Article pubs.acs.org/JAFC
Metabolic Response to Decaffeinated Green Tea Extract during Rest and Moderate-Intensity Exercise Doris M. Jacobs,*,§ Adrian B. Hodgson,† Rebecca K. Randell,† Krishna Mahabir-Jagessar-T,§ Ursula Garczarek,§ Asker E. Jeukendrup,† David J. Mela,§ and Silvina Lotito# †
School of Sport and Exercise Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom Unilever R&D, Olivier van Noortlaan 120, 3130 AC Vlaardingen, The Netherlands # Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom §
S Supporting Information *
ABSTRACT: We previously reported that a 7 day ingestion of caffeinated green tea extract (cGTE) induced marked metabolic differences during rest and exercise. Here, we report the metabolic effects of 1, 7, and 28 day ingestions of decaffeinated GTE (dGTE). In this crossover placebo-controlled study, 19 healthy males ingested dGTE or placebo (PLA) for 28 days, separated by a 28 day wash-out period. On days 1, 7, and 28, participants completed a 30 min cycling exercise 2 h after the ingestion of dGTE or PLA. Blood samples were collected at rest (t = 0 and 120 min) and during exercise (t = 150 min). Plasma was analyzed using untargeted four-phase metabolite profiling and targeted profiling of catecholamines and catechins. dGTE abolished several metabolic effects when compared to our previous study with cGTE. However, following 7 and 28 day dGTE ingestions, increases in 3-hydroxybutyrate, a metabolic marker of fat oxidation, were observed at t = 0 min. dGTE ingestion did not induce significant acute or acute-on-chronic effects on endogenous metabolites just prior to and during exercise. KEYWORDS: green tea, catechin, caffeine, metabolomics, exercise, fat oxidation
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INTRODUCTION Green tea (GT) has been reported to induce beneficial effects on weight management,1,2 glucose control,3 and cardiovascular risk factors.4,5 In particular, chronic ingestion of green tea extract (GTE) has been shown to promote weight loss,6 possibly due to elevated fat oxidation and total energy expenditure. Although a number of studies have shown increases in fat oxidation at rest7−10 and during exercise11,12 after GT intake, the current evidence from the literature still is inconclusive,13 as some studies have also reported no significant effects.14−16 The inconsistent study outcomes may be due to various factors, such as the duration of GTE intake, dosing, formulation and composition of GTE, sample size, population as well as variations in the measurements of fat oxidation parameters.13 With regard to the GTE composition, the catechins, especially (−)-epigallocatechin3-gallate (EGCG), are thought be the major bioactive components as demonstrated in many cell culture, animal, and human studies.17 Moreover, the amount of caffeine present in GTE is assumed to contribute to altering fat oxidation in humans. Several studies have shown that caffeine stimulates fat oxidation in humans at rest.18−20 Yet, the thermogenic effect of GTE containing caffeine has been found to be greater than that of an equivalent amount of caffeine, suggesting that GT catechins also stimulate energy expenditure at rest.9 Synergistic effects between caffeine and catechins have even been hypothesized.13,21 The potential molecular mechanisms of action affecting energy expenditure, fat oxidation, fat absorption, and energy intake after ingestion of catechin- and caffeine-rich tea have been reviewed elsewhere.22 This study follows our previous study16 in which a 7 day supplementation of caffeinated GTE (cGTE) was not found to © 2014 American Chemical Society
significantly alter whole body fat oxidation rates during exercise in healthy, physically active males. Yet, cGTE induced increased lactate concentrations during exercise,23 possibly indicating higher glycolytic activity, which is known to be associated with reduced fat oxidation.24 Considering that caffeine ingestion has been observed to increase lactate in arterial blood during exercise,25 we speculated that caffeine could have counteracted the up-regulation of fat metabolism during physical activity in our previous study. For this reason and because of the results of another study showing that decaffeinated GTE (dGTE) was able to augment fat oxidation during exercise,12 we had hypothesized that dGTE is more effective than cGTE in increasing fat oxidation