Associations of Observational and Genetically Determined Caffeine Intake With Coronary Artery Disease and Diabetes Mellitus

Abstract

Background

Caffeine is the most widely consumed psychostimulant and is associated with lower risk of coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM). However, whether these associations are causal remains unknown. This study aimed to identify genetic variants associated with caffeine intake, and to investigate evidence for causal links with CAD or T2DM. In addition, we aimed to replicate previous observational findings.

Methods and Results

Observational associations were tested within UK Biobank using Cox regression analyses. Moderate observational caffeine intakes from coffee or tea were associated with lower risks of CAD or T2DM, with the lowest risks at intakes of 121 to 180 mg/day from coffee for CAD (hazard ratio [HR], 0.77 [95% CI, 0.73–0.82; P<1×10⁻¹⁶]), and 301 to 360 mg/day for T2DM (HR, 0.76 [95% CI, 0.67–0.86]; P=1.57×10⁻⁵). Next, genome‐wide association studies were performed on self‐reported caffeine intake from coffee, tea, or both in 407 072 UK Biobank participants. These analyses identified 51 novel genetic variants associated with caffeine intake at P<1.67×10⁻⁸. These loci were enriched for central nervous system genes. However, in contrast to the observational analyses, 2‐sample Mendelian randomization analyses using the identified loci in independent disease‐specific cohorts yielded no evidence for causal links between genetically determined caffeine intake and the development of CAD or T2DM.

Conclusions

Mendelian randomization analyses indicate genetically determined higher caffeine intake might not protect against CAD or T2DM, despite protective associations in observational analyses.

Nonstandard Abbreviations and Acronyms

CARDIoGRAMplusC4D

Coronary Artery Disease Genome wide Replication and Meta‐analysis plus The Coronary Artery Disease Genetics

DIAGRAM

Diabetes Genetics Replication And Meta‐analysis

eQTL

expression quantitative trait locus

MR

Mendelian randomization

T2DM

type 2 diabetes mellitus

Clinical Perspective

What Is New?

• Leveraging data from >400 000 individuals, we identified 51 novel genetic loci associated with caffeine intake.

• We confirmed phenotypic associations between caffeine intake and the development of coronary artery disease or type 2 diabetes mellitus, but by exploiting instrumental variable analyses we found no evidence for causality of this association.

What Are the Clinical Implications?

• Our data do not support recommending caffeine intake to protect against the development of coronary artery disease of type 2 diabetes mellitus.

Caffeine is the most commonly consumed psychostimulant in the world and is readily available in coffee, tea, and other food products. ¹ Previous observational studies and meta‐analyses have generally reported beneficial associations between moderate intake of coffee, the main dietary source of caffeine, ¹ and risk of cardiovascular disease ² and type 2 diabetes mellitus (T2DM), ³ as well as cardiovascular and all‐cause mortality. ⁴ , ⁵ Contrasting results have been reported as well for cardiovascular disease outcomes, including coronary artery disease (CAD), ² , ⁶ , ⁷ , ⁸ , ⁹ and therefore coffee and tea are not generally included in dietary guidelines. ¹⁰ Given its widespread consumption, altering caffeine intake might be an interesting way to influence population‐wide risk of developing CAD and T2DM.

Because of the observational design of previous studies, which include many cross‐sectional and case‐control studies, it is difficult to provide insight into causal relationships. Genome‐wide association studies (GWASs) have identified several single‐nucleotide polymorphisms (SNPs) associated with caffeine or coffee intake through genes such as AHR and CYP1A2, which affect the metabolism of caffeine. ¹¹ , ¹² , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ Unlike traditional observational studies, Mendelian randomization (MR) analyses have the advantageous applicability of uncovering causal links using genetic variants, which are randomly allocated at conception, as instrumental variables for modifiable risk factors to test potential causal links with disease outcomes. So far, MR analyses between genetically determined higher caffeine intake and risk of CAD ⁷ , ¹⁸ or T2DM ¹⁹ failed to provide support for a causal link. However, these studies used only few SNPs and investigated coffee as the sole source of caffeine.

