University of Cape Town Bioenergetics of Exercise Research Unit, University of Cape Town Medical School, Newlands 7725, South Africa
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ABSTRACT |
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We examined the variability and determinants of the respiratory exchange ratio (RER) at rest and during exercise in 61 trained cyclists. Fasting (10-12 h) RER was measured at rest and during exercise at 25, 50, and 70% of peak power output (Wpeak), during which blood samples were drawn for [lactate] and [free fatty acid] ([FFA]). Before these measurements, training volume, dietary intake and muscle fiber composition, [substrate], and enzyme activities were determined. There was large interindividual variability in resting RER (0.718-0.927) that persisted during exercise of increasing intensity. The major determinants of resting RER included muscle glycogen content, training volume, proportion of type 1 fibers, [FFA] and [lactate], and %dietary fat intake (adjusted r2 = 0.59, P < 0.001). Except for muscle fiber composition, these variables also predicted RER at 25, 50, and 70% Wpeak to different extents. The key determinant at 25% Wpeak was blood-borne [substrate], at 50% was muscle [substrate] and glycolytic enzyme activities, and at 70% was [lactate]. Resting RER was also a significant determinant of RER at 25 (r = 0.60) and 50% (r = 0.44) Wpeak.
muscle enzyme activities; muscle glycogen and triglyceride content; training volume; dietary intake
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INTRODUCTION |
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IT HAS LONG BEEN RECOGNIZED that both dietary fat and carbohydrate (CHO) serve as substrates for energy metabolism in exercising subjects, and that the relative contribution of these two substrates to power production can be influenced by factors such as the preexercise diet (3, 5, 38, 39), training status (3, 6, 8, 28), exercise intensity (3, 6, 37), and the relative hormonal milieu during exercise (17).
However, there also appears to be individual variability in substrate
oxidation during exercise in both untrained (22, 43) and
trained (5, 36) subjects. This variability in substrate utilization during exercise may be related to differences in skeletal muscle characteristics. Indeed, Wade et al. (43) found
that the respiratory exchange ratio (RER) during mild exercise (100 W)
in fed subjects was inversely associated with the proportion of type I
(slow-twitch) muscle fibers. More recently, Geerling et al.
(18) and Helge et al. (22) were unable to
corroborate these findings in untrained subjects exercising at the same
relative exercise intensity [55% relative exercise intensity
(O2 max)] in the fasted state.
Moreover, Zurlo et al. (47) also failed to demonstrate a
relationship between 24-h RER (24hRER) and muscle fiber composition in
a small sample of nonobese, sedentary men and women. However, they
did demonstrate an inverse correlation between 24hRER and muscle
-hydroxyacyl-CoA dehydrogenase activity, even after adjusting for
age, sex, and %body fat [r =
0.70,
P = 0.005 (47)].
Further studies examining the variability and determinants of substrate utilization were undertaken in untrained, often obese, subjects fed a standardized diet in a metabolic chamber. In a recent study of 71 healthy siblings from 31 families, Toubro et al. (42) found that a substantial part of the interindividual variation in 24hRER could be explained by age, gender, energy balance, current dietary intake, and fasting plasma insulin and free fatty acid (FFA) concentrations. However, they also found that there was a strong familial resemblance in 24hRER, which had previously been shown by Zurlo et al. (46).
However, there are no studies, of which we are aware, that have comprehensively examined the variability and possible determinants of substrate utilization both at rest and during exercise in trained athletes eating their habitual diets. Variability in substrate utilization in trained athletes has only been examined indirectly, as demonstrated by two recent studies from this laboratory (5, 36). These studies demonstrated that not all trained cyclists could increase the contribution of fat to oxidative metabolism during prolonged exercise trials associated with muscle glycogen depletion (5, 36). The cyclists who did not increase their relative rates of fat oxidation during prolonged steady-state exercise had higher starting and overall rates of CHO oxidation and fatigued earlier than those cyclists who had higher relative rates of fat oxidation (and lower rates of CHO oxidation).
Therefore, the aim of the present study was, first, to examine the variability in fasting whole body substrate utilization (RER) at rest and during steady-state exercise at different intensities in a group of cyclists with above-average performance ability. Second, this study aimed to identify relationships between substrate utilization at rest and during exercise, as well as possible determinants of RER, including skeletal muscle characteristics, training, dietary intake, and body composition.
