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Does Genetic Variation in PPARGC1A Affect Exercise-Induced Changes in Ventilatory Thresholds and Metabolic Syndrome?

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It has been demonstrated that single nucleotide polymorphism (SNP) in the peroxisome proliferator-activated receptor-Γ coactivator-1α gene (PPARGC1A, rs8192678, G/A) affect the exercise-induced change in maximal oxygen uptake (Vo2). However, studies investigating the effect of this SNP on submaximal exercise performance markers are quite sparse. Therefore, we investigated the effect of a 10-wk supervised cycling training (3× 60 min·wk-1) on Vo2 and work rate at the point of optimal ventilatory efficiency (POE), anaerobic threshold (ANT), respiratory compensation point (RCP), and maximum level in subjects with 2 different genotypes in PPARGC1A. Analyses were completed in 24 untrained men aged 58 ± 6 yrs. Regarding genotype (G/A; Gly482Ser), three groups were formed (3× n=8): GT1 (G/G, wild type, common allele frequency); GT2 (A/A, homozygous); and GT3 (G/A, heterozygous). Before and after the exercise intervention blood samples and body composition in the fasted state were tested, and an incremental cycle ergometer test (10 W·min-1) until volitional exhaustion with measurements of respiratory gas exchange and heart rate were completed. In sum, the occurrence rate of metabolic syndrome was not affected by genotype or short-term supervised cycling. Ten weeks of cycling at 80-100% ANT and 90-120% RCP improved Vo2 and work rate at POE and RCP significantly. Furthermore, repeated ANOVA revealed a significant interaction between genotype and exercise with the highest responder in GT1 compared to GT3 and GT2. The results of this prospective study point towards the hypothesis that the SNP rs8192678 affects the trainability of aerobic capacity measured as Vo2 or work rate at RCP of previously untrained middle-aged men.
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Journal of Exercise Physiologyonline
April 2014
Volume 17 Number 2
Vf
April 2014
Volume 17 Number 2
Editor-in-Chief
Tommy Boone, PhD, MBA
Review Board
Todd Astorino, PhD
Julien Baker, PhD
Steve Brock, PhD
Lance Dalleck, PhD
Eric Goulet, PhD
Robert Gotshall, PhD
Alexander Hutchison, PhD
M. Knight-Maloney, PhD
Len Kravitz, PhD
James Laskin, PhD
Yit Aun Lim, PhD
Lonnie Lowery, PhD
Derek Marks, PhD
Cristine Mermier, PhD
Robert Robergs, PhD
Chantal Vella, PhD
Dale Wagner, PhD
Frank Wyatt, PhD
Ben Zhou, PhD
Official Research Journal of
the American Society of
Exercise Physiologists
ISSN 1097-9751
JEPonline
Does Genetic Variation in PPARGC1A Affect Exercise-
Induced Changes in Ventilatory Thresholds and
Metabolic Syndrome?
Susanne Ring-Dimitriou1, Lyudmyla Kedenko2, Igor Kedenko2, René
G. Feichtinger3, Peter Steinbacher4, Walter Stoiber4, Holger Föerster5,
Thomas Felder6, Erich Müeller1, Barbara Kofler3, Bernhard
Paulweber2
1Department of Sport Science and Kinesiology, Paris Lodron-
University, Hallein, Austria; 2Department of Internal Medicine I,
Paracelsus Medical University and Landeskliniken Salzburg, Salzburg,
Austria; 3Research Program for Receptor Biochemistry and Tumor
Metabolism, Department of Pediatrics, Paracelsus Medical University
and Landeskliniken Salzburg, Salzburg, Austria; 4Department of Cell
Biology, Paris Lodron-University, Salzburg, Austria; 5Medical Office of
Pediatrics and Sports Medicine, Salzburg, Austria; 6Department of
Medical Chemistry/Central Labor Diagnostics, Paracelsus Medical
University and Landeskliniken Salzburg, Salzburg, Austria.
ABSTRACT
Ring-Dimiriou S, Kedenko L, Kedenko I, Feichtinger RG,
Steinbacher P, Stoiber W, Foerster H, Felder T, Mueller E, Kofler
B, Paulweber B. Does Genetic Variation in PPARGC1A Affect
Exercise-Induced Changes in Ventilatory Thresholds and Metabolic
Syndrome? JEPonline 2014;17(2):1-18. It has been demonstrated that
single nucleotide polymorphism (SNP) in the peroxisome proliferator-
activated receptor- coactivator-1
gene (PPARGC1A, rs8192678,
G/A) affect the exercise-induced change in maximal oxygen uptake
(VO2). However, studies investigating the effect of this SNP on
submaximal exercise performance markers are quite sparse.
Therefore, we investigated the effect of a 10-wk supervised cycling
training (3x 60 min·wk-1) on VO2 and work rate at the point of optimal
ventilatory efficiency (POE), anaerobic threshold (ANT), respiratory
compensation point (RCP), and maximum level in subjects with
different genotypes in PPARGC1A. Analyses were completed in 24
2
untrained men aged 58 ± 6 yrs. Regarding genotype (G/A; Gly482Ser), three groups were formed (3x
n=8): GT1 (G/G, wild type, common allele frequency); GT2 (A/A, homozygous); and GT3 (G/A,
heterozygous). Before and after the exercise intervention blood samples and body composition in the
fasted state were tested, and an incremental cycle ergometer test (10 W·min-1) until volitional
exhaustion with measurements of respiratory gas exchange and heart rate were completed. In sum,
the occurrence rate of metabolic syndrome was not affected by genotype or short-term supervised
cycling. Ten weeks of cycling at 80-100% ANT and 90-120% RCP improved VO2 and work rate at
POE and RCP significantly. Furthermore, repeated ANOVA revealed a significant interaction between
genotype and exercise with the highest responder in GT1 compared to GT3 and GT2. The results of
this prospective study point towards the hypothesis that the SNP rs8192678 affects the trainability of
aerobic capacity measured as VO2 or work rate at RCP of previously untrained middle-aged men.
Key Words: PPARGC1A, Exercise-Induced Trainability, Untrained Adults
INTRODUCTION
One primary goal of exercise intervention is the improvement of cardiorespiratory fitness (i.e., VO2
max), which is associated with a reduced all-cause mortality rate, a reduction in cardiopulmonary
and/or metabolic disorders, as well as an improvement in the health of subjects with chronic diseases
and those prone to the metabolic syndrome (MetS) (5,22,24,40-42). However, what is interesting
when assessing individual responses to regular exercise is that healthy untrained adults display a
large inter-individual variation in VO2 max that ranges from -20% to +50% (6,16,41).
The observed differences in the training effect may be partly explained by the dissimilarities in study
population. Other considerations include the exercise prescription, duration of exercise intervention,
and genetic factors (6,46). As to the latter factor, Bouchard (5) indicates that the exercise-induced
variation in VO2 max is 47% explained by heritability (5). Recently, the genetic variations of the
deoxyribonucleic acid (DNA) sequence of certain genes have been included when studying exercise-
induced training effects (1,18,28,29). The replacement of single nucleotides, known as single
nucleotide polymorphisms (SNP), are the most common type of genetic variation (43).
In the present study, we focused on the SNP rs8192678 (G/A, Gly482Ser) of the gene peroxisome
proliferator-activated receptor- coactivator-1 (PPARGC1A). This gene encodes the protein PGC-
1, which is a key co-activator of several nuclear transcription factors. It is a master regulator of
mitochondrial biogenesis, mitochondrial respiration, skeletal muscle fiber transformation (from fast to
slow twitch), glucose and fatty acid metabolism, and the anti-oxidation machinery (4,21,35,38,42,48).
PPARGC1A is expressed in cell types with high oxidative function (i.e., heart, skeletal muscle slow-
twitch fibers, liver, and pancreas) and in brown adipose tissue (49,50). In this context, exercise can
serve as a stressor that increases the expression of PPARGC1A via adenosine-monophosphate
kinase and sirtuin-1 (8,21).
Recently, it has been reported that the endurance trained phenotype (cyclists) showed significantly
higher messenger ribonucleic acid (mRNA) expression in PPARGC1A compared to healthy active but
untrained phenotype, sedentary inactive and spinal cord injured subjects (23). In addition, a
significant increase in PPARGC1A expression was found after 3 hrs cycling in young healthy subjects
(13). Beside the impact of exercise, it has been demonstrated that overweight healthy subjects with
3
no family history of type-2 diabetes mellitus (T2D) were characterized by a significantly higher
expression in PPARGC1A compared to a matched sample with a family history of T2D and to
subjects with manifest T2D (36).
Several studies have shown that SNPs in PPARGC1A are associated to a significant lower level in
aerobic power (i.e., VO2 max) in insulin resistant and untrained individuals as well as in athletes (1,28,
29,51,53). To date only one study (51) has investigated the effect of an exercise intervention on
insulin sensitivity and aerobic capacity (i.e., %VO2 max at submaximum level) in subjects with the
point mutation rs8192678 in the PPARGC1A gene. The data revealed that 9 months of regular
exercise caused an insufficient training response in insulin sensitivity and in VO2 at the anaerobic
threshold (ANT) in adults carrying the rare allele in PPARGC1A while subjects who were
homozygous for the common allele showed a significant increase in insulin sensitivity and VO2 at the
ANT (51).
The ANT is a submaximal marker of oxidative capacity, characterizing the individual’s performance
level where a transition from a predominantly aerobic to partially anaerobic energy metabolism occurs
(VT1). Beside the ANT, the point of optimal ventilatory efficiency (POE) should also be used since it
represents the work rate that is fully covered by aerobic energy supply. This point was described by
Hollmann (1959) as another approach to measure VT1 since differences were found in work rate at
POE compared to ANT (3,14,16,20,33,52,58,60). The transition from a partially anaerobic to
predominantly anaerobic energy consumption indicates a second inflection point (VT2) in incremental
pulmonary exercise testing (3,47,60). This point is often called the respiratory compensation point
(RCP, VT2). The RCP equals the work rate where the end-tidal pressure of carbon dioxide (PETCO2)
begins to decline after isocapnic buffering to compensate for metabolic acidosis (3,9,11,33,45,54,56,
58,60).
Despite the apparent impact of the aforementioned gene variant on the regulation of the skeletal
muscle metabolism, there is a lack of prospective studies investigating the effect of SNPs on markers
of the oxidative capacity and the MetS in untrained subjects. Therefore, the primary aim of the study
was to test the hypothesis, that untrained men who are homozygous or heterozygous carriers of the
rare allele in PPARGC1A will show a reduced change in oxygen uptake and work rate at submaximal
performance level compared to men characterized by the common genotype after 10 wks of
endurance exercise. We also investigated the effect of genotype on the occurrence rate of the MetS,
and tested the comparability of the three ventilatory thresholds as markers of aerobic capacity.
METHODS
Subjects and Group Assignment
For the prospective 10 wks of exercise intervention, 838 males of a study cohort (SAPHIR, (44)) were
genotyped for the SNP rs8192678 in the gene PPARGC1A. The criteria for exclusion of individuals
included: (a) manifest T2D; (b) anti-diabetic and/or anti-coagulation medications; (c) extreme diet (i.e.,
very-low carbohydrate diet); and (d) surgery within the last 6 months prior to the onset of the study.
One or more of these factors may have affected the outcome in the investigation (i.e., blood samples,
exercise performance). Distance to testing and exercise locations led to further loss of individuals,
resulting in a sample size of 44 subjects who met the following inclusion criteria: (a) untrained (≤1
hwk-1 sport activity); and (b) between 45 to 65 yrs of age. All subjects were informed verbally about
the purpose of the study, the testing procedures, risks, and exercise intervention. The study was
approved by the local ethics committee (E1243, 2010-10-04).
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Due to several medical problems not associated with the exercise training program that included time
constraints (compliance of <80% sessions completed) and missing gas-exchange or clinical data, the
final sample consisted of 24 subjects representing three genotype groups. Based on SNP analysis
(rs8192678), 8 subjects carried the common allele in PPARGC1A (G/G) and were defined as
genotype 1 (GT1), 8 men were homozygous (GT2), and 8 subjects were heterozygous (G/A) of the
rare allele (GT3).
Genotyping
Genomic DNA was isolated from whole blood by standard procedures (Puregene Kit, Gentra, MN,
USA) from all subjects of the SAPHIR population and stored at -20˚C. Genotyping for SNP
(rs8192678) was performed by the TaqMan SNP allelic discrimination method using an ABI 7900HT
instrument (Applied Biosystems, Foster City, CA, USA) and pre-designed TaqMan SNP genotyping
assays from Applied Biosystems (Foster City, CA, USA). A total of 5% of samples were genotyped in
duplicate to ensure concordance. Genotype frequencies were tested for compatibility with Hardy-
Weinberg equilibrium (HWE) using chi-square (
2) test (P0.05).
Assessment of Metabolic Syndrome (MetS)
Using the definition (2) of the International Diabetes Federation (IDF), subjects were characterized
with MetS if they had central obesity measured by a waist circumference ≥102 cm plus 2 of 4
additional factors such as an increase in fasting plasma glucose (FPG ≥100 mg·dL-1) or previously
diagnosed T2D, raised triacylglycerol level (TG ≥150 mg·dL-1), reduced high-density lipoprotein
cholesterol level (HDL-C <40 mg·dL-1) or specific medical treatment for lipid abnormalities, and raised
systolic and/or diastolic blood pressure (SBP ≥130 mmHg / DBP ≥85 mmHg) or previously diagnosed
hypertension.
One week before the first exercise session baseline ambulatory SBP and DBP of the day-phase were
measured via Riva Rocci method (Boso Rapid Manometer, Bosch and Son, Germany) and 10 mL
blood was collected from the anticubital vein after an overnight fast to assess blood measures. All
blood samples were coded and subsequently assayed blinded. FPG was measured using the glucose
oxidase method and inter-assay coefficient of variation (CV) was calculated to be 1.7% for the applied
method. Based on a turbidimetric measurement after agglutination of the antigen-antibody complex
(Tina-quant method, Roche Diagnostics, Austria) the concentrations of HDL-C (CV 1.85%) and TG
(CV 1.8%) were assessed.
Anthropometric Characteristics and Body Composition
Body height and body mass (BM, kg) were measured barefoot in a standing position wearing light
clothing by utilizing a standard balance and scale (SECA 715, Vogel and Halke, Hamburg, Germany).
From both measures the body mass index (BMI) was calculated to identify the overweight (BMI ≥25
to 29.9 kg·m-2) and obese (BMI of 30 to 39.9 kg·m-2) subjects according to the World Health
Organization (59). Regional body fat was determined by measuring the waist circumference 0.5 cm
below the umbilicus with a standardized spring-loaded elastic tape (Roche, Mannheim, Germany) in
the standing upright position with the feet 20 to 30 cm apart to determine the main characteristic of
the MetS (26). Lean body mass (LBM, kg) and body fat (BF, kg) were estimated by multi-frequency
bio-impedance analysis (B.I.A. Nutriguard-M, Data Input, Darmstadt, Germany). Electrodes
(BIANOSTIC) were attached on the frontal site of the right wrist and ankle of the subject lying in
supine position. The recording started after 5 min of rest according to the manufactures guidelines
(Data Input, Darmstadt, Germany).
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Exercise Testing and Assessment of Ventilatory Thresholds
Exercise testing was completed within the same hour pre- and post-cycling training. Prior to each
exercise test, each subject was instructed to abstain from sport activities 48 hrs, eating and drinking 3
hrs before testing. A ramp test protocol using an electronically-braked cycle ergometer (Ergoselect
100, Ergoline, GER) with 10-W increments per minute (60-70 rev·min-1) was conducted to determine
ventilatory thresholds and peak oxygen uptake (VO2peak) (7,45,57). Each subject started with a warm-
up, cycling at 50 W for 4 min. Volitional fatigue was attained and defined by the following criteria: 90%
of age-adjusted maximum heart rate (220-age), VO2peak change ≤2 mL·kg-1·min-1 with increasing load,
and ≥1.1 respiratory exchange ratio. Dependent on the protocol VO2peak was assessed in the last
stage as the mean value of 5 consecutive breaths with the 3rd breath including the maximal VO2-
value.
Gas exchange analysis was conducted in breath-by-breath mode to measure minute ventilation (VE),
breathing frequency (Bf), gas concentration of oxygen (VO2), and carbon dioxide production (VCO2)
(ZAN 600, nSpire Health GmbH, CO, USA). The inspired and expired air flow were continuously
measured with a pneumotachograph (VIPTM, nSpire Health GmbH, CO, USA) that was calibrated
prior to each test with a 1-liter calibration syringe (ZAN 600, nSpire Health, CO, USA). Known gas
concentrations of ambient and expired air (20.9/15.9 Vol% for O2, 0.03/5.0 Vol% for CO2, N2 in
equilibrium) were used to calibrate the gas analyzers.
