Several studies have suggested that muscle carnosine content may be an important variable to consider when evaluating high-intensity performance (10,18,47,48). Although carnosine is synthesized in the muscle from its 2 constituents, β-alanine and histidine (4), synthesis is limited by the availability of β-alanine (11,17). Beta-alanine supplementation has been shown to significantly increase the intramuscular carnosine content (3,10,18,46). Elevation of intramuscular carnosine content via β-alanine supplementation has been shown to improve performance limited by acidosis (i.e., short intense exercise). Hill et al. (18) demonstrated a 13% improvement in total work done after 4 weeks of β-alanine supplementation and an additional 3.2% increase after 10 weeks. Zoeller et al. (52) also reported significant increases in ventilatory threshold (VT) in a sample of untrained men after supplementing with β-alanine (3.2 g·d−1) for 28 days. In agreement, Kim et al. (32) also reported significant increases in VT and time to exhaustion (TTE) in highly trained male cyclists after 12 weeks of β-alanine (4.8 g·d−1) supplementation and training. However, more recently, an ergogenic effect of β-alanine has been less clear, demonstrating no effect on supramaximal sprinting (26), repeated sprint performance (40), 400-m running (10), and the onset of blood lactate (28). An increased muscle buffering capacity should be potentially advantageous under multiple bouts of high-intensity exercise, with short rest intervals (6,7), and therefore, β-alanine should potentially improve running capacity.
The critical velocity (CV) test is the running-based analogue of the original critical power (CP) concept proposed by Monod and Scherrer (34). The CP test was developed to identify aerobic power and anaerobic energy reserves using a hyperbolic relationship between power output and TTE. Ettema (12) proposed calculating a linear relationship between the distance run (dlim) and the TTE (Tlim) expressed as follows: dlim = (CV × Tlim) + anaerobic running capacity (ARC), where CV is the slope of the regressed line between dlim and Tlim and the y-intercept represents ARC. The CV test involves a series of runs to exhaustion at different velocities to determine the time-velocity relationship, thereby mathematically providing an aerobic and anaerobic measure of performance with little equipment. This concept was applied to running, theoretically representing the maximal running velocity that can be maintained for an extended period of time without fatigue (CV). In contrast, the ARC is the distance that can be run at a maximal velocity using only anaerobic energy stores within the muscle (19,23–25,37). This dlim-Tlim relationship can therefore be used to determine performance over varying intensities and distances and used to predict both aerobic power (CV) and anaerobic capacity (ARC).
Although the CV test is typically completed under laboratory-based constraints, it can conform to some sport-specific settings and can be used by coaches with minimal equipment (i.e., stopwatch). More so, CV represents a velocity corresponding to an upper sustainable limit during running and has been associated with an increase in bicarbonate buffering and a drop in pH (19) and has been suggested as an appropriate training intensity to induce metabolic adaptations. Although there is some question regarding the validity of the CV test to accurately predict a sustainable (60 minutes) velocity, there seems to be no difference between the velocities at CV compared with V[Combining Dot Above]O2max (23,37,42). Additionally, the anaerobic component seems to be associated with maximal lactate concentrations and maximal accumulated oxygen deficit during exercise (19,24), supporting both aerobic and anaerobic outcomes of the test. Of additional practicality, the CV test has previously been shown to detect changes in training and supplementation (14,15,42). So, while the majority of research evaluating the effect of β-alanine occurs on laboratory-based measures, the CV test can be repeated by the strength coach and can be generalizable to a sport-specific setting.
The CV test uses a series of high-intensity running trials, interspersed with short rest periods, with each subsequent trial resulting in diminished stores of adenosine triphosphate (ATP), phosphocreatine (PCr), and glycogenic substrates and a subsequent accumulation of metabolites [adenosine diphosphate (ADP), inorganic phosphate (Pi), hydrogen ions (H+), and magnesium (Mg+)], each of which may contribute to fatigue (1,38). The acute metabolic response is believed to be the driving force behind the fundamental chronic adaptations reported with the use of intervals, leading to an enhanced ability to delay the onset of acidosis (51). Similarly, the use of β-alanine may enhance the buffering of H+, further augmenting acute and chronic training adaptations. However, no evidence exists on the effects of β-alanine when delineated for aerobic and anaerobic energy demands, as measured by CV and ARC using the running-based CV test. The majority of the β-alanine literature suggests an ergogenic effect in activities limited by acidosis, generally ranging from 2 to 4 minutes. However, to date, there is no definitive data across mode, duration, and intensity supporting the use of β-alanine as an ergogenic aid in either aerobic or anaerobic environment. More so, the sport-specific data are lacking. Therefore, the purpose of this study was to evaluate the effects of 28 days of β-alanine supplementation on aerobic and anaerobic running performances and high-intensity running TTE. Secondarily, both sexes were included to aid in more conclusive evidence for men and strengthening the small existing body of evidence in women. It was hypothesized that ARC and high-intensity run performance would be improved with supplementation.
