Comparison and Performance Validation of Calculated and Established Anaerobic Lactate Thresholds in Running

ackground and Objectives: This study aimed to compare the calculated running veloc- ity at the anaerobic lactate threshold (cLT An), determined by a mathematical model for metabolic simulation, with two established threshold concepts (onset of blood lactate accumulation (OBLA; 4 mmol L 1 ) and modi ed maximal deviation method (mDmax)). Additionally, all threshold con- cepts were correlated with performance in different endurance running events.Materials and Methods: Ten sub-elite runners performed a 30 s sprint test on a cycle ergometer adjusted to an isokinetic mode set to a cadence of 120 rpm to determine maximal lactate production rate (VLamax), and a graded exercise test on a treadmill to determine maximal oxygen uptake (VO 2max). Running velocities at OBLA, mDmax, and cLT Anwere then compared with each other, and further correlated with running performance over various distances (3000 m, 5000 m, and 10,000 m).Results: The mean difference in cLT Anwas

of 120 rpm to determine maximal lactate production rate (VLamax), and a graded exercise test on a treadmill to determine maximal oxygen uptake (VO 2max). Running velocities at OBLA, mDmax, and cLT Anwere then compared with each other, and further correlated with running performance over various distances (3000 m, 5000 m, and 10,000 m).Results: The mean difference in cLT Anwas 0.13 0.43 m s 1 and 0.32 0.39 m s 1 compared to mDmax (p= 0.49) and OBLA (p< 0.01), respectively. cLT Anindicated moderate to good concordance with the established threshold concepts (mDmax: ICC = 0.87, OBLA: ICC = 0.74). In comparison with other threshold concepts, cLT Anexhibited comparable correlations with the assessed running performances (cLT An: r= 0.61–0.76, mDmax:r= 0.69–0.79, OBLA:r= 0.56–0.69).Conclusion: Our data show that cLT An can be applied for determining endurance performance during running. Due to the consideration of individual physiological pro les, cLT Anoffers a physiologically justi ed approach to assess an athlete's endurance performance. Keywords: aerobic capacity; anaerobic capacity; maximal lactate production rate; exercise testing; endurance performance; metabolism 1. Introduction Determination of the blood lactate response during exercise is among the most widely used performance diagnostic tools [1,2]. Blood lactate concentration increases above the resting value with increasing exercise intensity. However, as long as exercise is performed at a constant exercise intensity under a certain intensity threshold, blood lactate concentration remains constant, physiologically known as a steady-state condition [3,4]. At a certain exercise intensity, a minor increment in the workload induces an accelerated blood lactate accumulation and subsequent fatigue-related metabolic consequences, such as the negative impact of hydrogen ion accumulation (acidosis) on muscle function and performance [4–7]. This considerable point has been de ned as the anaerobic lactate threshold (LTAn), which is generally considered to be a good indicator of individual aerobic endurance performance and can be used for prescribing endurance training intensities [8,9]. Medicina2021,57, 1117.

Medicina2021,57, 1117 2 of 12 In recent decades, researchers have developed several concepts to determine LTAn. Most LTAnconcepts are usually applied to lactate performance curves derived from graded incremental exercise tests [8]. Most existing LTAnconcepts use either xed lactate con- centrations [4,10] or in ection points [11,12] as their determination criteria. However, these criteria are derived either arbitrarily or empirically from the graphical analysis of the lactate performance curve. Moreover, LTAnhas shown to be strongly dependent on the applied test protocol [13,14] and on the athlete's training status [15], which is critical because there is no clear standardized test procedure de ned, which thus hinders accurate data interpretation and comparison. Therefore, the physiological background and the validity/reliability/comparability of these LTAnconcepts have been questioned [8]. Lactate production and removal are ongoing processes, which are closely related to metabolic rate but not necessarily to oxygen delivery [5,6,16,17]. There is a continual exchange of lactate between various organs and cells, which can be used as an energy source for oxidative energy production and/or as a major precursor to gluconeogenesis [5,17]. This emphasizes the complexity of metabolic processes behind blood lactate concentrations during exercise or other conditions. Limiting interpretation solely to blood lactate kinetics in response to graded exercise tests allows only scarce insight into the complex metabolic processes of total energy production [18,19]. In 1984, Mader [20] suggested that the lactate performance curve and the correspond- ing exercise intensity at LTAnmay be in uenced by aerobic (maximal oxygen uptake; VO2max) or anaerobic (glycolytic) capacity (maximal lactate production rate; VLamax) sepa- rately [20]. Further research con rmed this assumption and showed that different com- binations of VO2maxand VLamaxcan result in two identical lactate performance curves with equal LTAn[18]. In a more differentiated approach, Mader and Heck [3] proposed a mathematical simulation model of energy production processes in skeletal muscle. Using Michaelis–Menten kinetics, these researchers described the activation of glycolysis as a lactate production system and the oxidative phosphorylation as a combustion system, both depending on the total metabolic rate [3]. Based on this theoretical construct, the term “maximal steady-state of blood lactate (MLSS)” was