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METHODS |
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Subject Selection and Sampling
Forty-five male and sixteen female endurance-trained cyclists participated in this investigation, which was approved by the Research and Ethics Committee of the Faculty of Health Science of the University of Cape Town. To participate in the study, subjects had to: 1) have completed a local 104-km cycle race in <3.5 h for men and in <4 h for women and 2) have no known metabolic conditions that may have adversely affected intermediary metabolism (e.g., diabetes, thyroid hormone disorder, hyperlipidemia). Subjects were not taking any medications for chronic conditions such as high blood pressure (e.g.,Females were used in the study to characterize any differences in substrate utilization at rest and during exercise between males and females. Because of time and logistical constraints, we were unable to control for the menstrual cycle in the female subjects.
Preliminary Testing
Anthropometry. Anthropometrical measurements, including the sum of seven skinfolds (biceps, triceps, subscapular, suprailiac, abdomen, thigh, and calf), waist and hip circumference, and muscle mass, were determined. Percentage body fat was estimated using the equations of Durnin and Womersley (13).
Peak power output and peak oxygen consumption.
Peak oxygen consumption (O2 peak) and
sustained peak power output (Wpeak) were measured on an
electronically braked cycle ergometer (Lode, Groningen, Holland)
modified with toe clips and racing handle bars, as described previously
by Hawley and Noakes (21). Work rates were started at 3.33 W/kg body mass for men and 150 W for women. After 150 s, the
workload was increased by 50 W and then by 25 W every 150 s until
the subjects were exhausted. Exhaustion was defined as a >10%
reduction in pedaling frequency, or an RER of >1.10, or both.
Wpeak was defined as the highest exercise intensity the
subjects completed for 150 s, in W, plus the fraction of time
spent in the final work rate multiplied by 25 W. The Wpeak
values were used to determine the relative workloads of the subsequent
experimental tests.
Training history. A detailed retrospective training history was obtained from each subject. In addition, the subjects completed a training diary during the 2 wk preceding the experimental trial. Training was quantified according to intensity and duration in metabolic equivalents (METS, per week).
Dietary analysis. Three days before the experimental trial, the subjects completed a weighed dietary record. The dietary records were analyzed with the Food Finder program (Medtech, Medical Research Council, Tygerberg, South Africa) to determine the subjects' energy intake and macronutrient consumption.
Experimental Trial
Muscle fiber type, substrate content, and enzyme activities.
The day before the experimental trial, the subjects were instructed to
train routinely for a duration not longer than 1 h before 1800. On
the subsequent day, the subjects reported to the laboratory after a 10- to 12-h overnight fast. Resting muscle biopsies were taken from the
vastus lateralis muscle of 56 of the subjects by means of the
percutaneous needle biopsy technique. A portion of the muscle sample
was frozen rapidly in liquid N2 and stored at 80°C for
subsequent analysis of glycogen and triglyceride content and enzyme
activities, including the activities of carnitine acyltransferase
(CAT), citrate synthase (CS), phosphofructokinase (PFK),
hexokinase (HK), 3-hydroxyacyl-CoA dehydrogenase (3-HAD). The
remaining sample was oriented and imbedded in Tissue-Tek (Miles Laboratories, Naperville, IL), frozen in liquid nitrogen-cooled n-pentane (Saarchem, Muldersdrift, South Africa), and stored
at
20°C for subsequent fiber type determination by the myofibrillar ATPase (mATPase) method. Briefly, serial sections (10 µm for pH 9.4 and 20 µm for pH 4.3 and 4.6) were cut in a cryostat at
20°C. Adjacent muscle sections were assayed for mATPase at pH 9.4 after acidic (pH 4.3 or 4.6) and alkaline (pH 10.4) preincubation to identify
the three major fiber types (12).
Steady-state RER.
After the muscle biopsy, the subjects rested for 30 min and/or until
their heart rate returned to prebiopsy levels.
O2,
CO2,
and RER were then measured for 15 min at rest and during a steady-state
cycle ride at 25, 50, and 70% of Wpeak, respectively, in
which a pedaling frequency of 90 rpm was maintained. These workloads
corresponded to 41, 63, and 80%
O2 peak, respectively. Gas exchange
measurements were recorded for 5 min at rest and at each exercise
workload, after a 10-min "stabilization period," as described above.
Blood sampling and analysis.
Blood samples (~6 ml) were drawn from a forearm vein at rest and
during the last minute of each workload. One aliquot (3 ml) was placed
in a tube containing potassium oxalate and sodium fluoride for
subsequent analysis of plasma lactate concentrations. The remaining
aliquot (3 ml) was placed in a tube containing gel and clot activator
for determinations of serum (nonesterified) FFA concentrations. All
samples were kept on ice until centrifuged at 3,000 rpm at 4°C for 10 min upon completion of the trial. The plasma and serum were then stored
at 20°C for later analyses.
Statistical Analysis.
Bivariate correlations were used to explore the relationships between steady-state RER, at rest and during exercise, and physiological characteristics, muscle characteristics, training, and diet. These variables were then used in multivariate analysis, by use of backward stepwise regression, to determine the model which best predicted RER at rest and during exercise at different intensities. A repeated-measures ANOVA was used to investigate differences in RER between males and females. All results are presented as means ± SD, and an ![]() |
RESULTS |
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Subject Characteristics
The subject characteristics are presented in Table 1. Although the subjects performed at similar levels (completing a 104-km cycle race in the top 20% of the field), there was a large intersubject variation in body composition and physical work capacity (Table 1). The subjects' reported energy and macronutrient intakes for the 3 days preceding the experimental trial are also presented in Table 1. As there were no differences between men and women for RER at rest and during exercise at 25, 50, and 70% of Wpeak (Fig. 1), all data were combined for all analyses performed in this study.
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Variability in RER
Resting RER ranged from 0.927 to 0.718, which corresponds to a relative rate of fat oxidation of 23 to 93% (Fig. 2A). As the exercise intensity increased, there was a corresponding increase in mean RER (Fig. 2B). Mean RER increased from 0.817 ± 0.051 at rest to 0.862 ± 0.037, 0.900 ± 0.040, and 0.976 ± 0.043 at 25, 50, and 70% Wpeak, respectively. However, the variability in RER persisted during exercise at all intensities, with RER during submaximal exercise (50% Wpeak) ranging from 0.818 to 0.983 (Fig. 2B).
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Correlations with RER at Rest and During Exercise
Bivariate analysis was used to describe the relationships between RER, at rest and during exercise, and different physiological and metabolic parameters (Table 2). Variables that were positively correlated with resting RER were the proportion of type I muscle fibers, resting muscle glycogen content, and plasma lactate concentrations. The proportion of type IIa muscle fibers, serum FFA concentrations, and dietary fat intake were negatively associated with resting RER. Resting RER was also significantly correlated with RER at both 25 and 50% Wpeak (r = 0.60, P < 0.0001, and r = 0.44, P < 0.0001, respectively) but not at 70% Wpeak (r = 0.17, not significant).
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At 25% Wpeak, muscle fiber composition and glycogen content were not correlated with RER. However, serum FFA and plasma lactate concentrations, dietary fat intake, and training volume were significantly correlated to RER. At 50% Wpeak, serum FFA and plasma lactate concentrations, dietary fat intake, and training volume were significantly correlated with RER (Table 2). In addition, muscle triglyceride content and CS activity were negatively correlated with exercising RER, whereas the PFK-to-CS and HK-to-CS ratios (PFK/CS and HK/CS), both representing glycolytic flux, were positively correlated with RER at 50% Wpeak. At 70% Wpeak, only plasma lactate concentrations, HK/CS and PFK/CS, and training volume were significantly associated with RER (Table 2).
Multivariate Analysis for the Determination of RER at Rest
The variables, proportion of type I muscle fibers, muscle glycogen content, training volume, dietary fat intake, and resting serum FFA and plasma lactate concentrations were included in the model that accounted for 59% of the variance in resting RER [P < 0.0001, standard error of the estimate (SEE) = 0.035, Table 3]. The proportion of type I muscle fibers was positively associated with resting RER and was the most important determinant of RER in this equation.
