Individual Participant Data Meta-Analysis Provides No Evidence of Intervention Response Variation in Individuals Supplementing With Beta-Alanine

in International Journal of Sport Nutrition and Exercise Metabolism
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  • 1 University of São Paulo
  • | 2 Robert Gordon University
  • | 3 Nottingham Trent University

Currently, little is known about the extent of interindividual variability in response to beta-alanine (BA) supplementation, nor what proportion of said variability can be attributed to external factors or to the intervention itself (intervention response). To investigate this, individual participant data on the effect of BA supplementation on a high-intensity cycling capacity test (CCT110%) were meta-analyzed. Changes in time to exhaustion (TTE) and muscle carnosine were the primary and secondary outcomes. Multilevel distributional Bayesian models were used to estimate the mean and SD of BA and placebo group change scores. The relative sizes of group SDs were used to infer whether observed variation in change scores were due to intervention or non-intervention-related effects. Six eligible studies were identified, and individual data were obtained from four of these. Analyses showed a group effect of BA supplementation on TTE (7.7, 95% credible interval [CrI] [1.3, 14.3] s) and muscle carnosine (18.1, 95% CrI [14.5, 21.9] mmol/kg DM). A large intervention response variation was identified for muscle carnosine (σIR = 5.8, 95% CrI [4.2, 7.4] mmol/kg DM) while equivalent change score SDs were shown for TTE in both the placebo (16.1, 95% CrI [13.0, 21.3] s) and BA (15.9, 95% CrI [13.0, 20.0] s) conditions, with the probability that SD was greater in placebo being 0.64. In conclusion, the similarity in observed change score SDs between groups for TTE indicates the source of variation is common to both groups, and therefore unrelated to the supplement itself, likely originating instead from external factors such as nutritional intake, sleep patterns, or training status.

Beta-alanine (BA) supplementation is an established nutritional strategy to improve exercise capacity (Saunders et al., 2017). This is likely due to its capacity to increase muscle carnosine (MCarn) content (Rezende et al., 2020), which acts as an intracellular buffering agent (Blancquaert et al., 2015; Dolan et al., 2019; Trexler et al., 2015). A recent meta-analysis provided evidence that BA supplementation exerted a positive, albeit small magnitude effect (d = 0.18) across a range of exercise protocols, while meta-regression identified that exercise type and duration were influential moderating factors, with BA exerting its greatest influence on exercise capacity-based tests lasting between 30 s and 10 min (d = 0.49) (Saunders et al., 2017). These results align with plausible physiological mechanisms, given that capacity-based tests of moderate duration are most likely to be limited by metabolic acidosis (Bishop et al., 2009). Despite evidence of a positive average effect at the group level, substantial variability in performance outcomes was identified between studies. Both positive and null effects were reported for the influence of BA on an isometric endurance hold test (Bassinello et al., 2019; Derave et al., 2007; Jones et al., 2017; Sale et al., 2012) or on a high-intensity cycling capacity test (the CCT110%) (Danaher et al., 2014; Hill et al., 2007; Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021). Both these tests should theoretically be amenable to BA supplementation, given that they are exercise capacity tests that induce large pH perturbations and are within the time durations most likely to be positively influenced by BA supplementation (Saunders et al., 2017).

Research investigating BA supplementation has also highlighted large within-study interindividual variability. In addition to reporting that BA moderately improved CCT110% performance for the group, Saunders et al. (2017) also identified a wide range of individual changes, with some participants demonstrating large performance improvements (up to 40 s), while others showed none, or even a worsening of performance (up to −20 s). Large interindividual variability is not limited to BA supplementation studies, but likely applies to most health- and performance-related interventions (Atkinson & Batterham, 2015). Improved understanding of the factors underpinning this variability could improve study standardization and intervention effectiveness, enabling more targeted and individualized recommendations. Investigation of individual response variation is, however, both methodologically and statistically challenging, as has been described in detail elsewhere (Atkinson & Batterham, 2015; Atkinson et al., 2019; Bonafiglia et al., 2019; Hecksteden et al., 2015; Senn, 2004; Swinton et al., 2018). Briefly, variance in observed change scores across an intervention generally comprises three sources (Atkinson & Batterham, 2015; Swinton et al., 2018) including measurement error (which comprises instrumentation noise and biological noise, both of which may cause day-to-day fluctuations in the observed score, even though the true score remains constant); biological variability (which represents actual change in true score across the intervention period, but which occurs independently of the intervention); and intervention response variation (which represents variation directly attributable to the investigated intervention). Theoretically, variation in the observed change scores of the control group will comprise measurement error and biological variability, while variation in the intervention group will comprise all three sources. As such, inclusion of a control group enables estimation of the variation in change scores attributable to the intervention itself.

