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Do exercise-associated genes explain phenotypic variance in the three components of fitness? a systematic review & meta-analysis

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The aim of this systematic review and meta-analysis was to identify a list of common, candidate genes associated with the three components of fitness, specifically cardiovascular fitness, muscular strength, and anaerobic power, and how these genes are associated with exercise response phenotype variability, in previously untrained participants. A total of 3,969 potentially relevant papers were identified and processed for inclusion. After eligibility and study selection assessment, 24 studies were selected for meta-analysis, comprising a total of 3,012 participants (male n = 1,512; females n = 1,239; not stated n = 261; age 28 ± 9 years). Meta-Essentials spreadsheet 1.4 (Microsoft Excel) was used in creating the forest plots and meta-analysis. IBM SPSS statistics V24 was implemented for the statistical analyses and the alpha was set at p ≤ 0.05. 13 candidate genes and their associated alleles were identified, which were associated with the phenotypes of interest. Analysis of training group data showed significant differential phenotypic responses. Subgroup analysis showed; 44%, 72% and 10% of the response variance in aerobic, strength and power phenotypes, respectively, were explained by genetic influences. This analysis established that genetic variability explained a significant proportion of the adaptation differences across the three components of fitness in the participants post-training. The results also showed the importance of analysing and reporting specific gene alleles. Information obtained from these findings has the potential to inform and influence future exercise-related genes and training studies.
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RESEARCH ARTICLE
Do exercise-associated genes explain
phenotypic variance in the three components
of fitness? a systematic review & meta-
analysis
Henry C. ChungID
1
*, Don R. Keiller
2
, Justin D. RobertsID
1
, Dan A. Gordon
1
1Cambridge Centre for Sport & Exercise Sciences, Anglia Ruskin University, Cambridge, United Kingdom,
2School of Life Sciences, Anglia Ruskin University, Cambridge, United Kingdom
*henry.chung@pgr.anglia.ac.uk
Abstract
The aim of this systematic review and meta-analysis was to identify a list of common, candi-
date genes associated with the three components of fitness, specifically cardiovascular fit-
ness, muscular strength, and anaerobic power, and how these genes are associated with
exercise response phenotype variability, in previously untrained participants. A total of
3,969 potentially relevant papers were identified and processed for inclusion. After eligibility
and study selection assessment, 24 studies were selected for meta-analysis, comprising a
total of 3,012 participants (male n = 1,512; females n = 1,239; not stated n = 261; age 28 ±9
years). Meta-Essentials spreadsheet 1.4 (Microsoft Excel) was used in creating the forest
plots and meta-analysis. IBM SPSS statistics V24 was implemented for the statistical analy-
ses and the alpha was set at p 0.05. 13 candidate genes and their associated alleles were
identified, which were associated with the phenotypes of interest. Analysis of training group
data showed significant differential phenotypic responses. Subgroup analysis showed;
44%, 72% and 10% of the response variance in aerobic, strength and power phenotypes,
respectively, were explained by genetic influences. This analysis established that genetic
variability explained a significant proportion of the adaptation differences across the three
components of fitness in the participants post-training. The results also showed the impor-
tance of analysing and reporting specific gene alleles. Information obtained from these find-
ings has the potential to inform and influence future exercise-related genes and training
studies.
Introduction
Current evidence shows that cardiovascular fitness, muscular strength, and anaerobic power
are key in determining an individuals’ health-related fitness, well-being, and quality of life, as
well as successful performance in many sporting events [15]. For example, the V
_O2max, is a
key index of cardiovascular and cardiorespiratory fitness and an increase in V
_O2max, improves
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OPEN ACCESS
Citation: Chung HC, Keiller DR, Roberts JD,
Gordon DA (2021) Do exercise-associated genes
explain phenotypic variance in the three
components of fitness? a systematic review &
meta-analysis. PLoS ONE 16(10): e0249501.
https://doi.org/10.1371/journal.pone.0249501
Editor: Stephen E. Alway, University of Tennessee
Health Science Center College of Graduate Health
Sciences, UNITED STATES
Received: March 15, 2021
Accepted: October 3, 2021
Published: October 14, 2021
Peer Review History: PLOS recognizes the
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process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0249501
Copyright: ©2021 Chung et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
the ability of the body to both supply and utilise oxygen. This prolongs the time to exhaustion
and the ability to sustain aerobic exercise for longer periods of time, at higher intensities [6].
For some, this could mean the difference between being able to walk upstairs easily, rather
than with effort and associated discomfort. Below average, V
_O2max is also correlated to
increased morbidity and mortality and improvement in V
_O2max can prevent early mortality
[7]. There is a similar rationale for improving both strength and power as well. Strength refers
to the force that can be generated during a voluntary muscle contraction and is required for
everyday tasks and mobility [2,5]. Anaerobic power is the ability of the neuromuscular system
to produce the greatest possible action in a given time period and is needed for quick bursts
explosive movements and agility [8]. Hence, it is beneficial for individuals to improve these
components, irrespective of their initial level of fitness and especially, for those classed as
untrained [46,912].
Although it has always been a key objective of health and exercise sciences to improve these
specific key components of fitness, Schutte et al. [13] and Sarzynski, Ghosh and Bouchard,
[14] stress that an individuals’ responsiveness to exercise training varies significantly, depend-
ing on the precise exercise-stimuli given. In this connection, previous studies show that a
genetic component, in the response to exercise training, can explain up-to 80% of the variabil-
ity in aerobic, strength and power adaptations [2,1517]. Such findings suggest that the current
health and exercise guidelines, promoting generic fitness classes and group exercises, are of
questionable value, without consideration of individuals’ genetic profile. Accordingly, several
well-studied genes, show significant associations with exercise trainability and successful
increases in performance, providing advantages in sports and athletic competitions [18,19].
Many other genes have been shown to influence all aspects of fitness, including, but not limited
to, energy-pathways, metabolism, storage, cell growth, protein, hormonal, and enzyme interac-
tions [13,1921]. All such genes have been termed ‘candidate genes’ [14,2225] and may be
useful indicators in predicting and producing successful training responses, to a particular
exercise intervention and maximising health benefits. However, the difficulty is identifying
and selecting key genes from the extensive suite of candidate genes, shown to be associated
with exercise responses [13,15,17,26]. Since most current research typically relies on studies
that only investigate a limited number of genes and/or single genes and their alleles, and
mainly in twins or elite and high-level performance, a literature review may well be more
suited in identifying multiple genes and their alleles and their applications on the untrained
[17,27,28]. Thus, Ahmetov et al. [29] and Williams and Folland, [30] reported that, regardless
of the high heritability of exercise response, no single gene, or polymorphism, has been shown
to be solely responsible for a particular physiological variable, due to the large number of
genetic polymorphisms associated with aerobic, strength and power phenotypes.
Accordingly, the aim of this review is to identify candidate genes and their alleles that best
define exercise phenotype responses to training interventions, with respect to the three com-
ponents of fitness, in the untrained population.