Parallel to exercise testing, heart rate (HR) was recorded with a beat-to-beat monitoring system (T4,
Suunto, Vantaa, FIN) to determine exercise training intensity. The intensity was set at a HR equaling
the pre-training work rate at ANT and RCP.
Based on the incremental test protocol, ventilatory thresholds were determined by visual inspection
and stepwise linear regression utilizing a tri-segmental model (10,47). POE was determined by the
disproportional increase in VE vs. VO2 (20). ANT was assessed by the time curve of the PETO2 and
PETCO2 as well as by the time course of the ventilation equivalent for oxygen and carbon dioxide
(VE/VO2, VE/VCO2) as described by Scheuermann and Kowalchuk (45). RCP was determined by the
disproportional increase in VE vs. VCO2 production according to Wassermann et al. (56). Test-retest
and the observer reliability of the determination of VTs accounted for POE r = 0.94 and r = 0.97, for
ANT r = 0.89 and r = 0.99, and for RCP r = 0.95 and r = 0.97, respectively, utilizing a 10 W·min-1
cycling increment (54).
Exercise Training
To improve aerobic capacity, a progressive stationary cycle training program was selected (14,19).
The 10-wk exercise program consisted of 3 sessions·wk-1 fully supervised, each including 5 min of
warm-up and cool-down, respectively, and 35 min for the 1st 3 wks followed by 50 min for remainder
of the core workout. The sessions were completed at least 48 hrs apart. The exercise prescription is
depicted in detail in Table 1, where three zones of exercise intensity were prescribed via the HR
equaling a workload at 80 to 100% of ANT, 90 to 100% of RCP, and 100 to 120% of RCP,
respectively. Heart rate was recorded every session to assess differences between planned and
completed exercise dosage. Subject data were included when an exercise training compliance of
≥80% was achieved.
Table 1. Distribution of Exercise Intensity Zones (min) Over 10 wks.
6
Week
Session 1 (min)
Session 3 (min)
1
45a
45a
2
60a
60a
3
60a
60a
4
60 (2 x 10 +40)b
60 (2 x 10 +40)b
5
60 (3 x 10 +30)b
60 (2 x 15 +30)b
6
60 (2 x 10 +40)b
60 (4 x 5 +40)c
7
60a
60
8
60 (3 x 10 +40)b
60 (4 x 5 +40)c
9
60 (3 x 10 +40)b
60 (6 x 5 +30)c
10
60 (5 x 5 +35)c
60 (6 x 5 +30)c
Exercise intensity zones as: aHR at 80 to 100% of ANT, bHR at 90 to 100% of RCP, and cHR at 100 to 120%
of RCP.
Statistics
Normality was tested with Shapiro-Wilk statistics as suggested for small sample sizes (39). Chi-
squared test was utilized to test the allele frequencies for Hardy-Weinberg equilibrium. The one way
analysis of variance (ANOVA) was conducted to determine mean differences between genotypes of
primary outcomes at baseline (Table 2 and 3). Interaction of genotype and exercise training was
tested with analyses of variance (ANOVA) for repeated measures, and training effects within groups
were tested with paired sample t test and reported as absolute differences of post minus pre
measures in Table 4. Because genotype group were not different from each other in all outcome
measures at baseline and because of the small range in age of the investigated male sample no
covariates were used to adjust the measures. Significance level was set at P≤0.05. Statistical
procedures were carried out using the Statistical Package for Social Sciences (SPSS 17.0, Chicago,
IL, USA).
RESULTS
All allele frequencies were in Hardy-Weinberg equilibrium as tested by Chi-squared test. The overall
genotyping success rate was 96.6% for rs8192678 (Ser482 encoding allele). Rescreening of 5% of
the 440 subjects resulted in 100% identical results. All data were normally distributed for each group
(GT1, GT2, GT3) at both time points (0, 10 wks). Accordingly, repeated ANOVA was performed and
the F-test results were used because of equally distributed variance for all variables.
Baseline Outcomes: Anthropometric, Blood and Exercise Performance Data
As depicted in Table 2, male subjects were middle-aged with 58 ± 6 yrs, overweight according to a
BMI ≥25 kg·m-2 and characterized by an elevated cardiovascular risk according to a waist
circumference of >94 cm with a normal body fat composition (26,59). Based on the IDF definition (i.e.,
waist circumference, blood pressure, TG-concentration, HDL-C, and fasting plasma glucose), about
every third participant suffered from the MetS with no effect of the investigated genotype on the
occurrence rate of the MetS (see Table 2).
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The performance level at baseline, assessed as VO2 or work rate (P) at ventilatory thresholds and at
maximum load, did not differ significantly between genotypes as recorded in Table 3. The subjects
are characterized by an average cardiorespiratory fitness of 35.3 ± 5.4 mL·kg-1·min-1 regarding their
age strata (32).
Table 2. Baseline Characteristics of Subjects According to Genotype.
Groups
Total
GT1
GT2
GT3
SNP-PPARGC1A
G/A, G/A, A/A
G/G (Gly/Gly)
A/A (Ser/Ser)
G/A (Gly/Ser)
P
Sample size (n)
24
8
8
8
Age (yrs)
58.3
± 5.7
58.5
± 6.2
57.6
± 5.4
58.8
± 6.2
ns
BM (kg)
87.2
± 7.6
88.2
± 4.6
88.2
± 9.1
85.3
± 8.8
ns
BMI (kg·m-2)
27.6
± 2.7
27.2
± 1.9
28.2
± 2.9
27.3
± 3.4
ns
LBM (kg)
37.7
± 2.2
38.2
± 1.3
37.4
± 2.2
37.6
± 3.0
ns
BF (kg)
16.6
± 5.0
16.3
± 2.8
18.4
± 6.1
15.1
± 5.6
ns
Waist (cm)°
101.3
± 7.3
101.4
± 4.7
104.1
± 8.5
98.5
± 8.1
ns
SBP (mmHg)°
136
± 15
131
± 8
137
± 16
139
± 20
ns
DBP (mmHg)°
85
± 8
83
± 5
86
± 11
85
± 8
ns
TG (mg.dL-1
99.8
± 52
97.3
± 61.5
95.6
± 43.5
106.0
± 52.0
ns
HDL-C (mg.dL-1
58.3
± 15.1
54.0
± 11.5
64.9
± 14.7
56.8
± 18.4
ns
FPG (mg.dL-1
96.1
± 9.0
97.5
± 10.3
96.4
± 10.2
94.5
± 7.2
ns
MetS (n within group)
9/24
(38%)
3/8
(38%)
2/8
(25%)
4/8
(50%)
Values are means ± SD after one-way ANOVA; GT1 = common allele type in PPARGC1A (G/G), GT2 =
homozygous for rare allele frequency in PPARGC1A (A/A), GT3 = heterozygous for rare allele frequency in
PPARGC1A (G/A); BM = body mass, BMI = body mass index, LBM = lean body mass, BF = body fat, SBP =
systolic blood pressure, DBP = diastolic blood pressure, TG = triacylglycerol, HDL-C = high density lipoprotein
cholesterol, FPG = fasting plasma glucose, MetS = subjects with metabolic syndrome; °variables of MetS; P =
significance level between the genotype groups; ns = not significant.
Table 3. Submaximal and Maximal Performance Markers by Genotype at Baseline.
8
Groups
Total
GT1
GT2
GT3
SNP-PPARGC1A
G/A, G/A, A/A
G/G (Gly/Gly)
A/A (Ser/Ser)
G/A (Gly/Ser)
P
Sample size (n)
24
8
8
8
VO2-POE (mL·kg-1·min-1)
20.0
± 3.3
19.3
± 4.0
20.7
± 2.6
20.1
± 3.2
ns
VO2-ANT (mL·kg-1·min-1)
20.6
± 5.2
17.5
± 3.5
21.6
± 4.1
22.7
± 6.1
ns
VO2-RCP (mL·kg-1·min-1)
28.0
± 4.7
26.7
± 4.7
28.2
± 4.5
29.1
± 5.3
ns
VO2peak (mL·kg-1·min-1)
35.3
± 5.4
34.1
± 4.8
35.7
± 5.1
36.3
± 6.6
ns
PPOE (W)
109.1
± 17.0
105.0
± 20.7
117.1
± 15.0
106.3
± 14.1
ns
PANT (W)
119.6
± 30.3
105.0
± 29.3
127.5
± 23.8
126.3
± 35.0
ns
PRCP (W)
166.7
± 26.5
160.0
± 22.7
172.5
± 26.0
167.5
± 32.0
ns
Pmax (W)
217.5
± 31.1
208.8
± 24.7
223.8
± 37.0
220.0
± 32.5
ns
Values are means ± SD after one-way ANOVA; VO2 = minute volume of the relative oxygen uptake, P =
absolute work rate; POE = point of optimal respiratory efficiency, ANT = anaerobic threshold, and RCP =
respiratory compensation point; P = significance level between GT-groups; ns = not significant.