Accurate estimation of interindividual variability can be difficult, and generally requires relatively large sample sizes, which may not be feasible in single studies due to costs, need for invasive procedures, and/or time constraints. Estimation is particularly challenging in studies of ergogenic aids as expected effects tend to be small (Saunders et al., 2017), while performance-related outcomes can be influenced by substantive measurement error and biological variability. Statistical meta-analysis presents an approach to mitigate the limitations of small effects, noisy measurement outcomes, and small sample sizes by pooling data across studies to better estimate parameters of interest (Page et al., 2021). Most meta-analyses are, however, based upon aggregate data and can only provide information on the mean response. In contrast, individual participant data meta-analyses, which source raw data from previous studies, have been described as the “gold standard” of meta-analytic approaches because they allow for assessment of participant-level effects and interactions (Kelley & Kelley, 2019). Accordingly, we conducted an individual participant data meta-analysis, the aim of which was to estimate the mean response to BA supplementation on high-intensity cycling capacity and to quantify intervention response variation (namely that attributable to the BA intervention itself).

Methods

This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (Page et al., 2021). Eligibility criteria were defined according to the Population, Intervention, Comparator, Outcomes, and Study Design (PICOS), as described in Table 1. Only studies that used the CCT110%—a high-intensity cycling capacity test in which individuals are required to cycle at 110% of their previously established maximal power output until volitional exhaustion—were included. This test was selected as a model for this analysis because it is a widely used protocol in which a positive response to BA supplementation is theoretically likely (Saunders et al., 2017).

Table 1

Inclusion and Exclusion Criteria According to PICOS

CriteriaDescription
PopulationHealthy participants of any age or physical activity level.
InterventionStudies investigating the effects of chronic BA supplementation (≥4 weeks) on exercise test performance.
ComparatorChange in the BA group versus PLA group.
OutcomesThe primary outcome of interest was change in TTE in the CCT110% test, along with intervention response variation. Changes in MCarn content were considered a secondary outcome, along with the proposed moderator analyzes described in the analysis section.
Study designRandomized, double-blinded, PLA-controlled intervention studies.

Note. PICOS = Population, Intervention, Comparator, Outcomes, and Study Design; BA = beta-alanine; PLA = placebo; TTE = time to exhaustion; CCT110% = high-intensity cycling capacity test; MCarn = muscle carnosine.

Search Strategy and Data Extraction

To identify eligible studies, the included studies from a previous meta-analysis that investigated the influence of BA supplementation on exercise test performance were screened (Saunders et al., 2017). The full search strategy is described in the previous meta-analysis. Three databases (PubMed, Google Scholar, and Web of Science) were searched using the terms “β-alanine OR beta-alanine” concatenated with “supplementation OR exercise OR training OR athlete OR performance OR carnosine” and only double-blinded, placebo [PLA]-controlled trials were selected. The same strategies were repeated in September 2020 to identify any additional studies that had been published in the interim. Aggregate data were extracted from studies that matched our inclusion criteria (Table 1). Authors were contacted to request individual participant data, and these were compiled into a prepiloted Excel spreadsheet.

Risk of Bias and Certainty in Cumulative Outcomes

Certainty in systematic review outcomes (namely each combined meta-analytic result based upon all available data sets) was ascertained using the recommendations of the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) working group (Guyatt et al., 2008). Five potential downgrading factors were considered, namely risk of bias (assessed using the Cochrane Collaborations RoB2 tool; Sterne et al., 2019), indirectness, inconsistency, imprecision, and publication bias. Potential upgrading factors included large effects, evidence of a dose–response, or the presence of plausible residual confounding factors (Guyatt et al., 2008). All studies in the current review were initially defined as “high certainty” given that only double-blinded, randomized, PLA-controlled designs were included in the review. Application of the Grading of Recommendations, Assessment, Development and Evaluations strategy subsequently allowed for this a priori certainty rating of “high” to be maintained, or to be downgraded to “moderate,” “low,” or “very low.”

Statistical Analysis

The present study comprised both aggregate and individual participant data meta-analyses from a Bayesian perspective. Analyses were performed on change in time to exhaustion (TTE) performance (s) as the primary outcome, and change in MCarn (mmol/kg dry muscle) as the secondary outcome. For aggregate analyses, PLA controlled mean difference and variance difference effect sizes (ESs) were calculated to describe the effects of supplementation on mean response and response variation, respectively. Calculated values were pooled using three-level hierarchical models including random effects to account for within-studies variation, between-studies variation (τ) and covariance (intraclass correlation coefficient [ICC]) of multiple outcomes reported in the same study. SEs for mean differences (Morris & DeShon, 2002) and variance differences (Williamson et al., 2018) were calculated according to previously described formulas. The required pre–post correlation for mean difference SEs was estimated from available data and set to .5 for both outcome variables. Intervention response variation estimates were transformed by taking the square root (σIR) of the absolute variance values and then reapplying the positive or negative sign (Williamson et al., 2018).