Here, we investigated the observational associations between habitual caffeine intake from coffee, tea, or both with new‐onset CAD and T2DM in a large prospective observational cohort. To further our knowledge of the genetic architecture underlying caffeine intake, we carried out GWASs for caffeine intake from coffee, tea, or both in over 400 000 participants from the UK Biobank to identify novel variants for caffeine intake. Using this set of SNPs, we aimed to investigate the causal relationship between caffeine intake with CAD and T2DM in large independent cohorts.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request. GWAS summary statistics generated during the present study will be made available in the following repository: https://doi.org/10.17632/d8nwkm7p9p.1.

Study Population

The UK Biobank study is a population‐based prospective cohort whose design and population have been described previously. ²⁰ From 2006 to 2010, >500 000 individuals between the ages of 40 and 69 years were recruited in the United Kingdom. All participants gave informed consent, ²¹ and the UK Biobank study was approved by the North West Multi‐centre Research Ethics committee. ²² Details regarding the UK Biobank study population are provided in Data S1.

Ascertainment of Coffee and Tea Intake

During the first visit to the assessment center, daily coffee and tea intake were assessed by asking participants, “How many cups of coffee do you drink each day? (Include decaffeinated coffee)” and “How many cups of tea do you drink each day? (Include black and green tea).” In addition, coffee drinkers were asked what type of coffee they usually drink. Caffeine intake was calculated as the number of cups of coffee or tea multiplied by the caffeine content per cup. ²³ Combined caffeine intake from both coffee and tea was calculated as the sum of the daily caffeine intake from coffee and tea from individuals who provided data on both. Full details on the ascertainment of coffee, tea, and daily caffeine intake are provided in Data S1.

CAD and T2DM Prevalence and Incidence in the UK Biobank

Prevalence at baseline and incidence of new‐onset CAD and T2DM cases within UK Biobank were, per prior analysis, ²⁴ based on self‐reported data, International Classification of Diseases, Ninth Revision (ICD‐9) and Tenth Revision (ICD‐10) ²⁵ coded primary and secondary diagnoses, operation codes, ²⁶ and death attributable to either condition from inclusion in the UK Biobank until end of follow‐up (March 31, 2017, for participants from England; February 29, 2016, for Wales; and October 31, 2016, for Scotland) as described in Data S1. Incident cases that were based on self‐reported diagnoses during follow‐up visits were included only if there were no events recorded according to the ICD‐9 or ICD‐10 or operation codes data and only if the participant did not report this in the previous visit. If the participant was the same age as the reported age of diagnosis, the median date between the visit and the participant's birthday was taken as date of event. If the age of diagnosis was before the participant's current age, we took the median date of the year of the reported age of diagnosis counted from the participant's birthday. If age of diagnosis was not available, we took the median date between the visit of the first self‐reported diagnosis and the previous visit. Individuals with a history of CAD or T2DM at inclusion were excluded from the respective observational analyses.

Covariates

At the first visit, weight (in kilograms) and height (in centimeters) were measured and used to calculate the body mass index (in kilograms per square meter). Age was calculated as the difference between date of birth and date of inclusion in the UK Biobank. Sex, ethnicity, weekly alcohol intake (UK units) and active smoking at inclusion were self‐reported. Weekly alcohol intake was right‐skewed and therefore log2 transformed for participants who provided this data. For participants without these accurate data on the number of units, we estimated the weekly alcohol intake using a more crude questionnaire of alcohol intake frequency where participants were asked, “About how often do you drink alcohol?” For this, we fitted a linear regression between with the log2‐transformed weekly alcohol intake and alcohol intake frequency in participants with both measures, and predicted weekly alcohol intake on the remaining individuals. The Townsend Deprivation Index, a proxy for socioeconomic status, was provided by the UK Biobank and inverse rank normalized because of a right‐skewed distribution. ²⁴

Genotyping and Imputation in UK Biobank

UK Biobank participants were genotyped using custom Affymetrix Axiom (UK Biobank Lung Exome Variant Evaluation ²⁷ or UK Biobank) arrays. The genotyping methods, arrays, and quality‐control procedures have been described previously in detail ²⁸ , ²⁹ and are briefly described in Data S1.

