Available via license: CC BY 4.0
Content may be subject to copyright.
The effects of creatine
supplementation on cognitive
performance - a randomised controlled
study
Julia Fabienne Sandkühler1, 2, Dr. Xenia Kersting2, 3, Annika Faust2, Eva Kathrin Königs2,
George Altman4, Prof. Dr. Ulrich Ettinger1, Prof. Dr. Silke Lux2, Prof. Dr. Alexandra Philipsen2,
Prof. Dr. Helge Müller2, 5, Dr. Jan Brauner5, 6, 7
1Department of Psychology, University of Bonn, Germany
2Department of Psychiatry and Psychotherapy, University Hospital Bonn, Germany
3Klinik für Psychiatrie und Psychotherapie, Universitätsmedizin der Johannes
Gutenberg-Universität Mainz
4Manchester University NHS Foundation Trust, United Kingdom
5Department of Health, Witten/Herdecke University, Germany
6Department of Computer Science, University of Oxford, United Kingdom
7Future of Humanity Institute, University of Oxford, United Kingdom
Word count of manuscript text (excluding title page, abstract, references): 4285
Corresponding author: Julia Fabienne Sandkühler, email: jf.sandkuehler@gmail.com,
Department of Psychology, Kaiser-Karl-Ring 9, 53111 Bonn, Germany
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
Abstract
Background. Creatine is an organic compound that facilitates the recycling of
energy-providing adenosine triphosphate (ATP) in muscle and brain tissue. It is a safe,
well-studied supplement for strength training. Previous studies have shown that
supplementation increases brain creatine levels, which might increase cognitive
performance. The results of studies that have tested cognitive performance differ greatly,
possibly due to different populations, supplementation regimens and cognitive tasks. This is
the largest study on the effect of creatine supplementation on cognitive performance to date.
As part of our study, we replicated Rae et al. (2003).
Methods. Our trial was cross-over, double-blind, placebo-controlled, and randomised, with
daily supplementation of 5g for six weeks each. Like Rae et al. (2003), we tested participants
on Raven’s Advanced Progressive Matrices (RAPM) and on the Backward Digit Span (BDS).
In addition, we included eight exploratory cognitive tests. About half of our 123 participants
were vegetarians and half were omnivores.
Results. There was no indication that vegetarians benefited more from creatine than
omnivores, so we merged the two groups. Participants’ scores after creatine and after
placebo differed to an extent that was not statistically significant (BDS: p = 0.064, η2P=
0.029; RAPM: p = 0.327, η2P= 0.008). Compared to the null hypothesis of no effect, Bayes
factors indicate weak evidence in favour of a small beneficial creatine effect and strong
evidence against a large creatine effect. There was no indication that creatine improved the
performance of our exploratory cognitive tasks. Side effects were reported significantly more
often for creatine than for placebo supplementation (p = 0.002, RR = 4.25).
Conclusions. Our results do not support large effects of creatine on the selected measures
of cognition. However, our study, in combination with the literature, implies that creatine
might have a small beneficial effect. Larger studies are needed to confirm or rule out this
effect. Given the safety and broad availability of creatine, this is well worth investigating; a
small effect could have large benefits when scaled over time and over many people.
Key words. Creatine, cognition, intelligence, cognitive performance, Raven’s Advanced
Progressive Matrices, Backward Digit Span, working memory, deductive reasoning,
randomised controlled trial, RCT
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Introduction
Given the important role cognition plays in daily life, substances that enhance cognition
safely and cheaply are highly desirable. Creatine is safe, well-tolerated, and cheap (Kreider
et al., 2017). Strength athletes have benefited from creatine supplementation for over 30
years (Branch, 2003; Butts et al., 2018). Slight weight gain due to water retention is the only
consistently reported side effect (Bender et al., 2008; de Souza e Silva et al., 2019; Kreider
et al., 2017; Kutz & Gunter, 2003).
While the safety and athletic benefits of creatine are well established, its potential cognitive
benefits are still unclear. A systematic review tentatively suggests that creatine
supplementation may improve “short-term memory”/working memory and
“intelligence/reasoning” in healthy individuals (Avgerinos et al., 2018). The few studies that
have tested this have had heterogeneous results, but they have also used very different
populations (such as vegetarians, omnivores, varying age groups), supplementation doses
and durations and cognitive tasks (including different kinds of memory, reaction time,
reasoning, inhibitory control, attention and task switching). The study with the largest effect,
Rae et al. (2003), tested the effect of creatine supplementation in 45 young vegetarian adults
on working memory and abstract reasoning using the Backwards Digit Span (BDS) and
Raven’s Advanced Progressive Matrices (RAPM), respectively. Their study was
placebo-controlled, randomised and double-blind. Rae et al. (2003) found creatine
supplementation had a large and highly significant positive effect on both tasks. We deemed
this study particularly worth replicating.
Why might supplementing creatine benefit cognition? Muscle and brain cells use creatine to
access more energy when demand is high. They store creatine as phosphocreatine, which
acts to regenerate the energy-providing adenosine triphosphate (ATP) (Lowe et al., 2013;
Persky & Brazeau, 2001). The energy demand of neurons can increase rapidly; maintaining
ATP concentration despite increased demand may explain the potential effect of creatine
intake on cognition (Ainsley Dean et al., 2017). The crucial role of creatine in brain
metabolism is supported by evidence from Cerebral Creatine Deficiency Syndromes.