Experimental Approach to the Problem
Using a randomized, double-blind placebo controlled design, the effects of β-alanine loading on aerobic (CV) and anaerobic (ARC) running performances, TTE, and lactate levels were evaluated. During pretesting, participants completed an initial run to establish their maximal oxygen consumption (V[Combining Dot Above]O2max) and to determine the peak velocity (PV). Twenty-four to 48 hours after the initial visit, participants were taken through a series of 3 runs to exhaustion at 100%PV, 90%PV, and 110%PV with 15 minutes of rest between each bout to determine CV and ARC. Capillary lactate samples were taken at baseline, immediately after each run, and 15 minutes after run. Participants were then randomly assigned to a β-alanine (BA) or placebo (PL) group and underwent a 28-day loading period. After 28 days of supplementation, participants returned to the laboratory for posttesting, which included the same runs to exhaustion at identical velocities as pretesting. Previous research has failed to show improvements in sprint performance and repeated sprints. To date, the CV test has been used as an effective and sensitive method to assess changes after various supplementation protocols. The use of β-alanine on CV and ARC has not yet been evaluated.
Fifty recreationally active (1–5 h·wk−1) men and women volunteered to participate in this investigation (Table 1). According to the American College of Sports Medicine, both male and female participants ranked above the 70th percentile for maximal oxygen consumption values, yielding above average fitness levels for this age group. All subjects completed a health history questionnaire containing a brief survey to quantify each participant's physical activity and supplementation status. None of the participants reported any current or ongoing musculoskeletal injury at the time of initiation. Participants were asked to refrain from caffeine before testing weeks. All participants were moderately trained, engaging in 3–7 days per week of aerobic, resistance, or recreational activities. All procedures were approved by the University's Institutional Review Board for Human Subjects, and all subjects completed a written informed consent.
Randomization and Supplementation
Using a computer-generated allocation system, participants were randomly assigned to a placebo (PL; 800 mg per tablet of maltodextrin; 2 tablets 3 times daily) or β-alanine supplementing group (800 mg per tablet; 2 tablets 3 times daily; CarnoSyn; Natural Alternatives Inc., San Marcos, CA, USA). Supplements were identical in appearance and taste and were blinded by the donating company for separate sexes to maintain equal groups. Participants were instructed to consume 2 tablets orally with water, 3 times daily, allowing a minimum of 2 hours between consumptions. Product intake, compliance, and adverse effects were assessed midway through, and at posttesting, from dosing logs. To ensure that subjects ingested their assigned supplement, participants were required to return the supplement bottle to the investigators at posttesting to be counted. Furthermore, dietary intake was assessed using 3-day diaries during the first and final week of the study. Participants were encouraged to report any symptoms or adverse effects. Five participants reported mild symptoms of paresthesia. Of those 5, only 2 were taking the active supplement. All subjects were maintained for analysis. Throughout the duration of the study, participants were asked to maintain their physical activity regime. Dietary food logs were distributed to all participants and completed (2 nonconsecutive weekdays and 1 weekend day) at pre- and posttesting to evaluate any changes in total calories and protein intake.
Determination of Peak Velocity
All participants performed a graded exercise test (GXT) to volitional exhaustion on a treadmill (Woodway Pro Series, Waukesha, WI, USA) to determine PV. Based on the protocol of Peake et al. (36), the initial GXT velocity was set at 10 km·h−1 at a 0% grade and increased 2 km·h−1 every 2 minutes up to 16 km·h−1, followed by 1 km·h−1 increments per minute up to 18 km·h−1. The gradient then increased by 2% each minute until V[Combining Dot Above]O2max was achieved, and the speed corresponding to the end of the test was considered PV. This speed was used to establish individual running velocities for the CV test.
