Review Article | Published:

Coffee consumption and risk of hypertension: a systematic review and dose–response meta-analysis of cohort studies

Journal of Human Hypertensionvolume 32pages8393 (2018) | Download Citation

Abstract

Some debates exist regarding the association of coffee consumption with hypertension risk. We performed a meta-analysis including dose–response analysis aimed to derive a more quantitatively precise estimation of this association. PubMed and Embase were searched for cohort studies published up to 18 July 2017. Fixed-effects generalized least-squares regression models were used to assess the quantitative association between coffee consumption and hypertension risk across studies. Restricted cubic spline was used to model the dose–response association. We identified eight articles (10 studies) investigating the risk of hypertension with the level of coffee consumption, including 243,869 individuals and 58,094 incident cases of hypertension. We found no evidence of a nonlinear dose–response association of coffee consumption and hypertension (P nonlinearity = 0.243). The risk of hypertension was reduced by 2% (relative risk (RR) = 0.98, 95% confidence interval (CI) 0.98–0.99) with each one cup/day increment of coffee consumption. With the linear cubic spline model, the RRs of hypertension risk were 0.97 (95% CI 0.95–0.99), 0.95 (95% CI 0.91–0.99), 0.92 (95% CI 0.87–0.98), and 0.90 (95% CI 0.83–0.97) for 2, 4, 6, and 8 cups/day, respectively, compared with individuals with no coffee intakes. This meta-analysis provides quantitative evidence that consumption of coffee was inversely associated with the risk of hypertension in a dose–response manner.

Introduction

Hypertension, a key risk factor for cardiovascular diseases, is also the leading cause of premature death and the third cause of disability. It affects one billion people worldwide, leading to heart attacks, strokes, and renal failure [1,2,3,4]. The total number of people with hypertension is expected to increase to 1.56 billion by 2025 [5]. Given its significant burden, the importance of preventing hypertension by adopting a healthy lifestyle is imperative and undoubted.

Coffee is one of the most widely consumed beverages in the world, with a global consumption of 500 billion cups per year [6]. Because of the wide consumption of coffee, its effects on hypertension could have considerable public health and clinical implications. Since early 1930s, coffee consumption has attracted interest as a potential risk factor because of an acute pressor effect of caffeine on blood pressure (BP), but the long-term effect on hypertension risk remains controversial in a number of randomized controlled trials and cohort studies [7,8,9,10,11,12,13,14,15,16]. Two previous meta-analyses of coffee consumption and risk of hypertension have been published [17, 18]. s and incident hypertension. Given the different definitions of coffee exposure among studies, the association between coffee consumption and risk of hypertension could not be analyzed precisely. We therefore performed a systematic review and dose–response meta-analysis of all available data of cohort studies on the association of coffee consumption with the risk of hypertension.

Materials and methods

Literature search strategy

We searched the electronic databases PubMed and Embase for all articles of cohort studies investigating the association between coffee consumption and hypertension that were published up to 18 July 2017. Our search included both free-text and Medical Subject Heading (MeSH) terms, such as “coffee [MeSH]”, “caffeine [MeSH]”, “coffee”, “caffein*”, “hypertension [MeSH]”, “cardiovascular diseases [MeSH]”, “high blood pressure*”, OR “cardiovascular disease”, “hypertens*”, “HBP”, and “CVD”. Details of the wide search terms are shown in Supplementary Table 1. The reference lists of all retrieved articles [9,10,11,12,13,14,15,16] and previous systematic reviews [17,18,19] were manually searched for additional relevant studies. We restricted the search to studies on humans and written in English. The meta-analysis was conducted and reported in accordance with the Meta-analysis of Observational Studies in Epidemiology guidelines [20].


