Conceived and designed the experiments: DCN NDG DAH WS RAS AMK LCK FJ. Performed the experiments: DCN NDG DAH WS RAS AMK LCK FJ. Analyzed the data: DCN WS. Contributed reagents/materials/analysis tools: NDG DAH RAS AMK LCK FJ. Wrote the paper: DCN NDG DAH WS RAS AMK LCK FJ.
DCN, DAH, WS, RAS, AMK, and LCK have no conflicts of interest to report other than funding to the university from Dole Foods. NDG and FJ are scientists in the Dole Nutrition Research Laboratory, and were a part of the research team conducting this study. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
This study compared the acute effect of ingesting bananas (BAN) versus a 6% carbohydrate drink (CHO) on 75-km cycling performance and post-exercise inflammation, oxidative stress, and innate immune function using traditional and metabolomics-based profiling. Trained cyclists (N = 14) completed two 75-km cycling time trials (randomized, crossover) while ingesting BAN or CHO (0.2 g/kg carbohydrate every 15 min). Pre-, post-, and 1-h-post-exercise blood samples were analyzed for glucose, granulocyte (GR) and monocyte (MO) phagocytosis (PHAG) and oxidative burst activity, nine cytokines, F2-isoprostanes, ferric reducing ability of plasma (FRAP), and metabolic profiles using gas chromatography-mass spectrometry. Blood glucose levels and performance did not differ between BAN and CHO (2.41±0.22, 2.36±0.19 h, P = 0.258). F2-isoprostanes, FRAP, IL-10, IL-2, IL-6, IL-8, TNFα, GR-PHAG, and MO-PHAG increased with exercise, with no trial differences except for higher levels during BAN for IL-10, IL-8, and FRAP (interaction effects, P = 0.003, 0.004, and 0.012). Of 103 metabolites detected, 56 had exercise time effects, and only one (dopamine) had a pattern of change that differed between BAN and CHO. Plots from the PLS-DA model visualized a distinct separation in global metabolic scores between time points [R2Y(cum) = 0.869, Q2(cum) = 0.766]. Of the top 15 metabolites, five were related to liver glutathione production, eight to carbohydrate, lipid, and amino acid metabolism, and two were tricarboxylic acid cycle intermediates. BAN and CHO ingestion during 75-km cycling resulted in similar performance, blood glucose, inflammation, oxidative stress, and innate immune levels. Aside from higher dopamine in BAN, shifts in metabolites following BAN and CHO 75-km cycling time trials indicated a similar pattern of heightened production of glutathione and utilization of fuel substrates in several pathways.
Heavy exertion induces transient inflammation and oxidative stress, and wide ranging perturbations in the immune system
Bananas are a cost effective energy source and used by endurance athletes because of the perception that they are a good source of carbohydrate and potassium. One medium banana (∼118 g) contains about 27 g carbohydrate (half as sugars), 3.1 g dietary fiber, 105 kilocalories, and is a good source of potassium (422 mg) and vitamin B6 (0.43 mg)
In previous studies conducted by our research group, we showed that 60 g carbohydrate per hour in beverage form relative to placebo partially countered exercise-induced increases in cytokines and changes in innate immunity
Subjects included 14 male cyclists (ages 18–45) who regularly competed in road races (category 1 to 5) and had experience with cycling time trials. Subjects trained normally, maintained weight, and avoided the use of large-dose vitamin and mineral supplements, herbs, and medications known to affect inflammation and immune function for the duration of the study. All subjects signed informed consent and all study procedures were approved by the Institutional Review Board at Appalachian State University.
One week prior to the first 75-km time trial, each athlete completed orientation/baseline testing in the North Carolina Research Campus Human Performance Laboratory operated by Appalachian State University in Kannapolis, NC. Demographic and training histories were acquired with questionnaires. During orientation, a dietitian instructed the subjects to follow a diet moderate in carbohydrate (using a provided food list) during the 3-d period before each 75-km time trial. Subjects recorded food intake in 3-d food records, and were then analyzed using a computerized dietary assessment program for energy and macronutrient content (Food Processor; ESHA Research, Salem, OR).
During baseline testing, maximal power, oxygen consumption, ventilation, and heart rate were measured during a graded exercise test (25 Watts increase every two minutes, starting at 150 Watts) with the Cosmed Quark CPET metabolic cart (Rome, Italy) and the Lode cycle ergometer (Lode Excaliber Sport, Lode B.V., Groningen, Netherlands). Body composition was measured with the Bod Pod body composition analyzer (Life Measurement, Concord, CA).
