Comparison of calculated and experimental power in maximal lactate-steady state during cycling
© Hauser et al.; licensee BioMed Central Ltd. 2014
Received: 5 February 2014
Accepted: 16 May 2014
Published: 27 May 2014
The purpose of this study was the comparison of the calculated (MLSSC) and experimental power (MLSSE) in maximal lactate steady-state (MLSS) during cycling.
13 male subjects (24.2 ± 4.76 years, 72.9 ± 6.9 kg, 178.5 ± 5.9 cm, : 60.4 ± 8.6 ml min−1 kg−1, : 0.9 ± 0.19 mmol l-1 s-1) performed a ramp-test for determining the and a 15 s sprint-test for measuring the maximal glycolytic rate ( ). All tests were performed on a Lode-Cycle-Ergometer. and were used to calculate MLSSC. For the determination of MLSSE several 30 min constant load tests were performed. MLSSE was defined as the highest workload that can be maintained without an increase of blood-lactate-concentration (BLC) of more than 0.05 mmol l−1 min−1 during the last 20 min. Power in following constant-load test was set higher or lower depending on BLC.
MLSSE and MLSSC were measured respectively at 217 ± 51 W and 229 ± 47 W, while mean difference was −12 ± 20 W. Orthogonal regression was calculated with r = 0.92 (p < 0.001).
The difference of 12 W can be explained by the biological variability of and . The knowledge of both parameters, as well as their individual influence on MLSS, could be important for establishing training recommendations, which could lead to either an improvement in or by performing high intensity or low intensity exercise training, respectively. Furthermore the validity of -test should be focused in further studies.
KeywordsMaximal lactate-steady-state Calculation Lactate-production rate Elimination of lactate
Over the last 35 years, incremental graded exercise tests have been established for detecting endurance performance on the basis of a lactate-performance curve and the application of several different lactate-threshold concepts . Most of these lactate concepts have the aim to approximate the power output achieved at maximal lactate-steady-state (PMLSS), which is one criterion of endurance performance [1, 2]. PMLSS is defined as the highest workload where lactate-formation and lactate-elimination in the muscle cell are maintained at a steady-state [2–4]. However, Hauser et al.  compared the power at "onset of blood lactate accumulation" (OBLA) [6, 7], the "individual anaerobic threshold" (IAT)  and the " + 1.5 mmol·l−1 lactate model"  with power in MLSS, measured during 30-minutes constant load tests. They found high significant correlations between OBLA and MLSS: r = 0.89 (mean difference −7.4 W); IAT and MLSS: r = 0.83 (mean difference 12.4 W), +1.5 mmol·l−1 lactate model and MLSS: r = 0.88 (mean difference −37.4 W). However, based on Bland-and-Altman, the comparison of power of all threshold-concepts with power in MLSS showed large individual differences, which deceive the high regression coefficients and small mean differences between these methods.
Furthermore, it is problematical that lactate-threshold concepts are based solely on the blood-lactate-concentration (BLC), which is mainly influenced by lactate formation, −transport, −diffusion and -elimination. Therefore, BLC may not represent the true metabolic processes occurring within the muscle cell. Mader [10, 11] and Bleicher et al.  have previously suggested that the same lactate-performance-curve may result from different combinations of maximal oxygen uptake ( ) and maximal lactate production rate ( ). Furthermore, the shift of a lactate-performance curve could also be achieved by changing or separately.
Indeed, Bleicher et al.  verified, that two different athletes, (soccer and track), had exactly the same velocity for onset of blood lactate accumulation (OBLA) of 4.4 m s−1, yet the individual parameters of and were higher for the soccer player when compared to the track athlete ( : 70 vs. 63 ml min−1 kg−1; 0.93 vs. 0.65 mmol l−1 s−1, respectively). That confirms, therefore that identical MLSS could be originate by completely different combinations of - and -values. Using either , or BLC alone, it is not possible to explain differences of PMLSS between two athletes or the effects of training on the MLSS. As such, it would be beneficial to understand, how MLSS is controlled by glycolysis and oxidative phosphorylation within the muscle cell.