Individual participant data meta-analyses were conducted by calculating observed change scores from baseline and fitting hierarchical distributional models enabling both the location (mean) and spread (SD) to be estimated assuming Gaussian distributions. Models for both the mean and SD included a group factor (PLA vs. intervention) and for the mean a participant random effect intercept was added. Moderator analyses investigating group-level characteristics were completed for total BA consumption in both outcome variables. However, moderator analyses for individual variation in the primary outcome (TTE performance change) were only conducted where evidence was obtained that variation was influenced directly by the intervention (Atkinson & Batterham, 2015). Inferences from all analyses were performed on posterior samples generated using the Hamiltonian Markov Chain Monte Carlo method (five chains, 100,000 iterations, and 50,000 warm-up). Interpretations were based on the median value (0.5 quantile) and credible interval (CrI). In addition, posterior samples were used to estimate the proportion of individuals that would observe a small, medium, and large change (small: 0.2; medium: 0.5; and large: 0.8 times baseline SD), and the probability that the intervention change score SD was larger than PLA for individual participant data analyses. Analyses were performed using the R wrapper package brms (R Foundation for Statistical Computing, Vienna, Austria) interfaced with Stan to perform sampling (Bürkner, 2017). Convergence of parameter estimates was obtained for all models with Gelman–Rubin R-hat values below 1.1 (Gelman et al., 2013).

Results

Overview of Available Studies

Six studies that investigated the influence of BA supplementation on performance in the CCT110% were identified (Danaher et al., 2014; Hill et al., 2007; Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021). All corresponding authors were contacted, and individual participant data from four studies were obtained (Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021). A summary of the study design, population, and dosing protocol of all included studies is available in Table 2. The CCT110% protocols employed were compared with the standard protocol described in a reliability study of the CCT110% (Saunders et al., 2013; see Supplementary Material 1 [available online]). The differences between study designs were minor and deemed unlikely to impact interpretation of the results, particularly given that each study contained its own PLA-controlled comparison group.

Table 2

Characteristics of Included Studies

Author (year)Study designPopulationDosing protocol
Hill et al. (2007)Double blinded RCT, with tests conducted after 0, 4, 8, and 10 weeks of supplementation.Healthy, physically active males (BA: n = 13; PLA: n = 12).BA: Week 1, 4 g/day; Week 2, 4.8 g/day; Week 3, 5.6 g/day; Week 4, 6.4 g/day. Dose maintained until Week 10 for eight participants (8 × 800 mg doses; total dose = 145.6 g). PLA: maltodextrin.
Sale et al. (2011)Double-blinded RCT, with tests conducted after 0 and 4 weeks of supplementation.Healthy, physically active males (BA: n = 10; PLA: n = 10).BA: 6.4 g/day (4 × 1,600 mg) for 4 weeks (total dose = 179.2 g). PLA: maltodextrin.
Danaher et al. (2014)Double blinded, within subject cross-over, RCT, with test sessions separated by a 12-week washout.Healthy, physically active males (n = 8).BA: 4.8 g/day (6 × 800 mg) for 4 weeks, then 6.4 g/day (8 × 800 mg) for 2 weeks (total dose = 224 g). PLA: calcium carbonate.
Saunders et al. (2017b)Double-blinded RCT, with tests conducted after 0, 4, 8, 12, 16, 20, and 24 weeks of supplementation.Healthy, physically active males (BA: n = 15; PLA: n = 9).BA: 6.4 g/day of BA (4 × 1,600 mg) for 24 weeks (total dose = 1,075.2 g). PLA: maltodextrin.
Yamaguchi et al. (2021)Double-blinded RCT, with tests conducted after 0 and 8 weeks of supplementation.Healthy, physically active, omnivorous males (BA: n = 11; PLA: n = 4).BA: 6.4 g/day (4 × 1,600 mg) for 8 weeks (total dose = 358.4 g). PLA: maltodextrin.
Patel et al. (2021)Double-blinded RCT, with tests conducted after 0 and 4 weeks of supplementation.Healthy, physically active, omnivorous males (BA: n = 10; PLA: n = 9)BA: 6.4 g/day (4 × 1,600 mg) for 4 weeks (total dose = 179.2 g). PLA: celluloses plus excipients.

Note. RCT = randomized control trial; BA = beta-alanine; PLA = placebo.