One week following baseline testing, subjects completed the first 75-km time trial. Subjects were randomized to banana and 6% carbohydrate beverage conditions, and then crossed over to the opposite condition during the second 75-km time trial three weeks later. On the date of each 75-km time trial session, subjects consumed a standardized meal at 12:00 noon consisting of Boost Plus at 10 kcal/kg (41.9 kJ/kg) (Boost Plus; Mead Johnson Nutritionals, Evansville, IN). Subjects reported to the lab at 2:45 pm and provided a blood sample. At 2:50 pm, subjects ingested 0.4 g/kg carbohydrate from bananas (BAN) or from a standard 6% carbohydrate beverage (CHO) (Gatorade™, Chicago, IL). Subjects ingested 0.2 g/kg body weight every 15 minutes of BAN or CHO during the 75-km time trials. BAN were consumed with water to equal what was consumed with CHO. BAN were provided by Dole Foods (Westlake Village, CA) and were at a level six ripening stage (completely yellow with no brown spots).
Subjects cycled (3:00 pm start) on their own bicycles on CompuTrainer Pro Model 8001 trainers (RacerMate, Seattle, WA) with heart rate and rating of perceived exertion (RPE) recorded every 30 minutes, and workload continuously monitored using the CompuTrainer MultiRider software system (version 3.0, RacerMate, Seattle, WA). A mountainous 75-km course with moderate difficulty was chosen and programmed into the software system for use in each time trial. Fingertip capillary blood samples were drawn using heparin-lined microcapillary tubes pre-exercise, 1-h into the 75-km time trial, and post-exercise. Blood samples were immediately placed in microfuge tubes lined with EDTA dipotassium salt (RAM Scientific Inc., Germany), and analyzed using the YSI 2300 STAT Plus Glucose and Lactate analyzer (Yellow Springs, OH).
Blood samples were taken via venipuncture immediately after completing the 75-km time trial, and then 1-hr post-exercise. Subjects completed symptom logs, which included questions on digestive health (heartburn, bloating, diarrhea, and nausea). Subjects indicated responses using a 12-point Likert scale, with 1 relating to “none at all”, 6 “moderate”, and 12 “very high”.
Routine complete blood counts were performed by our clinical hematology laboratory using a Coulter Ac.TTM 5Diff Hematology Analyzer (Beckman Coulter, Inc., Miami, FL) and provided hemoglobin and hematocrit for the determination of plasma volume change
Total plasma concentrations of nine inflammatory cytokines (IL-6, TNFα, granulocyte-macrophage colony stimulating factor [GM-CSF], IFNγ, IL-1β, IL-2, IL-8, IL-10, and IL-12p70) were determined using an electrochemiluminescence based solid-phase sandwich immunoassay (Meso Scale Discovery, Gaithersburg, MD, USA). All samples and provided standards were analyzed in duplicate, and the intra-assay CV ranged from 1.7 to 7.5% and the inter-assay CV 2.4 to 9.6% for all cytokines measured. The minimum detectable concentration of IL-6 was 0.27 pg/ml, TNFα 0.50 pg/ml, GM-CSF 0.20 pg/ml, IFNγ 0.53 pg/ml, IL-1β 0.36 pg/ml, IL-2 0.35 pg/ml, IL-8 0.09 pg/ml, IL-10 0.21 pg/ml, and IL-12p70 1.4 pg/ml. Pre- and post-exercise samples for the cytokines were analyzed on the same assay plate to decrease inter-kit assay variability.
Plasma F2-isoprostanes were determined using gas chromatography mass spectrometry (GC-MS)
Total plasma antioxidant power was determined by the ferric reducing ability of plasma (FRAP) assay, a single electron transfer reaction
Dopamine hydrochloride (4-(2-aminoethyl)benzene-1,2-diol hydrochloride, 99%) was purchased from Acros Organics (New Jersey). Cavendish bananas (ripening stage 6) were obtained from a local grocery store and assayed on the day of purchase. Approximately 50 grams of banana flesh were blended with 150 mls of 70% aqueous methanol for three minutes, and analyzed for dopamine content using liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS-MS) (Thermo Scientific LTQ Velos system, West Palm Beach, FL).