Study selection

Studies included in this meta-analysis met the following criteria: (1) the study design was cohort; (2) the articles were published in English; (3) reported relative risks (RRs), odds ratios (ORs), or hazard ratios (HRs) with 95% confidence intervals (CIs) or data to calculate them; (4) the exposure was coffee consumption; (5) the outcome was risk of hypertension; and (6) provide the frequency and amount of coffee consumption, number of cases, exposed person-years, or participant numbers for the dose–response analysis. Abstract of only publications, comment, or conference articles were excluded. A search for unpublished literature was not performed.


Data extraction and quality assessment

Data were extracted by using a pre-designed extraction form. The following information was collected: the first author; publication year; country where the study was conducted; study name; study design; follow-up period; number of incident MetS cases and total population; mean or median age of the study population at baseline; coffee consumption ranges or median or mean categories at baseline; RRs, HRs, ORs, and 95% CIs for the association; definition for hypertension; and covariates on which the analyses were adjusted. When the required data were not reported in the original articles, we emailed authors to obtain further details. If a study only reported caffeinated coffee consumption instead of total coffee consumption, caffeinated coffee consumption was also included in the total coffee consumption analysis. If the numbers of cases in each category were unavailable, these data were inferred on the basis of the number of total cases, level-specific exposed participant numbers, and the reported effect size. If the exposed participant numbers were not reported in each category, group sizes were assumed to be approximately equal [21]. Cohort study quality was assessed by the Newcastle–Ottawa Scale, which allows for a total score of up to nine points summarizing eight aspects of each study the scale gives a maximum of 9 stars [22]. All retrieved articles were screened by two independent reviewers (C.X. and L.C.). Data extraction and quality assessment were done by C.X. and checked by L.C. Any disagreements were discussed until agreement was reached.


Statistical methods

To analyze the trend of coffee consumption and risk of hypertension, we used both semiparametric and parametric methods. For the semiparametric method, four coffee consumption groups were generated: lowest, third highest, second highest, and highest. For each included study, the lowest and the highest coffee consumption categories corresponded to the lowest and highest groups, respectively. For studies with four exposure categories, the second and third categories corresponded to the second and third highest groups, respectively. For studies with three exposure categories, the middle category corresponded to either the second or the third highest group, whichever median coffee consumption amount was most similar. If the study had more than four exposure categories, two consumption groups, other than the lowest and highest, were chosen on the basis of their similarity of the amount of coffee consumption in that category to the second and third highest groups. For studies reporting HRs or ORs for hypertension, we assumed that the HRs and ORs were approximately RRs [23]. A fixed-effects model [24] was used to calculate the summary RR estimates.

For the parametric method, generalized least-squares (GLS) regression was used to estimate study-specific dose–response association. GLS regression model estimates the linear dose–response coefficient, taking into account the covariance for each exposure category within each study, because they are estimated relative to a common referent PA exposure category [25, 26]. A fixed-effects model was used to pool the study-specific dose–response RR estimates [24]. First, a linear association was assumed; study-specific RR estimates were calculated per one cup/day of coffee consumption increment and then pooled. In addition, we examined possible nonlinear associations by modeling coffee consumption using a restricted cubic spline with three knots located at the 25th, 50th, and 75th percentiles of the distribution [27].

To perform the dose–response meta-analysis, we assigned the median or mean coffee consumption in each category of consumption to the corresponding RR for each study. If the mean or median consumption per category was not reported, the midpoint of the upper and lower boundaries in each category was assigned as the mean consumption. If the highest or lowest category was open-ended, the width of the interval was assumed to be the same as in the closest category [28]. Only studies reporting risk estimates for at least three coffee consumption exposure levels for risk of hypertension were included in this analysis. The P value for nonlinearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to zero.