Statistical Analysis

We performed multivariable Cox regression analyses to test the association of observational caffeine intake per 60 mg caffeine (equivalent to the caffeine content of 1 cup of instant coffee or 2 cups of tea) with new‐onset CAD and T2DM in the UK Biobank. Hazard ratios with 95% CIs were calculated for 1 to 60, 61 to 120, 121 to 180, 181 to 240, 241 to 300, 301 to 360, or >360 mg of caffeine from coffee or combined, compared with individuals who drank 0 mg. Because of the lower caffeine content per cup of tea compared with caffeinated coffee, the hazard ratios and 95% CIs for caffeine from tea were calculated for 1 to 60, 61 to 120, 121 to 180, or >180 mg (equivalent to >6 cups of tea) of caffeine compared with individuals who had 0‐mg intake from tea. The time scale for the Cox regression analyses was from inclusion in the UK Biobank until the outcome of interest, death or end of follow‐up. Cox regression analyses were performed unadjusted and adjusted for age, sex, body mass index, active smoking, Townsend Deprivation Index, and weekly alcohol intake using Stata version 15 (StataCorp, College Station, TX).

All genetic analyses were adjusted for age, sex, genotyping array, and the first 30 genetic principal components to adjust for population stratification. We performed separate GWASs for inverse rank normalized combined caffeine intake, caffeine from coffee, and caffeine from tea in 19 400 838 SNPs using BOLT‐LMM version 2.3.1 software (Broad Institute, Cambridge, MA). ³⁰ A Bonferroni corrected P<1.67×10⁻⁸ (traditional GWAS significance threshold of 5×10⁻⁸/3) was considered genome‐wide significant. This significance threshold is conservative, considering that our phenotypes are correlated with Spearman's rank correlation coefficients between phenotype pairs ranging from r=−0.33 to 0.71 (Table S1). Details of the GWAS analyses, functional annotation of candidate genes, ³¹ , ³² , ³³ , ³⁴ , ³⁵ and biological pathways are provided in Data S1.

We performed MR analyses using previously published summary statistics from the CARDIoGRAMplusC4D (Coronary Artery Disease Genome wide Replication and Meta‐analysis plus The Coronary Artery Disease Genetics) consortium (123 504 controls and 60 801 [33.0%] cases) ³⁶ and the DIAGRAM Diabetes Genetics Replication And Meta‐analysis)) consortium (132 532 controls and 26 676 [16.8%] cases) ³⁷ to gain insight into potential causal relationships between caffeine intake and CAD or T2DM, respectively. Lead SNPs of each caffeine intake trait that reached P<1.67×10⁻⁸ were used to create a weighted genetic risk score and were also used as instrumental variables in the MR. Each genetic risk score was created using an additive model per GWAS, summing the number of effect alleles (0, 1, or 2) per individual after multiplying it with the effect size between the SNP and the GWAS phenotype. Statistical power for the MR with a binary outcome was calculated using an alpha of 0.05 and the explained variance of each genetic risk score, as described previously. ³⁸ For the MR, SNPs that were not available in CARDIoGRAMplusC4D or DIAGRAM were replaced with proxies with R ²>0.8, and were otherwise excluded from the MR analyses if no eligible proxies were available. SNP effects were harmonized across studies using the built‐in feature of the TwoSampleMR package in R (R Foundation for Statistical Computing, Vienna, Austria). The association between genetically determined higher caffeine intake and CAD or T2DM was assessed using fixed‐effects inverse‐variance weighted meta‐analyses. Odds ratios (ORs) with 95% CIs are presented for the MR outcomes. To maximize the likelihood of reporting true findings, α was set at 0.005 instead of 0.05. ³⁹ Associations with P<0.05 were considered suggestively significant. We assessed potential weak instrument bias per SNP using the F‐statistic ⁴⁰ and I² _GX. ⁴¹ We determined the I² index. ⁴² Cochran's Q, Rücker's Q′, and Q‐Q′ ⁴³ to test for heterogeneity and thus potential pleiotropy. MR‐Egger, ⁴³ MR Pleiotropy Residual Sum and Outlier ⁴⁴ and MR inverse‐variance weighted random effects ⁴³ were used as pleiotropy analyses. MR‐Steiger filtering ⁴⁵ was performed to remove variants more strongly associated with the outcome than the exposure. Weighted median and weighted mode‐based estimator MR analyses ⁴⁶ were performed as additional sensitivity analyses. Details of the MR analyses are provided in Data S1.

Results

Cohort Characteristics

Of 502 525 UK Biobank individuals, 362 316 were available for the combined caffeine intake analyses, 373 522 for caffeine from coffee, and 395 866 for caffeine from tea (Figure S1). Baseline characteristics are shown in Table, per caffeine intake trait in Table S2, and stratified by caffeine intake in Tables S3 through S5. Median (interquartile range) combined caffeine intake was 205 (120–290) mg/day, from coffee 85 (3–180) mg/day, and from tea 90 (60–150) mg/day.

Table 1.

Baseline Characteristics of All Included 407 072 UK Biobank Participants

Characteristics	Men	Women
Total, N	186 968	220 104
Age, y, mean (SD)	57.16 (8.08)	56.72 (7.92)
Daily caffeine intake, mg/d, median (IQR)
Combined caffeine	210 (150–300)	180 (120–270)
Caffeine from coffee	85 (6–180)	60 (3–170)
Caffeine from tea	90 (60–150)	90 (60–150)
Blood pressure, mm Hg, mean (SD)
Systolic	139.60 (16.15)	128.74 (17.88)
Diastolic	84.69 (8.22)	79.94 (8.20)
Active smoker, N (%)
No	164 791 (88.1)	200 946 (91.3)
Yes	22 177 (11.9)	19 158 (8.7)
Body mass index , kg/m2, mean (SD)	27.85 (4.23)	27.05 (5.13)
Weekly alcohol intake, UK units, median (IQR)	15.40 (5.50, 28.40)	6.40 (1.60, 13.20)
Hypertension, N (%)
No	119 965 (64.2)	160 881 (73.1)
Yes	67 003 (35.8)	59 223 (26.9)
Hyperlipidemia, N (%)
No	139 471 (74.6)	188 444 (85.6)
Yes	47 497 (25.4)	31 660 (14.4)

Combined caffeine intake was calculated as the sum of caffeine intake from coffee and tea. Body mass index was calculated as weight in kilograms divided by height in meters squared. Smoking status and weekly alcohol intake were self‐reported at inclusion. IQR indicates interquartile range.

Associations of Observational Caffeine Intake With CAD and T2DM

During nearly 10 years (median, 8.1 years; interquartile range, 7.5–8.6) of follow‐up in 345 809 participants without history of CAD and 347 718 participants without history of T2DM, 14 681 (4.2%) individuals developed CAD, and 6982 (2.0%) developed T2DM in the combined caffeine cohort. Results for unadjusted analyses are presented in Tables S6 and S7. In multivariable adjusted analyses (Tables S8 and S9), combined caffeine intake was very modestly or not associated with CAD or T2DM. However, the individual components, caffeine from coffee or tea, did show associations with lower risks of new‐onset CAD and T2DM (Figure 1A and 1B, respectively). Overall, the associations between caffeine from coffee or tea with CAD and T2DM followed U‐curve–type shapes, with the highest protective effects of caffeine intake from coffee on CAD at moderate intakes (121–180 mg/day), compared with no, lower, or higher intakes. Associations between caffeine from coffee with CAD or T2DM were not appreciably different when additionally adjusted for caffeine from tea, nor were the associations for caffeine from tea when additionally adjusted for caffeine from coffee (Table S10). Overall, caffeine intake from coffee was associated with lower risks of CAD and T2DM compared with caffeine from tea or combined. To determine whether this may be attributable to confounding by other, noncaffeine, substances, we stratified the analyses by cups of decaffeinated or caffeinated coffee and found similar results. Both caffeinated and decaffeinated coffee were associated with lower risk of CAD and T2DM compared with no or high (>6 cups for caffeinated coffee; >3 for decaffeinated coffee) intake (Table S11).