Conditions causing brain creatine deficiency result in profound intellectual disability which
can be reversed by creatine supplementation (Clark & Cecil, 2015).
Creatine can be produced by the body and is present in common foods, so why would we
expect supplementation to make a difference? Dietary creatine is primarily contained in
meat, fish, and a small amount in some dairy products (Balestrino & Adriano, 2019; Brosnan
& Brosnan, 2016). However, typical supplementation doses of creatine (5g per day) are
equivalent to more than 1kg of meat consumption per day (Brosnan & Brosnan, 2016), which
is substantially higher than the combined dietary intake and synthesis in most people
(Brosnan & Brosnan, 2016).
Creatine intake increases the level of creatine in the blood serum (Harris et al., 1992)
(Schedel et al., 1999). Crucially, (Dechent et al., 1999) found brain creatine increased by
8.7% following a 20g/day 4-week supplementation regime; two further studies have
confirmed varying supplementation regimes can increase brain creatine (Lyoo et al., 2003;
Turner, Russell, et al., 2015).
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
It is unclear if creatine supplementation has similar effects on omnivores and vegetarians.
Rae et al. (2003) only included vegetarians. Another study comparing memory improvement
under creatine supplementation in omnivores and vegetarians found that creatine
supplementation improved memory only for vegetarians but not omnivores (Benton &
Donohoe, 2011). Vegetarians have been found to have lower serum and muscle creatine
concentration, but comparable total brain creatine to omnivores (Burke et al., 2003; Solis et
al., 2014, 2017). In this study, we included both omnivores and vegetarians to allow
comparison. We hypothesised that creatine supplementation would improve working
memory and reasoning ability in vegetarians. We also hypothesised that the improvement
would be greater in vegetarians than in omnivores.
To test these hypotheses, we approximately replicated the study design and treatment (5g
per day of creatine for six weeks) used by Rae et al. (2003). We included the same primary
outcome measures, the Backwards Digit Span and 10-minute standardised subtests of
Raven's Advanced Progressive Matrices. In addition, to investigate a broader range of
cognitive functions, we included exploratory tests on attention, verbal fluency, task switching,
and memory.
Methods
Trial design
We conducted a randomised, placebo-controlled, double-blind, cross-over study. The
primary endpoints are the scores in the cognitive tasks after 6 weeks of each
supplementation. Six weeks have been found to be a sufficient wash-out period (private
correspondence, (Turner, Byblow, et al., 2015)) and it is the duration used by Rae et al.
(2003). Unlike Rae et al. (2003), we did not have an extra washout period nor second
baseline testing after the first supplementation. The trial evaluated cognitive performance
after creatine compared to placebo. The trial design and participant flow are summarised in
Figure 1. The trial was prospectively registered (drks.de identifier: DRKS00017250,
https://osf.io/xpwkc/) and ethical approval was obtained from the ethics committee of the
University of Bonn (060/19). We follow the CONSORT guidelines.
Participants
Participants were 18 years or older (see appendix for full list of inclusion criteria). Half of
them reported to be on a vegetarian diet and half of them on an omnivore diet. Cognitive
assessments of participants took place in the clinic laboratory. Due to the contact restrictions
due to the COVID-19 pandemic, after 04/2020 participants were tested online via video call
instead. A screening questionnaire assessed if the eligibility criteria were met. Participants
who met these criteria went through the baseline assessment and were given their first
supplement to take home (or for participants tested online, received the two supplements via
the mail).
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Interventions and similarity of treatment groups
Participants took the supplements daily for six weeks, including the day of the testing. The
creatine supplement consisted of creatine monohydrate powder “CreaPure PG” produced by
the company Alzchem (Trostberg, Germany). The placebo supplement consisted of
maltodextrin powder “Maltodextrin 6” produced by the company Nutricia (Frankfurt am Main,
Germany.
The cans looked exactly the same except for clear markings of which one was the first and
which one the second supplement. The two powders looked exactly the same and were
flavourless. The solubility was somewhat different: While the placebo powder was
completely soluble in water and did not settle, the creatine powder slowly settled. We were
initially not aware of this difference in solubility. After we noticed it (after the first 40
participants), we asked participants to stir the powder into yoghurt or food with a similar
consistency, as we had found no perceptible difference then. To check to what extent
blinding was achieved, directly after the last testing participants were asked to guess what
their first supplement had been.
Outcomes
We had two primary outcomes:
● A standardised 10-minute subtest of Raven Advanced Progressive Matrices (RAPM)
(Rae et al., 2003)
● The Wechsler auditory Backward Digit Span (BDS) (Wechsler 1955)
RAPM is a test of abstract reasoning. Each item in the test consists of a 3x3 matrix with
pictures of geometric forms. One of the pictures is missing and the task consists of choosing
the right picture to fill this gap out of eight alternatives. The full RAPM consists of 80 items
and has a time limit of 40 minutes. We used the same standardised 10-minute subtests of
the RAPM as Rae et al. (2003), consisting of 20 items each. The subtests are constructed to
have equal levels of difficulty based on the published normative performance data and Rae
et al. (2003) additionally verified this in an independent sample (N = 20). The RAPM score
consists of the sum of correct responses.