Heterogeneity was tested by Cochran Q and I 2 statistics [29]. For the Q statistic, P < 0.1 was considered statistically significant; and for the I 2 statistic, I 2 values of ~25%, 50%, and 75% are considered to reflect low, moderate, and high heterogeneity, respectively. Subgroup analyses were conducted to investigate potential sources of heterogeneity, including study design, gender, age, region, diagnostic criteria number of cases, and the covariates adjusted in the analysis (age, smoking, alcohol consumption, physical activity, family history of hypertension, education, intake of sodium, and body mass index). We performed a sensitivity analysis by excluding one study at a time to assess the stability of results and potential sources of heterogeneity. Potential publication bias was evaluated by the Egger’s test [30] and results were considered to indicate publication bias at P < 0.10. All analyses involved use of Stata 12.1 (Stata Corp, College Station, TX, USA).

Results

Characteristics of studies

We identified eight articles of 10 cohort studies in PubMed and Embase for the meta-analysis (Fig. 1). In total, the review included 243,869 individuals and 58,094 incident cases of hypertension. The main characteristics of each study are presented in Table 1.

Fig. 1
Fig. 1

Flowchart of study selection for the meta-analysis

Table 1 Basic characteristics of cohort studies investigating the association of coffee consumption and incident hypertension

Overall, four studies were conducted in the United States, one in Asia, and five in Europe. In all, four articles did not distinguish between gender, one article described stratified analyses by gender, and three described analysis of only men or women. Of these, five articles defined hypertension as systolic BP ≥140 mm Hg or/and diastolic BP ≥90 mm Hg or use of antihypertensive medication and three defined as self-reported hypertension. Results of cohort study quality assessment (score 0–9) yielded an average score of 7.78 (Supplementary Table 2).

High vs. low coffee consumption analysis

The pooled results of different levels of coffee consumption categories compared with the lowest category are shown in Fig. 2. Compared with the lowest category (median consumption 0 cups/day), the pooled RR for incident hypertension was 0.95 (95% CI 0.91–0.99, I 2 = 44.0%, P heterogeneity = 0.066) for individuals in the highest category of consumption (6.2 cups/day), 0.96 (95% CI 0.89–1.03, I 2 = 61.8%, P heterogeneity = 0.011) for individuals in the second highest category of consumption (4.5 cups/day), 1.02 (95% CI 0.97–1.06, I 2 = 58.9%, P heterogeneity = 0.017) for individuals in the third highest category of consumption (1.5 cups/day). In the sensitivity analyses, removing one study at a time did not change the pooled risk substantially in any of the three coffee consumption levels. We found no evidence of publication bias for the highest (P = 0.305), for the second highest (P = 0.393), and for the third highest level of coffee consumption (P = 0.154) vs. the lowest consumption by Egger’s test.

Fig. 2
Fig. 2

Forest plot of relative risks (RRs) and 95% CIs for the association between coffee consumption and hypertension risk in cohort studies. Compared with the lowest category (median consumption 0 cups/day), the pooled RR for incident hypertension was 0.95 (95% CI 0.91–0.99, I 2 = 44.0%) for the highest category of consumption, 0.96 (0.89–1.03, I 2 = 61.8%) for the second highest, and 1.02 (0.97–1.06, I 2 = 58.9%) for the third highest category of coffee consumption

Dose–response analysis of coffee consumption with risk of hypertension

Data from 10 cohort studies were included in the linear dose–response analysis of coffee consumption with risk of hypertension. The risk of hypertension was reduced by 2% (RR = 0.98, 95% CI 0.98–0.99) with each one cup/day increment of consumption (Fig. 3).

Fig. 3
Fig. 3

Risk of incident hypertension for each 1cup/day increment in coffee consumption. RR relative risk, CI confidence interval, M men, W women

We found no evidence of nonlinear association between coffee consumption and hypertension risk (P nonlinearity = 0.243); therefore, restricted cubic spline was adopted to model the linear dose–response association. We found a linear negative correlation between coffee consumption and risk of hypertension (Fig. 4). Compared to those with no coffee consumption, the RR estimated directly from the cubic spline model was 0.97 (95% CI 0.95–0.99) for two cups/day, 0.95 (95% CI 0.91–0.99) for four cups/day, 0.92 (95% CI 0.87–0.98) for six cups/day, and 0.90 (95% CI 0.83–0.97) for eight cups/day.