Influence of BA Supplementation on TTE in the CCT110% (Primary Outcome)

Aggregate data for mean difference ESs were obtained for five studies (Hill et al., 2007; Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021) generating a total of 11 PLA-controlled ESs. One study was not included (Danaher et al., 2014) because no presupplementation data for TTE was available. This study did, however, report a positive influence of BA on this outcome when compared with PLA (p = .005; Danaher et al., 2014). Using the aggregate data, a large absolute mean difference ES was estimated (ES0.5 = 11.9, 95% CrI [6.3, 16.5] s; Figure 1a) with relatively low between-study variance (τ.5 = 2.4, 75% CrI [.5, 6.1] s) and covariance due to reporting of multiple time points (ICC = .13, 75% CrI [.02, .36]). Evidence of a group-level moderating effect was identified for total BA consumption. The mean difference between groups in TTE with a cumulative dose of 500 g was estimated to be (7.4, 95% CrI [0.8, 13.9] s), with linear regression estimating an increase of (0.6, 95% CrI [0.03, 1.2] s) per additional 100 g consumed. Aggregate data for response variation were calculated for four studies (Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021) generating a total of nine ESs. Large CrIs were obtained indicating equivalence or potentially greater variation in the PLA group (σIR = −6.1, 95% CrI [−15.5, 11.7] s; τ.5 = 10.4, 75% CrI [5.1, 15.3] s; and ICC.5 = .12, 75% CrI [.01, .30]).

Figure 1
Figure 1

—Influence of BA supplementation on TTE. (a) Aggregate data from PLA-controlled trials, showing mean difference effect sizes along with 95% CrIs for the shrunken effects of BA supplementation on TTE after applying the meta-analysis model. (b) Individual participant data with BA supplementation and PLA means along with 95% CrIs. BA = beta-alanine; TTE = time to exhaustion; CrIs = credible intervals; CCT110% = high-intensity cycling capacity test; PLA = placebo; ES = effect size.

Citation: International Journal of Sport Nutrition and Exercise Metabolism 31, 4; 10.1123/ijsnem.2021-0038

Individual participant data were obtained for four studies (Patel et al., 2021; Sale et al., 2011; Saunders et al., 2017; Yamaguchi et al., 2021) generating 210 sets of pretest, intermediate, and posttest data (127 BA and 83 PLA) across 78 participants (46 BA and 32 PLA). Using the distributional model, the mean difference in TTE with BA compared with PLA was estimated to be (7.7, 95% CrI [1.3, 14.3] s; Figure 1b), with very large individual random effect intercepts estimated (14.1, 95% CrI [10.6, 16.8] s). In agreement with the aggregate analysis, the distributional model estimated equivalent SD of change scores in the combined PLA (16.1, 95% CrI [13.0, 21.3] s) and supplement (15.9, 95% CrI [13.0, 20.0] s) analysis, with the probability that the SD was greater in the PLA group being 0.643. Using the distributional model estimates, the proportions of individuals expected to make at least a small, medium, and large improvement were substantively greater for BA supplementation (small: 0.63, 95% CrI [0.42, 0.81]; medium: 0.52, 95% CrI [0.31, 0.72]; and large: 0.42, 95% CrI [0.22, 0.62]) compared with PLA (small: 0.45, 95% CrI [0.24, 0.66]; medium: 0.35, 95% CrI [0.17, 0.56]; and large: 0.26, 95% CrI [0.11, 0.45]). No moderator analyses for individual variation were investigated due to the similarity in change score SDs (Williamson et al., 2017).

Influence of BA Supplementation on MCarn (Secondary Outcome)

MCarn aggregate data were obtained for four studies (Danaher et al., 2014; Hill et al., 2007; Saunders et al., 2017; Yamaguchi et al., 2021) generating a total of 10 PLA-controlled ESs. A large absolute mean difference ES was identified (ES0.5 = 13.7, 95% CrI [7.7, 19.6] mmol/kg DM) with moderate between study variance (τ.5 = 4.2, 75% CrI [.3, 12.0] mmol/kg DM) and substantive covariance due to the reporting of multiple time points (ICC.5 = .55, 75% CrI [.23, .82]). Evidence of a group-level moderating effect was identified for total BA consumption. The mean difference in MCarn with a cumulative dose of 500 g was estimated to be 15.1, 95% CrI [10.7, 19.5] mmol/kg DM, with linear regression estimating a 0.36, 95% CrI [0.09, 0.6] mmol/kg DM increase per additional 100 g. Aggregate data for response variation were calculated for two studies (Saunders et al., 2017; Yamaguchi et al., 2021) generating a total of seven ESs. Evidence was obtained indicating a large response variation (σIR = 5.8, 95% CrI [−2.4, 8.4] s; τ.5 = 3.9, 75% CrI [1.9, 6.5] s; and ICC.5 = .18, 75% CrI [.01, .38]).