Trainability of Aerobic Capacity within Genotype Groups
The distribution of the exercise intensity zones of all completed sessions for the entire sample
accounted for 80% at the heart rate equaling HR@80-100% of ANT, 13% at HR@90-100% of RCP,
and 7% at HR@100-120% of RCP. The compliance for 30 sessions was high and accounted for 99%
in GT1, 94% in GT2 and 100% in GT3.
Within the total sample supervised cycling resulted in a significant decrease in BMI (P≤0.001), in body
fat (-1 kg; P≤0.01), in diastolic blood pressure (-4 mmHg; P≤0.01), and in fasting plasma glucose
(P≤0.05; see Table 4). One subject in GT1 and two subjects in GT3 became normal regarding the
MetS (as depicted in Table 4). In addition, aerobic capacity (i.e., VO2 and mechanical power at
submaximal level) improved significantly in the entire group at POE (VT1, P≤0.05) and at RCP (VT2,
P≤0.001) at post-testing. Furthermore, aerobic power as VO2peak or Pmax increased significantly
(P≤0.001) after 10 wks of cycling.
Within groups, significant absolute changes were found in BMI in GT1 and to a smaller extent in GT3,
slightly in waist circumference in GT1 and in diastolic blood pressure in GT2. Aerobic capacity as VO2
improved significantly in GT1 at ANT, RCP and maximum level compared to a smaller increase at
RCP and maximum level in GT3. No improvement was found in the homozygous rare allele group
GT2. Work rate as absolute change in Watt increased significantly at all three submaximal markers,
compared to a significant increase of P at RCP in GT3, or no effect in GT2. Maximal work rate
improved in all genotypes, with the largest increase of +33 W in GT1, followed by GT3 (+31 W) and
by GT2 (+20 W).
9
Of interest is the finding of a significant interaction between genotype and exercise in the work rate at
RCP (P≤0.05), which demonstrates a significant smaller response in males carrying the homozygous
rare allele in PPARGC1A compared to a larger response in GT3 (heterozygous for the rare allele)
and the highest change of +28 W at RCP in the wild type group (Table 4).
Table 4. Exercise-Induced Trainability as Absolute Changes within 10 wks in Subject
Characteristics, Clinical Data, Submaximal and Maximal Performance Markers Regarding
Genotype.
Total
GT1
GT2
GT3
SNP-PPARGC1A
G/G
A/A
G/A
P
N
24
8
8
8
BMI (kg·m-2)
-0.4
± 0.5***
-0.4
± 0.2***
-0.5
± 0.7
-0.3
± 0.4*
ns
LBM (kg)
-0.3
± 1.3
-0.3
± 1.4
-0.4
± 1.7
0.0
± 0.9
ns
BF (kg)
-0.9
± 1.4**
-0.7
± 1.4
-1.2
± 1.8
-0.8
± 1.1
ns
Waist (cm)
-1.8
± 4.6
-1.8
± 1.5*
-3.9
± 7.4
0.5
± 1.3
ns
SBP (mmHg)
-6
± 13
-6
± 10
-7
± 15
-4
± 16
ns
DBP (mmHg)
-4
± 7**
-3
± 7
-6
± 6*
-2
± 7
ns
TG (mg·dL-1)
19.7
± 64.4
16.8
± 44.2
40.6
± 86.2
4.4
± 63.1
ns
HDL-C (mg·dL-1)
1.0
± 9.6
3.3
± 5.4
-3.4
± 11.7
2.8
± 10.7
ns
FPG (mg·dL-1)
-3.4
± 6.6*
-3.9
± 6.1
-3.1
± 8.2
-3.3
± 6.5
ns
MetS (n within group)
6/24
(25%)
2/8
(25 %)
2/8
(25%)
2/8
(25%)
VO2-POE (mL·kg-1·min-1)
1.4
± 2.4*
2.2
± 2.9
0.7
± 1.7
0.9
± 2.3
ns
VO2-ANT (mL·kg-1·min-1)
1.6
± 4.6
3.7
± 3.4*
0.1
± 4.0
1.1
± 5.8
ns
VO2-RCP (mL·kg-1·min-1)
2.0
± 3.3**
3.8
± 4.4*
0.5
± 2.6
1.8
± 1.6*
ns
VO2peak (mL·kg-1·min-1)
2.9
± 3.1***
4.0
± 3.7*
2.7
± 3.5
2.1
± 2.1*
ns
PPOE (W)
13.5
± 22.9**
18.8
± 15.5**
2.9
± 24.3
17.5
± 27.1
ns
PANT (W)
10.4
± 26.3
21.3
± 13.6**
-2.5
± 12.8
12.5
± 39.9
ns
PRCP (W)
17.5
± 20.7***
28.8
± 25.9*
3.8
± 16.9
20.0
± 9.3***
.04
Pmax (W)
27.9
± 13.2***
32.5
± 13.9***
20.0
± 12.0**
31.3
± 11.3***
ns
Values are means ± SD after paired sample t test as absolute differences (post pre), *P0.05, **P≤0.01, and
***P≤0.001 pre- vs. post-measures; P depicts the interaction of genotype and exercise training tested by
ANOVA with repeated measures; ns, not significant; other abbreviations see notes of Table 1.
Comparability of Ventilatory Thresholds
At pre- and post-testing VO2 and work rate at RCP were significantly higher compared to the mean
values at POE or AT. Although at baseline the work rate in Watt at POE was 11 W lower than at ANT,
the difference was not significant, and this difference was not found at post-testing. In summary, the
level of POE and ANT were nearly similar in untrained middle-aged male subjects (see Table 5).
Accordingly, VO2-POE and VO2-ANT were achieved at 57 and 58% of VO2peak compared to VO2-RCP, that
was achieved at 79% of VO2peak at baseline. After 10 wks of cycling, aerobic capacity as VO2-POE was
found at 56%, VO2-ANT at 58%, and VO2-RCP at 78% of VO2peak (38.3 ± 5.6 mL·kg-min-1), indicating
the relative stability of aerobic capacity over 10 wks of cycle training.
10
Table 5. Differences in VO2 and in Work Rate between Ventilatory Thresholds Before and After
10 wks Cycling.
N = 24
POE
ANT
RCP
M
± SD
M
± SD
M
± SD
P
VO2-pre
(mL·kg-1·min-1)
20.0
± 3.7
20.6
± 5.1
28.0
± 4.7a
.0001
VO2-post
(mL·kg-1·min-1)
21.3
± 3.0
22.2
± 4.5
30.0
± 5.3a
.0001
P-pre
(W)
109.1
± 17.0
121.4
± 32.6
166.1
± 28.6a
.0001
P-post
(W)
122.6
± 24.2
128.6
± 27.2
186.8
± 31.5a
.0001
Values are means ± SD tested by ANOVA for repeated measures at pre- and post-testing adjusted for multiple
comparison (Bonferroni); aRCP-level significant different from ANT and POE at P≤.0001; other abbreviations
see notes of Table 1.
DISCUSSION
In this prospective study, we investigated the effect of the rs8192678 point mutation in the
PPARGC1A gene on oxidative capacity of middle-aged untrained males after 10 wks of stationary
supervised cycling. Such endurance type exercise is highly recommended in adults to improve their
cardiorespiratory fitness and metabolic health (19,46).
Metabolic Syndrome
Because of the small sample size we discuss the data on a descriptive level. After 10 wks of training,
3 of 9 males were no longer characterized by MetS. The finding that regular exercise is able to
reduce the occurrence rate of MetS was reported widely over the last decade as reviewed by
Roberts, Hevener, and Barnard (42). Subjects with low expression of PPARGC1A showed a
dysfunction in the regulation of inflammation and lipid oxidation, two components among many others
associated with the development of the MetS (36,42). However, the results of our small sample could
not be proof of this observation since 2 to 3 subjects with MetS were found in each genotype group
pre- and post-testing.
Maximum Performance Level and Aerobic Power Were Not Affected by Genotypes
Although maximum work rate increased sufficiently by 13% after exercise training, no significant
effect of genotype could be detected because of the large variation in work rate and in VO2peak after
30 cycling sessions in formerly untrained men. This is in line with Stefan et al. (51), who detected a
smaller increase of +2% after a 2-yr lifestyle intervention with no effect of SNP in PPARGC1A on
aerobic power compared to our prospective study. On average, the entire sample improved aerobic
power significantly by +8%, a low-to-moderate response that is often found after short-term exercise
intervention (27,41).
