Since the early work of Hill and Lupton (23), endurance performance has been associated with a high peak V˙O2. However, although research has consistently shown that success in endurance events is associated with a high peak V˙O2(11,30), peak V˙O2 alone is not a good predictor of endurance performance when athletes of similar endurance ability are compared (10).
It has been suggested that parameters measured during submaximal exercise provide better predictors of endurance performance than peak V˙O2. In particular, the lactate response to incremental exercise appears to be strongly correlated with various types of endurance performance(Table 1). Moreover, when the lactate variable and peak V˙O2 have been correlated with endurance performance, the lactate variable has been more strongly related, both in trained athletes (14) and untrained subjects (42).
Despite the widespread use of blood and plasma lactate concentrations in both assessing performance (see Table 1) and prescribing training intensities (7,25,26), interpretation and application of changes in lactate levels have shown considerable variation. A number of methods are based on the observation that lactate levels change suddenly at some critical work rate and thus reflect a threshold phenomenon (4). For example, some authors consider the lactate threshold to be the work rate at which the lactate concentration first begins to increase above the resting level (42), whereas others have suggested that an increase of 1 mmol·L−1 can be used as an index of endurance ability (38). The Dmax method has been proposed to overcome the disadvantages of visual, subjective determination of thresholds (6). This method involves calculating the point that yields the maximal perpendicular distance from a curve representing work and lactate variables to the line formed by the two end points of the curve. Alternatively, a log-log transformation has been used to assist in the visual detection of the first rise in lactate concentration above resting levels (1). Other researchers have suggested that a fixed lactate level, such as 4 mmol·L−1, can be used as an index of endurance performance (26).
Irrespective of both the controversy surrounding the concept of the "lactate threshold" and of arguments over nomenclature, lactate parameters have proven useful as determinants of endurance performance. What is not clear, however, is which of these lactate parameters best predict 1-h endurance performance. The purpose of this study therefore, was to examine six commonly used lactate parameters and to determine which best predicted 1-h endurance performance in trained, female cyclists.
Subjects. Twenty-four female cyclists and triathletes, mean (± SD) age 29.3 (±9.8) years participated in this investigation. Their mean (± SD) height was 170 ± 34 cm, mass was 59.8 ± 6.3 kg, and peak V˙O2 was 48.1 ± 6.3 mL·kg−1·min−1. After being fully informed of the risks associated with the project, subjects gave their written consent to participate. The testing procedures were approved by the Medical Research Ethics Committee of The University of Queensland.
Experimental overview. Testing was conducted over 7 d and included an incremental test to determine both peak V˙O2 and the lactate parameters and one test to determine OHT performance; 1 wk separated the tests and each was conducted at a similar time of the day. Subjects were asked to maintain their normal diet and training throughout the study and to standardize their exercise routine for the day before each test. They were also instructed to be adequately hydrated and to not have eaten for 3 h before each test.
Peak oxygen uptake. Peak V˙O2 was determined on an electronically braked cycle ergometer (Lode Excalibur Sport, Quinton) using a continuous test modified from that described by Bruce et al. (5). The saddle and handle bar positions of the cycle ergometer were adjusted to resemble the cyclist's own bike and the subjects completed a 5-min warm-up cycling against a resistance of 50 W. The incremental test commenced at an initial workload of 50 W and increments of 25 W were applied at 3-min intervals until exhaustion. During the exercise protocol, expired air was collected each minute in Douglas bags and later analyzed for FEO2 and FECO2 using Ametek gas analyzers(SOV S-3A11 and COV CD3A, Pittsburgh, PA). Ventilation (V˙E) was also recorded every minute using a turbine ventilometer (Morgan, Model 096, Kent, England). The endpoint of work was volitionally determined by the subjects although each was verbally encouraged to continue for as long as possible.
The gas analyzers were calibrated immediately before and after each test using a certified beta gas mixture (Commonwealth Industrial Gas Ltd., Brisbane, Australia); the ventilometer was calibrated pre- and postexercise using a 1-L syringe in accordance with the manufacturer's instructions. In addition to calculating the subjects' peak V˙O2, the peak power output achieved at the end of the incremental test (Wpeak) was also recorded (19).
