The importance of high carbohydrate (CHO) availability before intense training sessions and/or competitions is well established and recommendations for athletes have been constructed on how this can be achieved efficiently (Burke et al., 2011; Thomas et al., 2016). Arguably one of the most important meals of the day for athletes is breakfast as this is the first provision of CHOs after an overnight fast. During the night, liver glycogen concentrations decline by ∼25%, whereas muscle glycogen concentrations remain relatively stable (Iwayama et al., 2021). Starting concentrations of both glycogen stores (i.e., muscle and liver) are associated with endurance exercise capacity (Bergström et al., 1967; Casey et al., 2000). Moreover, evidence in rodents demonstrating increased exercise capacity by genetic upregulation of hepatic glycogen concentrations indicates a causal link between liver glycogen stores and exercise tolerance (López-Soldado et al., 2021). Thus, it seems warranted that following an overnight fast, the composition of a meal should be such that liver glycogen concentrations would be topped-up (Noakes, 2022). It is currently recommended to athletes at their preevent meal (i.e., breakfast) to consume 1–4 g of CHOs per kilogram of body mass (Burke et al., 2011; Thomas et al., 2016), and the guidelines provide little detail about what the most appropriate CHO type is.
Recently, the effects of different monosaccharides on repletion of different glycogen pools (i.e., liver and muscle) were investigated. It was shown that co-ingestion of fructose with glucose after exhaustive exercise enhances liver glycogen repletion over ingestion of glucose-based CHOs only (Décombaz et al., 2011; Fuchs et al., 2016) but does not negatively affect muscle glycogen replenishment (Fuchs et al., 2016; Wallis et al., 2008) thus increasing whole-body glycogen storage (assuming negligible contribution from the kidney). Enhanced rates of liver glycogen synthesis after fructose co-ingestion are likely a result of multiple mechanisms. These include increase exogenous CHO availability, additional precursor availability by direct conversion of fructose to glucose for glycogen synthesis, and fructose-induced stimulation of hepatic glucose uptake via fructose-1-phosphate facilitation of glucokinase activity (Fuchs et al., 2019; Hengist et al., 2019; Sun & Empie, 2012). In line with this, studies demonstrated increased CHO availability (likely from liver glycogen and/or residual appearance from the gut) during a subsequent endurance exercise bout (Maunder et al., 2018; Podlogar & Wallis, 2020) and some also established functional benefits, for example, increased subsequent endurance exercise capacity (Gray et al., 2020; Maunder et al., 2018). These prior studies suggest that co-ingestion of fructose with glucose-based CHOs in recovery from exercise can enhance liver glycogen repletion and may improve subsequent exercise capacity. However, there is currently no evidence on the role of fructose in the preexercise meal. Since the metabolic status at rest versus postexercise can alter the partitioning of ingested CHOs—which may be due to relative changes in blood flow to the liver versus muscle and/or hormonal influences such as glucagon—findings from postexercise cannot necessarily be translated to preexercise (Gonzalez & Wallis, 2021).
Based on evidence from postexercise recovery, it could be hypothesized that a co-ingestion of glucose-based CHOs and fructose as opposed to ingestion of glucose-based CHOs only at breakfast should lead to more optimal replenishment of liver glycogen and subsequent improvement of endurance exercise capacity. Thus, the aim of this study was to test this hypothesis by investigating the effects of a breakfast consisting of glucose-based CHOs only or substitution of some glucose with fructose.
Methods
Study Design
This was a randomized, double-blinded, and a crossover designed study consisting of a preliminary testing, a familiarization trial, and two experimental trials. The study protocol was approved by the Committee of Republic of Slovenia for Medical Ethics (no. 0120-690/2017/8) and was conducted in accordance with the Declaration of Helsinki. Manuscript has been prepared according to Proper Reporting of Evidence in Sport and Exercise Nutrition Trials (Betts et al., 2020).
Participants
Eight trained male cyclists consented to take part in this experiment, characteristics are presented in Table 1. Recruitment of the study was open to both sexes, yet only male participants volunteered to take part. Participants of performance Level 3 or above (de Pauw et al., 2013), that is, peak oxygen uptake of >55 ml·kg−1·min−1, were eligible to take part in the study. Participants following a low-CHO diet or some other unconventional nutritional practice (e.g., intermittent fasting) were excluded from the study.