Critical Velocity and Anaerobic Running Capacity
To determine CV and ARC, the linear total distance (TD) model described and evaluated by Florence and Weir et al. (13) was used:
where the total distance achieved during each run to exhaustion (TD; y-axis) was plotted over the TTE (t; x-axis), and linear regression was used to calculate the y-intercept (ARC) and the slope (CV) of the line of best fit.
Three treadmill runs to exhaustion were performed to establish the distance-time relationships for the TD model for each subject. Each participant ran at velocities equivalent to 110, 90, and 100% of the treadmill velocity (in kilometers per hour) at which V[Combining Dot Above]O2max (PV) occurred. A minimum of 15 minutes was allotted between trials to allow heart rate to return within ±10 b·min−1 of resting. Time to exhaustion (in seconds), Tlim90%, Tlim100%, Tlim110%, respectively, and distance achieved (in kilometers) were recorded for each run. Intensities were run in a standardized order, 110, 90, and 100%, and at a similar time of day, for all subjects, at both pre- and posttestings. Total distance was calculated from the sum of the 3 work bouts for comparison between treatment groups. Test-retest reliability for CV and ARC from the authors' laboratory for college-aged men and women (n = 28) measured 1 week apart resulted in an intraclass correlation (ICC) of 0.97 and SEM of 0.76 km·h−1 (CV) and an ICC of 0.74 and SEM of 0.33 km (ARC).
A capillary blood sample was obtained from the finger and analyzed for lactate with the Lactate Plus Meter (Nova Biomedical Corp., Waltham, MA, USA). Lactate samples were taken immediately post (IP) each of the 3 runs to exhaustion, IP Tlim110%, IP Tlim90%, and IP Tlim100%, respectively, during the CV test.
Separate 2-way mixed-factorial analyses of variance (ANOVAs) (2 × 2; time [pre- vs. postsupplement] × treatment [placebo vs. β-alanine]) were used to evaluate CV, ARC, TTE performance data, and total distance in men and women, respectively. Lactate values were analyzed using a 3-way mixed-factorial ANOVA (3 × 2 × 2; bout [IP Tlim110% × IP Tlim90% × IP Tlim100%] × time [pre- vs. postsupplement] × treatment [placebo vs. β-alanine]) with analyses separated for sex. When appropriate, post hoc analyses for the ANOVA models were performed using lower-order ANOVAs and Bonferroni-corrected paired samples t-tests. All statistical assumptions were met. An alpha level was set at p ≤ 0.05, and all analyses were performed using PASW version 18.0 (SPSS, Inc., Chicago, IL, USA). Percentage change scores were also calculated for each participant for Tlim90%, Tlim100%, and Tlim110%, CV and ARC. These percentage change scores were averaged, and 95% confidence intervals were constructed around the mean values (Figure 1). When the 95% confidence interval includes zero, the mean percentage change score is not different from zero, which can be interpreted as no statistical change. Intervals were calculated and created in Microsoft Excel (Version 2007; Microsoft Corporation; The Microsoft Network, LLC, Redmond, WA, USA).
Time to Exhaustion and Total Distance
Time to exhaustion at 110%PV yielded no 2-way interaction (time × treatment, p = 0.912) and no main effect for treatment (p = 0.102), but there was a main effect for time (p = 0.023) in men. Marginal means collapsed across treatments indicate a significant increase from pre- to posttesting at 110%PV for both groups (p = 0.023; Table 2). Confidence intervals demonstrated no significant differences over time or treatment (Figure 1A).
Time to exhaustion at 110%PV illustrated no 2-way interaction (time × treatment, p = 0.630) and no main effect for time (p = 0.651) or treatment (p = 0.693) in women (Table 2). Confidence intervals display a significant increase over time (Figure 1B).
Time to exhaustion data illustrated no 2-way interaction (time × treatment, p = 0.069) and no main effect for time (p = 0.084) or treatment (p = 0.586) for men. Confidence intervals displayed a significant increase over time for the placebo group only (Figure 1A). Time to exhaustion data illustrated no 2-way interaction (time × treatment, p = 0.124) and no main effect for time (p = 0.399) or treatment (p = 0.412) for women.
Time to exhaustion data illustrated no 2-way interaction (time × treatment, p = 0.171) and no main effect for time (p = 0.737) or treatment (p = 0.189) for men. Confidence intervals resulted in a significant increase for the PL group only (Figure 1A).