The measured exercise-induced variation in VO2peak is in accordance with 6 wks of cycling at 70% of
VO2peak of young males 23 ± 5 yrs of age (range: -2% to 23%), but in contrast to 10 wks of cycling at
70 to 90% VO2peak (+31 ± 10%) in males aged 68 ± 7 yrs (34,55). One reason for a higher response in
older males could be the initial performance level, as they started with a 20% lower cardiorespiratory
11
fitness and a lower Pmax of 180 W compared to a Pmax of 218 W achieved in the present study. In
contrast to that argument are the results of the Heritage-Family Study, indicating that other factors
than the initial fitness level explain the variation in exercise-induced VO2 max after 20 wks of cycling
(46).
Another reason for the smaller response in VO2peak could be explained by the exercise intensity. In
the present study, the volume of high-intensity (HIT) bouts, that led to heavy muscle strain as
reported by our male subjects but was recommended for untrained subjects (17), accounted for 7% of
total sessions. However, the distribution of intensity zones in our study of 80-13-7% (HR@80-
100%ANT - HR@90-100%RCP - HR@100-120%RCP) was very close to Esteve-Leano et al. (14) of
80-10-10%. They showed that cycling below the RCP resulted in a significantly higher performance
level measured as a longer lasting time trial test compared to athletes completing a greater anaerobic
portion. Consequently, we are not able to draw the conclusion that exercise intensity was too low for
a sufficient improvement in aerobic power.
Moreover, we propose that the short exercise intervention of 10 wks and the smaller number of 30
sessions compared to 50 sessions in the Esteve-Leano et al. (14) study may have accounted for a
delay in adaptation at the cellular level. This would result in a smaller effect at maximal performance
level and a broad variation in trainability from low to high responder (16,41,55). Accordingly, we
suggest that the short exercise intervention determines the large variation in training response at
maximum level, where the structural adaptation on the cardiovascular and/or cellular level in some
participants is ongoing and in others is already finished (4). Another reason could be that the
rs8192678 SNP, who was not upon the 21 SNPs found to affect the trainability of VO2max, is not
associated to impact the trainability of aerobic power (5). As suggested recently by Vollaard et al. (55)
and by others, a more sensitive measure of trainability after a short-term exercise intervention may be
submaximal markers (30,31,37,52).
Genotype and Submaximal Markers at Baseline
Genotype did not significantly affect the occurrence of VTs at baseline as reported by Stefan and
colleagues (51). The finding that at baseline the workload at POE was lower than at ANT and at RCP
was found by Wisen and Wolhfart (60). However, the differentiation between POE and ANT was not
significant at baseline and diminished after 30 sessions of cycling by using the same exercise testing
protocol. Moreover, the result is not in line with further observations in our laboratory that especially
trained compared to untrained subjects exhibited a lower VO2 and work rate at POE vs. ANT, as the
differentiation diminished after exercise training. To date it seems that POE and ANT are markers for
the first VT and RCP identifies the second VT as suggested by Westhoff et al. (58).
Trainability
Genotype and Exercise Induced Changes of Aerobic Capacity: In agreement with the
retrospective study of Stefan and colleagues (51), we found a reduced effect in VO2 and work rate at
submaximal performance level. In addition to this, there is an indication that the effect is more
prominent in subjects homozygous for the rare frequency gene variant of PPARGC1A (A/A) than in
heterozygous males or in males with the common frequency allele.
Evidence for an impact of the PPARGC1A gene variant (rs8192678) on aerobic performance was
also found in endurance athletes who were characterized by a lower rare allele frequency compared
to untrained European males (1,15,28,29). Moreover, a smaller proportion of the investigated rare
allele in PPARGC1A was associated with a significantly higher cardiorespiratory fitness (VO2peak) and
12
percentage of slow-twitch fibers compared to untrained, unrelated healthy men and women (1).
Additionally, it has been shown in a Polish cohort that the frequency of the rare allele in PPARGC1A
was significantly less evident in endurance, strength-endurance and sprint-strength trained athletes
compared to untrained controls (5.6% vs. 13.2%, P<0.0001), which was analyzed with the same
genotyping method (29) as was used in the present study. Furthermore, a significant smaller
proportion of the rare allele frequency of rs8192678 (PPARGC1A - G/A) was found in soccer players
compared to healthy controls, indicating that regular moderate-to-vigorous physical activity is
associated with the PPARGC1A allele (18). In line with this finding, denHoed et al. (12) reported
recently that the SNP rs8192678 is negatively associated to the amount of habitual physical activity.
CONCLUSION
All together the data indicate that the rs8192678 SNP is associated to the exertion of chronic
exercise. However, short-term exercise intervention can diminish the impact of SNP in heterozygous
males, probably by affecting function-specific domains to the PPARGC1A gene and by altering a
subset of the multiple processes that PGC1 regulates on mitochondrial level as suggested by Lai et
al. (25) in diabetic subjects.
Despite a small sample size, the present study provides clear evidence that untrained males carrying
the rare allele in the PPARGC1A gene (rs8192678) respond less to a supervised stationary cycling
training based on VO2 and work rate at RCP compared to subjects carrying the common allele
frequency. This interaction is largest in the homozygous condition. The relevance of these results is
further underpinned by the fact that the subjects were prospectively selected by defined genotypes
containing the above SNP and by a supervised exercise intervention. Accordingly, we recommend
monitoring of submaximal markers rather than changes in peak oxygen uptake when investigating the
effects of an exercise intervention in combination with the PPARGC1A gene variants.
ACKNOWLEDGMENTS
We express our gratitude to the individuals who participated in the study. Additionally, we thank
Magdalena Humenberger, Jörg Stangl, and Markus Andress for performing the exercise testing and
supervising the exercise training sessions as part of their diploma thesis. Furthermore, we thank
Dave Bacharach, Jürgen Birklbauer, and Michael Buchecker for their inputs during the preparation of
the manuscript.
Grants and Disclosures: The gene-lifestyle study Salzburg was supported by funds of the
Oesterreichische Nationalbank (Anniversary Fund, project number: J14156) and by the research fund
of the Paracelsus Medical University (FFF-PMU Nr. E-09/09/055-PAU).
The authors declare no financial or other conflicts of interest.
SRD is the principle investigator and wrote the main part of the article. She is responsible for study
design, subject recruitment, exercise intervention, data analysis, funding, and coordination of the
research groups. LK and IK genotyped the DNA samples from the SAPHIR-cohort and wrote the
parts related to genotyping. RF, PS, WS, HF, TF, EM and BK were involved into the study design,
13
testing and in careful reading of the paper. BP is the senior researcher, who had the idea for the
study, was involved into the study design and in careful reading of the paper.
Address for correspondence: Dr. Susanne Ring-Dimitriou, Associate Professor, Paris Lodron-
University of Salzburg, Department of Kinesiology and Sport Science, Schlossallee 49, 5400 Hallein,
Austria, Telephone (work): ++43-662-8044-4890, Email: susanne.ring@sbg.ac.at
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Disclaimer
The opinions expressed in JEPonline are those of the authors and are not attributable to JEPonline, the
editorial staff or the ASEP organization.
... Research on the impact of genetic variation on exercise adaptation has identified a series of single nucleotide polymorphisms (SNPs) which may contribute to observed differences in response to aerobic training. Five of these SNPs from four different genes (VEGF [17], PPARGC1A [18], CRP [19,20], and two from ADRB2 [21][22][23]) have been collated into an algorithm used in a commercially available test. These SNPs affect different dimensions of cardiovascular function, and are associated with either VO 2max scores, or improvements in this capacity following aerobic training. ...
... The samples were sent to IDna Genetics Laboratory (Norwich, UK), where DNA was extracted and purified using the Isohelix Buccalyse DNA extraction kit BEK-50 (Kent, UK), and amplified through PCR on an ABI7900 real-time thermocycler (Applied Biosystem, Waltham, USA). Through this process, genetic information regarding SNPs determined to affect aerobic trainability (VEGF rs2010963, ADRB2 rs1042713 and rs1042714, CRP rs1205 & PPARGC1A rs8192678) [17][18][19][20][21][22][23] was determined. Each allele was given a score of between 0 and 4 points depending on the expected magnitude of its impact on improvements in aerobic fitness with training. ...