Methods
This review was conducted in accordance with the Cochrane guidelines of systematic reviews
and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [31
33]. This review was ethically approved by the Faculty of Science and Engineering Research
Ethics Panel at Anglia Ruskin University, Cambridge, UK. A comprehensive literature search
was performed using Scopus, Web of Science, PubMed and SportDiscus, between June and
July 2020. Multiple database thesauruses and MeSH terms were employed to pool keywords
for the systematic search. An email alarm system, on each database, was created for
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Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
notifications on new publications related to the search terms. All relevant studies were
exported and sorted within a bibliographic management system (Refworks, ProQuest, USA)
and an automatic deduplication tool was applied. Studies’ inclusion was screened in accor-
dance with title, abstract and full text. Potential studies that met the inclusion criteria were fur-
ther inspected [34]. The reference list of studies and grey literature was also explored, any
missing information was requested from study authors, via email, if possible, otherwise results
could not be included.
Eligibility criteria
The following PICOS criteria (Population/Participant, Intervention, Comparison, Outcome
and Study type) [35] were implemented. Firstly, a minimum effective intervention time-course
of two weeks (six sessions) was identified from studies such as, Astorino and Schubert, [26]
and Hautala et al. [2]. Relevant outcomes were defined as the common phenotype measure-
ments across all studies [3,5,17].
Population: (a) health untrained, human male and female participants with no stated medi-
cal conditions; (b) aged between 18–55 years; (c) training status below the ACSM norm values
for cycle ergometer cardio-respiratory fitness (V
_O2max), one repetition maximum (1RM) leg-
press and cycle ergometer peak power output (PPO), (V
_O2max 45 ml
.
kg
-1.
min
-1
for Males;
40 for females. Lower body 1RM 1.91 x mass for males; 1.32 x mass for females.
PPO 9.22 W/kg for males; 7.65 W/kg for females) [5].
Intervention: (d) training two weeks (minimum six sessions, three per-week); (e) include
either: 1) continuous endurance/aerobic Interval training, 2) resistance, or weight training, or
3) interval/sprint/anaerobic training; (f) no dietary manipulation.
Comparison: (g) Pre vs Post changes in primary phenotypes; (h) participants grouped by
gene, or genotype.
Outcome: (i) change in primary variable(s): V
_O2max, lower body 1RM or PPO.
Study type: (j) repeated measures method; (k) original research study; (l) English language.
Studies that did not meet all the above PICOS criteria were excluded from this review.
Quality and risk of bias assessment
A COnsensus-based Standards for the selection of health status Measurement INstruments
(COSMIN) checklist was implemented to evaluate the transparency and risk of bias of the
studies, by measuring methodological quality [36]. The COSMIN ‘worst score’ approach was
set for all items at 3, to meet the acceptable requirement of study quality and study selection
[31]. To confirm the consistency and reliability of the COSMIN tool, two reviewers (HC and
DG) independently evaluated the studies for inclusion. Each COSMIN item for all categories
was scored from 4–1 (31 items total), where 4 was low and 1 was high risk, respectively, and
any disagreements from authors were resolved using the mean COSMIN scores. Studies were
only included if 3. 95% Limits of Agreement (LoA) were calculated using the Bland and Alt-
man approximate method [37]. At least two studies, or groups, were required to report the
same gene of interest to establish a conclusive outcome measurement, otherwise this could not
be evaluated, by meta-analysis [38,39].
Statistical analysis
Means, Standard Deviations (SD), Standard Errors (SE) and 95% Confidence Intervals (CI)
were extracted from all studies and pooled for analysis. Pre-to-post intervention scores for
each study were converted into Effect Sizes (ES) and Standardised Mean Differences
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(SMD), to express intervention effects and genotypic variance between groups in standard-
ised units. Pooled standard deviation, SE, variance (s
1
), 95% Cl and the weighted Mean
Cohen’s d, were also calculated: classifying d values as 0.20–0.49 small, 0.50–0.79 medium
and 0.80 large effect [3941]. Meta-Essentials spreadsheet 1.4 (Microsoft Excel 2016,
Washington, USA) was used for the meta-analysis and creation of forest plots. IBM SPSS
statistics version 24 (SPSS, Chicago, Illinois) was used for all other statistical testing, with
alpha set at p 0.05. For the assessment of any genetic effects, a subgroup stratification
analysis was implemented. Here the training groups in each study were combined and then
split, based on genotypes, and then further analysed and compared. Normality and homoge-
neity of variance were calculated via Shapiro-Wilk test and Levene’s test, respectively.
Where necessary, a non-parametric Kruskal-Wallis H test was used to determine if there
were any significant differences between the gene groups. Partial Eta Squared and Mean
Ranks were used to determine the variability within subgroup genetics and the estimation
of gene and allelic variability and their contribution towards the change in training
phenotypes.
Results
A comprehensive flow diagram representing the study retrieval process and exclusions was
created (Fig 1). The figure also outlines the process of the PICOS and the COSMIN checklist
between reviewers. 29 studies were initially included for meta-analysis, however a further five
studies were excluded, as the genes in that study were only reported on one occasion [4246].
The final 24 studies contained 89 groups (43 aerobic; 29 strength; 17 power), with a total of
3,012 participants, and 13 candidate genes and associated alleles.
Fig 1. Flow diagram. Step-by-step method of collecting and excluding studies at each stage for this review. Also, where
other sources from unpublished material and grey literature were entered. This entire process was repeated twice.
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Genes associated with cardiorespiratory fitness (V
_O2max)
V
_O2max increased by 10.97 ±3.80%, with aerobic training interventions, across all studies in
this review. Forest plot analysis demonstrated that, irrespective of the genes, the overall results
represented a medium to large effect-size change in V
_O2max, which was classed as a very highly
significant improvement with training intervention alone (p <0.001) (Fig 2). On average,
these studies [12,18,24,25,4756] used durations of 36 minutes, with intensities of 74% V
_O2max,
or 77% HRmax, performed over 3-days a week and 12 weeks of training.
Between subgroup analysis revealed non-normal distributions across the nine aerobic
associated gene groups: D(43), .876, p<0.05. Here, Kruskal-Wallis H testing found signifi-
cant differences between the gene groups, H(8), 18.427, p= 0.018. Partial Eta Squared, cal-
culated by Pearson’s χ2 tests, found that 44% of the total variability in the increase in V
_O2max
post-training intervention was explained by nine gene subgroups (Table 1). Post-Hoc sub-
group analysis shows the distribution of the variance in V
_O2max scores between the genetic
groups.
Fig 2. V
_O2max forest plot. Effect sizes represent the change in V
_O2max post-intervention. For all plots the 95%
confidence intervals were calculated and the overall mean effect size is represented with the diamond, whereas, the
black squares are the individual effect size of each study. The weighting is adjusted for sample size, SD and variance.
Where G1 = group 1 and G2 = group 2. Genes listed in alphabetical order.
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Within this subgroup the ACE gene, regardless of allele, had the greatest mean rank and
weight on the increase in V
_O2max. ACE alleles (II, ID, DD) showed no significant difference in
effect sizes. Other “aerobic” genes (COX4I1, CS, HADH, PFKM and PGC-1α) showed no sig-
nificant differences between mean rank scores and contributed equally to the increase in
V
_O2max. However, these genes, which all showed a large effect size, had a higher mean rank
than those of ACTN3, AMPK and APOE groups. Interestingly, for the APOE genotype, which
showed medium effect sizes for certain alleles, the E3/E3 combination, in both males and
females, showed the lowest effect size on V
_O2max, at 0.18 and 0.14, respectively (Fig 2).
Here an F test found that there were no significant differences in APOE alleles effect size
scores across genders for E2, E3 and E4; p= 0.667, 0.488 and 0.776, respectively. However, fur-
ther analysis revealed that, as a whole, there were highly significant differences between allele
groups, regardless of gender F(2,6) = 59.52, p= 0.000. Paired samples t-test found significant
differences when comparing E2/E3 allele scores with E3/E3 at p= 0.013, similarly when com-
paring E3/E4 with E3/E3, p= 0.002. There was, however, no significant difference when com-
paring E2 and E4 (p= 0.952). Post-hoc LSD test found that E3 allele was the least effective,
compared to both E2 and E4, in terms of its effect size.
Genes associated with muscle strength (1RM)
Strength training intervention studies [11,23,48,52,55,5761] found an average increase in
lower body 1RM of 22.12 ±10.08% across the study groups, with an overall large effect size,
which was very highly significant (p <0.001) (Fig 3). Typically these studies did not report
activity duration, rather number of repetitions performed, which on average was 174 reps, ses-
sion at intensities of 75% 1RM, over 3-days per-week and 10 weeks of training.
Again, 1RM did not meet the requirements for parametric testing between the six strength
gene subgroups when split, D(29), 0.886, p <0.05. Kruskal-Wallis H test found very highly sig-
nificant difference between the gene specific groups; H (5), 20.081, p = 0.001. Partial Eta
Squared tests, revealed that 72% of the total variability in the increase of 1RM strength post-
training intervention, was explained by the genetic subgrouping (Table 2).
All strength associated gene groups, identified, were found to have a large influence in the
variability of lower body 1RM, with large effect-sizes, except for the ACTN3 group, which had
Table 1. Candidate genes for cardio-respiratory fitness.
Gene Groups No. of Study
groups
Total Group Sample
Size
Mean Rank (Not adjusted for
sample size)
Subgroup (Adjusted for sample size) %
of weight
Mean Group Effect
Size (d)
ACE 3 188 35.33 14.24 1.02
ACTN3 3 206 8.33 14.15 0.39
AMPK/PRKAA2 2 18 17.25 7.84 0.62
APOE 9 437 10.44 13.67 0.41
COX4I1 8 591 25.06 13.43 0.85
CS 5 46 28.00 12.12 0.90
HADH 6 106 25.25 10.58 0.96
PFKM 3 78 27.17 6.62 1.12
PGC-1α4 52 28.25 7.36 1.14
Total 43 1,722
The group with the highest mean rank shows the greatest number of high scores in V
_O2max and subgroup % shows how much of the overall 44% variance is accounted
for by each of the nine genes, adjusted for sample size, SE and within group variation in SMD.
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a medium effect. This relative lack of effect was noted, regardless of ACTN3 genotype and
allele (RR, RX, XX).
Genes associated with anaerobic power
Here the analysis revealed a mean increase in peak power output of 12.17 ±4.40%, irrespective
of gene groups. Forest plot analysis found all studies [51,53,6264] increased PPO by either a
medium, or large effect-size (smallest ES 0.57), with a very highly significant improvement
Fig 3. 1RM forest plot. The effect sizes are the change in 1RM post-intervention. For all plots the 95% confidence
intervals were calculated and the overall mean effect size is represented with the diamond. The black squares represent
the effect size of each study. Weighting was adjusted for sample size, SD and variance. Genes listed in alphabetical
order.
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Table 2. Candidate genes for strength.
Gene
Groups
No. of Study
groups
Total Group Sample
Size
Mean Rank (Not adjusted for
sample size)
Subgroup (Adjusted for sample size) % of
weight
Mean Group Effect Size
(d)
ACE 9 194 14.11 18.22 1.12
ACTN3 6 743 3.50 22.96 0.57
AKT1 4 39 24.00 11.71 2.27
COX4I1 4 506 15.50 22.72 1.09
mTOR 4 39 24.00 11.71 2.27
VEGF-A 2 17 16.50 12.68 1.27
Total 29 1,538
The group with the highest mean rank shows the greatest number of high scores in 1RM. Subgroup % shows how much each of the six genes contribute to the 72%
variance, adjusted for sample size, SE and within group variation in SMD.
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with training interventions (p <0.001) (Fig 4). These studies performed between 4–12 cycle
bouts, at average intensities of 90–110% V
_O2max or load of 0.075 per-kg bodyweight, over
3-days a week and 5 weeks of training.
As with previous data sets, PPO results were not normally distributed, D(17), .888,
p<0.05. However, there were no significant differences between gene subgroups, H(3), 1.592,
p = 0.661 and partial Eta Squared found that only 10% of the variability in the 12.17% increase
in PPO post-training, was explained by genetic sub-grouping (Table 3).
All genes showed a medium, or large effect size, with the HADH gene group showing the
largest effect size score, (d= 1.34). However, HADH showed the lowest weighting, of 18.95%,
when compared to other genes. MAFbx, with an effect size of 0.70, carried 34.36% of the
weighting and explained about 1/3 of the total variability in the 10% power increase.
Discussion
The aim of this systematic review and meta-analysis was to identify candidate genes associated
with the three key components of fitness. Additionally, to assess if these genes and their alleles,
are associated with exercise response phenotype variability, in untrained human subjects, fol-
lowing an exercise training intervention. The results from this review are important, not just
for the untrained, but all training populations. The inter-individual variation in the improve-
ments in health-related components of fitness, identified in this study, for V
_O2max, strength
and power can be explained genetics up to 44, 72 and 10% respectively. Such a finding empha-
sises the importance of assessing individuals’ genotype and planning training accordingly,
thereby, making these findings relevant to the wider field of sport and exercise sciences.
13 candidate genes were identified that show significant associations with the fitness vari-
ables of interest. Overall, this review found that a genetic component for exercise responsive-
ness can explain between 10–72% of the variability in these key components. Such findings are
consistent with previous studies, which reported variabilities of up to 80% in fitness pheno-
types [2,13,15,17,27]. In this review, the subgroup analysis of the 13 candidate genes showed
that nine were associated with cardiovascular fitness, six with muscular strength and four with
anaerobic power phenotypes. Although studies, reported the ACE, ACTN3 and APOE allelic
Fig 4. PPO forest plot. ES represents the change in PPO post-intervention. For all plots the 95% CI’s were calculated
and the overall mean effect size is represented with the diamond, whereas the black squares are the effect size of each
study. The weight is adjusted for sample size, SD and variance. Genes listed in alphabetical order.