and Heck [3] proposed a mathematical simulation model of energy production processes in skeletal muscle. Using Michaelis–Menten kinetics, these researchers described the activation of glycolysis as a lactate production system and the oxidative phosphorylation as a combustion system, both depending on the total metabolic rate [3]. Based on this theoretical construct, the term “maximal steady-state of blood lactate (MLSS)” was introduced (as another concept of LTAn), at which the extent of lactate formation by glycolysis is exactly equal to the maximal elimination rate of lactate by combustion. Thus, no lactate accumulation in blood lactate over time occurs (Figure) [ 3]. Thereby, it was suggested that accelerated accumulation of blood lactate during exercise is due to the saturation of the combustion system (oxidative phosphorylation) [3], which was later veri ed by subsequent investigations of lactate kinetics during exercise [6,21]. As this mathematical model considers both the maximal aerobic and anaerobic capacities for the determination of LTAn, it provides differentiated information about the energetic background of LTAn, as well as the physiological pro le of an athlete [18]. Based on Mader's approach, Hauser et al. [22] applied the mathematical model to calculate the power output at MLSS during cycling using individual VO2max- and VLamax- values and demonstrated a signi cant correlation with the experimental determined MLSS, and high reliability in the estimation of MLSS [23]. However, there is a lack of knowledge regarding the transferability of the model to running. Furthermore, the calculation method in the previous study [22] has only been compared to the empirically determined MLSS, but not to the actual athlete's competition performance, which is an essential aspect for a practical application of a laboratory testing parameter [8]. Therefore, this study aimed to calculate running velocity at LTAnusing individual VO2maxand VLamaxand an adapted mathematical method initially described by Mader and Heck [3] and Hauser et al. [22]. The calculated LTAn(cLTAn) was then compared with other established experimentally determined LTAnconcepts. Additionally, we aimed to validate cLTAnagainst the athlete's recent performance in endurance running events.

VO2maxand VLamaxand an adapted mathematical method initially described by Mader and Heck [3] and Hauser et al. [22]. The calculated LTAn(cLTAn) was then compared with other established experimentally determined LTAnconcepts. Additionally, we aimed to validate cLTAnagainst the athlete's recent performance in endurance running events.

Medicina2021,57, 1117 3 of 12Medicina 2021, 57, x FOR PEER REVIEW 3 of 12 Figure 1. An exemplary description of the mathematical model for metabolic simulation, presenting the gross lactate formation (VLa ss) and the maximal lactate elimination rate (VLaoxmax) depending on exercise intensity [22]. Maximal lactate steady state (MLSS) is defined as the exercise intensity at which the lactate formation is exactly equal to elimination. VO 2max = maximal oxygen uptake; VLamax = maximal lactate production rate; Ks4 = individual constant value of the relationship between oxy- gen demand and running velocity. Based on Mader’s approach, Hauser et al. [22] applied the mathematical model to calculate the power output at MLSS during cycling using individual VO 2max- and VLamax- values and demonstrated a significant correlation with the experimental determined MLSS, and high reliability in the estimation of MLSS [23]. However, there is a lack of knowledge regarding the transferability of the model to running. Furthermore, the calcu- lation method in the previous study [22] has only been compared to the empirically de- termined MLSS, but not to the actual athlete’s competition performance, which is an es- sential aspect for a practical application of a laboratory testing parameter [8]. Therefore, this study aimed to calculate running velocity at LT An using individual VO2max and VLamax and an adapted mathematical method initially described by Mader and Heck [3] and Hauser et al. [22]. The calculated LT An (cLTAn) was then compared with other established experimentally determined LT An concepts. Additionally, we aimed to validate cLTAn against the athlete’s recent performance in endurance running events. 2. Materials and Methods 2.1. Subjects Ten sub-elite male middle- and long-distance runners (age = 19.2 ± 3.5 years, body mass = 65.8 ± 5.8 kg, height = 181.7 ± 5.2 cm, VO 2max = 69.8 ± 6.7 mL∙kg −1 min −1 , VLamax = 0.39 ± 0.09 mmol L −1 s −1 ) participated in this study. Prior to signing the written informed con- sent of the investigation, all participants were informed about the experimental proce- dures. The protocols used in this investigation were approved by