The Backward Digit Span is a test of working memory. The tester reads increasingly longer
series of digits to the participant whose task it is to remember and repeat them in reverse
order. The task starts with two digits. Each length has two series of digits. The test ends after
wrong answers to two series of the same length. The BDS score consists of the sum of
correct responses.
We had eight further exploratory outcomes:
● The D2 Test of Attention (Brickenkamp, 2002), a test of sustained attention
● The Trail-Making-Test A (TMT-A), a test of visual attention (Reitan, 1958)
● The Trail-Making-Test B (TMT-B), a test of task switching (Reitan, 1958)
● The Block-Tapping-Test (BTT), a test of visuospatial working memory (Schellig, 1997)
● The Auditory Verbal Learning Test (AVLT, in German: VLMT), a word-learning test
including immediate recall, delayed recall and recognition (Lux et al., 2001)
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
● The Brief-Visuospatial-Memory Test – Revised (BVMT-R), a test of visuospatial
memory (Benedict et al., 1996)
● The Stroop test (in German: Farb-Wort-Interferenz Test, FWI-T), a test of inhibitiory
control (Bäumler & Stroop, 1985)
● Regensburger Wortflüssigkeitstest (RWT), a test of verbal fluency (Aschenbrenner et
al., 2000)
Participants reported side effects experienced during the supplementation period in a free
text form on the day of testing. How side effects would be grouped for the report was
determined after evaluating all entries. At baseline testing, participants performed a test of
crystallised intelligence called “Mehrfach-Wahl-Wortschatztest (MWT-B)” (Lehrl, 2005). In
this test, participants had to identify real German words among made-up words.
Sample size
The sample size of 123 was powered (with power = 0.8, alpha = 0.05, calculated with
GPower) to detect effects of Cohen’s d = 0.45. The sample size (preregistered as 120) was
chosen based on a conservative estimate (see appendix) of the effect size in Rae et al.
(2003) (d = 1 for both RAPM and BDS) with a substantial buffer to account for smaller
effects.
Block-tapping was originally performed with physical blocks and later on the website
Psytoolkit (Stoet, 2010, 2017) as part of remote testing during the COVID-19 pandemic.
Because the remote version was not immediately available, the participant number is lower
for this task.
Randomisation and blinding
The order of the two supplements was randomised with Excel by the pharmacy of the
university hospital Heidelberg. They labelled each of the cans of supplements with the
participant code and “A” or “B”, corresponding to the first and second supplement. The staff
members who tested participants also provided the participants with the supplement cans.
Allocation concealment was performed using sequentially numbered, opaque sealed
envelopes (SNOSE). Participants and all staff who interacted with them were kept blinded to
the allocation (also see intervention section).
Statistical methods
For each cognitive test, we conducted a mixed ANOVA with test score after supplementation
as the dependent variable, supplement (creatine vs placebo) as the within-subjects factor
and supplement order (creatine-first vs placebo-first) as the between-subjects factor. We did
not remove outliers in our main analysis, but conducted robustness checks which included
trimming and winsorising. We applied the Greenhouse-Geisser correction to all our analyses
but the correction did not change any value.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Confirmatory analyses
As preregistered, our two confirmatory cognitive tasks are the Backward Digit Span and
Raven’s Advanced Progressive Matrices. All other cognitive tasks are analysed in an
exploratory fashion.
There is one deviation from our preregistered analyses. We had preregistered t-tests, but
this was a mistake in the preregistration. The t-test is not appropriate here because
imbalances in the supplement order group sizes would bias the results. Instead we
conducted mixed ANOVAs with supplement (creatine vs placebo) as the within-subjects
variable, supplement order (creatine-first vs placebo-first) and diet (vegetarian vs omnivore)
as the between-subjects variables and test score after supplementation as the dependent
variable.
Robustness checks
We checked the robustness of our normality-assuming ANOVAs by performing: an ANOVA
on 20%-trimmed data, an ANOVA on 5%- and on 20%-winsorised data, and a robust
ANOVA which uses trimming and bootstrapping (performed with the sppb functions in the
WRS2 R package). The latter ANOVA provides the most robust estimate of these methods
(Field, 2013; Wilcox, 2011).
Bayes factors
For the calculation of the Bayes factors, we used the estimated marginal means (EMMs) of
the creatine and placebo score. The EMMs are the means weighed for the order groups
(creatine-first and placebo-first), so that imbalances in the sizes of the order groups do not
affect the means. So, we only had two groups for the Bayes factor calculation (creatine and
placebo), simplifying the analysis. The mean difference and standard error of the mean
difference were used to describe the data. Using the Bayesplay package (Colling, 2021), we
calculated the Bayes factors in several different ways. Approach 1 used point models for the
null hypothesis and the alternative hypotheses. Approach 2 compared a point null model
against half normal distributions centred on zero and with the standard deviation set to half
the maximum expected effect size. For the reasons behind this see the appendix.
Exploratory analyses
In addition to the confirmatory analyses of BDS and RAPM, we analysed the other cognitive
tasks in the same way in an exploratory fashion.