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contribution, a key limitation with the majority of studies, was that they assessed individual
genes, as a single variable, irrespective the gene’s allelic composition. Such an omission makes
it difficult to assess the exact role of the gene(s) as the minor and major alleles often affect the
phenotype differently, as shown in this study with APOE alleles [17,18]. Different genes also
interact to produce the final phenotypic response [65], and here Genome Wide Association
Studies (GWAS) will play an increasingly important role in identifying these and the variants.
Thus, Williams et al. [65] identified a total of 97 genes that predicted V
_O2max trainability, and
that the phenotype was dependent on several of these genotypes, which may contribute to
approximately 50% of an individual’s V
_O2max trainability [65]. Further, a recent study by Al-
Khelaifi et al. [66] also uncovered novel genes and associations using GWAS [66]. Using these
studies that have identified the genes and potential associations to training this study identified
13 candidate genes, that provides a useful focus for future exercise intervention studies and
how the variability of the phenotypes are affected.
In terms of cardiorespiratory fitness, all studies demonstrated an increased V
_O2max in
response to aerobic exercise interventions. Here the well-studied ACE gene and its polymor-
phisms; II, ID, DD, showed the greatest influence on the phenotype, despite only having three
groups in the analysis, with little differences between genotypes (188 participants out of 1,772,
Table 1). Following this, COX4I1, CS and HADH genes also showed large influences on aero-
bic improvements. A possible explanation is that these genes code for key mitochondrial
enzymes used in aerobic respiration. Thus, COX4I1 codes for cytochrome C oxidase, a key
component of mitochondrial electron transport, whilst CS codes for citrate synthase, found in
Krebs’ Cycle. Finally, HADH codes for 3-hydroxyacyl-CoA dehydrogenase, required for the
oxidation of fatty acids [15,17,25,67,68]. PFKM and PGC1-αalso displayed large influences
and effect sizes, but only contributed 6.62 and 7.36% of the total 44% gene variability, respec-
tively. A possible explanation is the combination of low study numbers and sample sizes (78
and 52 out of the 1,722 participants). AMPK had the smallest sample size, of 18 participants,
hence it is difficult to draw firm conclusions on its effect on cardiorespiratory fitness, when
compared to the other genes. However, AMPK has been found to directly affect PGC1- α,
which is the independent master regulator of mitochondrial biogenesis and aerobic respiration
[68,69]. Finally, APOE and ACTN3 results indicated no advantages and little influence on
cardiorespiratory fitness. The APOE E3/E3 allele is very common, at 78.3%, worldwide [70]
and is considered the neutral genotype, showing no effect on cardiorespiratory fitness, in
agreement with the findings in this review. However, high to medium effect sizes were
observed for the E2 and E4 alleles (Fig 2), concurring with the findings of Bernstein et al. [71]
and Deeny et al. [72]. In this connection Obisesan et al. [73] found APOE genotypes explained
significant variability in cardiorespiratory fitness, seen in training-induced increases after 24
Table 3. Candidate genes for peak power output.
Gene
Groups
No. of Study
groups
Total Group Sample
Size
Mean Rank (Not adjusted for
sample size)
Subgroup (Adjusted for sample size) % of
weight
Mean Group Effect Size
(d)
CS 6 66 9.33 26.15 1.15
HADH 4 39 10.88 18.96 1.34
MAFbx 3 27 6.17 34.36 0.70
PGC-1a 4 34 8.75 20.53 1.02
Total 17 166
The group with the highest mean rank shows the greatest number of high scores in PPO and subgroups show how much of the 10% variance is accounted for by each of
the four genes when normalised to 100%, adjusted for study sample size, SE and within group SD in SMD.
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weeks (p = 0.002). Further analysis in this review found that there was a highly significant dif-
ference when the E3 allele was compared to E2 and E4, in both males and females. This could
explain the observed 44% genetic influence seen in this review, and why it was lower than
reported rates in the literature, due to the confounding effect of APOE E3/E3 alleles
[2,15,16,74,75]. Additionally, this would also emphasise the importance of examining the con-
tributions of specific alleles of candidate genes, as opposed to whole gene analysis. These find-
ings would also suggest that it is more advantageous to carry E2 and E4 genotypes, as opposed
to E3, for improvements of V
_O2max post-training. Raichlen and Alexander, [70] stated, that
these specific candidate genes and genotypes do not necessarily aid physical performance phe-
notypes, such as cardiorespiratory fitness, but instead, in the presence of physical activity,
decrease health risks, such as coronary artery disease (CAD) and improve overall health status,
therefore, when this is included in the meta-analysis it consequently decreases the associated
gene variability [76].
Interestingly, the well-researched ACTN3 gene showed equivocal results in this review for
the both V
_O2max and 1RM. Theoretically, homozygosity for the X (deletion) allele should abol-
ish production of αactinin-3, leading to improved aerobic fitness, whilst the R allele should
decrease aerobic fitness, due to increased αactinin-3 expression [18,27,60,61,7779]. Addi-
tionally, Hogarth et al. [80] states that α-actinin-3 controls sarcomeric composition and mus-
cle function in an allele dose-dependent fashion and promotes strength adaptations, but this
was not observed in this review. However, the findings of this review agree with those of Gine-
viciene et al. [1], which directly assessed the influence of the ACTN3 R577X polymorphism on
aerobic fitness and found no significant differences between alleles for the variability and train-
ability of the exercise phenotype [14]. Similarly, for the well-studied ACE genotypes, this
review found no significant differences between ACE insertion (I) and deletion (D) alleles, as
both alleles were associated with significant improvements in cardiorespiratory fitness. Here
previous work has suggested that I allele is associated with greater increases in cardiorespira-
tory fitness and endurance, due to lower levels of ACE and increased maximal heart rate and
improved blood circulatory role [58].
In this review, muscular strength phenotype, assessed by 1RM, showed the largest observed
variability, in response to training interventions, with 72% accounted for by the six candidate
genes. AKT1 and mTOR had equally large contributions to the phenotype response, displaying
the largest mean rank and effect size. Previous studies [8183] are consistent in showing inter-
actions between Akt and mTOR regulation, which are activated through resistance exercise.
Akt and its downstream signalling pathways, such as mTOR (Akt-mTOR pathway) is the cen-
tral mediator of protein synthesis, associated with the control of skeletal muscle hypertrophy,
muscle mass and strength [8083]. However, due to the nature of the studies, reporting the
findings from whole gene analysis, it is still unclear on the specific role of different alleles.
Interestingly, the literature supports an upstream regulation in AMPK activation for endur-
ance, suppressing Akt-mTOR, meaning the increased levels of AMPK may be detrimental to
strength improvements [8487]. Additionally, the literature suggests that mTOR polymor-
phism (rs2295080) alleles G = 45.2% and T = 54.8% also show different results [88]. The G-
allele predominates in endurance athletes, whilst the T-allele frequency is greater in power and
strength-oriented athletes [89]. Therefore, suggests the need to review this gene at an allele spe-
cific level.