Phagocytosis was measured through the uptake of FITC-labeled bacteria and oxidative burst was measured through the oxidation of non-fluorescent hydroethidine (HE) to fluorescent ethidium bromide in cells stimulated with unlabeled bacteria. Unlabeled and FITC-labeled bacteria (Staphylococcus aureus; Molecular Probes, Eugene, OR) were suspended in phosphate buffered saline (PBS) to working concentrations of 133,333 particles/µL. For each sample, 100 µL of blood were dispensed into two polypropylene tubes. To one tube, 10 µL of HE working solution (10 µg HE/mL; Molecular Probes) were added and the tubes incubated at 37°C for 15 min, then cooled at 4°C for 12 min. Using a bacteria to phagocyte (neutrophils and monocytes) ratio of 8∶1, unlabeled bacteria were added to the HE loaded tubes and FITC-labeled bacteria were added to the second tube in the set. Both tubes were incubated at 37°C for 20 min, placed in an ice water bath and 100 µL of quench solution were added to allow suppression of surface bound FITC-bacteria fluorescence. Cells were washed twice with cold PBS and resuspended in 50 µL cold fetal bovine serum. Samples were processed on a Q-Prep™ Workstation (Beckman Coulter, Inc) and analysis was performed within 18-hr of blood collection using a Beckman Coulter FC-500 flow cytometer. After gating on the granulocyte and monocyte populations using forward scatter and side scatter, the mean fluorescence intensity (MFI; x-mean) and percent positive cells for FITC (FL1) and oxidized HE (FL2) were determined.
All samples (both plasma extracts and standards for the internal library) were analyzed on an Agilent 7890A GC system coupled to an Agilent 5975C EI/CI Mass Selective Detector. The raw data files generated by GC-MS were converted to NetCDF format. The converted data were processed using Leco ChromaTOF software v4.24 (St. Joseph, MI) including baseline de-nosing, smoothing, peak picking, and peak signal alignment (signal-to-noise ≥30). Metabolite annotation was performed by comparing unknown signal patterns from the study samples to those of reference standards from an internal library containing approximately 600 human metabolites (Sigma-Aldrich, St. Louis, MO) established on the GC-MS system. Commercial libraries including the NIST library 2008 and LECO/Fiehn Metabolomics Library for GC-MS metabolome data (similarity threshold of 70%) were also used for additional compound annotation. Heptadecanoic acid was added to the study samples as an internal standard to monitor analytical variations during the entire sample preparation and analysis processes, and precision was calculated by injecting six randomly selected samples five times. The average CV for heptadecanoic acid was less than 5%, and the mean CV across the entire sample analysis was 15.3%.
All data are expressed as mean ± SD. The biomarker data were analyzed using a 2 (condition)×3 (time) repeated-measures ANOVA, within-subject design. When interaction effects were significant (P≤0.05), changes between time points within BAN or CHO conditions were compared between trials using paired t tests, with significance set after Bonferroni adjustment at P≤0.025. For metabolomics data, a linear model with repeated measures was used to examine the effect of treatment (BAN or CHO) and time (pre-exercise, immediate post-exercise, 1 hour post-exercise) on metabolite concentration, where metabolite concentration was the response variable, and treatment, time, and treatment×time interaction were predictor variables. Due to the crossover design, the sequence (BAN→CHO or CHO→BAN), and visit (first or second) effects were also adjusted in the model. This analysis was performed in the MIXED procedure in SAS (version 9.2, SAS Institute, Inc., Cary, NC), and was performed for each metabolite separately. Benjamini-Hochberg method for False Discovery Rate (FDR) correction in the MULTTEST procedure in SAS was used for multiple testing correction. To improve the normality of the data, the concentration of each metabolite was log transformed, and outliers with studentized residue >3 or <−3 were excluded. Metabolites with significant (FDR adjusted p-value<0.05) treatment×time interaction effect were considered to have significantly different responses to the two treatments. Metabolites with significant (FDR adjusted p-value<0.05) time effect were considered to be significantly affected by exercise within a carbohydrate-fed context. Missing values for a given metabolite were imputed with the observed minimum after the normalization step. Partial Least Square Discriminant Analysis (PLS-DA) in SIMCA-P+ (Version 12, Umetrics, Umeå, Sweden) was used to detect metabolites that best distinguished the three time points. The default 7-round cross-validation in the SIMCA-P software package was applied with 1/7 of the samples being left out from the mathematical model in each round. Variable Influence on Projection (VIP) score was calculated based on the PLS weights and the variability explained in PLS-DA. Metabolites with VIP>1 were considered the most important metabolites responsible for the differentiation of the three time points. Similarly, PLS-DA was used to detect metabolites that best distinguished BAN from CHO treatment. In this analysis, the ratio of immediate post-exercise/pre-exercise for each subject was calculated, and used as input data for PLS-DA.