We also looked in an exploratory fashion at the first supplementation and the second
supplementation separately and at participants with a low and high baseline performance
separately (see appendix).
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Results
Participant flow
Figure 1. Participant flow through the study.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Recruitment
Participants were recruited through flyers and social media between 05/2019 and 05/2022
and tested between 05/2019 and 08/2022.
Baseline data
We analysed all available participant data apart from one task in the case of two participants
(see appendix). Participants were included irrespective of their adherence. For participant
characteristics, see Table 1.
Total
Creatine-first
Placebo-first
Age in years (M, SD)
30.6 (10.1)
31.5 (10.4)
29.8 (9.7)
Sex (% female)
57%
54%
60%
Weight in kg (M, SD)
70.3 (13.7)
71.8 (15.5)
68.8 (11.4)
MWT-B (M, SD)
26.31 (4.35)
26.32 (3.99)
26.31 (4.72)
Table 1. Participant characteristics. Data is given as mean (standard deviation) or as
percentage. The MWT-B (Mehrfach-Wahl-Wortschatztest) is a test of crystallised intelligence
(Lehrl, 2005).
Blinding, adherence and side effects
The last 73 participants were asked to guess the order of their supplements. Forty-three
(59%) guessed correctly and 30 (41%) guessed incorrectly. A binomial test reveals that the
probability of 43 or more correct guesses out of 73 by pure chance is p = 0.080. However,
most participants who guessed correctly reported being very unsure about their guess. We
recorded the reasons for the guesses of the last of the 33 participants. Of those participants
who had a reason for their guess, solubility was the most common, followed by negative side
effects and positive side effects. All three reasons seemed to improve guess accuracy (see
appendix).
A z-score test for two population proportions revealed that the proportion of participants
reporting any negative side effect was significantly higher for the creatine than the placebo
condition, p = 0.002, RR = 4.25 (Table 2). In addition, although we did not assess this
systematically, some participants reported positive side effects such as improvements in
strength (several participants) and mood (one participant). No patients discontinued the
study due to an adverse event.
Adherence (self-reported) was high (Table 2). All but one participant took the supplements in
the order assigned to them. This participant was analysed with their actual, not their
assigned, supplement order.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Creatine
Placebo
Days supplemented per week (M, SD)
6.89 (0.26)
6.87 (0.26)
Any side effects
Of these:
Digestion problems
Weight gain
Other
- Tiredness
- Thirst
- Weight loss
- Nightmares
- Cramps
- Thoughts racing
- Problems concentrating
- nervousness
17%
6%
3%
1x
1x
1x
1x
1x
1x
1x
1x
4%
2%
0%
1x
1x
0x
0x
0x
0x
0x
0x
Table 2. Adherence and negative side effects.
Interaction with diet
There was no significant interaction between diet and supplement nor between diet,
supplement and supplement order for neither BDS (p = 0.808 and p = 0.559) nor RAPM (p =
0.392 and p = 0.606), nor was the interaction in the predicted direction (we had hypothesised
that vegetarian participants would benefit more from creatine than omnivore participants).
This was also true when using the robust ANOVA based on bootstrapping. Bayes factors
favoured the null hypothesis. To be precise: They indicated strong support in favour of the
null hypothesis over the effect size in Benton and Donohoe (2011) (d = 0.36) and weak to
strong support in favour of the null hypothesis over smaller effect sizes (see appendix).
There was no indication for an effect of diet in the exploratory cognitive tasks either. For
more details on the analysis of diet, see the appendix.
Confirmatory analysis
There was a significant interaction between supplement and supplement order for both BDS
and RAPM. This seems to reflect a learning effect (see appendix). The effect of most
interest, the main effect of the supplement, was in the expected direction but not significant.
However, it bordered on significance for BDS (p = 0.067, η2P= 0.028). This means that 2.8%
of the variance in BDS scores that was not already explained by other variables was
explained by the supplement. For RAPM, it was 0.9%. The supplement effect was virtually
the same whether diet was included as a variable or not (Table 3). Thus we simplified
additional analyses (estimated marginal means, Bayes factors and robustness checks) by
dropping diet as a variable for these analyses.
In terms of raw scores, the effect size for BDS was 0.41 additional correct items, i.e. a 0.2
digits longer digit span, because there were always two digit spans of the same length. For
RAPM, the effect was 0.23 more matrices solved (Figure 2). Cohen’s d based on the
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
estimated marginal means of the creatine and placebo scores was 0.09 for RAPM and 0.17
for BDS. If these were IQ tests, this would mean 1.4 and 2.6 IQ points.
Task
N
Supplement effect
3-way ANOVA, inkl. diet
Supplement effect
2-way ANOVA
Crea.
Score
Pl.