during exercise. We found, however, that neither acute (1 day) nor 7 nor 28 day dGTE ingestion significantly changed whole body fat oxidation rates during exercise when compared to placebo (PLA).26 Nevertheless, we were interested in assessing the metabolic response in plasma similar to our previous study, in which our metabolomics approach has revealed changes in metabolites related to energy metabolism, yet not to adrenergic stimulation, at rest and during exercise.27 These effects were subtle and therefore may not have reached a level that would result in significantly altered whole body fat oxidation rates. Yet, it is conceivable that the metabolic response in plasma may be a sensitive measure at an early onset before any physiological effects become apparent. In addition, metabolomics is a comprehensive and unbiased approach and therefore particularly Received: Revised: Accepted: Published: 9936
June 10, 2014 August 20, 2014 September 7, 2014 September 8, 2014 dx.doi.org/10.1021/jf502764r | J. Agric. Food Chem. 2014, 62, 9936−9943
Journal of Agricultural and Food Chemistry
Article
Figure 1. Study design: (A, B) study arms. PLA, placebo; dGTE, decaffeinated green tea extract; w, week; D01, D07, D28, day 1, 7, or 28 of the respective supplementation period. All participants gave written informed consent to participate in the study. The study was approved by the Life and Sciences Ethical Review Committee at the University of Birmingham. All blood samples were collected in EDTA-containing tubes and stored on ice for no longer than 35 min. Subsequently, plasma was separated by centrifugation (3500 rpm, 15 min, 4 °C), aliquoted in 1 mL samples, and stored at −80 °C. Data Acquisition. Plasma Analysis of Polyphenols. Polyphenol concentrations in plasma from 19 subjects were analyzed at t = 0 min and t = 120 min on all six experimental days. In total, 225 samples (3 samples were missing) were measured by high-performance liquid chromatography multiple-reaction monitoring mass spectrometry (HPLC-MRMMS) with prior enzymatic deconjugation of glucuronides and sulfates, as described previously.23 In total, 9 phenolic compounds were quantified by means of 10-point calibration curves using external standards. They included (−)-catechin (C), (−)-epicatechin (EC), (−)-gallocatechin (GC), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-epigallocatechin gallate (EGCG), 3/4-O-methylgallic acid (3/4OMGA), 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone (3,4-diOH-VL), and 5-(3′-methoxy-4′-hydroxyphenyl)-γ-valerolactone (3-MeO-4-OHVL). The concentrations of 3-O-methylgallic acid (3-OMGA) and 4-Omethylgallic acid (4-OMGA) were combined to 3/4-OMGA because they could not be fully separated. Similarly, combined ECG values were reported for ECG and CG. Metabolite Profiling. Plasma metabolite profiles from 19 subjects were acquired from samples collected at t = 0, 120, and 150 min on four experimental days (dGTE_D01, dGTE7_D07, dGTE_D28, PLA_D28), respectively. Four-phase metabolite profiling and quantification of catecholamines were performed on human plasma samples at Metanomics Health GmbH, Berlin, Germany. Three types of mass spectrometry analyses were applied. GC-MS (gas chromatography−mass spectrometry; Agilent 6890 GC coupled to an Agilent 5973 MS-System, Agilent, Waldbronn, Germany) and LC-MS/MS (liquid chromatography-MS/ MS; Agilent 1100 HPLC-System (Agilent) coupled to an Applied Biosystems API4000 MS/MS-System (Applied Biosystems, Darmstadt, Germany)) were used for broad profiling, as described by van Ravenzwaay et al.29 SPE-LC-MS/MS (solid phase extraction-LC-MS/ MS; Symbiosis Pharma (Spark, Emmen, The Netherlands) coupled to an Applied Biosystems API4000 MS/MS-System (Applied Biosystems, Darmstadt, Germany) was used for the determination of catecholamine concentrations. Two hundred and one metabolites fulfilled the quality criteria for relative quantification, and absolute quantification was
useful in capturing the multifactorial and subtle influences of complex dietary ingredients on overall metabolism. Therefore, in the present study, we applied GC-MS- and LCMS-based untargeted four-phase metabolite profiling and targeted profiling of catecholamines to human plasma to compare the metabolic effects (i) between cGTE and dGTE supplementation and (ii) between 1, 7, and 28 days of dGTE ingestion at rest and during exercise.