The present study, therefore, hypothesised firstly that it would be possible to calculate the PMLSS using the method by Mader and Heck  and secondly that knowledge of and (and their interaction) would help to better understand the mechanisms of the MLSS. These outcomes could provide a benefit compared to lactate-threshold concepts and to time-extensive 30 min constant-load tests.
13 male subjects (age: 24.2 ± 4.76 yr, weight: 72.9 ± 6.9 kg, height: 178.5 ± 5.9 cm, : 60.4 ± 8.6 ml·min−1·kg−1, : 0.9 ± 0.19 mmol l-1 s-1) with different endurance levels participated in this study (training volume: n = 4 between 10 and 14 hours/week, n = 7 from 2 to 8 hours/week, n = 2 no sport). All subjects were informed about the aims of the study and subsequently provided written consent in accordance with the declaration of Helsinki .
All tests were performed on a Lode Excalibur Sport Ergometer (Lode, Groningen, NL). At the beginning of this investigation subjects performed, in a random order, a test for detecting the maximal glycolytic rate and a test for detecting the maximal aerobic performance. Using the method introduced by Mader and Heck , PMLSSC was calculated on the basis of the individual , and body weight. PMLSSC was used for the first constant-load-test and several 30 min constant load-tests were undertaken to detect PMLSSE. Each test was performed on different days.
Equation 1: Calculation of maximal glycolytic rate.
Abbreviations are as follows: LamaxPost = Maximal Post Exercise Bloodlactate, LaPre = Bloodlactate before test, ttest = test duration = 15 sec, talac = alactic time interval
The alactic time interval (talac) was defined as the time from the beginning of the sprint (0 sec) to when the maximum power decreases by 3.5%.
Subjects performed a ramp-test for measuring breath-by-breath (Oxycon Pro, Jäger, Höchberg, Germany) which included a warm up of 10 minutes at a constant load corresponding to 1.5 times of the participant’s body-weight, followed by a period of 2 min at a constant load of 50 W. The workload at the beginning of the test was set to 50 W for 2 min and was increased by 25 W every 30 s. The test was finished when subjects reached physically exhaustion, complaints of shortness of breath, dizziness or other physical complaints that unabled them proceeding the test . was calculated by the mean of all -values measured within the last 30s of the test.
Calculation of PMLSSC
Step 1: Biochemical elementary background
Equation 2: Elementary equation of Michaels-Menten-kinetics, where activation of an enzyme-substrate-complex ( ) depends on maximal performance ( ), 50%-activity-constant (KM) and substrate (S).
It is mostly agreed that under nomoxic conditions the main regulating substrate (S) for the activation of and is the level of free ADP concentration [3, 11, 16, 17]. With an increase of the workload and therefore a higher demand of ATP, ADP-concentration rises exponentially within the muscle towards and .
Step 2: Activation of oxidative phosphorylation ( )
Equation 3: Transformed equation of Michaels-Menten-kinetics to calculate the activation of oxidative phosphorylation ( ) – depending on maximal oxygen uptake ( ), 50%-activity-constant (Ks1) and substrate (ADP).
Step 3: Activation of glycolysis ( )
Step 4: Calculation of Lactate-elimination-rate depending on
Equation 5: Calculation of maximal lactate elimination rate ( ) – depending on lactate equivalent, lactate distribution volume and activity of oxidative phosphorylation.
However, there is no simple procedure to measure ADP-concentration and thus the activity rates of and in a daily endurance performance analysis. For an application of the model as a tool of endurance performance testing, and must be calculated without measuring the free ADP-concentration. This is possible when the mentioned equations are transposed from ADP in depended equations.
Step 5: Transformation from ADP depended equations into depended equations
Equation 6: Calculation for the activity of oxidative phosphorylation ( ) as dictated by workload (P) and bodyweight.
Equation 7: Calculation of free ADP-concentration with respect to activated oxidative phosphorylation ( ) and maximal oxygen uptake ( ).