Score
Crea.-Pl. Scores
M(SE) [95% CI]
total
crea
first
pl
first
F(df)
p
η2P
F(df)
p
η2P
BDS
121
61
60
3.41
(1, 117)
0.067
0.028
3.49
(1, 119)
0.064
0.029
8.85
(0.28)
8.44
(0.25)
0.41(0.22)
[-0.24; 0.844]
RAPM
118
60
58
1.02
(1, 114)
0.315
0.009
0.97
(1, 116)
0.327
0.008
12.39
(0.28)
12.16
(0.28)
0.23(0.23)
[-0.24; 0.70]
Table 3. Mixed 3-way ANOVA with supplement (creatine vs placebo) as the within-subjects
variable, supplement order (creatine-first vs placebo-first) and diet (vegetarian vs omnivore)
as the between-subjects variable and test score after supplementation as the dependent
variable. Mixed 2-way ANOVA without diet. The test score is given as estimated marginal
mean (standard error). P-values are two-tailed. The two cognitive tasks are the Backward
Digit Span and Raven’s Advanced Progressive Matrices.
Figure 2. a) Estimated marginal means for the Backward Digit Span (BDS) score. b)
Estimated marginal means for Raven’s Advanced Progressive Matrices (RAPM) score. Error
bars represent standard errors.
Bayes factors
To facilitate the interpretation of the results of the confirmatory analysis, we provide Bayes
factors. A Bayes factor (BF10) indicates how likely a null hypothesis is compared to an
alternative hypothesis given the data. A BF10 between ⅓ and 3 indicates low sensitivity of the
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
data (i.e. not enough data to be certain), with weak evidence in favour of the null hypothesis
if BF10 is below 1 and weak evidence in favour of the alternative hypothesis if it is above 1. A
BF10 above 3 (below ⅓ ) is considered moderate and above 10 (below 1/10) strong evidence
(van Doorn et al., 2021).
We compare several alternative hypotheses postulating small beneficial effects of creatine to
the null hypothesis. For RAPM, the data was very insensitive, very weakly favouring the
alternative hypotheses. For BDS, the data was more sensitive, providing weak to moderate
support in favour of the alternative hypotheses. Two different approaches to calculating
these Bayes factors were used (see statistical analysis) and the results were similar (Table
4).
There was strong evidence in favour of the null hypothesis compared to the alternative
hypothesis postulating the effect size found by Rae et al. (2003). The data was insensitive
(BDS) or weakly favoured the null hypothesis (RAPM) when compared to the half normal
model based on Rae et al. (2003). The half normal model based on Rae et al. (2003) does
not assume their effect size is the true effect size in the population. Instead, the model
assumes their effect size is a moderate overestimation of the true effect size. The model
uses their effect size as a reference point to assign probabilities to effect sizes. It assigns
most of the probability weight to effect sizes that are smaller than this effect size, and some
probability to effect sizes up to twice that effect size. This is a common alternative model
when replicating studies. However, we did not use it as our only model, because we were
also interested in assessing the likelihood of smaller effect sizes and of the possibility that
the effect size in Rae et al. (2003) was the true population effect size.
The results were similar whether using normal or cauchy distributions. For more details on
this and the aforementioned calculations see the appendix.
In summary, this study provides weak evidence for a small cognitive benefit of creatine and
strong evidence against the effect size by Rae et al. (2003) being representative.
Approach 1: point models
Approach 2: half normal
Small effects
Rae-sized
Small effects
Max. = 2xRae-size
Task
0.1
0.2
0.4
2.5
max. 0.4
max. 1
max. 5
BDS
2.1
3.6
5.7
< 2e-7
2.9
3.3
1.0
RAPM
1.4
1.6
1.3
< 2e-7
1.4
1
0.3
Table 4. Bayes factors (BF10) comparing a range of alternative hypotheses to the null
hypothesis. The effect size is given as the raw score difference. Approach 1 compared a
point null model to point alternative models with a range of small effect sizes (0.1-0.4, i.e. d =
0.04-0.17) as well as an equivalent of Rae et al.’s effect size (2.5, i.e. d = 1, see calculation
in appendix). Approach 2 compared a point null model against half normal distributions
centred on zero and with the SD set to half the maximum expected effect size.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Robustness checks
We checked the robustness of our confirmatory analysis (the normal ANOVA) by performing:
an ANOVA on 20%-trimmed data, an ANOVA on 5%- and on 20%-winsorised data, and an
ANOVA which uses bootstrapping and 20% trimming.
For RAPM, all of these methods gave overall similar results to that of the normal ANOVA
(Table 5).
Task
Better
score
Max.
skew
p (Supplement)
normal
20% trim
5%
winsorisation
20%
winsorisation
bootstrap
and 20% trim
RAPM
creatine
-059
0.327
0.412
0.361
0.672
0.354
BDS
creatine
1.14
0.064
0.17
0.009
0.05
0.37
Table 5. Creatine effect p-values (two-tailed) for different ANOVAs. The given trim and
winsorisation percentages are applied to each side. Better score based on estimated
marginal means. “Max. skew” gives the highest skewness statistic in any combination of
conditions (supplement and supplement order).
For BDS, whose skewness statistic was slightly further from 0 than that of RAPM, these
methods gave results that differ from each other and from the normal ANOVA to a relevant
extent (Table 5). Most notably, the p-value for the supplement effect was 0.009 for the
5%-winsorisation and 0.370 for the bootstrap ANOVA. This seems to suggest that in the
normal ANOVA, the most extreme values made the effect of creatine appear smaller by
inflating the variance, while relying on possibly unjustified assumptions of normality made
the effect of creatine appear larger.