Lactate analysis. Finger-tip capillary blood (20 μl) was sampled in the last 30 s of each 3-min work bout during the incremental test. Plasma lactate was determined from these samples using reflectance spectrophotometry (Kodak Ektachem DT60, Doncaster, Australia). Six descriptors of lactate increase (Fig. 1, a to f) were calculated using a custom-written program (Labview 3.1.1, National Instruments): 1) LT, the power output at which plasma lactate concentration begins to increase above the resting level during an incremental exercise test (42); 2) LT1, the power output preceding a plasma lactate increase by 1 mmol·L−1 or more (38); 3) LTLOG, the power output at which plasma lactate concentration begins to increase when the log([La−]) is plotted against the log(power output) (1); 4) L4, the power output at which plasma lactate reaches a concentration of 4 mmol·L−1(21); 5) LTD, the lactate threshold calculated by the D-max method (6), identified as the point on the regression curve that yielded the maximal perpendicular distance to the straight line formed by the two end data points; and 6) LTMOD, a modified LTD described by the point on the polynomial regression curve that yielded the maximal perpendicular distance to the straight line formed by the LT and the final lactate point.
Endurance performance. Endurance performance was assessed by measuring the second-by-second and ultimately the average power output(W) produced during the OHT. This test has been shown to be both a valid (13) and reliable (2) measure of endurance performance in cyclists. Exercise was performed on a calibrated, wind-braked cycle ergometer (South Australian Sports Institute, Adelaide, Australia), in controlled environmental conditions (temperature= 19-21°C, relative humidity = 55-65%, and barometric pressure = 760-770 mm Hg). The ergometer was equipped with racing handle bars and seat as well as "aero bars" and the cyclist's own pedals for cleated shoes. The subjects were instructed to generate the highest power output possible throughout the 60 min of cycling. During the initial 8 min of exercise, the power output was preset; subjects cycled at 70% of their Wpeak determined from the incremental test. After the initial 8 min, subjects could vary both pedal cadence and force. They were continually provided with visual feedback of pedaling cadence, power output, HR, and elapsed time.
Statistics. Correlation coefficients between dependent variables were calculated using Pearson's product moment (r). For all tests, α was set at P < 0.05. Friedmans's repeated measures on ranks was used to test for differences between the various lactate parameters and OHT performance.
Mean (± SD) indices of exercise performance are listed in Table 2. Analysis (Table 3) indicated that the six lactate parameters were significantly correlated with each other (0.54 < r < 0.94; P < 0.01). Of the six lactate parameters, LTD correlated best with OHT performance (r = 0.84,P < 0.001). There was also a highly significant relationship between Wpeak and OHT performance (r = 0.81, P < 0.001). Each of the six lactate parameters (0.61 < r < 0.84) and Wpeak (r = 0.81) were more strongly correlated with endurance performance than was peak V˙O2 (r = 0.55, P < 0.01).
Of the six lactate parameters calculated, only LTD and LT1 were not significantly different from the average power output achieved during the OHT. For the majority of subjects(19 of 24), LTD was within 10% of their average power output achieved during the OHT (Fig. 2). The difference between LTD and OHT performance was not significantly related to OHT performance (r = −0.09, P > 0.05). Although LT1 was also not significantly different from the average power output achieved during the OHT, fewer subjects (15 of 24) had their LT1 within 10% of their average power output during the OHT (Fig. 3). It should also be noted that because of its determination method, LT1 was less able to discriminate between subjects; among the 24 subjects there were only five different LT1 recorded, whereas each subject recorded a different LTD.
The major finding of this study is that every lactate parameter calculated in this study was better correlated with average power output during the OHT than was peak V˙O2. This finding is consistent with previous studies reporting various lactate parameters to be more closely related to endurance performance than was peak V˙O2(see Table 1). Of the six lactate parameters compared in this study, LTD was most highly correlated with OHT performance (r = 0.84, P< 0.001). The correlation coefficients for LTMOD (r = 0.83, P< 0.001) and L4 (r = 0.81, P < 0.001) were only slightly lower. This contrasts somewhat with the results of previous studies reporting that LT was better correlated than OBLA with 12-min run performance in 19 untrained females (42) and with marathon performance in 12 well-trained males (36).