Time to exhaustion data illustrated no 2-way interaction (time × treatment, p = 0.069) and no main effect for time (p = 0.199) or treatment (p = 0.483) for women. Confidence intervals displayed a significant increase for the BA group only (Figure 1B).
There were no significant differences in total distance for either men (p = 0.258) or women (p = 0.117).
Critical Velocity (in Kilometers per Hour) and Anaerobic Running Capacity
There were no 2-way interaction (time × treatment, p = 0.522) and no main effect for time (p = 0.868) or treatment (p = 0.959) for CV in men (Table 2). And there were no significant 2-way interaction (time × treatment, p = 0.716) and no main effect for time (p = 0.514) or treatment (p = 0.163) for ARC in men (Table 2).
There were no 2-way interaction (time × treatment, p = 0.173) and no main effect for time (p = 0.494) or treatment (p = 0.782) for CV in women (Table 2). And there were no significant 2-way interaction (time × treatment, p = 0.405) and no main effect for time (p = 0.914) or treatment (p = 0.728) for ARC in women (Table 2).
For men, there were no 3-way interaction (acute × chronic × treatment, p = 0.121) and no 2-way interaction for chronic × treatment (p = 0.806) or acute × treatment (p = 0.426). However, there was a significant interaction for acute × chronic (p = 0.003; Figure 2A). The marginal means for acute lactate levels (collapsed across chronic and treatment) yielded significantly lower values for IP Tlim110% compared with IP Tlim90% and IP Tlim100% (p < 0.01), with no differences between 100%PV and 90%PV. Marginal means (collapsed across acute and treatment) indicated no difference from pre- to posttreatment (p = 0.510).
For women, there were no 3-way interaction (acute × chronic × treatment, p = 0.387); no 2-way interactions for acute × chronic (p = 0.605), chronic × treatment (p = 0.588), and acute × treatment (p = 0.846); and no main effect for treatment (p = 0.871) and chronic (p = 0.076). However, there was a main effect for acute (p = 0.001; Figure 2B). The marginal means for acute lactate levels (collapsed across chronic and treatment) demonstrated that IP 110%PV lactate values were significantly lower than IP 90%PV and IP 100%PV (p < 0.01).
There was no significant difference between groups for their supplement compliance rate. Analyses of the dietary recalls demonstrated no significant differences in caloric intake (p = 0.391), carbohydrate (p = 0.783), protein (p = 0.158), or fat (p = 0.402) intake from pre- to post-supplementation.
The purpose of this study was to evaluate the use of β-alanine on aerobic (CV) and ARC while using an intermittent-based running assessment to establish an environment limited by acidosis. Neither CV nor ARC was improved with supplementation for either sex. Also, there were no significant improvements in TTE at Tlim90% to Tlim110%. Tests lasting between 2 and 4 minutes have been suggested to be limited by acidosis and thereby ideal for displaying ergogenic effects of β-alanine (2,18,39). It was hypothesized that TTE at supramaximal speeds (110%PV) would be improved with supplementation. With an average run time of 1.95 minutes, it is apparent that this exhaustive bout would be limited by metabolite accumulation. Additionally, average run time at 100% was 2.59 minutes, which would also highlight the benefits of β-alanine supplementation by reducing H+ accumulation. In contrast, there were no significant improvements because of supplementation [Table 2; Figure 3 (individual responses)]. These results are in line with the previous studies demonstrating no change in 400-m running performance (10), sprint endurance (26), and repeated sprints (26), all within the ideal time environment to be improved with an enhanced buffering capability. Whereas the current growing body of β-alanine literature demonstrates some positive effects on VT (47,52), TTE (18,45), and training volume (20), another consistent body of evidence demonstrates no direct effect on performance (10,26,28,40,43,45,50) leading researchers to investigate other mechanisms that may indirectly affect performance and recovery. It has been suggested that β-alanine may play a role in excitation-contraction coupling (5), although no human data have been established. More recently, it has been shown that β-alanine may have an effect on reducing oxidative stress by reducing lipid peroxidation (44) and therefore have a role in recovery. The current results suggest that there are no significant direct effects of β-alanine supplementation on moderately fit men (3.9 ± 0.5 L·min−1) and women (2.6 ± 0.3 L·min−1), on aerobic or anaerobic running, or lactate levels.