ACE and VEGF-A made similar contributions to increased strength variability, despite the
low number of participants (17 participants) in the VEGF-A study. The results further show
that ACE and COX4I1 accounted for 40.9% of the 72% total variability found. Theoretically
the ACE D allele results in increased ACE activity, which has been shown to be associated with
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strength performance and decreases in V
_O2max [23,29,90], however no such effect was noted in
this study. Finally, ACTN3 had the lowest mean rank score, and the lowest effect size, when
compared to the other genes. However, it is important to note that the increases in strength,
associated with ACTN3, were still significant, which is consistent with previous findings
[27,59,91]. Moreover, when sub-group analysis and sample size were accounted for, ACTN3
was the largest contributor to overall variability in the strength phenotype. However, this may
simply reflect the high proportion of participants within this group (743 out of the 1,538).
PPO displayed the lowest genetic influence (10% variability) but was still significantly
improved by the training intervention. Here one new candidate gene was identified, by this
review, that had not already been linked with another component of fitness, the MAFbx
(FBXO32), or Atrogin-1 gene. Despite low study numbers (27 participants), MAFbx accounted
for 34.36% of the total 10% variability. MAFbx has previously been found to be associated with
muscle strength gains during exercise induced muscle hypertrophy [65,9294]. In agreement,
Mascher et al. [93] found that this gene is involved in muscle breakdown and atrophy, and that
resistance training reduced its expression, hence on this basis, might be associated with
strength. Such findings reflect a paucity of investigations into candidate genes for the PPO
phenotype, as only five studies were included in this review. Moreover, the current analysis
also revealed three of the identified genes, were also associated with cardiorespiratory fitness.
Again, the allele compositions of these genes might have been more informative but were not
reported. Previous work [1] has identified that PPARGC1A gene (PGC-1α) is associated with
power variables, as has this review.
It is important to note that, one possible reason why the anaerobic power candidate genes
may be associated with different phenotype responses and low variability rates, could be due to
studies incorrectly measuring anaerobic power and rather, measuring metabolic power, which
is a mixture of energy sources. This may be a significant flaw in many power assessment stud-
ies [10]. In this connection, it is very common, when measuring power, to use 30 second Win-
gate tests (WAnT). Here energy from anaerobic phosphagenic, glycolytic and aerobic
mitochondrial respiration metabolism, contributes to 31.1%, 50.3% and 18.6%, respectively
[8,95]. Hence, many studies are misinterpreting power phenotypes, making the assessment
more difficult. Therefore, we would recommend studies that include, all-out burst and brief
sprints with durations of up to 10 seconds, where the initial energy source is primarily drawn
from anaerobic metabolism only, and reported as peak power output, rather than mean power
output over time [10,96,97].
A major strength of this meta-analytical was the ability to compare all studies, regardless of
intervention, by grouping studies and assessing them by the genotype sub-groups. For exam-
ple, in this review, all studies that assessed the same genes and alleles were combined, any effect
on the phenotype was averaged and the overall variability assessed. This was then compared
with the influences of the other candidate genes following the same method, rather than
directly comparing studies against each other using different interventions. Such an approach
helps account for the variation caused by the training intervention and other external influ-
ences, such as the environment on the phenotype. Another key strength of this review was that
the analysis compared the contribution of multiple genes towards the total variance of the phe-
notypes, rather than a more restricted, single gene analysis approach. It is also important to
note that the genetic make-up, alone, does not determine the phenotype, only the potential for
expression of the phenotype in response to a particular lifestyle, environment, and interven-
tion [13,17].
The main limitation to this review was the lack of allele specific analysis in the included
studies. Another limitation is the possible exclusion of other candidate genes, potentially
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Exercise genetics and phenotypic variance in the three components of fitness
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reducing the number of candidate genes identified. Such limitations reflect a generic problem
with a systematic review, that it is limited to current published information and the require-
ment to ensure the comparability of different studies. Another factor to consider, is that base-
line-training status also affects the adaptation responses to exercise. Although, this review
attempted to address this by specifically selecting studies in which all participants were classed
as untrained, according to widely accepted norms, it is clear there were still baseline differ-
ences between individuals. This is reflected in this review by the within-groups non-normal
distributions and heterogeneity [2,4]. Moreover, the predisposition of the genetic heritability
for advantageous baseline phenotypes, shows genes and specific alleles heavily influence adap-
tations and trainability, even before training interventions are implemented [14]. Finally, for a
number of particular genes in this review, due to the low group sample size, it is not clear, nor
possible to draw firm conclusions for the precise role of these genes on the phenotype and fur-
ther investigation is required. Nevertheless, this review has identified 13 candidate genes,
which explain a significant proportion of the variability and their contribution in the pheno-
type responses to trainability for the three components of fitness.
Practical applications
This review demonstrates that the candidate genes provide valuable information regarding
genotype-specific training and variability in the phenotype responses. In theory a possible prac-
tical application of this could be to identify a person’s genotype and tailor a specific individual
training intervention programme, based on their genotype. This would be more advantageous
than implementing generic training programmes, which may provide relatively little value in
terms of phenotype gains and improvements. These inferences also support and strengthen the
evidence, that genes have on training variability suggested in the research literature.
Supporting information
S1 Fig. Bland Altman plot. The results of both reviewers using the quality assessment tool is
mapped as the difference in scores against the average score (Bias). The 95% LoA are also cal-
culated and represented as the upper and lower 1.96 dashed lines. The confidence intervals for
the 95% LoA were calculated using Bland Altman’s approximate method.
(PDF)
S1 Table. Search terms and results. Search terms implemented for all the databases and the
number of results shown by hits.
(PDF)
S2 Table. COSMIN assessment tool and Post-Hoc power. The average score between review-
ers was taken for the final inclusion. Power threshold was based at 0.8 (80%).
(PDF)
S1 File. Meta-analysis on genetic association studies checklist.
(PDF)
S2 File. PRISMA 2009 checklist.
(PDF)
Acknowledgments
This meta-analytical review is non-profited and non-funded research. This work was sup-
ported by Anglia Ruskin University, the staff from library services and the Faculty of Science
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Exercise genetics and phenotypic variance in the three components of fitness
PLOS ONE | https://doi.org/10.1371/journal.pone.0249501 October 14, 2021 12 / 18
and Engineering (FSE). All data is available at the Cambridge Centre for Sport & Exercise Sci-
ences, Anglia Ruskin University, UK.
Author Contributions
Conceptualization: Henry C. Chung, Don R. Keiller, Justin D. Roberts, Dan A. Gordon.
Data curation: Henry C. Chung, Don R. Keiller.
Formal analysis: Henry C. Chung.
Investigation: Henry C. Chung, Don R. Keiller.
Methodology: Henry C. Chung, Dan A. Gordon.
Project administration: Henry C. Chung.
Resources: Don R. Keiller, Justin D. Roberts, Dan A. Gordon.
Software: Dan A. Gordon.
Supervision: Don R. Keiller, Justin D. Roberts, Dan A. Gordon.
Validation: Henry C. Chung, Dan A. Gordon.
Visualization: Henry C. Chung, Don R. Keiller, Justin D. Roberts, Dan A. Gordon.