... PPARGC1A encodes for PGC-1α, the master regulator of mitochondrial biogenesis. G allele carriers at rs8192678 typically have higher VO 2max values following exercise [18]. The SNPs used in this algorithm are not exhaustive, but represent those that have been well replicated. ...
Article
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Recent research has demonstrated that there is considerable inter-individual variation in the response to aerobic training, and that this variation is partially mediated by genetic factors. As such, we aimed to investigate if a genetic based algorithm successfully predicted the magnitude of improvements following eight-weeks of aerobic training in youth soccer players. A genetic test was utilised to examine five single nucleotide polymorphisms (VEGF rs2010963, ADRB2 rs1042713 and rs1042714, CRP rs1205 & PPARGC1A rs8192678), whose occurrence is believed to impact aerobic training adaptations. 42 male soccer players (17.0 ± 1y, 176 ± 6 cm, 69 ± 9 kg) were tested and stratified into three different Total Genotype Score groups; “low”, “medium”and “high”, based on the possession of favourable polymorphisms. Subjects underwent two Yo-Yo tests separated by eight-weeks of sports-specific aerobic training. Overall, there were no significant differences between the genotype groups in pre-training Yo-Yo performance, but evident between-group response differentials emerged in post-training Yo-Yo test performance. Subjects in the “high” group saw much larger improvements (58%) than those in the ‘medium” (35%) and “low” (7%) groups. There were significant (p<0.05) differences between the groups in the magnitude of improvement, with athletes in the “high” and medium group having larger improvements than the “low” group (d = 2.59 “high” vs “low”; d = 1.32 “medium” vs “low”). In conclusion, the magnitude of improvements in aerobic fitness following a training intervention were associated with a genetic algorithm comprised of five single nucleotide polymorphisms. This information could lead to the development of more individualised aerobic training designs, targeting optimal fitness adaptations.
... Nevertheless, it is common practice to determine the AT via the point of optimal ventilatory efficiency (POE), which is defined as the first disproportional increase of ventilation (VE) related to oxygen uptake (VO 2 ) (Binder et al., 2008;Hollmann, 2001;Meyer et al., 2005;Westhoff et al., 2013). But some authors have reported differences in the work rates between AT and POE (Gaskill et al., 2001;Ring-Dimitriou et al., 2014;Santos and Giannella-Neto, 2004;Tschentscher and Ring-Dimitriou, 2010). Therefore, it is important to point out the difference between AT and POE to avoid incorrect exercise intensity prescription. ...
... Therefore, we hypothesize that inter-individual differences in the breathing patterns at POE and AT in response to incremental exercise exist in untrained adults. And as a consequence different work rates at POE and AT will be detected (Binder et al., 2008;Gaskill et al., 2001;Ring-Dimitriou et al., 2014;Santos and Giannella-Neto, 2004;Westhoff et al., 2013). In order to prescribe reliable exercise intensities based on CPET, it is therefore important to confirm the difference between the POE and the AT and to analyse the possible underlying ventilatory mechanisms. ...
... A 5-min recovery phase at 10 W was performed after exhaustion. Attainment of volitional exhaustion (and therefore VO 2peak ) was confirmed by at least two of the following criteria (Ekkekakis et al., 2008;Ring-Dimitriou et al., 2014;Wasserman et al., 2011): (1) a plateau in VO 2 (changes of less than 2 ml ⋅ kg -1 ⋅ min -1 following an increase in workload); (2) EQO 2 > 30; (3) respiratory exchange ratio (RER) > 1.1; (4) achieving 90% of age predicted maximum heart rate (Tanaka et al., 2001); (5) pedalling rate < 50 rpm due to leg fatigue or shortness of breath. Exercise testing was terminated if any complications and contraindications occurred (Ross, 2003). ...
Article
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Background The point of optimal ventilatory efficiency (POE) and the anaerobic threshold (AT) are traditionally considered the same ventilatory indices, but recently differences between them have been reported. Therefore, the aim of this study was to identify different response patterns regarding POE and AT, and to analyse differences in breathing patterns as a possible explanation. Methods 118 females and 199 males aged 50 to 60 years performed an exercise test with gas analysis. POE and AT were determined, and the breathing patterns concerning ventilation, breathing frequency and tidal volume were assessed. Results and Conclusion Our study identified two different response patterns concerning the ventilatory indices POE and AT. Participants with a work rate difference between POE and AT (82% of all participants) were not different regarding breathing patterns of breathing frequency and tidal volume. However, the difference in work rate was explained by an early increase in ventilation and a higher aerobic capacity.
... The PPARGC1A rs8192678 Gly/Gly genotype has been associated with greater increases of an individual's anaerobic threshold [17], a greater increase of slow muscle fibers [18], greater mitochondria activity [18], a greater decrease of low-density and total lipoprotein cholesterol [19] and a greater VO 2 peak increase after aerobic training than PPARGC1A rs8192678 Ser allele carriers. Moreover, PPARGC1A rs8192678 Ser allele carriers had no response in slow muscle fibers' changes, changes in low-density and total lipoprotein cholesterol and VO 2 peak [20] after aerobic training. ...
... This review summarized the best responders for aerobic training in relation to PPARs and their coactivators' genes polymorphisms (PPARGC1A rs8192678 Gly/Gly, PPARD rs1053049 TT, PPARD rs2267668 AA, PPARD rs2016520 T allele carriers and PPARG rs1801282 Ala allele carriers) in a common population [17,18,20,21]. On the other hand, the evaluation summary on the effects in PPARD rs2267668 G allele carriers and PPARG rs1801282 Pro/Pro homozygotes showed several negative responses to aerobic training. ...
Article
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Background: The peroxisome proliferator-activated receptors (PPARA, PPARG, PPARD) and their transcriptional coactivators' (PPARGC1A, PPARGC1B) gene polymorphisms have been associated with muscle morphology, oxygen uptake, power output and endurance performance. The purpose of this review is to determine whether the PPARs and/or their coactivators' polymorphisms can predict the training response to specific training stimuli. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses, a literature review has been run for a combination of PPARs and physical activity key words. Results: All ten of the included studies were performed using aerobic training in general, sedentary or elderly populations from 21 to 75 years of age. The non-responders for aerobic training (VO₂peak increase, slow muscle fiber increase and low-density lipoprotein decrease) are the carriers of PPARGC1A rs8192678 Ser/Ser. The negative responders for aerobic training (decrease in VO₂peak) are carriers of the PPARD rs2267668 G allele. The negative responders for aerobic training (decreased glucose tolerance and insulin response) are subjects with the PPARG rs1801282 Pro/Pro genotype. The best responders to aerobic training are PPARGC1A rs8192678 Gly/Gly, PPARD rs1053049 TT, PPARD rs2267668 AA and PPARG rs1801282 Ala carriers. Conclusions: The human response for aerobic training is significantly influenced by PPARs' gene polymorphism and their coactivators, where aerobic training can negatively influence glucose metabolism and VO₂peak in some genetically-predisposed individuals.
... Additionally, 88% of athletes and 93% of support staff respondents believed that genetics has a sizeable (>25%) impact on an individual's improvements following a training programme. Again, this perspective is mirrored in the research literature; individual SNPs, such as ACTN3 and PPARGC1A, appear to modify the magnitude of post-training adaptations (Pickering & Kiely, 2017;Ring-Dimitriou et al., 2014). More recently, studies have started to explore the potential utility of Total Genotype Scores in explaining the variation in training response Jones et al., 2016;He et al., 2018;Moraes et al., 2018) and potentially in maximising adaptations to exercise (Jones et al., 2016). ...
Article
Recently, knowledge of the genetic influence on the attainment of elite athlete status, along with aspects such as training adaptations and injury risk, has grown. At present, there are various direct-to-consumer genetic tests targeted at athletes. Here, we aimed to determine to prevalence of, and attitudes towards, genetic testing in a group of athletes, coaches, and support staff. 243 participants (110 athletes and 133 support staff) took part in an internet-based survey. 51% of athletes had competed internationally, and 54% of support staff reported that their main contact time was with international athletes. The frequency of genetic testing was relatively low, with 10% of athletes and 11% of support staff stating they had utilised such a test. Overall, the majority of athletes and support staff felt that genetics played a role in the attainment of sporting success and training adaptation. The main barriers to undertaking genetic testing were a lack of awareness, high cost, and a lack of scientific evidence. The majority (73% of athletes and 64% of support staff) of participants utilising genetic tests found the information useful.