Fourteen subjects completed all aspects of the study, and subject characteristics indicated that they were well trained and experienced cyclists (mean age 37.0±7.1 y, body fat 17.8±4.5%, maximal power 379±46.8 Watts, VO2max 58.6±5.2 ml.kg.−1min−1, training and racing history 8.4±6.4 y). Subjects averaged 272±86.1 km/wk during the 3-month period prior to the study. Three-day food records before each of the two time trials revealed no significant differences in energy or macronutrient intake. Energy intake was 2486±625 kcal/day (10.5±2.47 MJ/day) and 2539±662 kcal/day (10.2±2.66 MJ/day), with carbohydrate representing 60.4±5.6% and 59.4±6.0%, protein 15.9±2.2% and 16.1±3.4%, and fat 23.7±5.6% and 24.5±5.2% of total energy for BAN and CHO conditions, respectively. The three-day food records also revealed no significant differences in potassium 2041±700 mg and 2454±625 mg, vitamin C 102±58.0 and 115±76.0 mg, and fiber 30.5±10.3 g and 33.8±11.0 g intake for BAN and CHO, respectively.
Mean power (225±43.0, 233±43.8 Watts, P = 0.178), heart rate (91.1±4.9, 89.3±3.4%HRmax, P = 0.096), rating of perceived exertion (14.6±1.5, 14.4±1.1 RPE units, P = 0.613), and total time (2.41±0.22, 2.36±0.19 h, P = 0.258) did not differ between BAN and CHO 75-km cycling time trials, respectively. The patterns of increase over time during the 75-km cycling trials were similar between BAN and CHO for serum glucose (23% and 19%, respectively, interaction effect, P = 0.849) and blood lactate (220% and 227%, respectively, interaction effect, P = 0.439). Mean carbohydrate intake during BAN and CHO trials was 150±19.5 grams. Subjects reported feeling significantly more full (P = 0.003) and bloated (P = 0.014) during the BAN versus CHO trial. Subjects lost 0.4 kg more body weight during the BAN versus CHO trial (mean weight change, −1.5±0.7, −1.1±1.1 kg, respectively, P = 0.015). Plasma volume shifts were less than 2% following exercise and did not differ between trials (P = 0.711).
The patterns of increase in plasma F2-isoprostanes did not differ between BAN and CHO trials (
Variable | Pre-Exercise | Post-Exercise | 1-h Post-Exercise | Time; interaction P values |
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BAN | 6.03±1.68 | 8.82±2.02 | 8.70±2.17 | <0.001; 0.104 |
CHO | 5.64±1.31 | 7.21±2.02 | 7.00±1.72 | |
|
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BAN | 0.90±0.56 | 14.6±9.35 | 10.9±7.93 | <0.001; 0.540 |
CHO | 0.99±0.56 | 12.2±9.80 | 8.66±7.86 | |
|
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BAN | 1.27±0.71 | 1.71±0.71 | 1.62±0.90 | 0.036; 0.185 |
CHO | 1.50±0.86 | 1.52±0.71 | 1.48±0.79 | |
|
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BAN | 2.12±0.56 | 11.1±5.84 |
8.82±3.14 |
<0.001; 0.004 |
CHO | 2.19±0.79 | 7.38±3.74 | 6.75±3.07 | |
|
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BAN | 2.61±1.57 | 11.0±5.13 |
13.2±12.3 |
0.003; 0.003 |
CHO | 2.24±1.76 | 5.98±3.52 | 6.96±7.82 | |
|
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BAN | 270±71.1 | 373±134 | 435±171 | 0.001; 0.215 |
CHO | 284±134 | 321±164 | 346±147 | |
|
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BAN | 137±50.9 | 233±104 | 281±117 | <0.001; 0.191 |
CHO | 156±87.6 | 206±117 | 224±100 | |
|
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BAN | 12.0±4.94 | 12.2±4.12 | 11.6±4.60 | 0.492; 0.697 |
CHO | 12.9±7.11 | 11.4±5.91 | 11.0±6.73 | |
|
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BAN | 8.70±2.17 | 10.2±2.39 | 9.19±3.11 | 0.207; 0.810 |
CHO | 8.95±3.89 | 9.56±3.67 | 9.13±5.05 | |
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BAN | 80.2±14.4 | 104±15.5 | 98.5±13.4 | <0.001; 0.145 |
CHO | 82.9±11.9 | 98.3±15.3 | 90.3±9.95 | |
|
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BAN | 460±51.3 | 604±79.3 |
575±76.0 |
<0.001; 0.012 |
CHO | 442±71.8 | 521±75.6 | 510±63.6 |
P<0.025, difference in change, pre-exercise when comparing BAN and CHO conditions.