Participants’ Characteristics
| Age (years) | 35 ± 4 |
| Mass (kg) | 72.1 ± 6.8 |
| Height (cm) | 179 ± 9 |
| 4.44 ± 0.24 | |
| 62.1 ± 5.4 | |
| VT1 (W) | 239 ± 20 |
| VT1 (W/kg) | 3.3 ± 0.4 |
| RCP (W) | 293 ± 25 |
| RCP (W/kg) | 4.1 ± 0.6 |
Note. Data are presented as mean ± SD. VT1 = first ventilatory threshold; RCP = respiratory compensation point intensity;
Preliminary Testing
Participants performed two exercise bouts to accurately determine peak oxygen uptake and the intensities corresponding to the first ventilatory threshold (VT1) and the respiratory compensation point (RCP) as per a previously described protocol (Iannetta et al., 2020). In brief, the test started with a 2-min warm-up at 80 W followed by 6 min of cycling at 120 W (moderate-intensity exercise domain). This transitioned into a ramp incremental protocol increasing the exercise intensity by 30 W/min until task failure. Respiratory data were immediately analyzed by two experienced researchers that independently determined oxygen uptake associated with VT1 (
During the test, gas exchange measurements were performed using an automated online gas analysis system (MetaLyzer 3B-R3, Cortex). Prior to each trial gas, analyzers were calibrated with fresh ambient air and a standardized gas mixture (15.10% O2, 5.06% CO2, Linde Gas) taking into account the environmental conditions (i.e., barometric pressure, temperature, and humidity), and the volume transducer was calibrated with a 3-L calibration syringe (Cortex). During all tests, the participants used their own bicycles mounted onto an electrically braked cycle ergometer (Kickr V5, Wahoo).
Experimental Trials
Familiarization and the two experimental trials consisted of two visits to the laboratory on consecutive days, and the trials were separated by 7–14 days. The order of the experimental trials was randomized and counterbalanced and was performed using an online randomizer software (https://www.randomizer.org) by a researcher not involved in data collection. The familiarization and experimental trials were identical without the blood sampling during the former. Participants were provided with a food and activity diary for the day of the trial and the day prior to it and asked to replicate dietary and activity patterns (confirmed by using Strava online app) before both experimental visits. They reported to the laboratory in the afternoon (i.e., 4:00 p.m. and 5:00 p.m.) and performed an exercise training session lasting 135 min with the aim of reducing and normalizing glycogen stores. In brief, the session consisted of two 2-min long oxygen uptake priming intervals performed at the RCP intensity, followed by four 8-min long intervals at the intensity corresponding to 104% RCP. In between the intervals, there was 4 min of recovery (i.e., easy cycling). After the successful completion of the intervals, participants cycled for 60 min at the intensity corresponding to 90% of VT1.
Immediately after completion of this workout, participants received a standardized meal consisting of 30 g whey protein and 50 g of CHOs in the 1:0.8—maltodextrin:fructose ratio (REGEN, Nduranz), which should stimulate both glycogen formation (Alghannam et al., 2018) and muscle protein synthesis (Churchward-Venne et al., 2020). This was followed by ingestion of three more CHO-rich meals, consisting of two meals with 1.3 g·kg·body·mass−1 and one of 1.5 g·kg·body·mass−1 of CHOs in the form of basmati rice. These were scheduled to be ingested hourly so that the last one was scheduled to be had just before bedtime at 9–10 p.m. and thus complied with the recommendations for optimal CHO intake in the postexercise recovery period (Burke et al., 2011; Thomas et al., 2016).
The following morning participants entered the laboratory after an overnight fast and were served a breakfast consisting of 2 g·kg·body·mass−1 of CHOs consisting of rice mixed with a CHO powder (dextrose [GLU + RICE] or fructose [FRU + RICE]; both from bulk, United Kingdom) so that the composition of CHOs was in 2:1 ratio (rice vs. CHO powder). Powder was weighed and packaged by a person not involved in data collection making the study double blinded.