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In addition to the muscle fiber composition, muscle glycogen content
was also an important determinant of resting RER in this equation.
Training volume, resting FFA concentration, and dietary fat intake were
less well correlated, but still significant determinants of resting
RER. Muscle enzyme activities, muscle triglyceride content, percent
body fat, and O2 peak were not
significant predictors of RER at rest and were therefore not included
in the model predicting RER at rest.
Determinants of RER During Low- to Moderate-Intensity Exercise
The statistical model that best predicted RER at 25% Wpeak still included muscle glycogen and training volume as important determinants but did not include muscle fiber type. Rather, muscle CS activity, serum FFA concentrations, and dietary fat intake contributed to the model significantly. When these variables were used in the equation, 45% of the variance in RER at 25% Wpeak was accounted for (P < 0.0001, SEE = 0.029, Table 4). However, when resting RER was used as a variable in the equation, the adjusted R2 increased to 0.575 (P < 0.0001, SEE = 0.029, Table 4). In this model, training volume and muscle glycogen content became less important determinants of RER at 25% Wpeak, whereas the relative importance of the muscle enzymes, including CS and PFK activities, increased.
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Although the intensity of the exercise bout doubled from 25 to 50%
Wpeak, the variables found to best predict RER were similar (Table 5). However, at this higher
exercise intensity, muscle triglyceride content became a significant
predictor of RER, whereas circulating FFA concentrations contributed
less to the model than at 25% Wpeak. This model accounted
for 42% of the variance in RER at 50% Wpeak
(P < 0.0001, SEE = 0.031, Table 5).
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When resting RER was used as a variable in the model to predict RER at
50% Wpeak, the fit of the model improved (adjusted R2 = 0.50, SEE = 0.029, P < 0.0001; Table 5). Other variables in the equation
included the proportion of type I muscle fibers, muscle triglyceride
content, and, less importantly, circulating plasma lactate
concentrations. Interestingly, the proportion of type I muscle fibers
was a better predictor of RER at 50% than the proportion of type IIa
muscle fibers (b weighting = 0.47 and
0.16 for the proportions
of type I and IIa fibers, respectively).
Determinants of RER During High-Intensity Exercise
During high-intensity exercise (70% Wpeak), circulating plasma lactate concentration contributed significantly to the model predicting RER (Table 6). This model, which also included the variables of dietary fat intake, training volume, muscle glycogen content, and circulating FFA concentrations, accounted for 56% of the variance in RER (P < 0.0001, SEE = 0.031, Table 6). Dietary fat intake and training were both negatively associated with RER and contributed equally to the model, whereas muscle glycogen content was positively associated with RER but contributed less.
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Possible Confounding Variables
There are a number of variables that were associated with, and may have confounded, the interpretation of the relationships with RER described above. Age was significantly correlated with percentage body fat and
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DISCUSSION |
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Variability in Fasting RER at Rest and During Short-Term Steady-State Exercise
The first major finding of this study was the large variability in RER at rest and during exercise at different intensities in endurance-trained athletes. The relative rate of fat oxidation at rest ranged from 23 to 93%, which is nearly a fourfold difference in fat oxidation in these athletes with above-average performance ability (Fig. 2A). Similar variability in resting RER or 24hRER has been previously demonstrated (0.771-0.931) in untrained subjects (33, 45-47). More recently, Helge et al. (22) reported a large variability in RER (0.83-0.95) during low-intensity steady-state exercise. However, to our knowledge, this is the first study that has described the large variability in substrate utilization at rest and during exercise in endurance-trained athletes with above-average performance ability. Furthermore, we are not aware of any other study demonstrating that this variability in resting RER persists during short-term low-, moderate-, and high-intensity exercise.A second important finding of this study, not previously reported, was the normal distribution of RER at rest that was maintained during exercise of increasing intensity (Fig. 2B). The normal distribution was found even at the relatively high exercise intensity of 70% Wpeak.
However, we cannot rule out the possible influence of the muscle biopsy
procedure itself on respiratory measures and substrate oxidation. There
are limited data suggesting that the concentrations of stress hormones
such as cortisol and norepinephrine may remain elevated in plasma for
20 min after biopsy (22, 24). However, in these studies,
the hormonal changes were not directly associated with changes in
fasting plasma metabolite concentrations (22, 24).
