Coffee Consumption And Metabolic Syndrome
Overview
This study explores the relationship between coffee consumption and metabolic syndrome, along with its individual components, while also examining the influence of milk, sugar, and artificial sweeteners on these associations.
The research involved a cross-sectional analysis of 351,805 participants from the UK Biobank. Data on coffee consumption were gathered through food frequency questionnaires and 24-hour dietary recalls. Metabolic syndrome was identified using blood biochemistry results and self-reported medication usage. To assess associations, multivariable logistic regression models were employed, with findings further validated via two-sample Mendelian randomization analysis.
Consuming one to two cups of coffee daily was linked to a reduced likelihood of metabolic syndrome (1 cup/day: OR: 0.88, 95% CI: 0.85–0.92; 2 cups/day: OR: 0.90, 95% CI: 0.86–0.93). However, higher coffee intake showed minimal or no associations with metabolic syndrome. Mendelian randomization analysis did not indicate a causal relationship between coffee intake and metabolic syndrome. Additionally, both self-reported and genetically predicted consumption of four or more cups per day were associated with an increased risk of central obesity. The inverse relationship between coffee consumption and metabolic syndrome was more pronounced for ground coffee compared to instant coffee. While adding milk or sugar did not significantly alter the results, the use of artificial sweeteners with coffee was associated with a higher risk of metabolic syndrome and its components.
In conclusion, moderate coffee consumption appears to be associated with a reduced risk of metabolic syndrome, although it may increase the risk of central obesity at higher intake levels. Further investigation is warranted to clarify the health implications of artificial sweeteners when consumed with coffee.
Introduction
Metabolic syndrome is a cluster of interrelated metabolic disorders that includes central obesity, high blood pressure, dyslipidemia, and hyperglycemia. This condition significantly increases the likelihood of developing type 2 diabetes, cardiovascular disease, and early mortality, making it a critical public health issue, with an estimated global prevalence of 20–30%. Addressing modifiable risk factors for metabolic syndrome is essential to reduce its widespread impact.
Some observational studies suggest that coffee consumption may be inversely related to the risk of developing metabolic syndrome. Proposed mechanisms include caffeine’s role in regulating energy balance and the anti-inflammatory properties of coffee polyphenols. However, evidence from randomized controlled trials has not established a causal link between coffee consumption and metabolic risk factors such as blood glucose and lipid levels. Furthermore, longitudinal studies raise concerns about confounding influences from lifestyle and socioeconomic variables.
While overall coffee consumption may not significantly impact metabolic syndrome risk, limited research has explored variations based on coffee types and additives like milk, sugar, or artificial sweeteners. Investigating these specific factors could yield valuable insights into the potential metabolic effects of coffee.
This study sought to evaluate the associations between total coffee consumption, coffee subtypes, and additive use with metabolic syndrome and its individual components in a large cohort. Additionally, a Mendelian randomization approach, leveraging genetic variants assigned at conception, was employed to validate the observed associations while minimizing confounding factors. These findings aim to guide future research into the complex relationship between coffee consumption and metabolic health.
Method
This study analyzed data from the UK Biobank, a large-scale prospective cohort study of middle-to-older-aged adults across the United Kingdom. The UK Biobank recruited approximately 500,000 participants aged 40–69 years between 2006 and 2010, collecting detailed information on demographics, socioeconomic factors, lifestyle habits, medical history, and biological measures. Participants underwent comprehensive assessments at 22 centers, including physical measurements and the collection of blood samples for biochemical analysis and genotyping using the Affymetrix UK BiLEVE Axiom and UK Biobank Axiom arrays. Ethical approval was granted by the North West Multi-Centre Research Ethics Committee (reference: 11/NW/0382), and all participants provided written informed consent. This analysis was conducted under UK Biobank application number 44407.
Habitual coffee consumption was measured through a food frequency questionnaire (FFQ) and multiple 24-hour dietary recalls. The FFQ captured participants’ average daily coffee intake over the past year, categorized by type (e.g., instant, ground, decaffeinated), with a standard cup defined as 200–250 mL. The dietary recalls documented coffee consumption from the previous day, detailing intake by subtype and additions like milk, sugar, or artificial sweeteners.
The main outcome was metabolic syndrome, defined using harmonized criteria from the International Diabetes Federation. Participants were classified as having metabolic syndrome if they met criteria for central obesity (based on population-specific waist circumference cut-offs) and at least two other conditions: elevated fasting glucose, elevated triglycerides, high blood pressure, or low HDL cholesterol. Waist circumference was measured using a SECA tape, while blood pressure and fasting biomarkers were assessed with standardized equipment and protocols. Information on medication use for managing dysglycemia, dyslipidemia, or hypertension was self-reported.