Fig. 4
Fig. 4

Linear dose–response association between coffee consumption and hypertension modeled by using restricted cubic spline

Subgroup, sensitivity analyses, and publication bias

To explore the sources of heterogeneity, we performed subgroup analyses by study design, gender, age, region, diagnostic criteria, number of cases, the covariates (age, smoking, alcohol consumption, physical activity, family history of hypertension, education, intake of sodium, and BMI) adjusted in the analysis (Table 2). In general, the association was consistent in most analyses. The heterogeneity appeared to be lower in Americans, age >50 years old and number of case ≥1000 populations, with I 2 reduced to 0.0%, 12.7%, and 28.3%, respectively. No significant changes of heterogeneity occurred in other subgroup analyses.

Table 2 Dose–response subgroup analysis of risk of hypertension with coffee consumption

When performing sensitivity analyses by removing one study at a time, none of the individual studies changed the pooled risk substantially. No publication bias was detected by Egger’s test (P = 0.618).

Discussion

The findings from this systematic review and meta-analysis, based on 247,659 participants and 54,639 incident cases of hypertension, demonstrate an inverse association between risk of hypertension and coffee consumption, with a reduction of 2% per one cup/day increment of coffee consumption. With the linear cubic spline model, the RRs of hypertension risk were 0.97 (95% CI 0.95–0.99), 0.95 (95% CI 0.91–0.99), 0.92 (95% CI 0.87–0.98), and 0.90 (95% CI 0.83–0.97) for 2, 4, 6, and 8 cups/day, respectively, compared with individuals with no coffee intakes.

Controversy exists in the current studies investigating the effects of coffee intake on hypertension risk. In a meta-analysis of five cohort studies, Zhang et al. found an inverse J-shaped association between caffeinated coffee intake and hypertension risk, with the risk increasing with up to three cups coffee/day compared with less than one cup/day and then decreasing at higher intakes [18]. On the basis of the same original studies, Steffen et al. reported opposite findings, suggesting no statistically significant effect of coffee consumption on the risk of hypertension [17]. Given that three additional articles have been published since the previous two meta-analyses and the inconsistent results between them, a more precise method is needed to access the association of coffee consumption with hypertension risk.

The linear inverse association between coffee consumption and hypertension risk might be true based on plausible biological mechanisms. First, coffee is rich in BP-lowering minerals (that is, vitamin E, niacin, potassium, and magnesium) and antioxidant compounds (polyphenols) that may have the antihypertensive effects of caffeine [31, 32]. Second, coffee consumption lowers the risk of type 2 diabetes (the consequences of hyperinsulinemia and insulin resistance) [33]; moreover, hyperinsulinemia and insulin resistance may contribute to hypertension via the effects of insulin on the retention of sodium increasing sympathetic nervous system activity and vascular smooth muscle proliferation [34], for another possible mechanism of the antihypertensive effect of coffee.

Except for the mechanisms mentioned above, genetics, smoking, alcohol consumption, and other aspects of the diet may modify the effects of coffee intakes on hypertension [35].There have been several studies certifying that the background characteristics, such as smoking and alcohol use, influence the effect of coffee on hypertension risk [14, 36, 37]. Almost all the included studies have adjusted the confounding factors, and the pooled results remained to be significantly negative correlation, indicating the consistency of the findings. Previous reports have suggested that obvious inter-individual differences in the sensitivity of the coffee effect were mainly due to the variants of caffeine metabolism-related gene [35, 38,39,40]. Given that only a few studies investigated the role of genes in the association of coffee consumption with hypertension risk, further researches based on genetic aspects are warranted.