It has been suggested that carnosine can contribute up to 7% of total muscle buffer capacity during intense exercise, causing an accumulation of H+. As described by Housh et al. (22), the CV test involves a series of runs to exhaustion at various supramaximal running velocities to determine the relationship between TTE and velocity. This interval-based test should be sufficient to cause a drop in pH, calling upon bicarbonate buffering. The CV test has been shown to be a valid measure of aerobic (CV) and anaerobic (ARC) abilities in trained and untrained men and women (30,41). In addition, the CV test is reliable and sensitive to changes in performance with high-intensity training or supplementation interventions (14,16,27,30,35,41). However, we are not aware of any previous studies that have examined the influence of β-alanine on CV performance. Only one study has evaluated running performance, demonstrating no acute effects of BA consumption (10). Conversely, several studies have evaluated the effectiveness of BA on separate cycle ergometer aerobic capacity and anaerobic strength measures (10,18,20,31,47–49,52). The use of BA supplementation alone does not seem to improve aerobic capacity or maximal strength, but when combined with training can be quite effective on both parameters (18,31,45,49), and improving training volume and reducing feelings of fatigue (20,43,44). In support, the current study revealed no significant influence on CV or ARC, in either men or women (Table 2; Figure 1). The lack of effect could be attributed to the large rest time (15 minutes) allowing for metabolites to disperse between running bouts. A protocol involving more intermittent bouts with reduced rest time may benefit more from an enhanced buffering. Additionally, despite the ease of administering the CV test, the results are highly dependent on motivated subjects, completing 3 all-out runs to exhaustion at intensities near or above maximum. Therefore, motivation could have also had an influence on the results. As illustrated in Figure 3, the male placebo group improved more than the BA group at Tlim90 and Tlim100. This could have been a result of motivation or superfluous exercise over the 4-week supplementation period. Additionally, 95% confidence interval results from the women yielded significantly greater results at Tlim100 for the BA group (Figure 1B) providing evidence to support the use of BA in women.
Hydrogen ions and lactate can be buffered and removed intracellularly by proteins, dipeptides (such as carnosine), and phosphate within the muscle, which represents the first line of defense against acidosis and lactate accumulation (29). According to Brooks (8), lactate production acts as a beneficial metabolic H+ buffer for contracting muscle, facilitating the removal of H+ from muscle fibers. Elevated muscle buffering and lactate clearance mechanisms would allow the muscle to produce lactate and protons before reaching lactate threshold and pH limits (33). Lactate kinetics have been evaluated in 4 previous studies (9,28,49,52) with 2 of those under a running stimulus (9,28). Van Thienen et al. (49) failed to show a significant change in lactate response after a final 30-second sprint following a 110-minute cycle ride. Similarly, Derave et al. (9) demonstrated no increase in blood lactate accumulation 90 and 180 seconds after a 400-m run. In contrast, Zoeller et al. (52) reported a significant increase in power output at lactate threshold and Jordan et al. (28) revealed a delay in the onset of blood lactate (OBLA) during a treadmill GXT. The present study demonstrated a significant decrease in blood lactate values after the first bout of exercise (Tlim110%) following treatment and a significant increase in lactate values IP Tlim90% and Tlim100% runs. However, there were no significant differences between treatments (Figure 2). Blood lactate may not be sensitive enough to reflect an immediate change after exercise, and the 15-minute rest time between bouts could have blunted the lactate response and the need for enhanced buffering capacity. More so, lactate measurements, although reliable, do not directly reflect H+ accumulation. With appropriate time and equipment, pH measurement would be a more valuable measurement tool to evaluate the effectiveness of β-alanine on muscle buffering capacity.
Although muscle carnosine concentration was not measured directly in this investigation, several studies have shown significantly elevated carnosine levels (+60%) after 28 days of β-alanine supplementation (17,18). Furthermore, the dosing strategy was similar to that of Derave et al. (10) suggesting that muscle carnosine levels were increased. The present study used the time release formula at 4.8 g daily in divided doses (CarnoSyn; Natural Alternatives Inc.) that has been demonstrated to significantly augment muscle carnosine levels by 27–39% in fast- and slow-twitch muscle fibers, respectively (3), under a similar dosing scenario. This study is further limited by the increases demonstrated in the placebo group after 4 weeks of supplementation, which could be attributed to changes in exercise patterns, because the subjects were testing in the Fall semester (right after summer break). Unfortunately, this is beyond the control of the investigators.
