Frequent and regular physical activity has significant benefits for health, including reduction of the risk of cardiovascular diseases, diabetes, cancer, and improvement of mental health (Rankinen and Bouchard, 2008). Additionally, properly selected exercises improve body composition and help control weight. Promoting training programs, particularly in those subjects who are genetically predisposed, is a significant step towards controlling the presently increasing epidemic of obesity (Ahmad et al., 2013; Li et al., 2010). Some studies have confirmed that physical activity is connected with a reduction of up to 40% in the genetic predisposition to obesity (Ahmad et al., 2013; Li et al., 2010). Understanding the genetic determinants of reactions occurring in the human body allows detailed and accurate prediction of the consequences of performed exercises. Studies on gene x physical activity interactions will help identify people who are expected to respond well or poorly to exercise, thus making training programs more efficient (possibility of accurate prediction of the training results) and safer (early prevention of possible overload or other injuries). Nevertheless, so far very few studies have been conducted with the focus on gene x lifestyle interactions (Ahmad et al., 2013; Franks, 2011; Li et al., 2010).
The melanocortin-4 receptor (MC4R) is a well known major regulator of food intake and energy expenditure (Hebebrand et al., 2010). The MC4R is a 332-amino-acid, which belongs to the family of seven trans-membrane G-protein-coupled receptors (GPCR) and is expressed in brain regions involved in regulation of food intake (Gantz et al., 1993; Mountjoy et al., 1994). The MC4R transduces its signal by integrating a satiety signal provided by its agonist α-melanocyte-stimulating hormone (α-MSH) and an orexigenic signal provided by its antagonist agouti-related protein (AGRP) (Lu et al., 1994). These ligands are expressed in distinct neuronal populations of the arcuate nucleus of the hypothalamus and are regulated by the adipocyte-secreted hormone, leptin, to control food intake and maintain long-term energy homeostasis (Schwartz et al., 2000). Thus, the
Genetic variants within the
The minor allele (C) is associated with increased intake of total energy and dietary fat, and as a consequence higher prevalence of common human obesity (Qi et al., 2008). Each copy of the risk allele is associated with an increase in the BMI of ~0.22 kg/m2 in adults (Loos et al., 2008). Moreover, the C allele was also related with a 14% increased risk of type 2 diabetes (Qi et al., 2008). Sex has repeatedly been shown to affect the association between rs17782313 and obesity markers, showing a more pronounced effect for female than male mutation carriers (Qi et al., 2008).
Taking into account the rs17782313 polymorphism’s role in body weight homeostasis, we decided to examine whether selected traits observed in physically active participants would be modulated by the genotype. To test this hypothesis, we performed a genetic association study that aimed to detect the relationship between the near
All the procedures followed in the study were approved by the Ethics Committee of the Regional Medical Chamber in Szczecin (Approval number 09/KB/IV/2011) and were conducted according to the principles of the World Medical Association, Declaration of Helsinki and ethical standards in sport and exercise science research. Furthermore, the experimental procedures were conducted in accordance with the set of guiding principles for reporting the results of genetic association studies defined by the Strengthening the Reporting of Genetic Association studies (STREGA) Statement. All participants signed a consent form and were provided with written information concerning the study, providing all pertinent details (purpose, procedures, risks, and benefits of participation).
Two hundred and one Polish Caucasian women aged 21 ± 1 years (range 19–24) met the inclusion criteria and were selected for the study. None of these individuals had engaged in regular physical activity in the previous 6 months. They had no history of any metabolic or cardiovascular diseases. Participants were nonsmokers and refrained from taking any medications or supplements known to affect the metabolism. Prior to the beginning of the intervention, participants were asked to maintain a balanced diet of approximately 2000 kcal/day.
Before the commencement of the training program, HRmax of each participant was evaluated using a continuous graded exercise test on an electronically braked cycle ergometer (Oxycon Pro, Erich JAEGER GmbH, Hoechberg, