Does beta-alanine help teen distance runners?
Learn to train smart, run fast, and be strong with this endurance performance nerd alert from Thomas Solomon, PhD.
Beta-alanine supplementation improves time to exhaustion, but not aerobic capacity, in competitive middle- and long-distance runners
Marko et al. (2026) J Int Soc Sports Nutr (click here to open the original paper)
What type of study is this?
◦ This study is a randomised controlled trialThe “gold standard” approach for determining whether a treatment has a causal effect on an outcome of interest. In such a study, a sample of people representing the population of interest is randomised to receive the treatment or a no-treatment placebo (control), and the outcome of interest is measured before and after the exposure to treatment/control. comparing beta-alanine supplementation with a placebo in elite adolescent middle- and long-distance runners.
What was the authors’ hypothesis or research question?
◦ The authors hypothesised that 4 weeks of beta-alanine supplementation would increase time to exhaustionA test where you work at a fixed intensity and keep going until you can’t maintain it anymore. The score is simply how long you last. It’s a tough way to measure endurance capacity and fatigue resistance. during maximal exercise in adolescent runners, without increasing aerobic capacity (V̇O2peakV̇O2peak is the highest oxygen uptake measured during a test, even if VO₂max wasn’t fully reached. I.e., it is the best effort recorded, but not always your max.).
What did the authors do to test the hypothesis or answer the research question?
◦ Twenty-seven competitive adolescent middle- and long-distance runners (13 males, 14 females, age about 17 years, training 5 times per week and 2000 km per year) were randomly assigned to receive beta-alanine (6.4 g/day males, 4.8 g/day females) or a maltodextrin placebo for 4 weeks.
◦ All athletes performed 3 graded exercise tests to exhaustion and 3 submaximal 1500 m treadmill runs at 80% of individual V̇O2peakV̇O2peak is the highest oxygen uptake measured during a test, even if VO₂max wasn’t fully reached. I.e., it is the best effort recorded, but not always your max., at baseline, week 2, and week 4.
◦ During graded exercise tests, the researchers measured: time to exhaustion (TTE), V̇O2peak, maximal heart rate, ventilatory thresholdsPoints during exercise where your breathing suddenly ramps up. It’s your body’s signal that the effort level has shifted and you’re relying more on faster energy sources. They can be used as points to set training zones., respiratory exchange ratio (RER)RER is the ratio of carbon dioxide you breathe out to oxygen you use. It hints at the type of fuel your body is predominantly burning: around 0.7 = mostly fat, around 1.0 = mostly carbs, >1.0 = all-out effort., blood lactate 3 minutes post-test, and rating of perceived exertion (RPE)Rating of perceived exertion (RPE) is a simple way to score how hard exercise feels to you, not to a machine. You pick a number on a scale (often 1–10), where low numbers mean “this feels easy” and high numbers mean “I’m really pushing it.” It blends how heavy your breathing is, how tired your muscles feel, and how much effort you think you’re putting in.. During submaximal 1500 m runs, they measured average heart rate, average oxygen uptake, RER, blood lactate 3 minutes post-run, and RPE. Training and diet were kept consistent, and participants were supervised for supplement compliance via daily messaging.
What did the authors find?
◦ Data were analysed for 23 runners (beta-alanine n=12, placebo n=11). Baseline anthropometrics and performance were similar between groups, and any baseline difference in final RERRER is the ratio of carbon dioxide you breathe out to oxygen you use. It hints at the type of fuel your body is predominantly burning: around 0.7 = mostly fat, around 1.0 = mostly carbs, >1.0 = all-out effort. during graded exercise tests was adjusted for statistically.
For the maximal text:
◦ V̇O2peakV̇O2peak is the highest oxygen uptake measured during a test, even if VO₂max wasn’t fully reached. I.e., it is the best effort recorded, but not always your max. showed only a trivial change with beta-alanine over 4 weeks (about a 1–2% increase), which the authors note is smaller than expected day-to-day test variability; the placebo group slightly decreased. Statistically, there was no meaningful between-group difference and no clinically convincing change in aerobic capacity.