Writing – original draft: Henry C. Chung.
Writing – review & editing: Henry C. Chung, Don R. Keiller, Justin D. Roberts, Dan A.
Gordon.
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... While previous research has reported IIRD associated with exercise, including genetic interactions, for a variety of outcomes [7], these studies have traditionally placed the "cart before the horse" by not appropriately testing for within-subject random variation first [5]. Put simply, these studies did not determine if the variation in response to exercise was the result of the exercise itself versus random variation (measurement error biological variability, etc.) and/ or behavioral changes (sleep, diet, etc.) not associated with the treatment response to exercise. ...
... As a result, this could lead to potentially false conclusions and unethical follow-up studies [8]. From a clinical perspective, this could lead to the recently stated and possibly false suggestion of developing exercise programs based on one's genotype [7] as opposed to a focus on more general exercise guidelines [2][3][4], the latter of which should have greater reach, especially for marginalized populations. ...
... As a classic example of a lack of true IIRD at the study level, low cardiorespiratory fitness has been shown to be a potent predictor for all-cause mortality as well as coronary heart disease and cardiovascular disease events in healthy men and women [16], with heritability estimates reportedly ranging from 25 to 65% for changes in cardiorespiratory fitness as a result of aerobic exercise [7,17]. However, a recent review that included 186 published studies found that none had appropriately quantified IIRD, with only one including a control arm comparator [18]. ...
... Physical activity is associated with a reduced risk of multiple non-communicable diseases [18] and is crucial in the prevention of becoming overweight/obese. Individuals with the same genotype respond more similarly to training than those with different genotypes, indicating that genes play an important role in the determination of individual differences in response to training [19,20]. Moreover, the effects of exercise differ greatly among individuals, depending on lifestyle factors and genetic backgrounds [21]. ...
... The Ethics Committee of the Regional Medical Chamber in Szczecin (approval num- [19][20][21][22][23][24] with normal weight (i.e., BMI < 25.0 kg/m 2 ) and height (167.6 ± 5.7 cm). The women were also involved in the study with a larger sample size [11]/ and therefore previously published participants' description partly matches the current description (Table 1). ...
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There is a wide range of individual variability in the change of body weight in response to exercise, and this variability partly depends on genetic factors. The study aimed to determine DNA polymorphisms associated with fat loss efficiency in untrained women with normal weight in response to a 12-week aerobic training program using the GWAS approach, followed by a cross-sectional study in athletes. The study involved 126 untrained young Polish women (age 21.4 ± 1.7 years; body mass index (BMI): 21.7 (2.4) kg/m2) and 550 Russian athletes (229 women, age 23.0 ± 4.1; 321 men, age 23.9 ± 4.7). We identified one genome-wide significant polymorphism (rs116143768) located in the ACSL1 gene (acyl-CoA synthetase long-chain family member 1, implicated in fatty acid oxidation), with a rare T allele associated with higher fat loss efficiency in Polish women (fat mass decrease: CC genotype (n = 122) −3.8%; CT genotype (n = 4) −31.4%; p = 1.18 × 10−9). Furthermore, male athletes with the T allele (n = 7) had significantly lower BMI (22.1 (3.1) vs. 25.3 (4.2) kg/m2, p = 0.046) than subjects with the CC genotype (n = 314). In conclusion, we have shown that the rs116143768 T allele of the ACSL1 gene is associated with higher fat loss efficiency in response to aerobic training in untrained women and lower BMI in physically active men.
... The precise effect of genetics on training responsiveness is a topic much-speculated upon. A recent systematic review highlighted the significant genetic influence on the variance of phenotype response specific to training type [33]. The analysis demonstrated that genetic variability contributed 44%, 72% and 10% of adaptation differences in aerobic, strength and power phenotypes, respectively [33]. ...
... A recent systematic review highlighted the significant genetic influence on the variance of phenotype response specific to training type [33]. The analysis demonstrated that genetic variability contributed 44%, 72% and 10% of adaptation differences in aerobic, strength and power phenotypes, respectively [33]. However, the findings from the Studies of Twin Responses to Understand Exercise as Therapy (STRUETH) study suggest genetics may not play as substantive a role as first thought [18]. ...
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There is a wide variance in the magnitude of physiological adaptations after resistance or endurance training. The incidence of “non” or “poor” responders to training has been reported to represent as high as 40% of the project’s sample. However, the incidence of poor responders to training can be ameliorated with manipulation of either the training frequency, intensity, type and duration. Additionally, global non-response to cardio-respiratory fitness training is eliminated when evaluating several health measures beyond just the target variables as at least one or more measure improves. More research is required to determine if altering resistance training variables results in a more favourable response in individuals with an initial poor response to resistance training. Moreover, we recommend abandoning the term “poor” responders, as ultimately the magnitude of change in cardiorespiratory fitness in response to endurance training is similar in “poor” and “high” responders if the training frequency is subsequently increased. Therefore, we propose “stubborn” responders as a more appropriate term. Future research should focus on developing viable physiological and lifestyle screening tests that identify likely stubborn responders to conventional exercise training guidelines before the individual engages with training. Exerkines, DNA damage, metabolomic responses in blood, saliva and breath, gene sequence, gene expression and epigenetics are candidate biomarkers that warrant investigation into their relationship with trainability. Crucially, viable biomarker screening tests should show good construct validity to distinguish between different exercise loads, and possess excellent sensitivity and reliability. Furthermore “red flag” tests of likely poor responders to training should be practical to assess in clinical settings and be affordable and non-invasive. Early identification of stubborn responders would enable optimization of training programs from the onset of training to maintain exercise motivation and optimize the impact on training adaptations and health.
... For example, aerobic performance traits such as endurance capacity are bolstered by efficient cardiac function and oxygen delivery, whereas burst traits such as sprint speed are anaerobic and require investment in the development and growth of skeletal muscle comprising appropriate muscle fiber types. These different performance traits incur distinct costs (Husak and Lailvaux, 2017) and likely also elicit activity in disparate metabolic and biochemical pathways (Chung et al., 2021;Husak and Lailvaux, 2022). Despite the attention paid to the physiological and genetic factors underlying locomotor performance (Sorci et al., 1995;Bouchard, 2012;Sharman and Wilson, 2015;Chung et al., 2021), it remains unclear how increased investment in specific types of performance mechanistically affects other aspects of the integrated phenotype. ...
... These different performance traits incur distinct costs (Husak and Lailvaux, 2017) and likely also elicit activity in disparate metabolic and biochemical pathways (Chung et al., 2021;Husak and Lailvaux, 2022). Despite the attention paid to the physiological and genetic factors underlying locomotor performance (Sorci et al., 1995;Bouchard, 2012;Sharman and Wilson, 2015;Chung et al., 2021), it remains unclear how increased investment in specific types of performance mechanistically affects other aspects of the integrated phenotype. This poor understanding in turn impedes our ability to comprehend both the proximate trade-offs involved in performance expression, as well as the effects of such trade-offs on developmental and evolutionary trajectories Husak, 2014, Husak andLailvaux, 2022;Garland et al., 2022). ...