= 181.7 ± 5.2 cm, VO 2max = 69.8 ± 6.7 mL∙kg −1 min −1 , VLamax = 0.39 ± 0.09 mmol L −1 s −1 ) participated in this study. Prior to signing the written informed con- sent of the investigation, all participants were informed about the experimental proce- dures. The protocols used in this investigation were approved by the Ethics Committee of the university and are in line with the Declaration of Helsinki. Figure 1. An exemplary description of the mathematical model for metabolic simulation, presenting the gross lactate formation (VLass) and the maximal lactate elimination rate (VLaoxmax) depending on exercise intensity [22]. Maximal lactate steady state (MLSS) is de ned as the exercise intensity at which the lactate formation is exactly equal to elimination. VO 2max= maximal oxygen uptake; VLamax= maximal lactate production rate;Ks4 = individual constant value of the relationship between oxygen demand and running velocity. 2. Materials and Methods 2.1. Subjects Ten sub-elite male middle- and long-distance runners (age = 19.2 3.5 years, body mass = 65.8 5.8 kg, height = 181.7 5.2 cm, VO2max= 69.8 6.7 mL kg 1 min 1 , VLamax= 0.39 0.09 mmol L 1 s 1 ) participated in this study. Prior to signing the written informed consent of the investigation, all participants were informed about the experimental procedures. The protocols used in this investigation were approved by the Ethics Committee of the university and are in line with the Declaration of Helsinki. 2.2. Design The present investigation consisted of two different performance tests completed on a single day. The body mass was measured before the performance testing (Tanita Corp., Tokyo, Japan). Participants were instructed to arrive in the laboratory in a rested, 2 h postprandial, and well-hydrated state. They were ordered to avoid strenuous exercise for at least 24 h before the test. First, the participants performed a 30 s isokinetic sprint test on a cycle ergometer with subsequent measurements of whole-blood lactate concentration for the determination of VLamax. After a 60 min break, a graded exercise test on a treadmill (second test) was performed to determine VO2maxand running

were ordered to avoid strenuous exercise for at least 24 h before the test. First, the participants performed a 30 s isokinetic sprint test on a cycle ergometer with subsequent measurements of whole-blood lactate concentration for the determination of VLamax. After a 60 min break, a graded exercise test on a treadmill (second test) was performed to determine VO2maxand running velocity at the onset of blood lactate accumu- lation (OBLA; 4 mmol L 1 ) [4] and at the modi ed maximal deviation point (mDmax) [24]. The cLTAnwas determined according to the calculation scheme described by Mader and Heck [3], as well as by Hauser et al. [22], and subsequently compared with OBLA and mDmax. To evaluate the validity of cLTAn, OBLA, and mDmax as indicators of endurance performance, running velocities at each concept were compared with the participant's per- formance (average velocity (m s 1 )) over various distances (3000 m, 5000 m, and10,000 m).

Medicina2021,57, 1117 4 of 12 One participant did not provide performance data, so only data from nine participants were included in correlation analysis. 2.3. Isokinetic Sprint Test and VLamaxDetermination (Performance Capacity of Glycolysis) The participants rst performed a 10 min standardized warm-up at 1.5 W kg 1 body mass. After an additional passive rest for 5 min, a 30 s sprint test was performed on a cycle ergometer adjusted to an isokinetic mode set to a cadence of 120 rpm [25,26]. Participants were instructed to perform the test in a sitting position and were verbally encouraged throughout the test to achieve and maintain maximal effort. After the sprint, participants took a rest in a sitting position for 10 min. Immediately before sprint testing, as well as every minute after the sprint bout (1 0 –10 0 ), 20 L of capillary blood was taken from the earlobe for lactate analysis (Biosen C-line; EKF Diagnostic Sales, Magdeburg, Germany). The VLamaxwas calculated using the following equation [27]: VLamax(mmol L 1 s 1 ) = ([La] peak [La]rest) (texerc t alac) 1 (1) where La peak(mmol L 1 ) is the peak post-exercise lactate concentration, Larest(mmol L 1 ) is the resting lactate concentration,texerc(s) is the duration of exercise, andt alac(s) is the period at the beginning of exercise in which no lactate formation is assumed. According to Heck et al. [27], t alacwas set to 5.5 s for all participants. 2.4. Graded Exercise Running Test and VO2maxand LTAnDetermination The graded exercise test was performed on a treadmill (Woodway, Weil am Rhein, Germany), which started at 2.4 m s 1 and increased by 0.4 m s 1 every 5 min until volitional exhaustion was reached. After each step of the graded exercise test, a 30 s rest was given for blood sampling. Furthermore, heart rate (HR) (H7, Polar Electro Oy, Kempele, Finland) and breath-by-breath expired gases (Cortex Metalyzer II, Leipzig, Germany) were continuously measured throughout the test. The VO2maxcorresponded to the highest value measured (moving average of 30 s) during the test. Blood lactate concentrations during the incremental tests were plotted against running