Following a 2-hr long passive rest, participants started the exercise trial performed with a 5-min long standardized warm-up and transitioned to cycling at the intensity corresponding to the VT1 until task failure. Every 15-min expired gas was analyzed using a metabolic cart (described above). After equilibration was achieved, data were averaged over 3 min. Substrate oxidation rates were calculated using indirect calorimetry equations in which protein oxidation was deemed negligible (Jeukendrup & Wallis, 2005). Rating of perceived exertion (RPE) was determined using the 6–20 Likert scale (Borg, 1982), and heart rate was measured using a chest strap (Polar H10, Polar), and fingertip measurements of blood glucose (Accu-Chek Aviva, Roche) and blood lactate (Lactate Plus, Nova Biomedical) were obtained. Once the participants could not sustain the required workload (i.e., cadence dropped to the point that pedaling was impossible), the time to task failure time was noted. Participants received no feedback on the time elapsed nor were they motivated by the researchers present in the laboratory. While exercise capacity tests have been criticized for poor reliability and ecological validity (Currell & Jeukendrup, 2008), in elite sports (e.g., cycling and running) sustaining a set pace by the leader of a group of which an athlete is a part of, often determines the outcomes of competitions, and, thus, exercise capacity tests are relevant for a wide range of endurance competitions (Alghannam et al., 2014). In addition to this, they allow continuous assessment of metabolism.
Data Analysis
Sample size calculation performed using G*Power app (version 3.1.9.6, Heinrich Heine Universität Düsseldorf) and was based on the results of a similar study employing running to task failure (Maunder et al., 2018), with an anticipated effect size of d = 1.84, a statistical power of β = 0.8, and α = .05 yielded a minimum sample size of n = 5. Data were initially tested for normality by the Kolmogorov–Smirnov test. A t test was used to compare the differences in time to task failure time for both conditions and the order of the trials (i.e., order effect), glycemic area under the curve, and total CHO oxidation. A two-way analysis of variance for repeated measures was used to compare differences in blood glucose, blood lactate respiratory exchange ratio, RPE, heart rate, CHO oxidation, and FAT oxidation at different time points and for the interaction of condition and time. All values are presented as mean ± SD. Statistical significance was set at p < .05.
Results obtained at 15-min intervals have been analyzed between 15- and 75-min time points as at these time points data were available for all the participants. To avoid using the same data twice, 90-min time point data were excluded from this part of the analysis as it was subsequently used when comparing last time point when measurements have been performed in either of the trials. This measure represented the last known physiological values before failure to maintain the required power output. Incremental area under the glycemic curve was calculated as suggested previously (Chlup et al., 2008). Total CHO oxidation was calculated as the area under the CHO oxidation curve, taking into account all data points to task failure for each participant. The area under the curve was calculated using the trapezoidal numerical integration function (trapz) in MATLAB. Data analysis was performed using MATLAB (version R2020b).
Results
Task failure time is presented in Figure 1 (137.03 ± 22.72 min [FRU + RICE], and 130.06 ± 19.87 min [GLU + RICE], with statistically significant difference between them; p = .046). No order effect was detected (p = .146).
—Time to task failure during cycling following a breakfast containing GLU + RICE or FRU + RICE. Bars represents mean; circles and connecting lines represent individual participants (N = 8). GLU + RICE = rice with added glucose; FRU + RICE = rice with added fructose.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 32, 6; 10.1123/ijsnem.2022-0067
Blood glucose, blood lactate, RPE, and HR at the 15-, 30-, 45-, 60-, and 75- min time points and at the last time point before task failure are shown in Table 2. A two-way analysis of variance revealed there were no statistically significant interactions between conditions for blood glucose (p = .481), blood lactate (p = .634), heart rate (HR; p = .667), or RPE (p = .729). Similarly, there was no statistically significant interaction between condition and time for blood glucose (p = .756), blood lactate (p = .518), HR (p = .976), or RPE (p = .686). However, there was a statistically significant effect of time for blood glucose (p < .001), HR (p = .003), and RPE (p < .001), but this effect was not present for blood lactate concentrations (p = .104).