Figure 1. Associations between observational caffeine intake with new‐onset coronary artery disease (A) and type 2 diabetes mellitus (B).

Hazard ratios (HR) with 95% CIs were calculated using Cox regression analyses, adjusted for age, sex, active smoking, body mass index, and log‐transformed weekly alcohol intake. Estimates <1 indicate a beneficial association between caffeine intake and outcome. Sixty milligrams of caffeine is equivalent to 1 cup of instant coffee or 2 cups of tea.

GWAS on Caffeine Intake Traits

We identified 62 SNPs in 37 loci: 32 novel, associated with combined caffeine intake (Figure 2; Table S12); 27 SNPs in 24 loci (20 novel) with caffeine from coffee (Figure S2; Table S13); and 27 SNPs in 24 loci (21 novel) with caffeine from tea (Figure S3; Table S14). When combined on the basis of the lowest P value over all traits, 73 unique SNPs in 5 known and 51 novel loci were associated with ≥1 caffeine trait (Figure S4, Table S15). In total, 15 of 20 previously reported SNPs were replicated within 1 MB of our sentinel SNPs (Table S16). Regional association plots for each independent locus per trait are presented in Figures S5 through S7 and QQ plots in Figures S8 through S10. The sentinel SNPs identified in the combined caffeine, caffeine from coffee, and caffeine from tea GWAS explained 1.32%, 0.59%, and 0.45% of variance in caffeine intake of their respective trait. The heritability rate (${\mathrm{h}}_{\mathrm{g}}^2$) for all SNPs in the GWAS was 8.2% for combined caffeine intake, 6.1% for caffeine from coffee, and 7.1% for caffeine from tea.

Figure 2. Manhattan plot for combined caffeine intake.

Manhattan plot showing the results for the genome‐wide associations with combined caffeine intake in the UK Biobank with the −log10 P value on the vertical axis. The sentinel single nucleotide polymorphisms that reached genome‐wide significance (P<1.67×10⁻⁸) are colored red.

Using the genetic risk score of each GWAS, each unit change in genetically determined caffeine intake was consistent with 131.6 mg combined caffeine intake, 134.5 mg caffeine intake from coffee, and 86.1 mg caffeine intake from tea. In coffee drinkers, depending on the type of coffee usually drunk, each unit related from 1.5 cup of decaffeinated coffee to 2.1 cups of instant coffee (Table S17).

Candidate Genes and Deeper Insights Into Biology

We explored the potential biology of the sentinel SNPs per GWAS by prioritizing potentially causal genes in these loci based on proximity, expression quantitative trait locus (eQTL) analyses, and data‐driven expression‐prioritized integration for complex traits. In total, we identified 48 candidate genes for combined caffeine intake, 27 for caffeine from coffee, and 40 for caffeine from tea (Figure 3). We identified the previously reported AHR, CYP1A1, and POR genes in all 3 GWASs. In addition, 2 novel genes, GOLPH3L and HORMAD1, were associated with all caffeine traits.

Figure 3. Venn diagram of candidate genes associated with caffeine intake.

Candidate genes were prioritized based on proximity, data‐driven expression‐prioritized integration for complex traits, and expression quantitative trait locus mapping for combined caffeine intake, caffeine from coffee, and caffeine from tea.