Individual participant data on the MCarn response to supplementation were obtained for two studies (Saunders et al., 2017; Yamaguchi et al., 2021), generating 156 sets of pre–post data (101 BA and 55 PLA) across 39 individuals (26 BA and 13 PLA). The mean difference in MCarn in the supplement condition compared with PLA was estimated to be 18.1, 95% CrI [14.5, 21.9] mmol/kg DM, with individual random effect intercepts estimated as 4.3, 95% CrI [3.0, 6.0] mmol/kg DM. When pooling data across studies using the distributional model, the intervention response SD was estimated as 5.8, 95% CrI [4.2, 7.4] mmol/kg DM.

Certainty in Outcomes

All outcomes were assigned an a priori certainty rating of “high” because they were all based upon data from double-blinded, randomized, PLA-controlled trials (as defined by the eligibility criteria). Results of the RoB2 assessment are summarized in Figure 2 (generated using R package robvis, R Foundation for Statistical Computing; McGuinness & Higgins, 2021). Possible sources of bias included a lack of information on specific randomization and concealment approaches (Domain 1; Danaher et al., 2014; Hill et al., 2007; Sale et al., 2011); nonreporting of adherence or compliance information (Domain 2; Danaher et al., 2014; Hill et al., 2007); and lack of information about the extent of, or reasons for, participant withdrawal (Domain 3; Danaher et al., 2014; Hill et al., 2007; Sale et al., 2011; Saunders et al., 2017). In addition, no study provided a preregistered protocol or analysis plan (Domain 5). These issues were largely due to a lack of detail in reporting and were deemed unlikely to meaningfully bias the available data. As such, certainty in outcomes were not downgraded due to RoB2 (the complete analysis and decision rationale is available in Supplementary Material 2 [available online]). The studies in this review used commonly recommended dosing protocols and were conducted on young, healthy, recreationally active but nonspecifically trained men and so were not downgraded based upon indirectness, but downgrading of certainty in these outcomes may be advisable for investigators interested in other populations, for example, highly trained athletes. All MCarn outcomes, and the influence of total BA consumption on TTE performance, were downgraded due to potential imprecision as they were based upon a subset of the available data. MCarn outcomes were subsequently upgraded, however, because they were consistent with recent meta-analytic results based upon all available data related to the MCarn response to BA supplementation (Rezende et al., 2020), thus leading to a greater level of certainty in the results obtained. Overall, the majority of outcomes were deemed to have a “High” degree of certainty, with the exception of the influence of total BA consumption on TTE, which was deemed to be of “Moderate” certainty (Table 3).

Figure 2
Figure 2

—Risk of bias in individual studies. D1 = Domain 1; D2 = Domain 2; D3 = Domain 3; D4 = Domain 4; D5 = Domain 5.

Citation: International Journal of Sport Nutrition and Exercise Metabolism 31, 4; 10.1123/ijsnem.2021-0038

Table 3

GRADE Quality Assessment and Summary of Findings

Summary of findings
Quality assessmentNumber of participants
Outcome (number of studies)ROBConsistencyPrecisionPublication biasUpgradeBAPLAES [95% CrI]Quality
TTE main effect of BA (AD) (5)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁594411.9 [6.3–16.5] s⨁⨁⨁⨁

TTE main effect of BA (IPD) (4)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁46327.7 [1.3–14.3] s⨁⨁⨁⨁

TTE influence of total BA consumption (500 g) (2)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁◯⨁⨁⨁◯⨁⨁⨁◯26137.4 [0.8–13.9] s⨁⨁⨁◯

TTE response variation (σIR) (4)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁4632⨁⨁⨁⨁

MCarn main effect of BA (AD) (4)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁◯⨁⨁⨁◯⨁⨁⨁⨁402713.7 [7.7–19.6] mmol/kg DM⨁⨁⨁⨁

MCarn main effect of BA (IPD) (2)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁◯⨁⨁⨁◯⨁⨁⨁⨁261318.1 [14.5–21.9]

mmol/kg DM
⨁⨁⨁⨁

MCarn influence of total BA consumption (500 g) (2)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁◯⨁⨁⨁◯⨁⨁⨁⨁261315.1 [10.7–19.5] mmol/kg DM⨁⨁⨁⨁

MCarn response variation (σIR) (2)⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁⨁◯⨁⨁⨁◯⨁⨁⨁⨁26135.8 [4.2–7.4]

mmol/kg DM
⨁⨁⨁⨁

Note. ⨁⨁⨁⨁ = high; ⨁⨁⨁◯ = moderate; ⨁⨁◯◯ = low; ⨁◯◯◯ = very low; ROB = risk of bias; BA = beta-alanine; PLA = placebo; ES = effect size; CrI = credible interval; TTE = time to exhaustion; AD = aggregate data; IPD = individual participant data; MCarn = muscle carnosine; DM = dry muscle; GRADE = Grading of Recommendations, Assessment, Development and Evaluations; σIR = intervention response SD.