To discover potential sources of heterogeneity, we performed various analyses, and the results generally supported our overall finding. We found that the presence of heterogeneity among studies may be related to geographical area. A slight reduction of hypertension risk and no heterogeneity were observed among studies conducted in Americans rather than Europeans, perhaps because of varied coffee composition, different types of coffee powder, and serving size across countries. In addition, different genotypes and gene–environment interactions may partially explain the discrepancy across regions. Results from studies stratified by sex were more consistent and robust than from studies of mixed gender, indicating an interaction with hypertension risk of coffee intake between men and women (such as the different habits of coffee consumption and body constitutions), which suggest us to separate them while analyzing the relationship in the future [11].

Our meta-analysis contains several strengths. Primarily, the meta-analysis included 10 high-quality cohort studies and a large number of participants, minimizing the potential sampling error and providing sufficient power to detect the association. Also, a dose–response analysis was also performed to clarify the quantitative estimation of the association between hypertension risk and coffee consumption, which provided a comprehensive description of the shape of the relationship. What’s more, the large number of studies enabled us to conduct subgroup analyses based on study design, sex, age, region, BMI, diagnostic criteria, number of cases, the covariates' adjustment, and sensitivity analyses to clarify the consistency and robustness of the results.

However, the limitations of the study should also be considered. All of the studies were observational, of which the inherent residual confounding may affect them to reach more plausible conclusion. Nevertheless, because of the observational nature of the studies, a causal relationship cannot be established with these data alone. Moreover, almost all of the exposures of coffee intakes were measured by self-reporting questionnaires, which is inevitable to introduce the misclassification of exposure. However, results from validation studies indicated that self-reported questionnaires can assess the coffee consumption with relatively high validity [41, 42]. Furthermore, information on the type of coffee (such as caffeinated and decaffeinated coffee) and the size of standard coffee cups was limited. Whether caffeinated and decaffeinated coffee have different effects on BP and hypertension risk is not clear till now [10, 43]. Also, the different size of coffee cups can distort the real relationship. Finally, we included only English language publications because of resource constraints. However, small study bias (including publication bias) was investigated by the funnel plot and Egger’s test, with no such bias detected.

Conclusion

In conclusion, this meta-analysis provides quantitative evidence that consumption of coffee was inversely associated with the risk of hypertension in a dose–response manner. Longer-term randomized controlled trials are needed to establish causality and testify the true association.

Summary

What is known about this topic?

  • Coffee consumption has attracted interest as a potential risk factor due to an acute pressor effect of caffeine on blood pressure, but the long-term effect on hypertension risk remains controversial.

  • Given the different definitions of coffee exposure among studies, the association between coffee consumption and risk of hypertension could not be analyzed precisely.

What this study adds?

  • The risk of hypertension was reduced by 2% (RR=0.98, 95% CI: 0.98-0.99) with each 1 cup/day increment of coffee consumption.

  • Compared with individuals with no coffee intakes, the RRs of hypertension risk were 0.97 (95% CI: 0.95-0.99), 0.95 (95% CI: 0.91-0.99), 0.92 (95% CI: 0.87-0.98), and 0.90 (95% CI: 0.83-0.97) for 2, 4, 6 and 8 cups/day, respectively.

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Acknowledgments

This research was supported by the National Social Science Foundation of China (Grant number 15BSH043). This research was supported by the National Social Science Foundation of China (Grant number 15BSH043). The National Social Science Foundation of China had no role in the design/conduct of the study, collection/analysis interpretation of the data, and preparation/review approval of the manuscript.

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Affiliations

  1. Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, Henan, 450001, China

    • Chen Xie
    • , Lingling Cui
    • , Jicun Zhu
    •  & Changqing Sun
  2. The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, People’s Road, Henan, China

    • Kehui Wang
  3. Terry College of Business, University of Georgia, Athens, GA, USA

    • Nan Sun

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The authors declare that they have no competing financial interest.

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Correspondence to Changqing Sun.

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https://doi.org/10.1038/s41371-017-0007-0

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