Across 209 tissue and cell types, central nervous system tissues were most enriched for SNPs associated with caffeine from tea and combined, but none with caffeine from coffee (Table S18). Furthermore, 6 combined caffeine intake loci, and 3 loci each of caffeine from coffee or tea, contained variants with eQTLs in at least 1 tissue. The strongest associations were found for rs768283768 near HORMAD1 and GOLPH3L, which tagged multiple tissues (Table S19).

Genetically Determined Caffeine Intake and CAD

The association between genetically determined caffeine intake and CAD was tested in the independent CARDIoGRAMplusC4D cohort (123 504 controls and 60 801 [33.0%] cases). In total, 35 SNPs from caffeine for combined caffeine intake, 22 for caffeine from coffee (rs2298527 excluded based on intermediate allele frequency in CARDIoGRAMplusC4D), and 24 for caffeine from tea (Table S20 through S22). F‐statistics indicated low chances of weak instrument bias (Table S23) and I² _GX indicated low chances of measurement error in MR‐Egger (Table S24). However, I² and Cochran's Q indicated heterogeneity, and thus potential pleiotropy, for all caffeine traits (Table S24). Using the random effects inverse‐variance weighted method as indicated by the nonsignificant Q‐Q′ and MR‐Egger intercepts, we found that genetically determined caffeine intake from combined or coffee were not associated with CAD (OR, 1.12 [95% CI, 0.80–1.40], P=0.31; OR 1.26 [95% CI, 0.82–1.93], P=0.28, respectively). MR‐Egger was used for caffeine from tea because the Q‐Q′ was significant; however, also for caffeine from tea, no association with CAD was indicated (OR, 1.60 [95% CI, 0.75–3.44], P=0.24). MR Pleiotropy Residual Sum and Outlier analyses corroborated these findings for all traits, with and without trimming outlier SNPs (Table S25). MR‐Steiger filtering also did not attenuate the results for any caffeine trait (Table S26). Finally, weighted median and mode‐based analyses also indicated no association between genetically determined caffeine intake and CAD. Individual SNP effects are shown in Figures S11 through S13 and the MR analyses in Figure 4A.

Figure 4. Mendelian randomization results for genetically determined higher caffeine intake (per SD) on coronary artery disease (A) and type 2 diabetes mellitus (B).

Odds ratios (OR) with 95% CIs are provided per standard deviation increase in genetically determined caffeine intake from combined, coffee, or tea. Number of single‐nucleotide polymorphisms (SNPs) included are shown per method. Estimates <1.0 indicate a beneficial association between genetically determined caffeine intake and outcome. MR‐PRESSO indicates Mendelian Randomization Pleiotropy Residual Sum and Outlier.

Genetically Determined Caffeine Intake and T2DM

The association between genetically determined caffeine intake and T2DM was investigated in the DIAGRAM cohort (132 532 controls and 26 676 [16.8%] cases). In DIAGRAM, 35 SNPs for combined caffeine intake, 23 SNPs for caffeine from coffee, and 24 SNPs for caffeine from tea were used (Tables S27 through S29). Also here, I² indices and Cochran's Q indicated pleiotropy for all traits, and the MR‐Egger intercept was not significant. However, because the Q‐Q′ was significant for all traits, we focused on the MR‐Egger estimate for the causal effect. The MR‐Egger analyses indicated no association between genetically determined higher caffeine intake from any trait with risk of T2DM (OR, 1.06 [95% CI, 0.67–1.68], P=0.79 for combined caffeine intake; OR, 1.07 [95% CI, 0.33–3.54], P=0.91 for caffeine from coffee; OR, 2.36 [95% CI, 0.62–8.91], P=0.22 for caffeine from tea; Figure 4B; estimates per SNP in Figures S14 through S16). Additional analyses using MR Pleiotropy Residual Sum and Outlier and MR‐Steiger also found no associations between caffeine intake with T2DM after respectively trimming outliers and filtering (Tables S25 and S26). Finally, also weighted and mode‐based estimator MR analyses were in line with these findings and indicated no association with T2DM.