Discussion

The principal finding of this investigation was the lack of intervention response variation to BA supplementation on high-intensity cycling performance. Large, but similar, variances in individual change scores were observed for both the BA and PLA groups, indicating that variation in response to the intervention itself had a negligible contribution, meaning that observed changes were mainly attributable to factors common to both groups, namely measurement error (instrument and biological noise) and/or biological variability.

The finding of a positive effect of BA supplementation on MCarn aligns with previous research (Harris et al., 2006; Rezende et al., 2020) and confirms that the biological mechanism for improved performance was present. Comparisons between BA and PLA groups identified a large intervention response SD for MCarn changes (σIR = 5.8, 95% CrI [4.2, 7.4] mmol/kg DM), demonstrating that a substantial proportion of observed variation was attributable to the intervention itself, which may relate to factors involved in the biokinetics of MCarn synthesis, such as the rate of BA uptake to the skeletal muscle, or the activity level of the synthesizing enzyme carnosine synthase, or in intervention adherence. Previous research also indicates that BA supplementation has a large effect on MCarn (Rezende et al., 2020), and that in the absence of intervention, MCarn remains relatively stable across similar time periods as were investigated herein (Baguet et al., 2009; da Eira Silva et al., 2020; Rezende et al., 2020). Both factors (large effect and small biological variation) may facilitate identification of intervention response variation. In contrast, while large variations in observed change scores were also observed for TTE, comparisons between BA and PLA groups revealed similar SDs. This finding indicates that intervention response variation is negligible, and that observed variation must be primarily attributable to factors common to both the PLA and BA group (namely measurement error and biological variability). These differences in intervention response findings for MCarn and TTE highlight the importance of not conflating mechanistic and performance outcomes—just because the biological mechanism to improve exercise performance is present, does not necessarily mean that all individuals will experience an associated magnitude dependent performance improvement.

The finding of a group effect for BA supplementation on performance on this high-intensity capacity test, along with negligible intervention variation, has positive implications for individuals who supplement with BA, as it suggests there is a consistent group effect and that most individuals who supplement have the capacity to improve performance. This does not mean, however, that everyone who supplements will record improvements in performance. As evidenced by the large interindividual variation observed (Figure 1), large measurement errors and true changes (both positive and negative) in performance can be caused by sources external to the supplement. Although beyond the scope of the current investigation to parse out the relative contribution of potential factors underpinning this variation, it seems unlikely that the instrumentation noise component of measurement error would noticeably impact performance. As such, biological noise (i.e., biological factors that cause the observed score to fluctuate even though the true score remains stable) and biological variability (actual change in the true score due to factors outside the intervention) are the most likely explanations for this finding. Similar findings of negligible variation attributable to the intervention itself have been reported in other interventions (Islam & Gurd, 2020), including the effect of exercise training on maximal oxygen consumption (Williamson et al., 2017) and weight loss (Williamson et al., 2018), in a pain management intervention in adults with chronic musculoskeletal pain (Watson et al., 2021), and in changes in muscle size and strength following resistance training (Dankel et al., 2020). Collectively, these findings support consideration of more holistic approaches to intervention delivery.

Factors that may influence an individual’s response to intervention include nutritional status (both acute and chronic), physical activity levels, sleep, environmental conditions, and external sources of motivation (Mann et al., 2014) such as intervention expectancy (Marticorena et al., 2021). A logical next step for future research would be to attempt to parse out the relative influence of these factors on individual response variation, although this is undoubtedly challenging. Initially, parameters of interest must be defined. For example, although it seems logical to predict that factors such as nutritional intake may contribute to observed variability, the precise parameters required to test this hypothesis remain to be determined (e.g., Macronutrient composition? Micronutrient adequacy? Energy availability?). It is also important to consider whether parameters of interest can be measured with a reasonable level of accuracy. Further complicating these assessments is the possibility that a combination of factors, exerting potentially opposing directional effects, is likely to underpin observed variation, in which case very large samples, in combination with sophisticated analysis techniques, may be necessary to parse out their relative contribution. Sampling error can lead to different outcomes between studies, and this is particularly relevant when investigating small effects and large variability (as is common in sport supplement interventions), and therefore results from any one study should be interpreted with caution. Strategies to increase sample size and thereby to reduce sampling error (e.g., through multicenter studies or individual participant data meta-analyses as was conducted herein) may be required to further advance understanding of factors underpinning individual variation. Similarly, reducing measurement error through selection of the most reliable tests, rigorous control, and standardization of potential confounding variables, as has been described elsewhere (Betts et al., 2020; Burke & Peeling, 2018), may facilitate further investigation of individual response variation.