The pattern of increase in granulocyte (GR) and monocyte (MO) phagocytosis (PHAG) did not differ between BAN and CHO (
Of 103 metabolites detected through our GC-MS metabolomics system, 56 had significant time effects following the 75-km cycling bouts. Only one (dopamine) of the 56 metabolites had a pattern of change that differed between BAN and CHO, and overall treatment effects were not separated by PLS-DA modeling. Dopamine significantly increased in BAN compared to CHO, as shown in
* P<0.025, difference in change from pre-exercise when comparing BAN and CHO. Data shown as mean±SD.
A satisfactory separation was obtained between time points groups (Q2Y = 0.766).
Serum Metabolite | Mean Fold Change |
Palmitoleic Acid | 22.0 |
2,3,4-Trihydroxybutanoic Acid | 3.1 |
Malic Acid | 5.6 |
Succinic Acid | 12.7 |
Palmitic Acid | 3.9 |
Oleic Acid | 6.1 |
Heptadecanoic Acid | 2.8 |
D-Fructose | 11.5 |
2-Hydroxybutyric Acid | 1.9 |
L-Isoleucine | 0.7 |
L-Glutamic Acid | 1.7 |
2-Aminobutyric Acid | 0.7 |
L-Methionine | 0.9 |
Pyruvic Acid | 2.8 |
L-Proline | 0.9 |
LC-ESI-MS-MS analysis of banana flesh indicated a dopamine concentration of 0.42 mg/100 grams. The average subject ingested 794±94.3 grams banana during the banana cycling trial (6–7 total bananas), for a total banana dopamine intake of 3.33±0.41 mg.
These data from a randomized, crossover study with 14 trained cyclists indicate that acute ingestion of BAN or CHO supported 75-km cycling performance and underlying metabolic processes to a similar degree when the rate of carbohydrate delivery was equated. Exercise-induced inflammation, oxidative stress, and changes in innate immune function were also comparable between BAN and CHO trials, with the exception of a few biomarkers including IL-10 and IL-8. BAN compared to CHO resulted in higher antioxidant capacity (as measured through FRAP) and serum dopamine levels. The 75-km cycling time trials caused wide ranging increases in serum metabolites, and the data support a similar pattern of intensified production of glutathione and utilization of fuel substrates in several pathways during both BAN and CHO.
Metabolomics utilizes analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry to measure the multicomponent metabolic composition of biological fluids such as urine, plasma, or serum
Using an untargeted approach with GC-MS analytical technology, our data indicate substantial shifts in metabolites related to glutathione production and fuel substrate usage following prolonged and intense exercise, with no differences between BAN and CHO. Our metabolomics data also support that these shifts in metabolites persisted, but with some attenuation, for at least one hour post-exercise. Lipid peroxidation increases during intense, prolonged exertion, as supported by the 20–30% increase in plasma F2-isoprostanes experienced by our subjects
Lewis et al.
Dopamine is a polyphenolic found in small quantities in banana flesh, and in large quantities in the banana peel
The CHO used in this study is sweetened with a sucrose-dextrose-fructose blend (each 0.5 liter contains 26.2 g total sugar, with 11.0 g glucose, 9.1 g fructose, and 4.6 g sucrose)
Mitchell et al.
In previous studies, our research group has shown that CHO compared to placebo ingestion during prolonged and heavy exertion attenuates exercise-induced increases in plasma inflammation measures and granulocyte phagocytosis, in part due to higher serum glucose levels and reduced plasma epinephrine and serum cortisol
In conclusion, in this randomized, crossover study, cyclists ingesting BAN or CHO at a rate of 0.2 g/kg carbohydrate every 15 min (and one 0.4 g/kg carbohydrate dose pre-exercise) were able to complete 75-km cycling trials with no differences in performance measures. Changes in blood glucose, inflammation, oxidative stress, and innate immune measures were also comparable between BAN and CHO 75-km cycling trials, and similar to what we have previously reported for carbohydrate-fed athletes
We acknowledge the assistance of Manuela Konrad from Medical University, Graz, Austria, and Jun Deng, Qi Jiang, Sarah Schwartz, and Brandon Ore from the David H. Murdock Research Institute. We acknowledge the skillful assistance of Pamela Krasen, Dustin Dew, and Eric Vail in the administration of this study.