a 30 s rest was given for blood sampling. Furthermore, heart rate (HR) (H7, Polar Electro Oy, Kempele, Finland) and breath-by-breath expired gases (Cortex Metalyzer II, Leipzig, Germany) were continuously measured throughout the test. The VO2maxcorresponded to the highest value measured (moving average of 30 s) during the test. Blood lactate concentrations during the incremental tests were plotted against running velocity and then tted by a third-order polynomial function. Running velocity at OBLA was set as the point at which blood lactate concentration reached 4 mmol L 1 [4]. mDmax was identi ed as the point on the third-order polynomial curve that yielded the maximal perpendicular distance to a straight line formed by the peak lactate point, and by the point of the rst rise in blood lactate concentration at which the slope of the tted lactate curve was equal to 1.00 [24]. 2.5. Calculation of Running Velocity at cLTAn To determine cLTAn, the oxidative and glycolytic energy production depending on exercise intensity must initially be known, which can be expressed as the activity of oxidative phosphorylation (VO2ss) and glycolysis (VLass), respectively [3]. The theoretical background of the applied equations and constants is explained in detail by previous publications [3,22]. According to Mader and Heck [3], the implementation of the metabolic simulation model requires knowing the free ADP concentration, which is the main regulating substrate for the activation of VO2ssand VLass. Since there is no simple and practical procedure for measuring free ADP concentration, the ADP-dependent equations in the previous study were transposed into VO2ss-dependent equations [22]. On this occasion, the term “VO2ss” represents the steady-state oxygen consumption at a constant work rate [3,22]. Hauser et al. [22] calculated the VO2ssin relation to exercise intensity based on the assump- tion of a linear relationship between oxygen demand (VO2) and workload. Thereby, a constant value for VO2per 1 W (Ks4 = 11.7 mL O2W 1 ) was used for all participants based on the data of previous cycling experiments [3,28]. However, it should be noted that VO2 in running is more affected by an athlete's exercise economy (i.e., metabolic cost at

of a linear relationship between oxygen demand (VO2) and workload. Thereby, a constant value for VO2per 1 W (Ks4 = 11.7 mL O2W 1 ) was used for all participants based on the data of previous cycling experiments [3,28]. However, it should be noted that VO2 in running is more affected by an athlete's exercise economy (i.e., metabolic cost at a given workload) than in cycling. Running economy was shown to be in uenced by several physi-

Medicina2021,57, 1117 5 of 12 ological and biomechanical factors [29], which can lead to greater inter-individual variation in comparison to the cycling economy due to weight-bearing activity [30]. Therefore, it is necessary to determineKs4 (mL kg 1 min 1 per 1 m s 1 running velocity) individually, by plotting VO2during incremental tests against running velocity. TheKs4 corresponded to the slope of linear regression (y = mx + b) between VO2and running speed (Figure).Medicina 2021, 57, x FOR PEER REVIEW 5 of 12 2.5. Calculation of Running Velocity at cLT An To determine cLT An, the oxidative and glycolytic energy production depending on exercise intensity must initially be known, which can be expressed as the activity of oxi- dative phosphorylation (VO 2ss) and glycolysis (VLass), respectively [3]. The theoretical background of the applied equations and constants is explained in detail by previous pub- lications [3,22]. According to Mader and Heck [3], the implementation of the metabolic simulation model requires knowing the free ADP concentration, which is the main regulating sub- strate for the activation of VO 2ss and VLass. Since there is no simple and practical procedure for measuring free ADP concentration, the ADP-dependent equations in the previous study were transposed into VO 2ss-dependent equations [22]. On this occasion, the term “VO 2ss” represents the steady-state oxygen consumption at a constant work rate [3,22]. Hauser et al. [22] calculated the VO 2ss in relation to exercise intensity based on the assump- tion of a linear relationship between oxygen demand (VO 2) and workload. Thereby, a con- stant value for VO 2 per 1 W (Ks4 = 11.7 mL O 2 W −1 ) was used for all participants based on the data of previous cycling experiments [3,28]. However, it should be noted that VO 2 in running is more affected by an athlete’s exercise economy (i.e., metabolic cost at a given workload) than in cycling. Running economy was shown to be influenced by several phys- iological and biomechanical factors [29], which can lead to greater inter-individual varia- tion in comparison to the cycling economy due to weight-bearing activity [30]. Therefore, it

The study compares calculated and established anaerobic lactate thresholds in running.