Equation 8: Calculation of glycolysis activity with respect to activated oxidative phosphorylation (VO2ss) and maximal glycolytic rate ( ).
Furthermore, can also be calculated as demonstrated in Equation 5.
Step 6: Calculation of PMLSSC depending on
Equation 9: Calculation in the activity of glycolysis with respect to the activation oxidative phosphorylation ( ) and maximal glycolytic rate ( ).
Equation 10: Calculation of power in MLSS (PMLSSC) depending on the activity of oxidative phosphorylation ( ), bodyweight and oxygen/workload-constant (Ks4).
Therefore the relation between and power expressed as Ks4 must be known. In the present paper Ks4 was set to a constant value of 11.7 O2/W .
Constant load tests
Subjects performed at least two 30 min constant load exercise tests at a cadence of 70–80 rpm for determination the PMLSSE. The first constant-load test according to PMLSSC started after a warm-up of 3 minutes at a power corresponding to 60% of the PMLSSC rate. Blood samples were taken during rest, after 4 and 8 min, and at subsequent 2 min intervals until the end of the test. The PMLSSE was defined as the highest workload that can be maintained without an increase of blood-lactate-concentration of more than 0.05 mmol·l−1·min−1 during the last 20 minutes of the test. Depending on blood-lactate-concentration, power in the next constant load test was set higher or lower by 10 W.
All data were analyzed using the software SPSS version 14. Descriptive statistics were calculated from the data (means, standard deviations (SD), minimum and maximum values). Normal distribution was verified using the Shapiro-Wilk-Test. Relationship between variables was investigated using orthogonal regression and correlation. The level of significance was set at α = 0.05 for all analyses.
Results of maximum metabolic performance tests and calculated and experimental power in maximal lactate-steady state
Difference PMLSSC- PMLSSE (W)
0.91 ± 0.18
60.4 ± 8.6
72.9 ± 6.8
229 ± 47
217 ± 51
12 ± 20
The aim of the present investigation was to compare the calculated and experimentally determined power output in MLSS. The comparison of PMLSSC and PMLSSE showed a highly significant correlation (0.92), with only a mean difference of 12 W ± 20 W between the two methods. The results of the present paper accords to previous comparisons between the different lactate-concepts and MLSS. It is well known that different lactate threshold concepts approximate in average MLSS rather well. Van Schuylenbergh et al.  published highly significant correlations between MLSS and OBLA and the Dmax method (r = 0.94 and r = 0.89, respectively). Heck  also evaluated correlations between MLSS and OBLA and individual anaerobic threshold of r = 0.92 and r = 0.87, respectively. However, as already mentioned, the investigation of Hauser et al.  showed large individual differences comparing power of threshold-concepts with power in MLSS. Therefore the calculation method is at least as useful the application of lactate-concepts to detect MLSS.
In contrast to lactate-concepts, however, by using the calculation method it is also possible to show the influence of individual and on MLSS, as well as their combined effects. This can be highlighted for subjects with similar values, for example subject 5 and 12 at 61.0 and 62.7 ml·min−1·kg−1 respectively. Using the classical interpretation, endurance performance of these subjects would be nearly the same, yet interestingly PMLSSE of subject 5 and 12 were completely different (172 vs. 278 W). To explain this difference of 106 W, it is not possible to use only , but differences in of both subjects (1.39 vs. 0.94 mmol·l−1·s−1) is also required. Therefore, subject 5 produces significantly more lactate within the muscle cell per second in contrast to subject 12. When related to the same , this higher lactate production rate leads to a reduction of MLSS [10, 12].
On the other hand it also seems pertinent to focus on subjects with the same PMLSSE, for example subject 5 and 13 (172 vs. 180 W). It is essential to mention that and values of these subjects are completely different (61 vs. 49 ml·min−1·kg−1 and 1.39 vs. 0.81 mmol·l−1·s−1, respectively). This particular example explains, why individuals with the same MLSS could originate by completely different combinations of and as previously suggested by Bleicher et al. . Therefore the knowledge of and and the application of the calculation method could help for a better interpretation of MLSS.