Combined Caffeine Intake–Specific Variants

In total, 18 variants were associated with combined caffeine intake, of which the annotated genes do not overlap with those of caffeine from coffee or caffeine from tea. However, these variants were most strongly associated with combined caffeine intake compared with caffeine from tea or coffee and had concordant betas across all traits (Table S15). This suggests that these variants act on both caffeine from coffee and caffeine from tea. We repeated the MR analyses using these variants or their proxies available in CARDIoGRAMplusC4D and DIAGRAM. Similar to the MR using all combined caffeine intake variants, we found no associations with CAD or T2DM.

Moderate Versus Extreme Caffeine Intakes From Coffee or Tea

Because of the U‐shaped curve observed in the observational analyses between caffeine from coffee and caffeine from tea with CAD or T2DM, we performed exploratory analyses to investigate variants associated with moderate caffeine intake from coffee or tea separately. Extremes of caffeine intake (0 and >360 mg/day for coffee and 0 and >120 mg/day for tea) were taken together and values between the extremes as moderate intake. A total of 373 522 individuals (99 427 [26.6%] with moderate intake) were included in the GWAS for moderate caffeine consumption from coffee, and 395 866 (188 013 [47.8%] with moderate intake) in the GWAS for moderate caffeine consumption from tea. However, GWAS on either phenotype found no variants at P<1.67×10⁻⁸ or P<5×10⁻⁸.

Discussion

In this large prospective study, we observed U‐type associations between observational caffeine intake with CAD and T2DM, although similar intakes from different sources had dissimilar effect sizes. In addition, we identified 51 novel genetic loci associated with caffeine intake, more than tripling the number of known loci. ¹¹ , ¹² , ¹³ , ¹⁴ , ¹⁵ , ¹⁶ , ¹⁷ In contrast to the observational analyses, genetic causal inference analyses indicated that genetically determined caffeine intake was not associated with CAD or T2DM.

Our observational findings are concordant with previous studies showing inverse or U‐type associations between caffeine intake with CAD ² , ⁴⁷ and T2DM. ³ , ⁴⁷ , ⁴⁸ A meta‐analysis in 1 283 685 individuals (28 347 CAD cases) estimated a relative risk of 0.89 (95% CI, 0.85–0.94) for CAD at 3 to 5 cups of coffee daily and a neutral effect at higher intakes (>360 mg or >6 cups of coffee) compared with no intake. ² A plausible explanation for the U‐type shape of the association is that coffee is a liquid extract of coffee beans and it contains a complex chemical mixture of biologically active compounds, some with beneficial and others with harmful effects. ⁴⁹ At moderate intakes, the beneficial effects could outweigh or counteract the harmful effects, whereas at higher intakes the harmful effects may counterbalance this. ² Our results for T2DM are in line with the most recent meta‐analysis, which reported a relative risk of 0.70 (95% CI, 0.65–0.75) in individuals who consumed 5 cups of coffee per day compared with nondrinkers, although they reported no U‐type associations. ⁵⁰ The hypothesis that moderate caffeine intake may have beneficial effects compared with extreme intakes is also not supported by our findings for combined caffeine intake. The null findings of the observational analyses for combined caffeine intake indicate that caffeine by itself is unlikely to affect disease risk. The current study used the largest number of caffeine SNPs to date from different dietary sources, which is relevant for this UK population, where tea is the second‐largest source of caffeine ¹ and may confound the association. Using these SNPs in robust causal inference analyses, we found no associations between genetically determined higher or lower caffeine intake and CAD or T2DM. These findings are in line with previous MR studies of caffeine intake on CAD and T2DM. ⁷ , ¹⁸ , ¹⁹ The null findings of the combined caffeine intake SNPs can be considered a negative control for the observational findings. There is accumulating evidence that previous beneficial associations between caffeine intake with outcomes were attributable to residual confounding, most likely because of other compounds found in coffee ³ , ⁷ , ¹⁸ , ¹⁹ or smoking, ⁵¹ since no difference in outcomes is reported between decaffeinated and caffeinated coffee for CAD ⁸ or T2DM. ³ Also, in the current study, we found that observational decaffeinated coffee consumption was associated with similar effect sizes compared with caffeinated coffee. Caffeinated coffee was more robustly associated with outcomes, but this is likely attributable to the larger number of caffeinated coffee drinkers. Furthermore, caffeine from coffee was generally associated with lower estimates compared with caffeine from tea or combined, arguing against an independent effect of caffeine. In addition, both previous and the current MR analyses consistently lack evidence for causality, providing further argument against a protective effect of genetically determined higher caffeine intake.