This study is original in its approach to evaluate the effects of β-alanine supplementation on intermittent running and aerobic/anaerobic-derived parameters. The results fall in line with the equivocal collective view on the current body of β-alanine literature. To date, there have been no effects on aerobic performance. Some data suggest an improvement in anaerobic activities delimited by 2–4 minutes of exercise (18,39) and anaerobic thresholds (45,49,52). Yet, other studies demonstrate no change on sprint or intermittent sprint performance. More so, several other performance studies have failed to show significant ergogenic effects of β-alanine on performance (10,20,21,28,44,50). The current study also did not show significant improvements. It can be argued that more sport-specific studies should be completed to establish stronger guidelines for β-alanine use, and further investigations are needed regarding other potential mechanisms β-alanine may have in regard to muscle contraction, oxidative stress, and recovery. Furthermore, the current study is one of few (44,47) to include women in their evaluation. There seems to be no difference in the responses between men and women. As the current body of β-alanine research continues to grow, it is advised that the strength coach and trainer follow the published findings as they evolve. To date, with both positive and negligible, but no harmful, effects reported, the use of β-alanine may be applicable in individuals with low muscle carnosine levels (vegetarians, older adults, women) and those individuals with a naive muscle buffering capacity.
The authors thank Natural Alternatives Inc. for supplying and blinding the active and placebo products. The results of the present study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.
1. Allen DG, Lamb GD, Westerblad H. Skeletal muscle fatigue: Cellular mechanisms. Physiol Rev 88: 287–332, 2008.
2. Artioli GG, Gualano B, Smith A, Stout J, Lancha AH Jr. Role of beta-alanine supplementation on muscle carnosine and exercise performance. Med Sci Sports Exerc 42: 1162–1173, 2010.
3. Baguet A, Reyngoudt H, Pottier A, Everaert I, Callens S, Achten E, Derave W. Carnosine loading and washout in human skeletal muscles. J Appl Physiol 106: 837–842, 2009.
4. Bakardjiev A, Bauer K. Transport of beta-alanine and biosynthesis of carnosine by skeletal muscle cells in primary culture. Eur J Biochem 225: 617–623, 1994.
5. Begum G, Cunliffe A, Leveritt M. Physiological role of carnosine in contracting muscle. Int J Sport Nutr Exerc Metab 15: 493–514, 2005.
6. Bishop D, Edge J, Goodman C. Muscle buffer capacity and aerobic fitness are associated with repeated-sprint ability in women. Eur J Appl Physiol 92: 540–547, 2004.
7. Bishop D, Edge J, Mendez-Villanueva A, Thomas C, Schneiker K. High-intensity exercise decreases muscle buffer capacity via a decrease in protein buffering in human skeletal muscle. Pflugers Arch 458: 929–936, 2009.
8. Brooks GA. What does glycolysis make and why is it important? J Appl Physiol 108: 1450–1451, 2010.
9. Derave W, Everaert I, Beeckman S, Baguet A. Muscle carnosine metabolism and beta-alanine supplementation in relation to exercise and training. Sports Med 40: 247–263, 2010.
10. Derave W, Ozdemir MS, Harris RC, Pottier A, Reyngoudt H, Koppo K, Wise JA, Achten E. Beta-alanine supplementation augments muscle carnosine content and attenuates fatigue during repeated isokinetic contraction bouts in trained sprinters. J Appl Physiol 103: 1736–1743, 2007.
11. Dunnett M, Harris RC, Soliman MZ, Suwar AA. Carnosine, anserine and taurine contents in individual fibres from the middle gluteal muscle of the camel. Res Vet Sci 62: 213–216, 1997.
12. Ettema J. Limits of human performance and energy production. Euro J App Physio and Occupational Physio 22: 45–54, 1966.
13. Florence S, Weir JP. Relationship of critical velocity
to marathon running performance. Eur J Appl Physiol Occup Physiol 75: 274–278, 1997.
14. Fukuda DH, Smith AE, Kendall KL, Dwyer TR, Kerksick CM, Beck TW, Cramer JT, Stout JR. The effects of creatine loading and gender on anaerobic running capacity
. J Strength Cond Res 24: 1826–1833, 2010.
15. Fukuda DH, Smith AE, Kendall KL, Stout JR. The possible combinatory effects of acute consumption of caffeine, creatine, and amino acids on the improvement of anaerobic running performance in humans. Nutr Res 30: 607–614, 2010.