The reason for the overestimation of PMLSSC is likely caused by methodological as well as physiological aspects related to its calculation. It is well known, that a high positive correlation between and PMLSS exists, which incidentally was confirmed in the present study, and highlights the importance of concerning PMLSS. The determination of is a valid test procedure and well established in performance and clinical diagnostics . However, Mader and Heck , Bleicher et al. , Heck and Schulz  and Mader  showed that on a theoretical basis, must have a significant influence on PMLSS. In the present investigation shows no correlation with PMLSSE, which was probably caused by the small range of values measured in this investigation. Furthermore, the missing correlation between and PMLSSE as well as the overestimation of PMLSSC may have been caused by the methodological procedure in determining the maximal anaerobic performance. For example, in the present study was measured by a sprint-test lasting 15 s. It is possible that testing by using a test duration lower than 15 s would lead to higher maximal glycolytic rates and therefore on the basis of the same to a lower PMLSS [3, 12, 14]. Hauser  showed, that increases by 8% when measured using a 13 s sprint-test compared to a 15 s sprint-test. If the present of 0.91 mmol·l−1·s−1 would be increased by 8%, the PMLSSC would have been 224 W. The bias between PMLSSC and PMLSSE would only be −7 W, which could from a practical point of view be neglected. Therefore test procedures of must receive greater focus in future investigations.
Another reason for the differences between the two methods could be the defined interval of 10 W between two constant-load tests, which was used because of time and economic reasons. Using the interval of 10 W it is possible, that PMLSSE is underestimated by a mean by 4 - 5 W. Consequently, it is possible that PMLSSE does not represent the PMLSS exactly. The possible increase of PMLSSE of 4 - 5 W would lead to a decrease in the difference between PMLSSC and PMLSSE of −7 W.
In addition, physiological reasons for differences could be based on the biological variability of the parameters and constants that were used in the calculation. As pointed out by Mader and Heck, the relation between and power output (Ks4) has an important influence on PMLSS . Again according to Mader and Heck  Ks4 was set to 11.7 O2/W in the present study. This relation corresponds exactly to the determined mean value of Ks4 used with the cycle ergometer. However, Ks4 varies on an interindividual basis , and only a theoretical increase of Ks4 by 2.5% would lead to a 224 W decrease in PMLSSC. In addition, the day-to-day variability of and also has important influences on PMLSS, with a mean within-subject variation of 5.6% of leading to deviations in PMLSSC of ± 30 W . In contrast, the biological variability of still remains unknown.
The mathematical method introduced by Mader and Heck  for the determination of PMLSS represents an accurate method similar to that of previous lactate-threshold concepts. In contrast to lactate-threshold concepts, however, this novel calculation method is based on and that can be used for explaining the origin of PMLSS and therefore the metabolic response. The knowledge of both parameters, as well as their individual influence on MLSS, could be important for establishing training recommendations, which could lead to either an improvement in or by performing high intensity or low intensity exercise training, respectively.
The experiments comply with the current laws of the country. The study was proved by Ethics Commission.
- CLarest :
Blood-lacate-concentration during rest
- Dmax method:
Lactate threshold concept
Individual anaerobic threshold
50%-activity constant of oxidative phosphorylation
50%-activity constant of glycolysis
Maximum post excercise blood lactate concentration
Calculated maximal lactate steady-state
- MLSSE :
Experimental maximal lactat steady-state
Onset of blood lactate accumulation
Power in maximal lactate-steady-state
- PMLSSC :
Power in calculated maximal lactate-steady-state
- PMLSSE :
Power in experimental maximal lactate-steady-state
- Pmax :
Revolutions per minute
Respiratory exchange ratio
- talac :
Alactic time intervall
The authors would like to thank Steffi Hallbauer and Jörg Kersten for their assistance in the laboratory and Scott Bowen for their help.
The publication coast of this article were founded by the German Research Foundation/DFG (Geschäftszeichen INST 270/219-1) and the Chemnitz University of Technology in the funding programme Open Access Publishing.
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