This disparity may be partially attributed to differences in blood sampling methods and/or test protocols. The choice of blood sampling site(arterial, venous, or capillary) and the choice of blood media analyzed (plasma, lysed or precipitated whole blood) have been shown to influence the exercise intensity corresponding to a fixed lactate concentration (3,15,40,43) but not to significantly alter LT (29,41). Lactate concentrations(16) and lactate parameters (21) have also been shown to be protocol specific. Therefore, protocol differences between the present study (cycling; increments = 25 W/3 min) and the studies by Yoshida et al. (42) (cycling; increments = 20 W/4 min) and Tanaka and Matsura (36) (running; increments = 20 m·min−1/3 min) may have contributed to the reported differences. Care should therefore be taken when comparisons are made between studies using lactate parameters detected from different blood compartments and/or from different protocols.
Although Tanaka and Matsura (36) reported that marathon performance (range: 2 h 22 min 40 s to 3 h 3 min 32 s) was more strongly correlated with the anaerobic threshold (AT; equivalent to LT in the present study) (r = 0.781; P < 0.01) than OBLA (equivalent to L4 in the present study) (r = 0.682; P< 0.05), their results are similar to those of the present study in that they reported that the lactate parameter that most accurately approximated the performance pace was the best single predictor of endurance performance. That is, the running velocity corresponding to AT (VAT; 4.57 m·s−1) was not significantly different from marathon velocity (VM; 4.49 m·s−1), whereas the running velocity corresponding to OBLA (VOBLA; 5.30 m·s−1) was significantly greater than VM. Similarly, in the present study, LTD(178.79 ± 24.07 W) was not significantly different from average power output during the OHT (183.01 ± 18.88 W) and had the highest correlation with OHT performance. Furthermore, for 19 of 24 cyclists their LTD was within 10% of their average power output achieved during the OHT (Fig. 2). It therefore appears that there may not be one lactate parameter that best predicts endurance performance in all events. For endurance events of different intensity and duration, different lactate parameters may provide a simple method of estimating a pace that does not result in premature fatigue.
Although the power output at LT1 was also not significantly different from the average power output during the OHT, it had a weaker correlation with OHT performance, possibly because it was a less effective discriminator in the homogenous group of subjects recruited for this study. Unlike LTD, which could essentially take on an infinite number of values, LT1 (and LT) could only take the discrete values of the specific work-rate stages. Improving the precision of this estimate may therefore result in a stronger correlation with endurance performance.
In the only other study to directly compare lactate parameters (42), it was reported that LT was a better predictor of 12-min run performance, in untrained females, (r= 0.84; P < 0.01) than LT1 (r = 0.77; P < 0.01), LT = 2 (r = 0.66; P < 0.01), or OBLA (r = 0.80; P < 0.01). It is difficult however, to compare these results with those of the present study, or the study by Tanaka and Matsura (36), because the velocity corresponding to each lactate parameter was not reported. It is likely, however, that 12-min run pace was considerably faster than the pace corresponding to any of the four lactate parameters measured. It therefore remains to be tested whether an alternate lactate parameter, which more closely approximates 12-min run pace, would provide an even better predictor of 12-min run performance. Interpretation of this study is also limited by the fact that the authors correlated lactate parameters determined from a cycling protocol with 12-min run performance. If lactate parameters are related to peripheral factors in the muscles, then it would seem necessary, when predicting performance, to employ a predictive test that employs more precisely the same muscles used in the criterion performance.
The question remains however, as to why lactate parameters should provide a better predictor of endurance performance than peak V˙O2. Although the exact determinants of peak V˙O2 are controversial, several authors have argued that peak V˙O2 is limited by the V˙O2 supply to the muscle mitochondria (31,39). However, although central factors are likely to limit peak V˙O2, it appears that the lactate response to exercise is primarily related to peripheral factors in the trained musculature such as the percentage of slow twitch fibers (14,37), the activities of key oxidative enzymes (33,34), and respiratory capacity (24). Substrate metabolism during submaximal exercise also appears to be primarily determined by peripheral factors, especially the respiratory capacity of the exercising musculature. With a greater respiratory capacity, sensitivity is increased and a given level of respiration can be obtained with lower levels of the postulated effectors such as ADP and inorganic phosphate (Pi) (17). These smaller metabolic alterations are likely to play a major role in accounting for the slower rate of glycogenolysis(8,18,27), resulting in less lactate formation(22) in endurance-trained muscle.