Furthermore, our measurements took place at a minimum of 40 min
postbiopsy and were conducted in the entire group, thereby minimizing
possible bias.
Determinants of Fasting RER at Rest
The determinants of resting RER included the proportion of type I muscle fibers, muscle glycogen content, dietary fat intake, training, and blood metabolites (plasma lactate and serum FFA concentrations), all of which accounted for ~56% of the variance in RER (Table 3). The positive association with type I (slow-twitch) muscle fibers and negative association with type IIa (fast-oxidative) muscle fibers and RER was unexpected. Type I muscle fibers more often use fatty acids as a fuel source, because they have a high oxidative capacity due to a high mitochondrial and capillary density (26). Moreover, Wade et al. (43) found that RER, measured during mild exercise at an absolute intensity of 100 W in 11 sedentary male subjects, was inversely related to the proportion of type I muscle fibers. However, Zurlo et al. (47), and more recently, Helge et al. (22) found no association between muscle fiber composition and RER in untrained subjects.We cannot fully explain the reasons for the positive association between the proportion of type I muscle fibers and resting RER in this study. A possible explanation may be the coexistence of myosin heavy-chain isoforms for types I and IIa fibers found in endurance-trained subjects but not in sedentary individuals (31). If there is a large coexistence of type I and type IIa isoforms in the muscle samples, as expected in these trained cyclists, the conventional mATPase techniques for fiber typing may react histochemically as type I fibers, overestimating the proportion of type I fibers. However, we cannot exclude the possibility that the relationship between muscle fiber type and RER may be influenced by other factor/s not identified in this study.
Association Between RER and Body Composition
We found no association between fasting RER at rest and body composition as demonstrated by Zurlo et al. (46) and Weyer et al. (45) in untrained subjects measured using underwater weighing and total body dual-energy X-ray absorptiometry, respectively (Table 2). The lack of association between RER and body composition may be due to the fact that our subjects were leaner and had a smaller range of body fat (9-30%) compared with the subjects in the studies of Zurlo et al. (46) (5-50%) and Weyer et al. (45) (3-53%). We also did not show any association between exercising RER and body composition, previously described by Wade et al. (43) (r = 0.538, P < 0.001) in 50 male subjects with a range in body fat similar to that in our study (~4-28%). Their subjects were, however, untrained and were not fasted when RER was measured during mild exercise at the same absolute, but not relative, exercise intensity (100 W). In agreement with our findings, both Helge et al. (22) and Geerling et al. (18), who replicated the study of Wade et al. (43), failed to find a relationship between exercising RER and body fatness in untrained, healthy male subjects.In our study and previous studies (1, 42) there is a consistent relationship between circulating FFA concentrations and RER (Tables 2 and 3). Havel et al. (20) demonstrated that most of the energy supplied to skeletal muscle at rest is derived from plasma FFAs. Furthermore, plasma FFA turnover is mainly controlled by substrate availability at rest (4) and is highly dependent on recent dietary intake (29). Therefore, preceding dietary intake can alter fuel utilization by modifying substrate availability (7). This relationship between dietary fat intake in the 3 days preceding the measurement of RER was demonstrated in the present study (Tables 2 and 3) and the study of Toubro et al. (42).
Determinants of Fasting RER During Short-Term Steady-State Exercise
Another important finding of this study was that the major determinants of fasting RER at rest were different from those during exercise. Furthermore, as the intensity of the exercise increased, the variables used in the models to predict RER at the different exercise intensities changed, or their relative contribution to the model changed. At rest, muscle fiber composition was the most important determinant of fasting RER, but it did not contribute significantly to exercising RER. During low-intensity exercise (25% Wpeak), blood-borne substrate concentrations were important determinants of RER, whereas during moderate-intensity exercise (50% Wpeak), muscle substrate concentrations and enzyme activities became more important variables in predicting substrate utilization. At high-exercise intensities (70% Wpeak), plasma lactate concentration was the most significant determinant of RER.Muscle glycogen content was an important determinant of both resting
and exercising substrate utilization in the fasted state. What may
appear surprising is that this association was present at rest and
during low-intensity exercise (25% Wpeak) (Tables 3 and
4). Romijn et al. (37) found that, at 25%
O2 max in trained subjects in the
fasted state, the majority of energy was provided by plasma FFA and
glucose, with little or no reliance on muscle glycogen. However,
Friedlander and co-workers (15, 16) found that CHO
accounted for >50% of total energy expenditure during exercise at
45%
O2 max in untrained male and
female subjects tested 2-3 h after they ingested a standardized
meal (448 kcal) containing 80 g of CHO. It therefore appears that, irrespective of the exercise intensity, substrate oxidation may be
regulated by substrate availability. Indeed, previous studies (5,
44) have shown that muscle glycogen utilization is determined, in part, by muscle glycogen content at the start of exercise.