◦ Time to exhaustion improved more in the beta-alanine group (about 6.5% increase, roughly 24 seconds longer) than in the placebo group (about 1.4% increase, roughly 5 seconds). This produced a small between-group effect that is plausible but not huge; for a competitive runner, it might be useful if it translates into race-relevant surges.
For the ventilatory thresholdsPoints during exercise where your breathing suddenly ramps up. It’s your body’s signal that the effort level has shifted and you’re relying more on faster energy sources. They can be used as points to set training zones.:
◦ At the first ventilatory threshold, VT1, beta-alanine produced moderate between-group increases in oxygen uptake and breathing rate compared with placebo. These changes were moderate in effect sizeA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. but modest in absolute terms, so are likely to have a small real-world impact.
◦ At the second ventilatory threshold, VT2, there were larger and more convincing differences: over 4 weeks, oxygen uptake at VT2 increased about 5% and velocity about 4–5% in beta-alanine, whereas the placebo group actually decreased in both. Between-group effects were large for oxygen uptake and moderate for velocity, suggesting a delayed onset of heavy breathing in the supplement group.
For the submaximal 1500 m treadmill run:
◦ Average heart rate fell by about 7 beats per minute (roughly 4%) in the beta-alanine group, with no meaningful change in placebo, giving a moderate between-group difference that looks practically helpful (lower heart strain at the same relative workload).
◦ Average RERRER is the ratio of carbon dioxide you breathe out to oxygen you use. It hints at the type of fuel your body is predominantly burning: around 0.7 = mostly fat, around 1.0 = mostly carbs, >1.0 = all-out effort. fell slightly in beta-alanine and stayed similar in placebo, again a moderate between-group effect but small absolute shift (about 0.04), so the physiology geeks will like it more than the athletes notice it.
◦ Average oxygen uptake during the submaximal run decreased in the placebo group and increased slightly in beta-alanine, producing a moderate between-group effect; however, this may be confoundedA third factor that influences both the exposure (or intervention) and the outcome, creating a false association (or effect). Adjusting for a confounding factor can change the story. by small body-mass changes and does not clearly indicate better or worse “running economy” in a way that runners would feel day to day.
◦ Lactate and breathing rate responses during the submaximal runs showed only trivial-to-small changes in both groups, largely within expected day-to-day noise.
Side effects:
◦ 8 of 11 beta-alanine participants reported mild paraesthesia (tingling) within 2 weeks, with no other reported side effects; the placebo group had no such issue.
◦ The authors concluded that 4 weeks of beta-alanine supplementation did not improve aerobic capacity (VO2peak) but may prolong time to exhaustion and raise ventilatory thresholds in elite adolescent runners, offering a potential ergogenic benefit during high-intensity segments of endurance performance.
What were the strengths?
◦ Methodologically, this is a nicely run small trial. RandomisationRandomization means assigning people to different parts of a study (e.g., groups in a randomised controlled trial) by chance, not by choice. This helps make the groups similar at the start and reduces bias, so any differences you see are more likely due to the treatment, not background differences. In a crossover study, randomization usually decides the order in which each person gets the treatments (for example, Treatment A first then B, or B first then A). This way, order effects—like learning, fatigue, or simple time passing—are less likely to skew the results., a placebo control, and double-blindingBlinding is when people in a study don’t know which treatment they’re getting. It stops expectations or beliefs (from patients or researchers) from skewing the results. “Single-blind” means participants don’t know; “double-blind” means participants and researchers don’t know; “triple-blind” means that the participants, researchers, and data analysts are kept in the dark. The goal is simple: fair tests and trustworthy findings. are all in place, with group allocation done by computer randomiser. Testing is standardised: same time of day, treadmill protocol, clear termination criteria for the graded exercise test, and validated methods for determining ventilatory thresholdsPoints during exercise where your breathing suddenly ramps up. It’s your body’s signal that the effort level has shifted and you’re relying more on faster energy sources. They can be used as points to set training zones., VT1 and VT2. The authors measure a wide panel of outcomes (time to exhaustionA test where you work at a fixed intensity and keep going until you can’t maintain it anymore. The score is simply how long you last. It’s a tough way to measure endurance capacity and fatigue resistance., thresholds, lactate, ventilation, heart rate, RERRER is the ratio of carbon