Thus, the result for RAPM was robust and for BDS much less so.
Exploratory cognitive tasks
There was no indication that creatine improved the performance of our exploratory cognitive
tasks. The distribution of p-values was what one would expect if there was no effect. For the
exploratory cognitive tasks, Table 6 only includes the p-values of the supplement effect. For
the full results, including the interaction effect (reflecting a learning effect) and the order of
supplement effect, see the appendix.
Task
N
Better
score
p (Supplement)
normal
20%
trim
5%
winsorisation
20%
winsorisation
bootstrap
and 20% trim
Blocktapping forward
71
creatine
0.779
0.564
0.865
0.678
0.826
Blocktapping backward
70
placebo
0.83
0.87
0.482
0.482
0.59
BVMT-R
119
creatine
0.543
0.809
0.746
0.112
0.67
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
D2 test
104
placebo
0.394
0.382
0.446
0.291
0.62
Forward digit span
117
placebo
0.714
0.795
0.838
0.721
0.52
Stroop - colors
118
placebo
0.813
0.184
0.432
0.054
0.568
Stroop - colorletters
119
placebo
0.626
0.877
0.861
0.547
0.856
TMT A
123
placebo
0.129
0.04
0.068
0.021
0.224
TMT B
122
creatine
0.855
0.622
0.745
0.567
0.56
VLMT immediate recall
119
creatine
0.87
0.744
0.996
0.883
0.996
VLMT recall after
interference
119
creatine
0.694
0.622
0.854
0.323
0.806
VLMT delayed recall
118
placebo
0.339
0.346
0.462
0.133
0.54
VLMT recognition
117
creatine
0.722
0.327
0.398
0.635
0.348
Word fluency
122
creatine
0.227
0.631
0.272
0.119
0.692
Table 6. Creatine effect p-values (two-tailed) for different ANOVAs. The given trim and
winsorisation percentages are applied to each side. Higher score based on estimated
marginal means.
Discussion
This is the largest study on the cognitive effects of creatine to date. As part of our study, we
aimed to replicate Rae et al. (2003), who found a large positive effect of creatine on the
abstract reasoning task Raven’s Advanced Progressive Matrices (RAPM) and on the
working memory task Backward Digit Span in healthy young adult vegetarians.
In our study, half of the participants were vegetarians and half of them were omnivores. We
found no indication that our vegetarian participants benefited more from creatine than our
omnivore participants. This is in line with Solis et al (2014, 2017) who did not find a
difference in brain creatine content between omnivores and vegetarians. Our Bayesian
analysis of their data provides moderate support for the lack of a difference (see appendix).
In contrast, Benton and Donohoe (2011) found that creatine supplementation benefited
memory in vegetarians more than in omnivores, with no difference in baseline performance.
However, given the high number of tests in that study, the chance of a false positive was
high, so we regard their finding as only an exploratory hint. The conflicting findings might be
due to possible differences in the amount of dietary creatine (not measured in this study nor
in Benton and Donohoe (2011)).
The preregistered frequentist analysis of RAPM and BDS found no significant effect at p <
.05 (two-tailed), although the effect bordered significance for BDS. The Bayes factors in this
study provide weak evidence for a small cognitive benefit of creatine and strong evidence
against the large effect size found by Rae et al. (2003). A larger sample size would be
necessary to provide stronger evidence on the question of a small benefit. In order for the
sample size to not have to be exceedingly large, we recommend being extremely careful in
reducing noise and choosing participants who are likely to benefit the most. In addition,
analogous to the compounding effect of creatine over time for strength training, it might be
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
possible to see a larger effect of creatine on cognition over time by training the tasks while
on the supplement.
In their review, Avgerinos et al. (2018) reported that a creatine effect is more likely for
“intelligence/reasoning” and “short term memory”/working memory than for other cognitive
domains. In line with this, we found a weak indication for a creatine effect for the two
confirmatory tasks reflecting these two domains but not for other domains. However, two of
our exploratory tasks, the forward digit span and the immediate recall part of the VLMT also
tested short-term memory and there was no indication for an effect for these tasks. Another
review, (Dolan et al., 2018) report that a creatine effect is more likely for more cognitively
demanding tasks. In line with this, we found some indication for a creatine effect for the
backward digit span (BDS) but not for the less demanding forwards digit span. The VLMT
may also be less cognitively demanding than the BDS, but this comparison is less obvious to
make.
There are a number of limitations to this study. Despite the large sample size compared to
other studies, a larger sample size would be needed to be powered for effects that are
smaller but still relevant. Some of the data (2%) could not be analysed because it was not
labelled with the participant and timepoint. The COVID-19 pandemic started in the middle of
the study, which likely added noise to the data, and meant that we had to switch from
in-person cognitive testing to testing via video call. Adherence was self-reported and not
checked with blood samples. Another limitation is that the proportion of participants who
correctly guessed their supplement order (59%) bordered on significance (p = 0.08).
However, most participants who guessed correctly reported being very unsure about their
guess. The largest contributing factor to correct guesses was likely the difference in the
solubility between the powders, followed by negative and positive side effects. We attempted
to counteract differences in solubility by recommending participants to stir the supplements
into yoghurt. For future studies we recommend cellulose as the placebo and a mixture of
cellulose and creatine as the treatment, as these two look extremely similar when dissolved
in water. The alternative solution with capsules would require participants to consume many
capsules per day. This would likely reduce adherence and massively increase costs.