16. Gaesser GA, Wilson LA. Effects of continuous and interval training on the parameters of the power-endurance time relationship for high-intensity exercise. Int J Sports Med 9: 417–421, 1988.
17. Harris RC, Tallon MJ, Dunnett M, Boobis L, Coakley J, Kim HJ, Fallowfield JL, Hill CA, Sale C, Wise JA. The absorption of orally supplied beta-alanine and its effect on muscle carnosine synthesis in human vastus lateralis. Amino Acids 30: 279–289, 2006.
18. Hill CA, Harris RC, Kim HJ, Harris BD, Sale C, Boobis LH, Kim CK, Wise JA. Influence of beta-alanine supplementation on skeletal muscle carnosine concentrations and high intensity cycling capacity. Amino Acids 32: 225–233, 2007.
19. Hill DW, Ferguson CS. A physiological description of critical velocity
. Eur J Appl Physiol Occup Physiol 79: 290–293, 1999.
20. Hoffman J, Ratamess N, Faigenbaum A, Ross R, Kang J, Stout J, Wise JA. Short-duration beta-alanine supplementation increases training volume and reduces subjective feelings of fatigue in college football players. Nutr Res 28: 31–35, 2007.
21. Hoffman J, Ratamess NA, Ross R, Kang J, Magrelli J, Neese K, Faigenbaum AD, Wise JA. Beta-alanine and the hormonal response to exercise. Int J Sports Med 29: 952–958, 2008.
22. Housh TJ, Cramer JT, Bull AJ, Johnson GO, Housh DJ. The effect of mathematical modeling on critical velocity
. Eur J Appl Physiol 84: 469–475, 2001.
23. Housh TJ, Johnson GO, McDowell SL, Housh DJ, Pepper M. Physiological responses at the fatigue threshold. Int J Sports Med 12: 305–308, 1991.
24. Housh TJ, Johnson GO, McDowell SL, Housh DJ, Pepper ML. The relationship between anaerobic running capacity
and peak plasma lactate. J Sports Med Phys Fitness 32: 117–122, 1992.
25. Hughson RL, Orok CJ, Staudt LE. A high velocity treadmill running test to assess endurance running potential. Int J Sports Med 5: 23–25, 1984.
26. Jagim AR, Wright GA, Brice AG, Doberstein ST. Effects of beta-alanine supplementation on sprint endurance. J Strength Cond Res, 2012. Apr 3 [Epub ahead of print].
27. Jenkins DG, Quigley BM. The influence of high-intensity exercise training on the Wlim-Tlim relationship. Med Sci Sports Exerc 25: 275–282, 1993.
28. Jordan T, Lukaszuk J, Misic M, Umoren J. Effect of beta-alanine supplementation on the onset of blood lactate accumulation (OBLA) during treadmill running: Pre/post 2 treatment experimental design. J Int Soc Sports Nutr 7: 20, 2010.
29. Juel C. Regulation of pH in human skeletal muscle: Adaptations to physical activity. Acta Physiol (Oxf) 193: 17–24, 2008.
30. Kendall KL, Smith AE, Graef JL, Fukuda DH, Moon JR, Beck TW, Cramer JT, Stout JR. Effects of four weeks of high-intensity interval training and creatine supplementation on critical power and anaerobic working capacity in college-aged men. J Strength Cond Res 23: 1663–1669, 2009.
31. Kern B, Robinson T. Effects of beta-alanine supplementation on performance and body composition in collegiate wrestlers and football players. J Int Soc Sports Nutr 25: 1804–1815, 2009.
32. Kim HJ, Kim CK, Lee YW, Harris RC, Sale C, Harris BD, Wise JA. The effect of a supplement
containing B-alanine on muscle carnosine synthesis and exercise capacity, during 12 week combined endurance and weight training. J Int Soc Sports Nutr 3: S9, 2006.
33. Messonnier L, Kristensen M, Juel C, Denis C. Importance of pH regulation and lactate/H+ transport capacity for work production during supramaximal exercise in humans. J Appl Physiol 102: 1936–1944, 2007.
34. Monod H, Scherrer J. The work capacity of a synergic muscular group. Ergonomics 8: 329–338, 1965.
35. Nebelsick-Gullett LJ, Housh TJ, Johnson GO, Bauge SM. A comparison between methods of measuring anaerobic work capacity. Ergonomics 31: 1413–1419, 1988.