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MATERIALS AND METHODS
Study Design. The study was designed as a double-blind, crossover, counterbalanced study (Figure 1) and has been described previously.26 In brief, 20 healthy, physically active, habitual caffeine-consuming, male participants [mean ± SD age, 21 ± 2 years; weight, 75.0 ± 7.0 kg; body mass index (BMI), 23.2 ± 2.2 kg/m2; maximal oxygen consumption (V̇ O2max), 55.4 ± 4.6 mL·mL·kg−1·min−1] completed two 28 day supplementation periods of dGTE and PLA ingestion, separated by a 28 day wash-out period. One subject dropped out of the study. Each supplementation period included three experimental days, namely, on the first day (dGTE_D01 or PLA_D01), after 7 days (dGTE_D07 or PLA_D07), and after 28 days (dGTE_D28 or PLA_D28) of supplementation. Before each experimental day, the subjects followed a 24 h controlled diet (consisting of three meals each containing ∼50% carbohydrate, ∼35% fat, and ∼15% protein, total energy intake = 2700 kcal) and were asked to refrain from any physical activity and not consume alcohol- or caffeine-based beverages. On each experimental day, a resting blood sample (5 mL) was taken at baseline (t = 0 min) after a 12 h overnight fast. Then the participants consumed two capsules of dGTE or PLA with 200 mL of water, rested for 2 h in a seated position and subsequently completed a 30 min cycle exercise bout at 50% Wmax (55 V̇ O2max). Blood samples were taken before the exercise bout commenced (t = 120 min) and at 30 min during the exercise bout. Participants ingested four capsules per day containing dGTE or PLA for a total of 28 days, respectively. Two capsules were consumed an hour before lunch (or 2 h before the exercise bout on the experimental days), and two additional capsules were consumed an hour before dinner. The dGTE was Sunphenon 90 DCF-T (lot 003191) obtained from Taiyo Europe (Fiderstadt, Germany). Each dGTE capsule contained 156 ± 3 mg of EGCG, 284 ± 6 mg of total catechins, and ∼3 mg of caffeine. (The catechin composition has been reported in ref 28.) The total catechin ingestion was 1136 ± 24 mg per day. The PLA capsule contained 273 ± 25 mg of cellulose. 9937
dx.doi.org/10.1021/jf502764r | J. Agric. Food Chem. 2014, 62, 9936−9943
Journal of Agricultural and Food Chemistry
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performed for additional 10 metabolites. From a total of 211 metabolites, 184 were known metabolites and 27 were not chemically identified with sufficient certainty (i.e., thus considered in the present study to be unknown analytes). The known metabolites belonged to different metabolite (ontology) classes: 22 amino acids; 17 metabolites related to amino acids; 12 carbohydrates and related metabolites; 84 complex lipids, fatty acids, and related metabolites; 16 metabolites related to energy metabolism; 13 hormones, signal substances, and related metabolites; 4 nucleobases and related metabolites; 7 vitamin, cofactors, and related metabolites; and 9 miscellaneous metabolites. Technical reference samples were measured in parallel with the study samples to allow the relative quantification of metabolites in the study samples. These technical reference samples were generated by pooling aliquots of plasma from all study samples. Relative quantification for each metabolite was obtained by normalizing peak intensity in the study samples to the median peak intensity of the corresponding metabolite in the technical reference samples measured in the same batch. For the metabolite profiling by GC-MS and LC-MS/MS, proteins were removed from plasma samples (60 μL) by precipitation. Subsequently, polar and nonpolar plasma fractions were separated for both GC-MS and LC-MS/MS analysis by adding water and a mixture of ethanol and dichloromethane. For GC-MS analyses, the nonpolar fraction was treated with methanol under acidic conditions to yield the fatty acid methyl esters derived from both free fatty acids and hydrolyzed complex lipids. The polar and nonpolar fractions were further derivatized with O-methyl-hydroxyamine hydrochloride (20 mg/mL in pyridine, 50 μL) to convert oxo groups to O-methyloximes and subsequently with a silylating agent (MSTFA, 50 μL) before GC-MS analysis.30 For LC-MS/MS analyses, both fractions were reconstituted in appropriate solvent mixtures. High-performance LC (HPLC) was performed by gradient elution using methanol/water/formic acid on reversed phase separation columns. Mass spectrometric detection technology was applied as described in U.S. patent 7196323, which allows targeted and high-sensitivity “multiple reaction monitoring” profiling in parallel to a full screen analysis. For the lipid phase, the broad profiling technology determines, for example, fatty acid concentrations after acid/methanol treatment, which is essential for derivatization preceding GC-MS analysis. As