Blood Glucose, Blood Lactate, RPE, and HR During Exercise Following a Breakfast Containing GLU + RICE or FRU + RICE
| Time (min) | Trial | Blood glucose (mmol/L) | Blood lactate (mmol/ L) | RPE | HR (bpm) |
|---|---|---|---|---|---|
| 15 | FRU + RICE | 3.8 ± 0.7 | 1.5 ± 0.3 | 12.6 ± 1.3 | 146 ± 9 |
| GLU + RICE | 4.1 ± 0.7 | 1.5 ± 0.4 | 12.5 ± 0.9 | 144 ± 8 | |
| 30 | FRU + RICE | 4.2 ± 1.1 | 1.5 ± 0.4 | 12.9 ± 1.5 | 149 ± 8 |
| GLU + RICE | 4.8 ± 0.7 | 1.4 ± 0.3 | 13.4 ± 1.1 | 148 ± 7 | |
| 45 | FRU + RICE | 4.6 ± 1.5 | 1.4 ± 0.4 | 13.5 ± 1.6 | 152 ± 8 |
| GLU + RICE | 5.1 ± 0.5 | 1.4 ± 0.2 | 14.0 ± 1.4 | 150 ± 7 | |
| 60 | FRU + RICE | 4.6 ± 1.3 | 1.7 ± 0.5 | 14.0 ± 1.8 | 152 ± 8 |
| GLU + RICE | 5.3 ± 0.6 | 1.3 ± 0.4 | 14.1 ± 1.2 | 149 ± 10 | |
| 75 | FRU + RICE | 4.6 ± 1.3 | 1.5 ± 0.4 | 14.4 ± 1.8 | 151 ± 10 |
| GLU + RICE | 5.1 ± 0.6 | 1.5 ± 0.4 | 14.9 ± 1.6 | 150 ± 7 | |
| End | FRU + RICE | 4.3 ± 1.2 | 1.7 ± 0.4 | 18.4 ± 1.1 | 156 ± 11 |
| GLU + RICE | 4.7 ± 0.5 | 1.8 ± 0.8 | 18.4 ± 1.2 | 155 ± 11 |
Note. Data are represented as mean ± SD. N = 8. RPE = rating of perceived exertion; HR = heart rate; GLU + RICE = rice with added glucose; FRU + RICE = rice with added fructose.
The VO2, VCO2, respiratory exchange ratio, CHO, and FAT oxidation rates are shown in Table 3. In addition, CHO and fat oxidation rates are graphically presented in Figure 2. There were no differences in VO2 or VCO2 between conditions (p = .923 and p = .936, respectively) and no Condition × Time interactions (p = .942 and p = .978, respectively) but both parameters changed over time (p = .040 and p = .012, respectively). Respiratory exchange ratio was not different between conditions (p = .970) nor was there an interaction in Condition × Time (p = .780) but it changed over time (p < .001). CHO and fat oxidation rates did not differ between conditions (p = .976 and p = .803, respectively) nor were there any Condition × Time interactions (p = .859 and p = .929, respectively) but both parameters changed over time (p = .005 and p < .001, respectively). The total amount of CHO oxidized was higher in FRU + RICE (326 ± 60 g) as compared with GLU + RICE (298 ± 61 g, p = .009). Moreover, the incremental area under the glycemic curve did not differ (p = .149) and was 552 ±125 min·mmol·L−1 in FRU + RICE and 524 ± 93 min·mmol·L−1 in GLU + RICE, respectively.