Training volume was also a major determinant of RER, both at rest and during exercise (Tables 3-6). This is confirmed by numerous studies (3, 8, 28) that have demonstrated higher rates of fat oxidation (and lower rates of CHO oxidation) in trained vs. untrained subjects, even during exercise at the same relative intensity.
As mentioned previously, blood-borne substrate concentrations,
particularly serum FFA concentrations, were important determinants of
RER during low-intensity exercise (25% Wpeak). However, as the exercise intensity increased to 50% Wpeak, the
relative weighting of serum FFA concentration decreased, and muscle
triglyceride content became a more important determinant of RER (Tables
4 and 5). These findings are consistent with those of Romijn et al.
(37) who found that peripheral lipolysis (and presumably oxidation) was higher during low-intensity exercise (25%
O2 max) and declined progressively as
the exercise intensity increased. Moreover, they showed that
intramuscular triglyceride content, measured indirectly with RER and
stable isotope tracers, did not contribute significantly to energy
production at 25%
O2 max (10% of
total fat oxidized), whereas at 65%
O2 max, intramuscular triglyceride
content accounted for 50% of the total fat oxidized.
Not only were intramuscular substrate concentrations important
determinants of RER during exercise, but muscle enzyme activities contributed significantly to the model predicting RER, especially during moderate-intensity exercise (50% Wpeak). CS
activity was inversely correlated with RER at 50% Wpeak
(r = 0.30, P = 0.026, Table 2) and
contributed significantly to the models that best predicted RER at 25 and 50% of Wpeak (Tables 4 and 5). Both Zurlo et al.
(47) and Helge et al. (22) failed to
demonstrate this association, most likely because their subjects were
untrained and had lower CS activities with a smaller range of values
than our subjects. Rather, Zurlo et al. demonstrated an inverse
relationship between 24hRER and 3-HAD activity (r = 0.75,
P = 0.002, Ref. 38) that was not supported
by our data (r = 0.008, P = 0.952). This
discrepancy in findings cannot be explained by differences in the range
of values but may relate to methodological differences or to possible
confounding factors outlined in Table 7.
We also demonstrated a positive relationship between RER and HK/CS and PFK/CS during moderate-intensity exercise (Table 2). These ratios provide an indication of glycolytic flux, which may attenuate fatty acid oxidation (39), possibly by controlling the rate of fatty acid transport into the mitochondria (38).
During high-intensity exercise (70% Wpeak), plasma lactate concentration was the most important determinant of RER in the regression equation (Table 6). During exercise of this intensity, CHO oxidation and muscle glycogen utilization are increased, with CHO providing more than two-thirds of the energy needed for exercise (for review, see Ref. 9). This results in accelerated rates of plasma lactate production, which may limit lipolysis (27). Moreover, during high-intensity exercise, FFA appearance in the plasma may also be decreased by entrapment of FFA within adipose tissue because of decreased blood flow (23).