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Locomotor performance is a key predictor of fitness in many animal species. As such, locomotion integrates the output of a number of morphological, physiological, and molecular levels of organization, yet relatively little is known regarding the major molecular pathways that bolster locomotor performance. One potentially relevant pathway is the insulin and insulin-like signaling (IIS) network, a significant regulator of physiological processes such as reproduction, growth, and metabolism. Two primary hormones of this network, insulin-like growth factor 1 (IGF1) and insulin-like growth factor 2 (IGF2) are important mediators of these processes and, consequently, of life-history strategies. We sprint-trained green anole (Anolis carolinensis) females to test the responsiveness of IGF1 and IGF2 hepatic gene expression to exercise training. We also tested how sprint training would affect glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and eukaryotic elongation factor 2 (EEF2). The former is a crucial enzyme for glycolytic function in a cell, and the latter is necessary for protein synthesis. Resistance exercise forces animals to increase investment of resources towards skeletal muscle growth. Because IGF1 and IGF2 are important hormones for growth, and GAPDH and EEF2 are crucial for proper cellular function, we hypothesized that these four genes would be affected by sprint training. We found that sprint training affects IGF and EEF2 expression, such that larger sprint-trained lizards express hepatic IGF1, IGF2, and EEF2 to a lesser extent than similarly sized untrained lizards. These results demonstrate that the IIS, and pathways connected to it, can react in a size-dependent manner and are implicated in the exercise response in reptiles.
... While a large body of evidence has confirmed the beneficial impact of sufficient PA and limited SB on health, this evidence is mainly based on populations from Western countries [23]. Owing to cultural and genetic differences [24], it is possible that the health effects of PA and SB in Western populations cannot be fully replicated in the Chinese population [3]. As such, under ideal circumstances, the Chinese PASBG would be based on studies conducted in Chinese populations. ...
Article
Full-text available
Physical inactivity has long been a global public health issue. In response to this, China published new Physical Activity and Sedentary Behaviour Guidelines for Chinese People in 2021 (PASBG 2021). This is a milestone in China’s public health, behavioural epidemiology and an important contribution to the Healthy China 2030 initiative. This commentary summarises the contents and highlighted the significance of the new guidelines. The new Chinese PASBG provide foundations for population-based estimates of healthy behaviours, strategies addressing physical inactivity and messages designed to encourage people to be more active. While the contents of the PASBG 2021 are mostly consistent with the World Health Organisation physical activity guidelines, it is unclear on what evidence they are based, and whether this included research in Chinese people. Physical activity research in China is very limited and it is urgently needed to advance national-based physical activity research in China in accordance with the behavioural epidemiology framework. The development of new PASBG is only the first step, now it is what is done to communicate and disseminate, provide opportunities and supportive environments that will make a difference to physical activity levels in China. As such, we hope the PASBG 2021 will not only become a document for educating Chinese people to move more, but also an impetus for improving population health research.
... Differences in the results might originate from differences in study populations, methods, and genetic variation in fitness levels 29 . Most of the previous studies determined LFC by ultrasound or fatty liver index, instead of the gold standard assessment methods liver biopsy and MRS, which may affect the results. ...
Article
Full-text available
The aim of this study was to investigate the associations between liver fat content (LFC), sedentary behaviour (SB), physical activity (PA), fitness, diet, body composition, and cardiometabolic risk factors in adults with metabolic syndrome. A total of 44 sedentary adults (mean age 58 [SD 7] years; 25 women) with overweight or obesity participated. LFC was assessed with magnetic resonance spectroscopy and imaging, SB and PA with hip-worn accelerometers (26 [SD 3] days), fitness by maximal bicycle ergometry, body composition by air displacement plethysmography and nutrient intake by 4-day food diaries. LFC was not independently associated with SB, PA or fitness. Adjusted for sex and age, LFC was associated with body fat%, body mass index, waist circumference, triglycerides, alanine aminotransferase, and with insulin resistance markers. There was and inverse association between LFC and daily protein intake, which persisted after further adjusment with body fat%. LFC is positively associated with body adiposity and cardiometabolic risk factors, and inversely with daily protein intake. SB, habitual PA or fitness are not independent modulators of LFC. However, as PA is an essential component of healthy lifestyle, it may contribute to liver health indirectly through its effects on body composition in adults with metabolic syndrome.
... A recent study reported that powerlifters and weightlifters possessed a few different DNA polymorphisms associated with power and strength status, respectively [25], suggesting that powerlifters and weightlifters have different genetic traits associated with each performance. Moreover, in a recent meta-analysis, Chung et al. [26] have shown that strength and power phenotypes respond differently and play different roles according to genetic variability. Therefore, the results of this study indicate the importance of examining the association between phenotypes and polymorphisms by differentiating between power-oriented (e.g., weightlifters) and strength-oriented (e.g., powerlifters) athletes. ...
Article
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The rs671 polymorphism is associated with the enzymatic activity of aldehyde dehydrogenase 2 (ALDH2), which is weakened by the A allele in East Asians. We recently reported the association of this polymorphism with the athletic status in athletic cohorts and the muscle strength of non-athletic cohorts. Therefore, we hypothesized the association of ALDH2 rs671 polymorphism with the performance in power/strength athletes. We aimed to clarify the relationship between the ALDH2 rs671 polymorphism and performance in power/strength athletes. Participants comprising 253 power/strength athletes (167 men and 86 women) and 721 healthy controls (303 men and 418 women) were investigated. The power/strength athletes were divided into classic powerlifting (n = 84) and weightlifting (n = 169). No differences in the genotypes and allele frequencies of the ALDH2 rs671 polymorphism and an association between performance and the ALDH2 rs671 genotype were observed in weightlifters. However, the relative values per body weight of the total record were lower in powerlifters with the GA + AA genotype than those with the GG genotype (7.1 ± 1.2 vs. 7.8 ± 1.0; p = 0.010, partial η2 = 0.08). Our results collectively indicate a role of the ALDH2 rs671 polymorphism in strength performance in powerlifters.
... A study found that the influence of genes on the results of cardiovascular exercise is estimated to be 44%. In comparison, genes' influence on short-term explosive exercise is estimated to be only 10%, which suggests that it is possible to formulate personalized exercise programs according to individual genetic characteristics in the future [20]. Looking forward to the future, our body's response to the same exercise is slightly different because everyone's genetic composition is different. ...
Article
Full-text available
Human muscle tissue undergoes dynamic changes in gene expression during exercise, and the dynamics of these genes are correlated with muscle adaptation to exercise. A database of gene expression changes in human muscle before and after exercise was established for data mining. A web-based searchable database, Exe-muscle, was developed using microarray sequencing data, which can help users to retrieve gene expression at different times. Search results provide a complete description of target genes or genes with specific expression patterns. We can explore the molecular mechanisms behind exercise science by studying the changes in muscle gene expression over time before and after exercise. Based on the high-throughput microarray data before and after human exercise, a human pre- and post-exercise database was created using web-based database technology, which researchers can use or share their gene expression data. The Exe-muscle database is accessible online.
