The number of people with diabetes is increasing rapidly, worldwide. The International Diabetes Federation reported that in 2015, 415 million adults had diabetes and that by 2040, this number is expected to rise to 642 million [1
]. In 2015, the global economic burden associated with diabetes reached US$
1.31 trillion, becoming a substantial global economic burden [2
]. Therefore, low-cost and easily accessible strategies for preventing diabetes are required. Globally, coffee and tea are widely consumed beverages and their consumption is integrated into people’s daily lives. A large body of epidemiological evidence, from prospective cohort studies, suggests a strong association between coffee/tea consumption and a decreased risk of diabetes [3
]. If the associations are causal, there would be substantial public health implications. However, whether or not these beverages reduce glucose metabolism remains uncertain. Previous meta-analyses of clinical trial data have investigated the effects of coffee or tea consumption on glucose metabolism [7
]. Unfortunately, these meta-analyses have not evaluated tea and coffee comprehensively; have included duplicate studies [7
]; have evaluated only acute effects [10
]; and did not evaluate the quality of evidence across studies [7
], using approaches such as the Grading of Recommendations Assessment Development and Evaluation (GRADE) system [11
]. Together, these weaknesses limit the interpretation of the results and provide insufficient information to make relevant judgments.
Network meta-analysis (NMA) is a method that enables the comparison of multiple interventions using direct and indirect comparison evidence even when direct comparisons of data are insufficient [12
]. Hence, we performed a systematic review that involved an NMA, using the GRADE system to comprehensively evaluate the effects of coffee and tea consumption on glucose metabolism.
2. Materials and Methods
2.1. Literature Search
This study was conducted according to a predetermined protocol (PROSPERO # CRD42015029597) developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols [13
]. We systematically searched PubMed, Embase, and the Cochrane library for eligible randomized, controlled clinical trials published before February 19, 2017 without any language restrictions. The literature search involved querying the terms “coffee,” “tea,” “glucose,” “HbA1c,” “insulin,” “insulin resistance,” and “randomized controlled trial (RCT).” In addition, we manually searched the references included in each original article retrieved. The details of the literature search strategy are described in the Supplementary Table S1
2.2. Study Selection
We included studies examining the effects of caffeinated coffee, decaffeinated coffee, black tea, oolong tea, and green tea (caffeinated or decaffeinated), given as a drink or crude extract, with follow-up durations of at least 2 weeks. We excluded studies examining multi-nutrient supplements, in addition to coffee or tea as interventions. Studies were selected for this analysis only if they also described an RCT in human adults involving either a parallel or crossover design and had at minimum, fasting blood glucose (FBG) or hemoglobin A1c (HbA1c) results available for each group. In cases involving multiple publications of the same study, the most informative article was included.
2.3. Data Extraction
Two investigators extracted data independently and resolved any discrepancies through discussion. The extracted information included study characteristics (authors, design, publication year, sample size, duration of intervention, and follow-up), participant characteristics (age, sex, ethnicity, body mass index, HbA1c, FBG concentration), 2-h post-load glucose concentration from an oral glucose tolerance test (75-g OGTT 2h-PG), fasting blood insulin (fasting IRI) concentration, homeostasis model assessment for insulin resistance (HOMA-IR) and HOMA-ß, and intervention (type of tea or coffee).
2.4. Risk of Bias Assessment
The risk of bias was assessed using Cochrane’s risk of bias tool [15
]. In addition, quality of evidence was assessed using the GRADE system [11
2.5. Strategy for Data Synthesis
We examined the effects of the studied interventions after combining green tea and green tea extract into a single group referred to as green tea. Decaffeinated green tea and decaffeinated green tea extract interventions were also combined into a single decaffeinated green tea group. A standard, pairwise meta-analysis was conducted for each pairwise direct comparison of interventions (caffeinated coffee, decaffeinated coffee, black tea, oolong tea, caffeinated green tea, and decaffeinated green tea). The outcome data (post-intervention values) were extracted for subsequent analyses. When studies reported data for different durations of intervention, the duration for the pre-defined primary endpoint in each study, was used in the analyses. If the standard deviations of the endpoints were missing, pooled standard deviations were calculated from the other included studies [16
]. We used a random-effects model to incorporate the assumption that different studies estimated different yet related intervention effects. Publication biases or small study effects were examined using conventional funnel plots.