a consequence, complex lipids are hydrolyzed to components of the lipid backbone (i.e., glycerol) and fatty acids. Hence, the concentration of a fatty acid determined by this procedure represents the sum of its occurrence in free and in lipid-bound form. Components of the backbone can be recognized by the term (“lipid fraction“) added to the metabolite name. As an example “glycerol, lipid fraction” represents glycerol liberated from complex lipids; in contrast, “glycerol, polar fraction” represents glycerol that had been present originally in the biological sample. The use of “additional” (add) indicates that quantification can be affected by the co-occurrence of metabolites exhibiting identical characteristics in the analytical methods. Literature data and/or comparison with alternative methods (e.g., LC-MS/MS, GC-MS) suggest that such metabolites are present at minor concentrations only. Catecholamines and their related metabolites were measured by online SPE-LC-MS/MS, as described by Yamada et al.31 Quantification was performed using stable isotope-labeled standards. Data Analysis of Metabolite Profiles. The data set included 215 metabolite profiles. (One subject was excluded because of protocol violation. Another sample was missing.) Analysis of the four-phase metabolite profiles and the catecholamines were based on pool-normalized ratios and on absolute concentrations in nanograms per milliliter, respectively. For the multivariate and univariate statistical analysis, the data were log-transformed to better match normal distribution. Principal component analysis (PCA) was performed using SIMCA P + version 12 software (Umetrics, Umea, Sweden). Data were centered and scaled to unit variance to introduce a common scale for all metabolites independent of their absolute variance. The explorative unsupervised multivariate analysis method PCA was used for the detection of trends, patterns, and groupings among samples and variables. PCA revealed outliers that belonged to samples from one subject and thus were excluded from further analysis.
Single-metabolite analysis of variance (ANOVA) analysis was performed using the R-software package nlme.32,33 For this analysis, additional six samples were excluded, because their plasma catechin concentrations significantly deviated from average concentrations. The analysis was performed for in total 211 metabolites including all SQ metabolites from four-phase metabolite profiling and all catecholamines quantified by targeted profiling. A mixed-effects ANOVA model was built using the factors treatment (PLA_D28, dGTE_D01, dGTE_D07, dGTE_D28), time (0, 120, 150), study arm (A, B) and subject. All possible secondary and tertiary interactions between fixed factors were evaluated in the model refinement process. Model diagnostics was performed to ensure adequacy of the model structure. To this end, residuals were inspected visually by scatter plots of standardized residuals versus fitted values. Additionally, factors or interactions resulting in a number of significantly changed metabolites beneath the expected false-positive rate were excluded. Inclusion of all other factors and interactions ensured that residuals did not correlate with fitted values and showed homogeneous variance. No interaction between the factors time and study arm was considered. Thus, the following model was used (R annotation):
fixed: ∼( treatment + time)2 + study arm random: ∼ 1|subject The t statistics results of the ANOVA models comprised estimates, standard deviations, t values, and p values and q values corrected according to the methods of Benjamini-Hochberg (q value) or Bonferroni (qval.Bonf). The resulting numbers of significantly changed metabolites were evaluated by binomial test to account for false positives due to multiple hypotheses testing.
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RESULTS Effect of dGTE on Exogenous Metabolites. To check the compliance, the concentrations of several catechins were measured in plasma after deconjugation of glucuronides and sulfates at baseline (t = 0 min) and 2 h after the ingestion of two capsules of dGTE or PLA (t = 120 min) on D01, D07, and D28, respectively. Six individual samples were identified as outliers, and thus removed, because they did not confirm dGTE exposure in the dGTE condition or indicated dGTE exposure in the PLA condition. The concentrations of the remaining 219 samples are shown as box plots in Figure 2. As expected, the concentrations of EGCG, EGC, EC, and ECG/CG significantly increased 2 h after dGTE ingestion. These catechins accumulated in plasma following 7 days of dGTE ingestion, as shown by the increased baseline levels at D07 when compared to D01. However, there was no further accumulation, because the baseline levels after 7 and 28 days dGTE intake were similar. The plasma concentrations of GC, C, and GCG were