The findings of the present study may have been influenced by the combination of relatively small mean intervention response alongside large measurement error. In contrast, studies investigating interventions with larger mean responses and lower measurement error may have greater precision to quantify the different error sources and potentially identify intervention response variation. It would also be interesting to investigate whether population characteristics impact findings. The participants investigated herein were all young, healthy, active men, but they were not trained cyclists. Replication of these analyses in elite athletes, who may, theoretically, be less subject to both measurement error and external sources of variation (e.g., by having more consistent sleep, nutrition, and training habits), may potentially allow for detection of variation attributable to the intervention itself.

Summary and Conclusion

In both aggregate and individual participant data meta-analyses, we identified a positive mean effect of BA supplementation on high-intensity cycling capacity as determined by the CCT110% test, although there was considerable interindividual variability in the observed change. The extent of this variation was similar between the PLA and BA groups, indicating that it was mainly due to factors common to both groups and with minimal contribution attributable to the BA intervention itself. Individuals who wish to supplement with BA should follow evidence-based dosing protocols (e.g., to ingest 3.2–6.4 g/day of BA for at least 4 weeks). In addition to following dosing recommendations, each individual should consider other modifiable lifestyle factors in order to enhance their own likelihood of a positive response, including, for example, maintaining dietary habits that support energy and nutrient requirements, recommended physical activity levels, and adequate sleep schedules.

Acknowledgments

The authors would like to thank the researchers and volunteers who conducted or participated in the studies included in this meta-analysis. The Risk of Bias figure was created using R package robvis. Authorship: Conceptualization, E. Dolan and P. Swinton; Methodology, E. Dolan and P. Swinton; Formal analysis, P. Swinton; Investigation, G.P. Esteves and E. Dolan; Data Curation, G.P. Esteves and E. Dolan; Writing—Original Draft, G.P. Esteves and E. Dolan; Writing—Review and Editing, G.P. Esteves, P. Swinton, C. Sale, R.M. James, G.G . Artioli, H. Roschel, B. Gualano, B. Saunders, E. Dolan. All authors approved the final version of the manuscript. Our research group has previously received financial support, supplements free of charge, and support for open access publication charges from Natural Alternatives International (NAI, a company that produces BA) for studies unrelated to this one. NAI has not had any input (financial, intellectual, or otherwise) to the present investigation. The authors have no other potential conflicts of interest to declare. G.P. Esteves, B. Saunders, B. Gualano, G.G. Artioli, and E. Dolan are supported by research grants from the São Paulo Research Foundation (FAPESP grants numbers 2020/07860-9, 2016/50438-0, 2017/12511-0, 2019/05616-6, 2019/25032-9, and 2019/26899-6). B. Saunders has also received a grant from Faculdade de Medicina da Universidade de São Paulo (2020.1.362.5.2).

References

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Esteves, Artioli, Roschel, Gualano, Saunders, and Dolan are with the Applied Physiology & Nutrition Research Group, School of Physical Education and Sport, Rheumatology Division, Faculdade de Medicina FMUSP, University of São Paulo, São Paulo, Brazil. Swinton is with the School of Health Sciences, Robert Gordon University, Aberdeen, United Kingdom. Sale and James are with the Musculoskeletal Physiology Research Group, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom. Gualano is also with the Food Research Centre, University of São Paulo, São Paulo, Brazil. Saunders is also with the Institute of Orthopaedics and Traumatology, Faculty of Medicine FMUSP, University of São Paulo, São Paulo, Brazil.

Dolan (eimeardolan@usp.br and eimeardol@gmail.com) is corresponding author.
  • View in gallery

    —Influence of BA supplementation on TTE. (a) Aggregate data from PLA-controlled trials, showing mean difference effect sizes along with 95% CrIs for the shrunken effects of BA supplementation on TTE after applying the meta-analysis model. (b) Individual participant data with BA supplementation and PLA means along with 95% CrIs. BA = beta-alanine; TTE = time to exhaustion; CrIs = credible intervals; CCT110% = high-intensity cycling capacity test; PLA = placebo; ES = effect size.

  • View in gallery

    —Risk of bias in individual studies. D1 = Domain 1; D2 = Domain 2; D3 = Domain 3; D4 = Domain 4; D5 = Domain 5.