Oxygen Uptake, Carbon Dioxide Production, RER, CHO, and Fat Oxidation Rates During Exercise Following a Breakfast Containing GLU + RICE or FRU + RICE
| Time (min) | Trial | VO2 (L/min) | VCO2 (L/min) | RER | CHO oxidation (g/min) | Fat oxidation (g/min) |
|---|---|---|---|---|---|---|
| 15 | FRU + RICE | 3.17 ± 0.23 | 2.96 ± 0.23 | 0.93 ± 0.02 | 3.07 ± 0.38 | 0.34 ± 0.13 |
| GLU + RICE | 3.17 ± 0.20 | 2.97 ± 0.21 | 0.94 ± 0.02 | 3.13 ± 0.39 | 0.31 ± 0.12 | |
| 30 | FRU + RICE | 3.23 ± 0.23 | 2.98 ± 0.23 | 0.92 ± 0.02 | 2.98 ± 0.33 | 0.41 ± 0.10 |
| GLU + RICE | 3.21 ± 0.19 | 2.97 ± 0.20 | 0.92 ± 0.02 | 2.98 ± 0.37 | 0.40 ± 0.13 | |
| 45 | FRU + RICE | 3.23 ± 0.22 | 2.95 ± 0.22 | 0.91 ± 0.02 | 2.85 ± 0.37 | 0.46 ± 0.12 |
| GLU + RICE | 3.20 ± 0.18 | 2.93 ± 0.18 | 0.92 ± 0.03 | 2.87 ± 0.37 | 0.43 ± 0.15 | |
| 60 | FRU + RICE | 3.23 ± 0.23 | 2.93 ± 0.25 | 0.91 ± 0.03 | 2.79 ± 0.43 | 0.48 ± 0.12 |
| GLU + RICE | 3.21 ± 0.18 | 2.93 ± 0.19 | 0.91 ± 0.03 | 2.82 ± 0.39 | 0.46 ± 0.14 | |
| 75 | FRU + RICE | 3.22 ± 0.23 | 2.92 ± 0.24 | 0.91 ± 0.03 | 2.75 ± 0.43 | 0.50 ± 0.12 |
| GLU + RICE | 3.22 ± 0.16 | 2.91 ± 0.17 | 0.91 ± 0.03 | 2.73 ± 0.35 | 0.50 ± 0.14 | |
| End | FRU + RICE | 3.17 ± 0.28 | 2.87 ± 0.28 | 0.91 ± 0.05 | 2.67 ± 0.43 | 0.57 ± 0.16 |
| GLU + RICE | 3.17 ± 0.22 | 2.84 ± 0.22 | 0.89 ± 0.02 | 2.56 ± 0.37 | 0.55 ± 0.11 |
Note. Data are represented as mean ± SD. N = 8. CHO = carbohydrate; RER = respiratory exchange ratio; GLU + RICE = rice with added glucose; FRU + RICE = rice with added fructose; VCO2 = carbon dioxide output.
—Carbohydrate (a) and fat (b) oxidation rates following a breakfast containing GLU + RICE or FRU + RICE. Individual points represent mean ± SD; (N = 8). GLU + RICE = rice with added glucose; FRU + RICE = rice with added fructose.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 32, 6; 10.1123/ijsnem.2022-0067
Discussion
The main objective of the present study was to test the hypothesis that combining fructose with glucose-based CHOs (i.e., fructose and rice) at breakfast as opposed to having only glucose-based CHOs (i.e., dextrose and rice) leads to improved endurance exercise capacity after an overnight fast. The obtained data confirm this hypothesis. In addition, the enhanced exercise capacity was observed in the presence of no detectable differences in whole-body substrate metabolism and physiological parameters.
Limited time in between two training sessions or competitions is a very common occurrence in elite sport, and thus, it is important that athletes replenish CHO stores as efficiently as possible within the time available. Yet previous studies mostly focused on the short-term recovery within the same day with <5 hr of recovery time. In these circumstances, replenishment of muscle glycogen stores was not different when athletes ingested fructose–glucose-based CHOs as compared with glucose-based CHOs only (Fuchs et al., 2016; Wallis et al., 2008). The replenishment of liver glycogen stores, however, was enhanced with a fructose–glucose combination (Casey et al., 2000; Fuchs et al., 2016). Based on these findings—but without measurements of liver and muscle glycogen content—it could therefore be tentatively speculated that the improvement of endurance exercise capacity observed in the present study was a result of equal muscle and higher liver glycogen storage before the initiation of the morning exercise bout.
As liver glycogen is responsible for the maintenance of blood glucose concentrations, it could have been hypothesized that lowering of blood glucose concentrations would be observed toward the end of the exercise trial (Casey et al., 2000; Coyle et al., 1986; Gonzalez & Betts, 2019). However, blood glucose concentrations remained stable throughout the trial in both experimental conditions and did not differ between conditions. This might not be surprising as it appears that at higher exercise intensities, task failure might not be preceded by low blood glucose despite being hypothesized that low liver glycogen content was the main culprit for the task failure (Gray et al., 2020). However, as exercise intensity was relatively high, it is well known that liver glucose output is increased (Howlett, Febbraio, et al., 1999; Howlett, Galbo, et al., 1999) either as a result of liver glycogenolysis or gluconeogenesis (Trefts et al., 2015). The latter could therefore be responsible for maintenance of glycemia, while there may be mechanisms that detect liver glycogen concentrations (or rates of depletion) independent from blood glucose concentrations. Consistent with this, hepatic vagotomy in rodents abolished the effects of liver glycogen on physical activity, suggesting that liver glycogen concentrations could be sensed via vagal afferents (López-Soldado et al., 2017), independent from circulating metabolite and hormonal signals.