Participants meeting criteria for metabolic syndrome across three or more components were labeled as cases, while those without criteria for at least three components were controls. Participants not meeting either case or control definitions were excluded.
Since only 6% of participants provided fasting blood samples, alternative criteria were applied for classifying cases. For example, individuals on medications for high fasting glucose or triglycerides were categorized accordingly, while others with missing fasting data were excluded. Similar approaches were used for other components like blood pressure and HDL cholesterol.
Socioeconomic status was evaluated using the Townsend Deprivation Index, while lifestyle factors such as smoking, alcohol intake, and physical activity were assessed through validated questionnaires. Dietary factors, including fruit, vegetable, and tea intake, were self-reported.
To examine the causal relationship between coffee intake and metabolic syndrome risk, a two-sample Mendelian randomization analysis was conducted. Genetic variants associated with coffee consumption were derived from a genome-wide association study by the Coffee and Caffeine Genetics Consortium. Independent single-nucleotide polymorphisms (SNPs) linked to coffee consumption and identified at genome-wide significance levels were analyzed in the UK Biobank dataset. Multivariable logistic regression adjusted for demographic and genetic factors was used to estimate genetic associations with metabolic syndrome and its components. Participants with poor genotyping quality, genetic inconsistencies, or familial relationships were excluded from the genetic analysis.
This work underwent rigorous review and editing to ensure accuracy, clarity, and coherence, with full responsibility for its content resting with the senior author.
Statistical Analysis
All analyses were conducted using R version 4.1.1. Mendelian randomization (MR) was performed utilizing the “TwoSampleMR” and “MVMR” packages. To account for multiple testing, a statistical significance threshold of p < 0.0083 was determined by dividing 0.05 by the six distinct outcomes assessed. Data cleaning and analysis scripts were provided in the Supporting Information.
Cross-sectional associations between coffee consumption and metabolic syndrome, along with its individual components, were explored using multivariable logistic regression. Individuals who did not consume coffee were set as the reference group. Effect estimates were adjusted for various confounders, including age, sex, Townsend deprivation index, educational attainment, smoking status, alcohol consumption, physical activity, and intake of fruits, vegetables, and tea. For 24-hour dietary recall data, coffee type was included as a covariate. Additional sub-analyses were conducted to examine the interactions between milk, sugar, and artificial sweeteners in coffee and their combined effects. Specifically, milk use was analyzed for instant and filtered coffee, while sugar and sweetener use were evaluated for all coffee types except espresso.
Instrument strength was assessed using R² and F statistics, calculated following established methodologies in Mendelian randomization. Stronger instruments were indicated by higher F statistics, minimizing weak instrument bias. Effect alleles of all genetic instruments were harmonized with alleles linked to increased coffee consumption. The inverse variance weighted (IVW) method with multiplicative random effects models was applied to investigate causal relationships between coffee consumption and metabolic syndrome, assuming balanced horizontal pleiotropy. Cochran’s Q test was used to detect heterogeneity among instrument-specific Wald ratios, potentially signaling invalid instruments.
Robustness of findings was ensured through sensitivity analyses. The MR-Egger estimator, accounting for directional pleiotropy under the InSIDE assumption, was applied, with a significant MR-Egger intercept (p < 0.05) suggesting pleiotropy. Additionally, the weighted median method was used, requiring at least 50% of the weight to derive from valid single-nucleotide polymorphisms (SNPs).
Given the confounding influence of smoking and alcohol consumption on coffee-related health outcomes, multivariable MR was performed, adjusting for genetic liabilities to these traits. Genetic instruments for smoking initiation and alcohol consumption were sourced from GWAS and the Sequencing Consortium of Alcohol and Nicotine Use studies, including European participants separate from the UK Biobank cohort. Instruments for coffee consumption were derived from the CCGC meta-analysis, restricted to European participants. SNPs in linkage disequilibrium were excluded using a lenient threshold (r² < 0.1) to retain statistical power. Proxy SNPs (r² ≥ 0.8) were substituted where target SNPs were absent.
Causal effects of coffee consumption on metabolic syndrome and its components were estimated using multivariable IVW and MR-Egger methods. SNP alignment ensured consistency across exposure and outcome data, including effect allele frequency corrections for palindromic SNPs. Detailed SNP lists are available in supplementary tables.
Result
After excluding participants who withdrew consent, were non-British, lacked coffee consumption data, or had missing covariate information, a total of 351,805 individuals were included in the analysis. Among them, coffee drinkers were more likely to be male, smoke, consume alcohol frequently, attain higher education levels, and drink less tea compared to non-coffee drinkers.