Resting RER as a Determinant of Exercising RER
In this study we also found that resting RER, independent of all other factors, was a good predictor of RER during low- and moderate-intensity exercise [r = 0.60 and r = 0.44 at 25 and 50% Wpeak (P < 0.0001), respectively]. Therefore, as exercise intensity increases from rest to exercise of increasing intensity, the short-term exercise RER of individuals with low resting RERs will increase but remain relatively low compared with those of individuals with high resting RERs, which will increase even further. When included in the regression equation to predict RER during both low- and moderate-exercise intensities, resting RER improved the fit of the models (Tables 4 and 5). This has important implications for exercise metabolism research because, first, it provides a useful tool to easily estimate relative substrate utilization in athletes without requiring that the athletes be studied during exercise. Second, in studies in which substrate utilization during exercise is under investigation, it may be important to consider the initial, individual fasting RER, because it may impact on the interpretation of these studies.Gender Differences
In the present study, there was no difference in fasting RER between the male and female subjects at rest or during exercise (Fig. 1). When gender was forced into the model as a variable to predict RER at rest and during exercise, it did not contribute significantly to the model or alter its interpretation. These findings are contrary to the findings of Horton et al. (25), Friedlander and colleagues (14, 16), and Tarnopolsky et al. (41), who found that women tested in the midfollicular phase of the menstrual cycle tended to oxidize more fat during exercise at the same relative intensity than men. Although there was no significant difference in average training per day between the male and female subjects in the present study (13.5 vs. 14.6 METS/day, respectively), there was a significant difference in their dietary intake. Both energy intake and %dietary fat intake were significantly higher, and %CHO intake was significantly lower, in the male subjects than in the female subjects (12,642 ± 3,247 vs. 8,377 ± 2,654 kJ, 32.6 ± 6.5 vs. 27.0 ± 9.0% fat, and 45.6 ± 6.9 vs. 53.4 ± 9.2% CHO, for males and females, respectively, P < 0.01). In contrast, in the studies of Tarnopolsky et al. (41) and Horton et al. (25), the dietary intake of the subjects was controlled for 2-3 days before testing. Although this discrepancy in CHO intake did not result in differences in muscle glycogen content of the male and female subjects (427 ± 126 vs. 436 ± 116 mmol/kg dry wt, respectively), it may have masked any potential gender differences in substrate utilization. Indeed, the female subjects had a significantly higher proportion of type I (slow-oxidative) muscle fibers than the male subjects (58.4 ± 10.0 vs. 49.6 ± 12.7%, respectively, P = 0.03), as previously shown (34). However, these data need to be interpreted with caution because of the small number of females in this study.Furthermore, the phase of the menstrual cycle was not controlled in the female subjects, which may have influenced the interpretation of the RER measurements. However, the results of existing research, examining whether the cyclic fluctuations of female sex hormones affect substrate utilization during exercise, are inconclusive (11, 19, 29). Moreover, Kanaley et al. (29) found that amenorrhea did not influence substrate utilization during prolonged exercise, compared with eumenorrheic athletes tested in the early and late follicular phases and midluteal phase of their menstrual cycle.
In the present study, we found a large variability in substrate utilization in a group of trained cyclists who have above-average performance ability. However, it is not known whether this variability in RER, measured during short-term steady-state exercise, will persist during more prolonged exercise, in the fed state, under conditions of low glycogen availability, or when CHO is ingested during exercise, for example.
The major determinants of this variability and/or their relative contribution to substrate utilization differed at rest and during exercise of different intensities. However, training, dietary intake, and the consequent muscle glycogen content and circulating substrates consistently predicted RER at rest and during exercise in this study. These findings suggest that, by manipulating training and/or dietary intake, an athlete's ability to alter substrate utilization during steady-state exercise may be modified. Furthermore, resting RER was a significant independent determinant of RER at low and moderate intensities. This finding may have important implications for metabolic research, both as a tool for estimating relative substrate utilization and in interpreting results of studies examining relative substrate utilization in response to various stimuli; however, further investigations are warranted.
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ACKNOWLEDGEMENTS |
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We thank all the subjects who so willingly took part in this study. We are grateful to Judy Belonje for expert technical assistance.
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FOOTNOTES |
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This study was funded by the National Research Foundation of South Africa, the Medical Research Council of South Africa, the Nellie Atkinson and Harry Crossley Staff Research Funds of the University of Cape Town, and Bromor Foods. Additional funding was received from the Technology and Human Resources for Industry Programme.
Address for reprint requests and other correspondence: J. H. Goedecke, MRC/UCT Bioenergetics of Exercise Research Unit, Univ. of Cape Town Medical School, Sports Science Institute of South Africa, PO Box 115, Newlands 7725, South Africa (E-mail: juliag{at}sports.uct.ac.za).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 27 January 2000; accepted in final form 10 August 2000.
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