As (all else being equal) commencing exercise with higher glycogen availability can increase CHO oxidation rates, it could be speculated that the higher CHO oxidation rates following fructose–glucose ingestion (vs. glucose only) observed in some previous recovery studies (Maunder et al., 2018; Podlogar & Wallis, 2020) is a marker of increased CHO availability. However, the absence of a detectable difference in CHO oxidation rates, as seen in the present study, and one other (Gray et al., 2020), does not necessarily rule out the possibility of a higher CHO availability. At a similar CHO oxidation rate, higher CHO availability will delay the depletion of CHO stores and thereby extend endurance capacity. Indeed, the current study did demonstrate an increased amount of total CHO oxidized. Based on this, it appears that substituting some glucose for fructose in the preexercise meal increases CHO availability, most likely in the form of improved liver glycogen storage. The reasons for the discrepant results between this study and prior studies with respect to CHO oxidation rates are unknown, but could include differences in the status in which the CHOs were consumed (e.g., postexercise vs. at rest) and/or the amount and composition of CHOs (e.g., drinks vs. food). Nevertheless, these data suggest that ingesting 2 g/kg of fructose–glucose-based CHOs at breakfast does not modify substrate oxidation rates during subsequent exercise, and more work is needed to understand the effect on CHO availability.
The amount of CHOs (i.e., 2 g/kg, average ingestion rate 1.2 ± 0.1 g/min) and the timing of ingestion (i.e., 2 hr) in the preexercise period was such that the absorption should have been nearly complete, and despite on average a slightly higher intake than the maximum suggested intestinal absorption rate of glucose-based CHOs (i.e., 1 g/min), indifferent blood glucose concentrations and substrate oxidation rates at the first measurement time point during exercise (i.e., 15 min) indicate that exercise was initiated in a similar metabolic state. Thus, the data of the present study indicate that 2 g/kg of CHOs in the form of rice and a glucose powder ingested 2 hr prior to the start of the exercise, can be efficiently metabolized. As the time between the two exercise bouts in this study was likely insufficient for a complete replenishment of either of the CHO stores (Gonzalez & Betts, 2019) especially considering the overnight fast, it remains to be seen if such results could be replicated in conditions when muscle and liver glycogen levels would have been completely replenished on the day before the intervention. However, even small doses of fructose can effectively stimulate liver glycogen synthesis (Casey et al., 2000; Décombaz et al., 2011; Fuchs et al., 2016), and thus it can be hypothesized that even in this case liver glycogen formation would have been affected.
The main limitation of the present study is lack of measurements of liver and muscle glycogen concentrations before and after the dietary intervention, in addition to a more complete blood markers (i.e., hormones and certain metabolites), which would collectively aid to better understanding of the findings in the present study. In addition to this, a more stringent control of the diet leading toward the first experimental trial could have been desired as reporting of dietary intake by study participants is often inaccurate.
Conclusion
This study demonstrates that combining fructose and glucose-based CHOs at breakfast could result in improved endurance exercise capacity of athletes during subsequent exercise. Further studies are required to better understand the underpinning mechanisms and the optimal dose and ratio of fructose–glucose co-ingestion.
Acknowledgments
The authors thank Pia Mušič and Blaž Grmek for their assistance with recruitment and data collection. The study was designed by Podlogar and Gonzalez; data were collected by Cirnski, Podlogar, and Bokal; data were analyzed by Podlogar and Verdel; and data interpretation and manuscript preparation were undertaken by Podlogar, Verdel, and Gonzalez. All authors approved the final version of the manuscript. Podlogar offers consulting services to a sports nutrition brand Nduranz. Gonzalez is an investigator on research grants funded by BBSRC, MRC, British Heart Foundation, Clasado Biosciences, Lucozade Ribena Suntory, ARLA Foods Ingredients and Knowledge center Sugar and Nutrition and has completed paid consultancy for The Dairy Council, PepsiCo, Tour Racing Ltd., and SVGC. This study was funded by Faculty of Health sciences, University of Primorska, Slovenia.
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