... 27 In a systematic review, Chung et al reported that individual's CRF 13 response to exercise training was associated with genetic variability, and suggested that 14 genotype-specific training programs are more advantagemous in improving CRF response than 15 generic training programs. 28 Future research should focus on identifying factors contributing to a 16 CRF response to CR. 17 A second possible reason is that the CR program was not designed specifically for AF 18 prevention, and so not all modifiable AF risk factors are addressed. Significant weight loss, a key 19 aspect of successful AF risk factor modification clinics, was not achieved in this program. ...
Article
Background Patients with coronary artery disease (CAD) are at risk for developing atrial fibrillation (AF). Whether attending a cardiac rehabilitation (CR) program can attenuate this risk is unclear. Methods This retrospective cohort study included patients who were free of pre-existing AF and referred to CR after coronary revascularization between April 2004 and March 2015 in Calgary, Canada. Patients with incident AF were identified using administrative data and the local electrocardiogram repository. Exposure variables and covariates were extracted from electronic medical records of a CR program and a clinical registry. Results The study included 11,662 patients [mean age (standard deviation), 60.9 (10.9) years; male, 80.6%]. In a median follow-up of 4.8 years, the cumulative incidence rate of AF was 1.04 per 100 person-years. There was no association between CR completion and the risk of incident AF after adjusting for baseline characteristics [Hazard ratio (HR): 0.97; 95% confidence interval (CI): 0.83-1.15]. However, each higher metabolic equivalent (MET) of baseline cardiorespiratory fitness (CRF) and each MET gain in CRF following CR were independently associated with a 12% (95% CI: 6%-18%) and 18% (95% CI: 6%-28%) lower relative risk of incident AF, respectively. The risk of incident AF declined progressively with the baseline CRF increasing up to 9.0 peak METs, and with the 12-week CRF increasing up to 10.3 peak METs; beyond these peak MET levels, benefits plateaued. Conclusions CR completion alone was not associated with a lower risk of incident AF. However, higher baseline CRF and greater CRF improvement had dose-dependent protective effects.
Chapter
Exercise may limit the development of several illnesses, whereas maintaining sedentary behaviors have been associated with multiple health risks. Even moderate amounts of exercise, such as walking 30 min a day, may be sufficient to provide metabolic gains, elevated microbiota diversity, reduced oxidative stress, enhanced immune activity, and diminished inflammation. As well, the actions of exercise may come about owing to epigenetic changes, such as those that affect immune functioning and processes related to energy production, as well as effects on DNA damage repair. Through the biological changes introduced, moderate exercise regimens may contribute to the prevention of some cancers and could serve as an adjunctive treatment to enhance the effects of cancer therapies. Moreover, moderate exercise might diminish secondary effects related to cancer (e.g., fatigue, depression) and side effects of cancer therapies (e.g., neuropathy, sleep disorders). The actions of exercise can be enhanced by procedures that augment the adoption of healthy behaviors, particularly group-based activities.
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Background The genetic predisposition to elite athletic performance has been a controversial subject due to the underpowered studies and the small effect size of identified genetic variants. The aims of this study were to investigate the association of common single-nucleotide polymorphisms (SNPs) with endurance athlete status in a large cohort of elite European athletes using GWAS approach, followed by replication studies in Russian and Japanese elite athletes and functional validation using metabolomics analysis.ResultsThe association of 476,728 SNPs of Illumina DrugCore Gene chip and endurance athlete status was investigated in 796 European international-level athletes (645 males, 151 females) by comparing allelic frequencies between athletes specialized in sports with high (n = 662) and low/moderate (n = 134) aerobic component. Replication of results was performed by comparing the frequencies of the most significant SNPs between 242 and 168 elite Russian high and low/moderate aerobic athletes, respectively, and between 60 elite Japanese endurance athletes and 406 controls. A meta-analysis has identified rs1052373 (GG homozygotes) in Myosin Binding Protein (MYBPC3; implicated in cardiac hypertrophic myopathy) gene to be associated with endurance athlete status (P = 1.43 × 10−8, odd ratio 2.2). Homozygotes carriers of rs1052373 G allele in Russian athletes had significantly greater VO2max than carriers of the AA + AG (P = 0.005). Subsequent metabolomics analysis revealed several amino acids and lipids associated with rs1052373 G allele (1.82 × 10–05) including the testosterone precursor androstenediol (3beta,17beta) disulfate.Conclusions This is the first report of genome-wide significant SNP and related metabolites associated with elite athlete status. Further investigations of the functional relevance of the identified SNPs and metabolites in relation to enhanced athletic performance are warranted.
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A common null polymorphism (rs1815739; R577X) in the gene that codes for α-actinin-3 (ACTN3) has been related to different aspects of exercise performance. Individuals who are homozygous for the X allele are unable to express the α-actinin-3 protein in the muscle as opposed to those with the RX or RR genotype. α-actinin-3 deficiency in the muscle does not result in any disease. However, the different ACTN3 genotypes can modify the functioning of skeletal muscle during exercise through structural, metabolic or signaling changes, as shown in both humans and in the mouse model. Specifically, the ACTN3 RR genotype might favor the ability to generate powerful and forceful muscle contractions. Leading to an overall advantage of the RR genotype for enhanced performance in some speed and power-oriented sports. In addition, RR genotype might also favor the ability to withstand exercise-induced muscle damage, while the beneficial influence of the XX genotype on aerobic exercise performance needs to be validated in human studies. More information is required to unveil the association of ACTN3 genotype with trainability and injury risk during acute or chronic exercise.
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Objectives: This study tests the hypothesis that individuals who achieve a plateau at V˙ O2max (V˙ O2plat) are more likely to possess alleles, associated with anaerobic capacity, than those who do not. Design: A literature survey, physiological testing and genetic analysis was used to determine any association between the aerobic and anaerobic polymorphisms of 40 genes and V˙ O2plat. Methods: 34, healthy, Caucasian volunteers, completed an exercise test to determine V˙ O2max, and V˙ O2plat. 28 of the volunteers agreed to DNA testing and 26 were successfully genotyped. A literature search was used to determine whether the 40 polymorphisms analysed were associated with aerobic, or anaerobic exercise performance. Results: The literature survey enabled classification of the 40 target alleles as aerobic [11], anaerobic [24], or having no apparent association (NAA) [5] with exercise performance. It also found no previous studies linking a genetic component with the ability to achieve V˙ O2plat. Independent t-tests showed a significant difference (p < 0.001) in the ability to achieve V˙ O2plat, but no other measured physiological variable was significantly different. Pearson's χ2 testing demonstrated a highly significant association (p = 0.008) between anaerobic allele frequency and V˙ O2plat, but not with V˙ O2max. There was no association between aerobic alleles and V˙ O2plat, or V˙ O2max. Finally there were no significant differences in the allelic frequencies, observed in this study and those expected of Northern and Western European Caucasians. Conclusion: These results support the hypothesis that the ability to achieve V˙ O2plat is associated with alleles linked to anaerobic exercise capacity.