogenesis of arthritis.

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.