36. Peake J, Wilson G, Hordern M, Suzuki K, Yamaya K, Nosaka K, Mackinnon L, Coombes JS. Changes in neutrophil surface receptor expression, degranulation, and respiratory burst activity after moderate- and high-intensity exercise. J Appl Physiol 97: 612–618, 2004.
37. Pepper ML, Housh TJ, Johnson GO. The accuracy of the critical velocity
test for predicting time to exhaustion during treadmill running. Int J Sports Med 13: 121–124, 1992.
38. Robergs RA, Ghiasvand F, Parker D. Biochemistry of exercise-induced metabolic acidosis. Am J Physiol Regul Integr Comp Physiol 287: R502–516, 2004.
39. Sale C, Saunders B, Harris RC. Effect of beta-alanine supplementation on muscle carnosine concentrations and exercise performance. Amino Acids 39: 321–333, 2010.
40. Saunders B, Sale C, Harris RC, Sunderland C. Effect of beta-alanine supplementation on repeated sprint performance during the Loughborough Intermittent Shuttle Test. Amino Acids 43: 39–47, 2012.
41. Smith AE, Fukuda DH, Kendall KL, Stout JR. The effects of a pre-workout supplement
containing caffeine, creatine, and amino acids during three weeks of high-intensity exercise on aerobic and anaerobic performance. J Int Soc Sports Nutr 7: 10, 2010.
42. Smith AE, Kendall KL, Fukuda DH, Cramer JT, Stout JR. Determination of aerobic and anaerobic performance: A methodological consideration. Physiol Meas 32: 423–431, 2011.
43. Smith AE, Moon JR, Kendall KL, Graef JL, Lockwood CM, Walter AA, Beck TW, Cramer JT, Stout JR. The effects of beta-alanine supplementation and high-intensity interval training on neuromuscular fatigue and muscle function. Eur J Applied Physiol 105: 357–363, 2009.
44. Smith AE, Stout JR, Kendall KL, Fukuda DH, Cramer JT. Exercise-induced oxidative stress: The effects of beta-alanine supplementation in women. Amino Acids 43–90: 77–90, 2011.
45. Smith AE, Walter AA, Graef JL, Kendall KL, Moon JR, Lockwood CM, Fukuda DH, Beck TW, Cramer JT, Stout JR. Effects of beta-alanine supplementation and high-intensity interval training on endurance performance and body composition in men; a double-blind trial. J Int Soc Sports Nutr 6: 5, 2009.
46. Stellingwerff T, Anwander H, Egger A, Buehler T, Kreis R, Decombaz J, Boesch C. Effect of two beta-alanine dosing protocols on muscle carnosine synthesis and washout. Amino Acids, 42: 2461–2472, 2011.
47. Stout JR, Cramer JT, Zoeller RF, Torok D, Costa P, Hoffman JR, Harris RC, O'Kroy J. Effects of beta-alanine supplementation on the onset of neuromuscular fatigue and ventilatory threshold in women. Amino Acids 32: 381–386, 2007.
48. Suzuki Y, Ito O, Mukai N, Takahashi H, Takamatsu K. High level of skeletal muscle carnosine contributes to the latter half of exercise performance during 30-s maximal cycle ergometer sprinting. Jpn J Physiol 52: 199–205, 2002.
49. Van Thienen R, Van Proeyen K, Vanden Eynde B, Puype J, Lefere T, Hespel P. Beta-alanine improves sprint performance in endurance cycling. Med Sci Sports Exerc 41: 898–903, 2009.
50. Walter AA, Smith AE, Kendall KL, Stout JR, Cramer JT. Six weeks of high-intensity interval training with and without beta-alanine supplementation for improving cardiovascular fitness in women. J Strength Cond Res 24: 1199–1207, 2010.
51. Weston AR, Myburgh KH, Lindsay FH, Dennis SC, Noakes TD, Hawley JA. Skeletal muscle buffering capacity and endurance performance after high-intensity interval training by well-trained cyclists. Eur J Appl Physiol Occup Physiol 75: 7–13, 1997.
52. Zoeller RF, Stout JR, O'Kroy JA, Torok DJ, Mielke M. Effects of 28 days of beta-alanine and creatine monohydrate supplementation on aerobic power, ventilatory and lactate thresholds, and time to exhaustion. Amino Acids 33: 505–510, 2007.