It therefore appears that lactate parameters may be related to a rate of energy expenditure at which muscle cell homeostasis is sufficiently disturbed to markedly stimulate glycogenolysis, resulting in accelerated glycogen utilization and lactate production. The consistently strong relationship between endurance performance and lactate parameters may therefore indicate that lactate parameters provide a simple method of estimating a rate of energy expenditure which does not prematurely deplete glycogen stores. This is supported by the results of a study by Coggan and Coyle (9), which reported that cycling at LT required a remarkably similar rate of muscle glycogenolysis among trained cyclists with the result that these athletes uniformly fatigued after approximately 3 h because of glycogen depletion.
The rate of glycogen utilization that will prematurely deplete glycogen stores will depend on the intensity and duration of the event. If lactate parameters are related to rates of glycogen utilization, then different lactate parameters should be more strongly related to performance in different events. It has been suggested that athletes select a given rate of muscle glycogenolysis that can be maintained during a race (12). The absence of a significant difference between the power output during the OHT and the LTD power output may therefore indicate that LTD provides a simple method of estimating the power output corresponding to a rate of glycogen utilization that can be maintained for 1 h. Events that can be maintained for 2-3 h (e.g., a marathon race) will demand a lower rate of glycogen utilization. This may explain why the LT (which occurs at a lower power output than LTD) has been reported to be strongly correlated with marathon performance (r = 0.78; (36)) but was less strongly correlated with 1-h performance in the present study. LTLOG, which detects the LT with less subjectivity, may also be more strongly correlated with longer duration endurance performance than with the 1-h endurance test employed in this study.
It is difficult to establish a physiological rationale for the strong correlation between OHT performance and both LTMOD (r = 0.83) and L4 (r = 0.81). Both produced threshold estimates that were significantly greater than average power output during the OHT. Furthermore, previous authors have questioned the physiological significance of a fixed lactate value of 4 mmol·L−1 (i.e., L4), which does not take into account the individual kinetics of the lactate concentration curve(12,35). However, the strong correlation between LTD and both LTMOD (r = 0.81) and L4 (r = 0.82) suggests that the strong correlation between OHT performance and both LTMOD and L4 can possibly be attributed to the relationship between these two parameters and LTD, rather than any physiological significance.
The second important finding of this study was the strong relationship between Wpeak and OHT performance in trained female cyclists (r = 0.81, P < 0.001). This is in agreement with previous research utilizing runners(28,32), cyclists (19), and swimmers(20). Consistent with previous studies, the present data also show that Wpeak is a better predictor of endurance performance (r = 0.81,P < 0.001) than peak V˙O2 (r = 0.55, P < 0.01). The strong correlation between Wpeak and LTD (r = 0.81; P< 0.001) may indicate that cyclists who record a high Wpeak do so because they are able to delay the accumulation of lactic acid. This may also explain the strong correlation between Wpeak and OHT performance. These results question the utility of measuring peak V˙O2 when assessing endurance performance, especially when few athletes have access to the expensive analyzers and equipment necessary for measuring peak V˙O2.
The strong correlation between Wpeak and OHT also suggests that the time-consuming and costly analysis of lactate is not necessary for the prediction of 1-h endurance performance. However, lactate parameters have also been suggested as useful for the prescription of training intensities (6,7,25,26,43). Therefore, although it may be simpler and less expensive to use Wpeak for the prediction of endurance performance, lactate parameters may be useful for assigning training intensities.
In summary, this investigation has shown that lactate parameters and Wpeak provide better predictors of endurance performance than peak V˙O2 in trained female cyclists. This has implications for the coach or athlete interested in monitoring the effects of training. Wpeak appears particularly useful as it does not require measurement of lactate or V˙O2 uptake and has similar predictive value for endurance performance as L4, LTD, and LTMOD. Of the six lactate parameters compared in this study, LTD was most highly correlated with OHT performance in trained female cyclists and also provided the best estimate of 1-h race pace. Although previous research has found other lactate parameters to be better predictors of endurance performance (for events of different type, intensity and duration), this may simply indicate different lactate parameters are more appropriate for different endurance events requiring different rates of substrate utilization.
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