To our knowledge, this is the largest study to date to investigate the association of both observational and genetically determined caffeine intake from multiple sources with CAD and T2DM. This study also reports the largest number of caffeine intake–associated SNPs, while also replicating previously reported SNPs. These newly identified variants were then used in independent disease‐specific cohorts for both CAD and T2DM in 2‐sample MR analyses. The explained variance of the sentinel SNPs is comparable with previously published GWASs on coffee ⁷ , ¹² or alcohol ⁵² intake, which range between 0.6% and 1.3%. However, the explained variance was of little influence on the statistical power for the MR.

This study has some limitations. In the current analyses, caffeine intake was calculated on the basis of self‐reported data at a single time point at baseline, which does not take into account possible changes in coffee‐ and tea‐drinking habits. Furthermore, because the caffeine content of coffee may differ depending on the method of preparation, ⁵³ , ⁵⁴ use of filter, ⁵⁵ and type of coffee bean, ¹ and individuals may drink several types of coffee, the actual caffeine intake per day may differ from our calculation. We did not take into account caffeine intake from other sources such as cola or energy drinks, as this information was not available. In addition, the main MR analyses assume linear associations, whereas the causal associations might be nonlinear, with higher risks at low and high intakes, such as the U‐shaped–curve associations observed in the observational analyses. However, it was not possible to examine nonlinear associations in the MR analyses because these require individual‐level data in the outcome cohorts, which were not available. The MR analyses should therefore be interpreted with caution at the extremes of caffeine intake. It remains unclear which genetic variants are responsible for the specific parts of the potential U‐shaped–curve association, and we cannot exclude the possibility that the variants associated with caffeine intake from coffee or tea could have bidirectional effects on the association. Exploratory analyses to investigate the nonlinear association within the UK Biobank, however, indicate that there may be no genetic variants solely associated with moderate or extreme caffeine intake from coffee or tea.

Also, despite our sensitivity analyses to test for and minimize bias, especially from genetic pleiotropy in which the instrumental variables may act on the outcome through other pathways than caffeine, this cannot be completely excluded. We found evidence for heterogeneity in the MR for CAD and T2DM for all caffeine traits, indicating that pleiotropy cannot be ruled out. We therefore report the correct model per degree of pleiotropy as the main results and performed several other sensitivity analyses to take this into account. Finally, the present analyses were performed in individuals of White British ancestry, which may limit the generalizability of the results to other populations.

In conclusion, this large prospective study showed inverse associations between observational caffeine intake with CAD and T2DM. However, effect sizes were similar between caffeinated and decaffeinated coffee; similar caffeine intakes from tea were associated with fewer inverse effects compared with caffeine from coffee. Furthermore, MR analyses in independent cohorts yielded no evidence for causality between genetically determined caffeine intake with CAD or T2DM. The main MR analysis results suggest that increasing caffeine intake may not be protective against the development of CAD or T2DM. However, these do not take into account the nonlinear association observed within the observational analyses. We therefore encourage reanalysis of the results when more advanced methods to study nonlinear associations within a summary‐based 2‐sample MR setting emerge, without individual‐level exposure data in the outcome cohort.

Sources of Funding

Dr Verweij is supported by a Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) VENI grant (016.186.125).

Disclosures

None.

Supporting information

Data S1

Tables S1–S29

Figures S1–S16

(J Am Heart Assoc 2020;9:e016808. DOI: 10.1161/JAHA.120.016808.)