Unfortunately, it is difficult to achieve perfect blinding when side effects occur with higher
frequency in the creatine condition. The side effects of creatine are well-known and not
dangerous (Bender et al., 2008; de Souza e Silva et al., 2019; Kreider et al., 2017; Kutz &
Gunter, 2003).
Conclusion
Supplementing creatine is safe, easy and very cheap. The real effect of creatine on cognition
is likely smaller than that reported in Rae et al. (2003). However, even small improvements
in cognition may be relevant, especially if accumulated over many people and over time. The
results of this study do not allow any strong conclusions, but it would be worthwhile to test
for a small effect of creatine in strategically designed, larger studies.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Acknowledgements
We thank the doctors of the University Clinic Bonn who collected blood samples. We thank
Dr. Lincoln Colling, Dr. Christian Stark, Jan Speller, Maximilian Meier and David Reinstein for
their feedback on statistical questions. We thank Thomas Szpejewski and Tom Lieberum for
their help with verifying data quality. We thank all data entry helpers.
Funding
Funding was provided by the non-profit organization Effective Ventures Foundation, 2443
Fillmore St., #380-16662, San Francisco, CA 94115. The trial funders had no role in the
design of the study, the collection, analysis or interpretation of data, the writing of the report,
or the decision to submit the article for publication.
Data
The appendix, data, code and output of this study are openly available at the Open Science
Framework, https://osf.io/xpwkc/.
References
Ainsley Dean, P. J., Arikan, G., Opitz, B., & Sterr, A. (2017). Potential for use of creatine
supplementation following mild traumatic brain injury. Concussion (London, England),
2(2), CNC34.
Aschenbrenner, S., Tucha, O., & Lange, K. W. (2000). Regensburger Wortflüssigkeits-Test:
RWT. Hogrefe, Verlag für Psychologie.
Avgerinos, K. I., Spyrou, N., Bougioukas, K. I., & Kapogiannis, D. (2018). Effects of creatine
supplementation on cognitive function of healthy individuals: A systematic review of
randomized controlled trials. Experimental Gerontology,108, 166–173.
Balestrino, M., & Adriano, E. (2019). Beyond sports: Efficacy and safety of creatine
supplementation in pathological or paraphysiological conditions of brain and muscle.
Medicinal Research Reviews,39(6), 2427–2459.
Bäumler, G., & Stroop, J. R. (1985). Farbe-Wort-Interferenztest nach JR Stroop (FWIT).
Hogrefe, Verlag für Psychologie.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Bender, A., Samtleben, W., Elstner, M., & Klopstock, T. (2008). Long-term creatine
supplementation is safe in aged patients with Parkinson disease. Nutrition Research ,
28(3), 172–178.
Benedict, R. H. B., Schretlen, D., Groninger, L., Dobraski, M., & Shpritz, B. (1996). Revision
of the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and
validity. Psychological Assessment,8(2), 145.
Benton, D., & Donohoe, R. (2011). The influence of creatine supplementation on the
cognitive functioning of vegetarians and omnivores. The British Journal of Nutrition,
105(7), 1100–1105.
Branch, J. D. (2003). Effect of creatine supplementation on body composition and
performance: a meta-analysis. International Journal of Sport Nutrition and Exercise
Metabolism,13(2), 198–226.
Brickenkamp, R. (2002). Test d2: Aufmerksamkeits-Belastungs-Test Hogrefe. Testzentrale:
Göttingen, Germany.
Brosnan, M. E., & Brosnan, J. T. (2016). The role of dietary creatine. Amino Acids,48(8),
1785–1791.
Burke, D. G., Chilibeck, P. D., Parise, G., Candow, D. G., Mahoney, D., & Tarnopolsky, M.
(2003). Effect of creatine and weight training on muscle creatine and performance in
vegetarians. Medicine and Science in Sports and Exercise,35(11), 1946–1955.
Butts, J., Jacobs, B., & Silvis, M. (2018). Creatine Use in Sports. Sports Health,10(1),
31–34.
Clark, J. F., & Cecil, K. M. (2015). Diagnostic methods and recommendations for the cerebral
creatine deficiency syndromes. Pediatric Research,77(3), 398–405.
Colling, L. (2021). ljcolling/go-bayesfactor: https://doi.org/10.5281/zenodo.4642331
Dechent, P., Pouwels, P. J., Wilken, B., Hanefeld, F., & Frahm, J. (1999). Increase of total
creatine in human brain after oral supplementation of creatine-monohydrate. The
American Journal of Physiology,277(3), R698–R704.
de Souza e Silva, A., Pertille, A., Reis Barbosa, C. G., Aparecida de Oliveira Silva, J., de
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Jesus, D. V., Ribeiro, A. G. S. V., Baganha, R. J., & de Oliveira, J. J. (2019). Effects of
Creatine Supplementation on Renal Function: A Systematic Review and Meta-Analysis.
Journal of Renal Nutrition: The Official Journal of the Council on Renal Nutrition of the
National Kidney Foundation,29(6), 480–489.