Analysis of 142,594 participants revealed a notable association between daily coffee consumption and the prevalence of metabolic syndrome after adjusting for sociodemographic and lifestyle factors. Drinking up to three cups per day was linked to reduced odds of metabolic syndrome compared to non-coffee drinkers. The odds ratios (OR) are listed below e:
- 1 cup/day: OR 0.88 (95% CI: 0.85–0.92)
- 2 cups/day: OR 0.90 (95% CI: 0.86–0.93)
- 3 cups/day: OR 0.94 (95% CI: 0.90–0.99)
Conversely, consuming four or more cups daily was potentially associated with increased odds of metabolic syndrome, with ORs ranging from 1.03 to 1.06, though these findings were less definitive. Coffee intake was positively linked to elevated fasting glucose and triglycerides but inversely associated with high blood pressure and low HDL-cholesterol levels. A J-shaped relationship was observed for central obesity.
The Mendelian Randomization (MR) analysis utilized genetic instruments to explore causal relationships between coffee intake and metabolic syndrome risk. Results showed no robust evidence supporting a causal effect of genetically predicted coffee consumption on metabolic syndrome risk. However, higher coffee intake was causally linked to increased central obesity risk and showed suggestive evidence for elevated fasting glucose levels.
Stratified analyses based on coffee subtypes largely mirrored the overall trends, with a few deviations. Instant coffee showed no association with metabolic syndrome or high blood pressure, while decaffeinated coffee was not linked to low HDL-cholesterol. Consumption of lattes or cappuccinos was inversely associated with metabolic syndrome and high blood pressure, whereas espresso intake showed no significant associations.
Regarding additives, milk and sugar did not substantially alter the associations, but artificial sweeteners were positively associated with adverse metabolic outcomes, particularly among instant coffee drinkers. Similarly, filtered coffee with sweeteners was linked to negative outcomes except for fasting glucose and triglycerides. Notably, avoiding sweeteners was associated with reduced risks of metabolic syndrome, central obesity, and low HDL-cholesterol among consumers of filtered and latte/cappuccino coffees.
Conclusion
This large, population-based study of over 350,000 UK Biobank participants examined the relationship between coffee consumption and metabolic health. Findings suggest that low-to-moderate coffee intake (1–3 cups daily) correlates with a lower prevalence of metabolic syndrome in observational analyses. However, Mendelian randomization (MR) studies did not support a causal link, indicating that these associations may be influenced by residual confounding, reverse causation, or bias. Interestingly, both high self-reported coffee consumption and genetically predicted elevated intake were linked to an increased risk of central obesity. Additionally, the use of artificial sweeteners in coffee emerged as a potential risk factor for metabolic syndrome, emphasizing the role of coffee additives in shaping health outcomes.
These findings are consistent with earlier MR studies, such as one conducted in a Danish cohort, which also reported protective observational associations that did not hold up under genetic scrutiny. By leveraging separate datasets for exposure and outcome, as well as larger sample sizes, this study achieved greater statistical power and robustness. Sensitivity analyses further supported the conclusion that lifestyle and socioeconomic factors, rather than coffee itself, likely drive the observed inverse associations with metabolic syndrome. Notably, beneficial effects were confined to ground and decaffeinated coffee consumers, which may reflect healthier diet and lifestyle patterns associated with these coffee types.
For central obesity, the study demonstrated consistent positive associations between coffee intake—both self-reported and genetically predicted—and obesity risk. These findings align with prior MR studies suggesting that coffee consumption may contribute to weight gain, despite observational studies suggesting the opposite. While caffeine and coffee polyphenols may offer anti-obesity properties, psychological factors such as stress and its biological responses could mediate these effects.
The association between artificial sweeteners in coffee and higher metabolic syndrome risk is another novel finding. It aligns with prior evidence linking artificial sweeteners to adverse effects on weight, glycemic control, and overall metabolic health. Proposed mechanisms include alterations in taste preferences, disruptions to energy regulation, and potential effects on gut microbiota. However, reverse causation remains a plausible explanation, as individuals with pre-existing metabolic conditions may preferentially use artificial sweeteners due to health concerns. Additional longitudinal and experimental studies are warranted to clarify these relationships.
This study’s strengths include its large sample size, detailed coffee intake data, and the integration of observational and genetic methodologies. However, limitations include reliance on nonfasting blood samples, reduced power in subgroup analyses, potential pleiotropy in genetic instruments, and the predominantly White British cohort, which may limit the generalizability of findings.
In conclusion, while low-to-moderate coffee consumption showed inverse associations with metabolic syndrome in observational analyses, genetic evidence does not support a causal protective effect. High coffee intake may elevate central obesity risk, and additives like artificial sweeteners could modify metabolic outcomes. Further research involving diverse populations and detailed coffee phenotyping is necessary to refine dietary recommendations and better understand the interplay between coffee, its additives, and metabolic health.