In addition, we conducted network meta-analyses within the frequentist framework using multivariate random-effects meta-analysis models, which consider the heterogeneity of effects across studies. The results for the comparative effects are presented as mean difference estimates and 95% confidence intervals (CIs). We plotted a comparison-adjusted funnel plot to detect the presence of publication bias in the network meta-analysis. We also evaluated the ranking of the intervention effect, i.e., the most efficacious beverage, second best, third best, etc. Inconsistency between the direct and indirect evidence on the network was evaluated using global and local inconsistency tests [17
Stata 14 software (Stata, College Station, TX, USA) was used for the analyses. The “metan” package [18
] was used to conduct the pairwise random-effects meta-analyses and the “metareg” package [19
] was used for the meta-regression analyses; the “network” package [20
] was used for the NMAs. The p
-values < 0.05 were considered statistically significant.
2.6. Subgroup or Subset Analysis
In the pairwise direct comparison meta-analyses, the heterogeneity of the intervention effects across studies were investigated using the heterogeneity test and the I2 statistic.
The sources of possible clinical heterogeneity were listed a priori, such as age, sex, ethnicity, body mass index, baseline fasting blood glucose level, and the baseline HbA1c level. Among these, data on age, sex, ethnicity, body mass index, baseline fasting blood glucose level were available. These were examined as effect modifiers in the meta-regression or subgroup analyses. Sensitivity analyses were also conducted for assessing possible biases.
In this systematic review and NMA, we evaluated the effects of various coffee and tea consumptions on glucose metabolism across available RCT data. We found 27 studies, involving 1898 subjects with study durations of 4–72 weeks. With regard to the primary endpoint, the studies with a moderate quality of evidence suggested that green tea consumption, but not consumption of caffeinated/decaffeinated coffee or black tea, may reduce FBG concentrations, as compared with a placebo/water. The effect estimates were also statistically significant for oolong tea, but the quality of evidence was very low due to the risk of bias and imprecision in the studies. As for the secondary endpoint, studies with a moderate quality of evidence indicated that caffeinated coffee consumption may increase insulin concentrations. The potential effects of green tea on glucose metabolism have substantial public health implications given the global diabetes epidemic, such that even a small potential downward shift in the distribution of FBG concentrations would result in substantial numbers of individuals avoiding diabetes. Although further efforts are required to confirm the evidence, our findings support the notion that green tea may be a preventative strategy for reducing the number of people developing diabetes.
Some mechanisms have been suggested to explain the ability of green tea to reduce FBG concentrations. Previous rodent-based studies reported that the potential beneficial effect of green tea on glucose metabolism may be mediated by epigallocatechin gallate (EGCG), the most abundant catechin present in green tea [48
]. Waltner-Law et al. reported that EGCG reduces hepatic glucose production by increasing tyrosine phosphorylation of the insulin receptor and insulin receptor substrate-1 in H4IIE rat hepatoma cell models [49
]. Recent studies have also suggested that green tea increases insulin sensitivity and glucose metabolism, helping to prevent type 2 diabetes from developing. Ortsäter et al. also reported that EGCG preserves islet structure and enhances glucose tolerance in genetically diabetic mice (young db
]. Further, Ueda et al. reported that EGCG may reduce hyperglycemic events by promoting glucose transporter-4 translocation in skeletal muscle via a mechanism that is partially different from the action of insulin because EGCG promoted the translocation in insulin-resistant L6 myotubes and had neither a synergistic nor an additive effect on insulin [51
]. Surprisingly, Fu et al. reported that EGCG increases the concentrations of the circulating anti-inflammatory cytokine, interleukin-10, and delayed type 1 diabetes onset in non-obese diabetic mice [52
]. In our meta-analysis, green tea and its extract reduced FBG concentrations. In the NMA, the effect of green tea on FBG concentrations was confined to younger (< 55-years-old) subjects. Beta-cell function is known to decrease continuously from euglycemia until the onset of type 2 diabetes [53