dioxide you breathe out to oxygen you use. It hints at the type of fuel your body is predominantly burning: around 0.7 = mostly fat, around 1.0 = mostly carbs, >1.0 = all-out effort.) and use effect sizesA standardised measure of the magnitude of an effect of an intervention. Unlike p-values, effect sizes show the size of the effect and how meaningful it might be. Common effect size measures include standardised mean difference (SMD), Cohen’s d, Hedges’ g, eta-squared, and correlation coefficients. and “minimum clinically meaningful differencesThe smallest change a person would notice or value. It’s the bar a treatment should clear to be worth it.” to interpret clinical significanceReflects how meaningful a change is to a person’s health or performance. A small change can be statistically significant but not clinically significant; but, if a change is big enough to matter to people in real life, then it is clinically significant (and doesn’t just tick a statistical significance box). rather than chasing P-valuesA p-value is a statistical measure that indicates the probability that the result is at least as extreme as that observed if the null-hypothesis was true. If P is small, the observed difference is big enough to disprove (reject) the null hypothesis. In very basic terms, P equals the probability that the effect could be explained by random chance, and a P-value of less than 0.05 means the results look so promising that there’s only a 1-in-20 (or 5%) chance that they would have occurred if the treatment had no effect at all. Common thresholds for statistical significance are 0.05, 0.01, and 0.001. and statistical significanceEvidence that a result is unlikely to be due to chance under a “no effect” model (or null hypothesis). Statistical significance is often judged by a p-value below 0.05 to flag that “something” is going on, but not how big or important that “something” is. One statistically significant result doesn’t mean proof; replication is needed. And, a statistically significant result doesn’t necessarily indicate clinical significance. alone, which is refreshingly grown-up. Inclusion/exclusion criteria, ethics approval, and informed consent are clearly described, and baseline differences were minor and appropriately adjusted for in analyses. For such a niche population (elite adolescent runners), having a tightly controlled, sport-specific lab protocol is a real plus.
What were the limitations?
◦ Despite the strengths, we’re still looking at a small N. Only 23 athletes completed the study, which means limited statistical powerStatistical power is the probability that a statistical test will correctly detect a real effect if there is one: a true positive. (In jargon: power is the probability that a statistical test correctly rejects a false null hypothesis). Higher statistical power reduces the risk of a false negative (failing to detect a true effect; or a Type II error). Power is typically influenced by sample size, effect size, significance level, and variability in the data, with a common target being at least 80% (or 0.8). and a higher risk of missing true effects (i.e., finding false negativesWhen a statistical test fails to detect an effect or difference when there actually is one. I.e, “a missed detection”. Studies with a small sample size (N, number of participants) are more likely to produce false negative results.) or overestimating the effects we see.
◦ The intervention lasted only 4 weeks, which the authors themselves point out is basically the minimum loading period for beta-alanine; longer durations might be needed to fully exploit changes in muscle carnosine and see any real effect on V̇O2 or race performance. There was no direct measurement of muscle carnosine (read about that at veohtu.com/beta-alanine-for-runners), so the supposed mechanism remains assumed rather than tested.
◦ Weight changes over the 4 weeks were correlated with changes in V̇O2, especially in the beta-alanine group, making it hard to cleanly attribute small capacity shifts to the supplement rather than simple “lighter runner, higher VO2 relative to body mass” effects.
◦ The authors did not pre-register the protocolPreregistration is when a detailed description of a study plan is deposited in an open-access repository before collecting the study data. It promotes transparency and accountability, and boosts research integrity. Without preregistration, it is easier for scientists to change outcomes after seeing the data, selectively report “exciting” results, or run many analyses and only show the ones that work, which can introduce bias and weaken the trustworthiness of the findings. or report a pre-study power calculationA power calculation is a way to figure out how many people or data points you need in a study so you can reliably spot a real effect if it exists. It balances four things: the size of the effect you care about, how much random variation there is, how strict you are about false alarms, and how likely you want to be to detect the effect. In plain terms: it helps you avoid running a study that’s too small to be useful or so big that it wastes time and money..