... The transcriptional co-activator PPARGC1A interacts with PPARD and regulates mitochondrial biogenesis, angiogenesis, lipolysis and adipogenesis [39]. Four candidate gene studies, predominantly in men, found consistent associations of rs8192678 within PPARGC1A and aerobic capacity in Europeans [38,[40][41][42]. While in the Han Chinese cohort another nearby SNP (rs6821591) was associated with VO 2max specifically, the G allele was associated with increased VO 2max compared to those carrying the A allele [43]. ...
Article
This review summarised robust and consistent genetic variants associated with aerobic-related and resistance-related phenotypes. In total we highlight 12 SNPs and 7 SNPs that are robustly associated with variance in aerobic-related and resistance-related phenotypes respectively. To date, there is very little literature ascribed to understanding the interplay between genes and environmental factors and the development of physiological traits. We discuss future directions, including large-scale exercise studies to elucidate the functional relevance of the discovered genomic markers. This approach will allow more rigour and reproducible research in the field of exercise genomics.
... The presence of the Ala allele PPARG rs1801282 and the C allele PPARA rs4253778 in elite athletes might be related to the molecular mechanisms required to sustain high anaerobic training loads [53]. Although PPARG rs1801282 Ala allele carriers have been found in individuals with better reactions to aerobic training in the typical population [54][55][56][57], their association in elite athletes might be related to the sustainability of periodic training, which requires tissue recovery and frequent training. Anaerobic training is accompanied by an increase in inflammatory markers, which are regulated by PPARs [8,9]. ...
Article
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Background: Although the scientific literature regarding sports genomics has grown during the last decade, some genes, such as peroxisome proliferator activated receptors (PPARs), have not been fully described in terms of their role in achieving extraordinary sports performance. Therefore, the purpose of this systematic review was to determine which elite sports performance constraints are positively influenced by PPARs and their coactivators. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used, with a combination of PPAR and sports keywords. Results: In total, 27 studies that referred to PPARs in elite athletes were included, where the Ala allele in PPARG rs1801282 was associated with strength and power elite athlete status in comparison to subelite athlete status. The C allele in PPARA rs4253778 was associated with soccer, and the G allele PPARA rs4253778 was associated with endurance elite athlete status. Other elite status endurance alleles were the Gly allele in PPARGC1A rs8192678 and the C allele PPARD rs2016520. Conclusions: PPARs can be used for estimating the potential to achieve elite status in human physical performance in strength and power, team, and aerobic sports disciplines. Carrying specific PPAR alleles can provide a partial benefit to achieving elite sports status, but does not preclude achieving elite status if they are absent.
... CC homozygotes in PPARA gene in this study showed better jump performance represented by RSI, which had been suggested by previous studies (1,48). However, the finding that this predisposition is identical in adults, and adolescents should be considered when training methods are selected, especially as PPARs and their coactivators are associated with improvements in training programs for weight reduction (29), aerobic performance (49,52,60,61), and resistance training load capabilities (1). In this manner, the ketogenesis and other metabolic factors determined by PPARA indicate an individual response to strength and power training (1), and satellite cell proliferation determined by GDF5 can indicate a potential to regenerate from a long-term physical load (18). ...
Article
Stastny, P, Lehnert, M, De Ste Croix, M, Petr, M, Svoboda, Z, Maixnerova, E, Varekova, R, Botek, M, Petrek, M, Lenka, K, and Cięszczyk, P. Effect of COL5A1, GDF5, and PPARA genes on a movement screen and neuromuscular performance in adolescent team sport athletes. J Strength Cond Res 33(8): 2057-2065, 2019-The risk of injury increases with adolescents' chronological age and may be related to limited muscle function neuromuscular, genetic, and biomechanical factors. The purpose of this study was to determine whether COL5A1, PPARA, and GDF5 genes are associated with muscle functions and stretch-shortening cycle performance in adolescent athletes. One hundred forty-six youth players (14.4 ± 0.2 years) from various team sports (basketball n = 54, soccer n = 50, handball n = 32) underwent a manual test for muscle function, maturity estimation, functional bend test (FBT), passive straight leg raise (SLR) test, leg stiffness test, test of reactive strength index (RSI), and gene sampling for COL5A1, PPARA, and GDF5. The χ test did not show any differences in allele or genotype frequency between participants before and after peak height velocity. Multivariate analysis of variance showed that COL5A1 rs12722 CT heterozygotes had worse score in FBT (p < 0.001), worse score in SLR (p = 0.003), and lower maturity offset (p = 0.029, only in females) than TT homozygotes. Male GDF5 rs143383 GG homozygotes showed better score in SLR than AA and AG genotypes (p = 0.003), and AA and AG genotypes in both sex had greater RSI than GG homozygotes (p = 0.016). The PPARA rs4253778 CC homozygotes had greater RSI than GG and GC genotypes (p = 0.004). The CT genotype in COL5A1 rs12722 is possible predictor of functional movement disruption in the posterior hip muscle chain, causing shortening in FBT and SLR, which includes hamstrings function. CT genotype in COL5A1 rs12722 should be involved in programs targeting hamstring and posterior hip muscle chain.
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Variation between individuals in response to a stimulus is a well-established phenomenon. This thesis discusses the drivers of this inter-individual response, identifying three major determinants; genetic, environmental, and epigenetic variation between individuals. Focusing on genetic variation, the thesis explores how this information may be useful in elite sport, aiming to answer the question “Is there utility to genetic information in elite sport?” The current literature was critically analysed, with a finding that the majority of exercise genomics research explains what has happened previously, as opposed to assisting practitioners in modifying athlete preparation and enhancing performance. An exploration of the potential ways in which genetic information may be useful in elite sport then follows, including that of inter- individual variation in response to caffeine supplementation, the use of genetic information to assist in reducing hamstring injuries, and whether genetic information may help identify future elite athletes. These themes are then explored via empirical work. In the first study, an internet-based questionnaire assessed the frequency of genetic testing in elite athletes, finding that around 10% had undertaken such a test. The second study determined that a panel of five genetic variants could predict the magnitude of improvements in Yo-Yo test improvements following a standardised training programme in youth soccer players. The third study demonstrated the effectiveness of a panel of seven genetic variants in predicting the magnitude of neuromuscular fatigue in youth soccer players. The fourth and final study recruited five current or former elite athletes, including an Olympic Champion, and created the most comprehensive Total Genotype Score in the published literature to date, to determine whether their scores deviated significantly from a control population of over 500 non-athletes. The genetic panels were unable to adequately discriminate the elite performers from non-athletes, suggesting that, at this time, genetic testing holds no utility in the identification of future elite performers. The wider utilisation of genetic information as a public health tool is discussed, and a framework for the implementation of genetic information in sport is also proposed. In summary, this thesis suggests that there is great potential for the use of genetic information to assist practitioners in the athlete management process in elite sport, and demonstrates the efficacy of some commercially available panels, whilst cautioning against the use of such information as a talent identification tool. The major limitation of the current thesis is the low sample sizes of many of the experimental chapters, a common issue in exercise genetics research. Future work should aim to further explore the implementation of genetic information in elite sporting environments.
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Abstract: Background: Traditional exercise prescription is based on the assumption that exercise adaptation is predictable and standardised across individuals. However, evidence has emerged in the past two decades demonstrating that large inter-individual variation exists regarding the magnitude and direction of adaption following exercise. Objective: The aim of this paper was to discuss the key factors influencing this personalized response to exercise in a narrative review format. Findings: Genetic variation contributes significantly to the personalized training response, with specific polymorphisms associated with differences in exercise adaptation. These polymorphisms exist in a number of pathways controlling exercise adaptation. Environmental factors such as nutrition, psycho-emotional response, individual history and training programme design also modify the inter-individual adaptation following training. Within the emerging field of epigenetics, DNA methylation, histone modifications and non-coding RNA allow environmental and lifestyle factors to impact genetic expression. These epigenetic mechanisms are themselves modified by genetic and non-genetic factors, illustrating the complex interplay between variables in determining the adaptive response. Given that genetic factors are such a fundamental modulator of the inter-individual response to exercise, genetic testing may provide a useful and affordable addition to those looking to maximise exercise adaption, including elite athletes. However, there are ethical issues regarding the use of genetic tests, and further work is needed to provide evidence based guidelines for their use. Conclusion: There is considerable inter-individual variation in the adaptive response to exercise. Genetic assessments may provide an additional layer of information allowing personalization of training programmes to an individual’s unique biology.
Chapter
Mit zunehmendem Alter steigt die Prävalenz zahlreicher Erkrankungen und gesundheitlicher Risikofaktoren (z. B. Herz-Kreislauf-Erkrankungen, Metabolisches Syndrom, Typ-2-Diabetes, Bluthochdruck, Adipositas, Fettstoffwechselstörung).