]. Therefore, early green tea intervention, which may help maintain beta-cell function, might be a prerequisite for its potential effects on FBG concentrations, at least via the proposed beta-cell protection mechanism. The reason for the discrepancy between results derived from Asian-based studies and non-Asian-based studies is unclear. In both Asian and non-Asian studies, 75% of studies used green tea extract for the intervention. If the EGCG content was not provided, we assumed that one cup of green tea contains 110 mg of EGCG [32
] and 100 mg of green tea extract contains 21.4 mg of EGCG [29
] from similar studies. The mean daily EGCG doses were not different between the Asian and non-Asian studies (367.3 ± 175.5 mg/day vs. 374.6 ± 186.8 mg/day, p
= 0.95). The meta-regression analysis showed there was no significant interaction between daily the EGCG dose contained in green tea and the FBG (β = 0.00; SE, 0.01; 95% CI, −0.02 to 0.01; p
= 0.54; Supplemental Figure S11
One possible explanation could be the differences in dietary habits or genetic predisposition for impaired glucose metabolism that might interact with the glucose-lowering effects of green tea. However, further investigations are certainly needed to understand this possible ethnic difference. Caffeine is known to reduce insulin sensitivity in the short term and have adverse effects such as arrhythmias, pregnancy complications, and drug interactions from clinical trials [55
]. Thus, such potential risks associated with caffeine should also be evaluated in order to make recommendations regarding caffeine-related beverages such as caffeinated coffee and tea. In our meta-analysis, green tea reduced FBG levels but did not reduce HbA1c levels. Two-thirds of the included green tea studies with HbA1c data were followed up in less than 12 weeks. This might be too short a period for assessing the effects for change in HbA1c levels accurately.
Oolong tea also showed potential protective effects on FBG concentrations in the NMA. Both green tea and oolong tea are derived from Camellia sinensis
, with their only difference being in the level of fermentation; green tea is unfermented whereas oolong tea is partially fermented. Therefore, both may exert protective effects on glucose metabolism through similar mechanisms. However, the two oolong tea studies included in our meta-analysis were supported by oolong tea manufacturers and had a very low quality of evidence due to the presence of very serious risks of bias and serious imprecisions. Thus, our inferences regarding the potential protective effects of oolong tea are limited. Indeed, a five-day, cross-over trial of 19 participants failed to show the protective effects of oolong tea on glucose metabolism, i.e., FBG concentrations and incremental glucose areas under the concentration time curve remained largely unchanged in the trial arms [56
]. To assess the true effects of oolong tea on glucose metabolism, more precisely designed, larger-scale RCTs are required. A series of prospective cohort studies reported that coffee and decaffeinated coffee intakes were linked to reducing the risk of type 2 diabetes [57
]. In a meta-analysis and systematic review that summarized the findings from 28 prospective studies, the relative risk of diabetes associated with a 1-cup/day consumption increase was 0.91 (95% CI, 0.89–0.94) for caffeinated coffee consumption and 0.94 (95% CI, 0.91–0.98) for decaffeinated coffee consumption [21
]. Further, these findings also suggested a dose-response relationship. However, in our meta-analysis, coffee and decaffeinated coffee consumption were unrelated to FBG concentrations. Furthermore, our meta-analysis showed that coffee resulted in slight increases in fasting IRI concentrations, raising concerns that coffee may impair insulin sensitivity. Because the follow-up periods of the included studies were far shorter than for the cohort studies and the total number of participants of coffee trials was small (below optimal information criterion), the long-term effect of coffee on glucose metabolism remains uncertain and longer (a few years) RCTs with a sufficient number of participants are required.
To date, this meta-analysis is the most comprehensive analysis, using a combined NMA and GRADE approach to evaluate the effects of tea and coffee on glucose metabolism. However, several limitations merit further consideration. First, complete blinding of coffee or tea interventions is difficult due to their taste. Thus, most studies were conducted using an open-label design. Second, the study durations were relatively short (median, 9 weeks; interquartile range, 6.5–16 weeks). Therefore, additional randomized studies, having longer durations and sufficient washout periods, are needed to determine the long-term effects of coffee and tea consumption on glucose metabolism.