  • Atkinson, G., & Batterham, A. (2015). True and false interindividual differences in the physiological response to an intervention. Experimental Physiology, 100(6), 577588. PubMed ID: 25823596 doi:10.1113/EP085070

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atkinson, G., Williamson, P., & Batterham, A. (2019). Issues in the determination of “responders” and “non-responders” in physiological research. Experimental Physiology, 104(8), 12151225. PubMed ID: 31116468 doi:10.1113/EP087712

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baguet, A., Reyngoudt, H., Pottier, A., Everaert, I., Callens, S., Achten, E., & Derave, W. (2009). Carnosine loading and washout in human skeletal muscles. Journal of Applied Physiology, 106(3), 837842. PubMed ID: 19131472 doi:10.1152/japplphysiol.91357.2008

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bassinello, D., de Salles Painelli, V., Dolan, E., Lixandrão, M., Cajueiro, M., de Capitani, M., . . . Roschel, H. (2019). Beta-alanine supplementation improves isometric, but not isotonic or isokinetic strength endurance in recreationally strength-trained young men. Amino Acids, 51(1), 2737. PubMed ID: 29905904 doi:10.1007/s00726-018-2593-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, J.A., Gonzalez, J.T., Burke, L.M., Close, G.L., Garthe, I., James, L.J., . . . Atkinson, G. (2020). PRESENT 2020: Text expanding on the checklist for proper reporting of evidence in sport and exercise nutrition trials. International Journal of Sport Nutrition and Exercise Metabolism, 30(1), 213. PubMed ID: 31945740 doi:10.1123/ijsnem.2019-0326

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bishop, D., Edge, J., Mendez-Villanueva, A., Thomas, C., & Schneiker, K. (2009). High-intensity exercise decreases muscle buffer capacity via a decrease in protein buffering in human skeletal muscle. Pflugers Archiv European Journal of Physiology, 458(5), 929936. PubMed ID: 19415322 doi:10.1007/s00424-009-0673-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blancquaert, L., Everaert, I., & Derave, W. (2015). Beta-alanine supplementation, muscle carnosine and exercise performance. Current Opinion in Clinical Nutrition and Metabolic Care, 18, 6370. doi:10.1097/MCO.0000000000000127

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonafiglia, J., Brennan, A., Ross, R., & Gurd, B. (2019). An appraisal of the SD IR as an estimate of true individual differences in training responsiveness in parallel-arm exercise randomized controlled trials. Physiological Reports, 7(14), 14163. doi:10.14814/phy2.14163

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burke, L.M., & Peeling, P. (2018). Methodologies for investigating performance changes with supplement use. International Journal of Sport Nutrition and Exercise Metabolism, 28(2), 159169. PubMed ID: 29468949 doi:10.1123/ijsnem.2017-0325

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using stan. Journal of Statistical Software, 80(1), 128. doi:10.18637/jss.v080.i01

    • Crossref
    • Search Google Scholar
    • Export Citation
  • da Eira Silva, V., de Salles Painelli, V., Katsuyuki Shinjo, S., Pereira, W., Cilli, E., Sale, C., . . . Artioli, G. (2020). Magnetic resonance spectroscopy as a non-invasive method to quantify muscle carnosine in humans: A comprehensive validity assessment. Scientific Reports, 10(1), 4908. PubMed ID: 32184463 doi:10.1038/s41598-020-61587-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Danaher, J., Gerber, T., Wellard, R.M., & Stathis, C.G. (2014). The effect of β-alanine and NaHCO3 co-ingestion on buffering capacity and exercise performance with high-intensity exercise in healthy males. European Journal of Applied Physiology, 114(8), 17151724. PubMed ID: 24832191 doi:10.1007/s00421-014-2895-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dankel, S.J., Bell, Z.W., Spitz, R.W., Wong, V., Viana, R.B., Chatakondi, R.N., . . . Loenneke, J.P. (2020). Assessing differential responders and mean changes in muscle size, strength, and the crossover effect to 2 distinct resistance training protocols. Applied Physiology, Nutrition, and Metabolism, 45(5), 463470. PubMed ID: 31553889 doi:10.1139/apnm-2019-0470

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derave, W., Ozdemir, M.S., Harris, R.C., Pottier, A., Reyngoudt, H., Koppo, K., . . . Achten, E. (2007). Beta-Alanine supplementation augments muscle carnosine content and attenuates fatigue during repeated isokinetic contraction bouts in trained sprinters. Journal of Applied Physiology, 103(5), 17361743. doi:10.1152/japplphysiol.00397.2007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolan, E., Saunders, B., Harris, R., Bicudo, E., Bishop, D., Sale, C., & Gualano, B. (2019). Comparative physiology investigations support a role for histidine-containing dipeptides in intracellular acid-base regulation of skeletal muscle. Comparative Biochemistry and Physiology Part A: Molecular and Integrative Physiology, 234, 7786. doi:10.1016/j.cbpa.2019.04.017

    • Crossref
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