Dolan, E., Gualano, B., & Rawson, E. S. (2018). Beyond muscle: the effects of creatine
supplementation on brain creatine, cognitive processing, and traumatic brain injury.
European Journal of Sport Science: EJSS: Official Journal of the European College of
Sport Science, 1–14.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. SAGE.
Harris, R. C., Söderlund, K., & Hultman, E. (1992). Elevation of creatine in resting and
exercised muscle of normal subjects by creatine supplementation. Clinical Science ,
83(3), 367–374.
Kreider, R. B., Kalman, D. S., Antonio, J., Ziegenfuss, T. N., Wildman, R., Collins, R.,
Candow, D. G., Kleiner, S. M., Almada, A. L., & Lopez, H. L. (2017). International
Society of Sports Nutrition position stand: safety and efficacy of creatine
supplementation in exercise, sport, and medicine. Journal of the International Society of
Sports Nutrition,14, 18.
Kutz, M. R., & Gunter, M. J. (2003). Creatine monohydrate supplementation on body weight
and percent body fat. Journal of Strength and Conditioning Research / National Strength
& Conditioning Association,17(4), 817–821.
Lowe, M. T. J., Kim, E. H., Faull, R. L. M., Christie, D. L., & Waldvogel, H. J. (2013).
Dissociated expression of mitochondrial and cytosolic creatine kinases in the human
brain: a new perspective on the role of creatine in brain energy metabolism. Journal of
Cerebral Blood Flow and Metabolism: Official Journal of the International Society of
Cerebral Blood Flow and Metabolism,33(8), 1295–1306.
Lux, S., Helmstaedter, C., & Lendt, M. (2001). Verbaler Lern- und Merkfähigkeitstest: VLMT ;
Manual. Beltz-Test.
Lyoo, I. K., Kong, S. W., Sung, S. M., Hirashima, F., Parow, A., Hennen, J., Cohen, B. M., &
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Renshaw, P. F. (2003). Multinuclear magnetic resonance spectroscopy of high-energy
phosphate metabolites in human brain following oral supplementation of
creatine-monohydrate. Psychiatry Research,123(2), 87–100.
Persky, A. M., & Brazeau, G. A. (2001). Clinical pharmacology of the dietary supplement
creatine monohydrate. Pharmacological Reviews,53(2), 161–176.
Rae, C., Digney, A. L., McEwan, S. R., & Bates, T. C. (2003). Oral creatine monohydrate
supplementation improves brain performance: a double-blind, placebo-controlled,
cross-over trial. Proceedings. Biological Sciences / The Royal Society,270(1529),
2147–2150.
Reitan, R. M. (1958). Validity of the Trail Making Test as an Indicator of Organic Brain
Damage. Perceptual and Motor Skills,8(3), 271–276.
Schedel, J. M., Tanaka, H., Kiyonaga, A., Shindo, M., & Schutz, Y. (1999). Acute creatine
ingestion in human: consequences on serum creatine and creatinine concentrations.
Life Sciences,65(23), 2463–2470.
Schellig, D. (1997). Block-tapping-test. Swets Test Services Frankfurt.
Solis, M. Y., Artioli, G. G., Otaduy, M. C. G., Leite, C. da C., Arruda, W., Veiga, R. R., &
Gualano, B. (2017). Effect of age, diet, and tissue type on PCr response to creatine
supplementation. Journal of Applied Physiology,123(2), 407–414.
Solis, M. Y., de Salles Painelli, V., Artioli, G. G., Roschel, H., Otaduy, M. C., & Gualano, B.
(2014). Brain creatine depletion in vegetarians? A cross-sectional 1H-magnetic
resonance spectroscopy (1H-MRS) study. The British Journal of Nutrition,111(7),
1272–1274.
Stoet, G. (2010). PsyToolkit: a software package for programming psychological experiments
using Linux. Behavior Research Methods,42(4), 1096–1104.
Stoet, G. (2017). PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires
and Reaction-Time Experiments. Teaching of Psychology ,44(1), 24–31.
Turner, C. E., Byblow, W. D., & Gant, N. (2015). Creatine supplementation enhances
corticomotor excitability and cognitive performance during oxygen deprivation. The
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint
Journal of Neuroscience: The Official Journal of the Society for Neuroscience,35(4),
1773–1780.
Turner, C. E., Russell, B. R., & Gant, N. (2015). Comparative quantification of dietary
supplemented neural creatine concentrations with (1)H-MRS peak fitting and basis
spectrum methods. Magnetic Resonance Imaging,33(9), 1163–1167.
van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A.,
Evans, N. J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharský, Š., Ly, A., Marsman, M.,
Matzke, D., Gupta, A. R. K. N., Sarafoglou, A., Stefan, A., Voelkel, J. G., &
Wagenmakers, E.-J. (2021). The JASP guidelines for conducting and reporting a
Bayesian analysis. Psychonomic Bulletin & Review,28(3), 813–826.
Wilcox, R. R. (2011). Introduction to Robust Estimation and Hypothesis Testing. Academic
Press.
. CC-BY 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 6, 2023. ; https://doi.org/10.1101/2023.04.05.23288194doi: medRxiv preprint