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The importance of normal distribution is undeniable since it is an underlying assumption of many statistical procedures such as t-tests, linear regression analysis, discriminant analysis and Analysis of Variance (ANOVA). When the normality assumption is violated, interpretation and inferences may not be reliable or valid. The three common procedures in assessing whether a random sample of independent observations of size n come from a population with a normal distribution are: graphical methods (histograms, boxplots, Q-Q-plots), numerical methods (skewness and kurtosis indices) and formal normality tests. This paper* compares the power of four formal tests of normality: Shapiro-Wilk (SW) test, Kolmogorov-Smirnov (KS) test, Lilliefors (LF) test and Anderson-Darling (AD) test. Power comparisons of these four tests were obtained via Monte Carlo simulation of sample data generated from alternative distributions that follow symmetric and asymmetric distributions. Ten thousand samples of various sample size were generated from each of the given alternative symmetric and asymmetric distributions. The power of each test was then obtained by comparing the test of normality statistics with the respective critical values. Results show that Shapiro-Wilk test is the most powerful normality test, followed by Anderson-Darling test, Lilliefors test and Kolmogorov-Smirnov test. However, the power of all four tests is still low for small sample size.
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While there is agreement that exercise is a powerful stimulus to increase both mitochondrial function and content, we do not know the optimal training stimulus to maximise improvements in mitochondrial biogenesis. This review will focus predominantly on the effects of exercise on mitochondrial function and content, as there is a greater volume of published research on these adaptations and stronger conclusions can be made. The results of cross-sectional studies, as well as training studies involving rats and humans, suggest that training intensity may be an important determinant of improvements in mitochondrial function (as determined by mitochondrial respiration), but not mitochondrial content (as assessed by citrate synthase activity). In contrast, it appears that training volume, rather than training intensity, may be an important determinant of exercise-induced improvements in mitochondrial content. Exercise-induced mitochondrial adaptations are quickly reversed following a reduction or cessation of physical activity, highlighting that skeletal muscle is a remarkably plastic tissue. Due to the small number of studies, more research is required to verify the trends highlighted in this review, and further studies are required to investigate the effects of different types of training on the mitochondrial sub-populations and also mitochondrial adaptations in different fibre types. Further research is also required to better understand how genetic variants influence the large individual variability for exercise-induced changes in mitochondrial biogenesis. The importance of mitochondria for both athletic performance and health underlines the importance of better understanding the factors that regulate exercise-induced changes in mitochondrial biogenesis. This article is part of a Special Issue entitled Frontiers of Mitochondrial Research.
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The reproducibility of the assessment of ventilatory thresholds was investigated in two test‐retest experiments, one performed on a cycle ergometer with 21 moderately active male subjects and the other on a treadmill with 20 well‐trained male subjects. The first (VT‐1) and the second (VT‐2) nonlinear increases in ventilation (VE) relative to O2 consumption were determined (a) by three independent evaluators coding separately (OIE), (b) as the mean of three independent evaluators (TIE) and (c) by two dependent evaluators (TDE). One of the evaluators repeated the assessment four months later (SELF). The VT‐1 and VT‐2 were also assessed from the graph of VE/VO2 relative to VO2. Under the SELF condition VT‐1 and VT‐2 in ml O2 per kg min proved to be reliable measurements with intraclass correlations of 0.84 and 0.91 respectively. Independent evaluators were individually reliable assessors of VT‐1 and VT‐2, with the exception of VT‐1 in the treadmill group in terms of ml O2 per kg min with coefficient ranging from 0.71 to 0.94. Similar results were obtained under the TDE condition. The VT‐1 assessments in the treadmill group were generally less reproducible than VT‐2 and less reproducible than either VT‐1 or VT‐2 in the bicycle group. The VT‐1 and VT‐2 expressed as percentages of VO2 max were not reproducible measurements under the conditions of this study. It is concluded that the condition described as TDE offers the most advantageous procedure for the determination of VT‐1 and VT‐2.
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This document replaces the DGP recommendations published in 1998. Based on recent studies and a consensus conference, the indications, choice and performance of the adequate exercise testing method in its necessary technical and staffing setting are discussed. Detailed recommendations are provided: for arterial blood gas analysis and right heart catherterization during exercise, 6-minute walk test, spiroergometry, and stress echocardiography. The correct use of different exercise tests is discussed for specific situations in respiratory medicine: exercise induced asthma, monitoring of physical training or therapeutical interventions, preoperative risk stratification, and evaluation in occupational medicine. © Georg Thieme Verlag KG Stuttgart · New York.
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The aim of this study was to determine the impact of ACE (I/D), PPARGC1A (G/A) and PPARA (G/C) polymorphisms on footballers performance among 199 Lithuanian professional footballers and 167 sedentary, healthy men (controls). Genotyping was performed using polymerase chain reaction and restriction fragment length polymorphism methods on DNA from leucocytes. Results revealed that the ACE genotype distribution was significantly different between total football players group (II 23.6%, ID 46.7%, DD 29.6%) and the controls (II 24.6%, ID 29.9%, DD 45.5%; P=0.002). Although investigating PPARGC1A (G/A) and PPARA (G/C) polymorphisms no significant results were obtained in the total football players group, however, significant differences were determined between forwards and controls (PPARGC1A: GG 54.6%, GA 29.5%, AA 15.9% vs. GG 49.7%, GA 44.3%, AA 6.0% (P=0.044); PPARA: GG 52.3%, GC 40.9%, CC 6.8% vs. GG 72.4%, GC 24.6%, CC 3.0% (P=0.034)). In the whole cohort, the odds ratio of the genotype [ACE ID+PPARA GG] being a footballer was 1.69 (95% CI 1.04-2.74), and of [ACE ID+PPARGC1A GG] 1.93 (95% CI 1.10-3.37), and of [ACE II+PPARA GC] 2.83 (95% CI 1.02-7.91) compared to controls. It was revealed that ACE ID genotype together with PPARA GG and PPARGC1A GG as well as ACE II genotype with PPARA GC is probably the ‘preferable genotype’ for footballers. Summing up, the present study suggests that the ACE, PPARGC1A and PPARA polymorphisms genotypes are associated, separately and in combination, with Lithuanian footballers’ performance.
Article
Metabolic syndrome (MS) is a collection of cardiometabolic risk factors that includes obesity, insulin resistance, hypertension, and dyslipidemia. Although there has been significant debate regarding the criteria and concept of the syndrome, this clustering of risk factors is unequivocally linked to an increased risk of developing type 2 diabetes and cardiovascular disease. Regardless of the true definition, based on current population estimates, nearly 100 million have MS. It is often characterized by insulin resistance, which some have suggested is a major underpinning link between physical inactivity and MS. The purpose of this review is to: (i) provide an overview of the history, causes and clinical aspects of MS, (ii) review the molecular mechanisms of insulin action and the causes of insulin resistance, and (iii) discuss the epidemiological and intervention data on the effects of exercise on MS and insulin sensitivity. © 2013 American Physiological Society. Compr Physiol 3:1-58, 2013.
Article
Thresholds in cardiopulmonary exercise testing are necessary for the evaluation of motivation and cooperation in exercise, for training programs, in transplant medicine, preoperative evaluation and medical assessments. There is a hardly comprehensible number of terminologies concerning these thresholds and their definitions. This hampers the comparison of protocols and studies and leads to incertainties in terminologies and interpretations of cardiopulmonary exercise tests. Based on literature a definition of thresholds was undertaken. Thresholds should be regarded from a conceptional and an operational (methodological) point of view. The conceptional model means, that there are two ventilatory thresholds (VT1 and VT2) and two metabolic thresholds (lactate threshold [LT] 1 and 2 ). These thresholds are pathophysiologically based. Both threshold concepts determinate the beginning and the end of the aerobic-anaerobic transition. The lactate thresholds determine the metabolic changes, whereas the ventilator thresholds 1 and 2 represent the ventilatory response to the metabolic changes. VT1 represents the subsequent increase of ventilation and CO2-output relative to oxygen uptake as a consequence of an increase of lactate and a necessary lactate buffering. VT2 is characterized by an exceeding of lactate-steady-state, resulting in excess lactate, metabolic acidosis and overproportional rise of ventilation. The operational concept describes the method, which is used for determination of the different lactate and ventilatory thresholds. In a further step this can be completed by indicating the exercise protocol which was applied. © Georg Thieme Verlag KG Stuttgart · New York.