◦ And, importantly, they did not test a real all-out running time trial (e.g., a 1500 m race effort); the submaximal 1500 m run at fixed relative intensity is more physiological than performance-relevant, so translation to race results is speculative.
How was the study funded, and are there any conflicts of interest that may influence the findings?
◦ The research was funded by grant SVV No. 260 847/2025 and the Cooperatio program for Sport Sciences – Biomedical & Rehabilitation Medicine. The authors explicitly state that they had no potential conflicts of interest. There is no indication that the supplement manufacturer funded or influenced the trial.
How can you apply these findings to your training or coaching practice?
◦ For coaches of high-level teenage middle- and long-distance runners, this paper nudges beta-alanine into the “maybe useful, but not a miracle” bucket. You might get a bit more tolerance to race-pace surges and a slightly lower heart rate at a given submaximal speed, without a bump in aerobic capacity itself. If you already have the basics nailed (training, nutrition, rest, sleep), beta-alanine could be a marginal-gains add-on rather than a cornerstone. The tingling side-effect in most of the beta-alanine group is mild but real, so athletes should also know what they’re signing up for. As for whether it will actually make a teenager kick harder in the last 300 m of a big race… the data are suggestive, not definitive. The force is kinda strong with this one, but I wouldn’t build a Death Star on it.
What is my Rating of Perceived scientific Enjoyment (RPsE)?
6 out of 10 → I experienced moderate scientific enjoyment because this is a well-executed, sport-specific, double-blind trial in a rare population (elite adolescent runners) with thoughtful stats and a plausible ergogenic signal on time to exhaustion and ventilatory thresholds, but the small sample, short duration, lack of pre-registration, and absence of real race-performance outcomes hold it back from greatness.
Important: Don’t make any major changes to your daily habits based on the findings of one study, especially if the study is small (e.g., less than 30 participants in a randomised controlled trial or less than 5 studies in a meta-analysis) or poor quality (e.g., high risk of biasRisk of bias in meta-analysis refers to the potential for systematic errors in the studies included in the analysis, which can lead to misleading or invalid results. Assessing this risk is crucial to ensure the conclusions drawn from the combined data are reliable. or low quality of evidenceA low quality of evidence means that, in general, studies in this field have several limitations. This could be due to inconsistency in effects between studies, a large range of effect sizes between studies, and/or a high risk of bias (caused by inappropriate controls, a small number of studies, small numbers of participants, poor/absent randomization processes, missing data, inappropriate methods/statistics). When the quality of evidence is low, there is more doubt and less confidence in the overall effect of an intervention, and future studies could easily change overall conclusions. The best way to improve the quality of evidence is for scientists to conduct large, well-controlled, high-quality randomized controlled trials.). What do other trials in this field show? (opens in new tab) Do they confirm the findings of this study or have mixed outcomes? Is there a high-quality systematic review and meta-analysis evaluating the entirety of the evidence in this field? (opens in new tab) If so, what does the analysis show? What is the risk of biasRisk of bias in a meta-analysis refers to the potential for systematic errors in the studies included in the analysis. Such errors can lead to misleading/invalid results and unreliable conclusions. This can arise because of issues with the way participants are selected (randomisation), how data is collected and analysed, and how the results are reported. or certainty of evidenceCertainty of evidence tells us how confident we are that the results reflect the true effect. It’s based on factors like study design, risk of bias, consistency, directness, and precision. Low certainty means more doubt and less confidence, and that future studies could easily change the conclusions. High certainty means that the current evidence is so strong and consistent that future studies are unlikely to change conclusions. across the included studies? I’ve written a deep-dive article on this topic; check it out at veohtu.com/beta-alanine-for-runners.
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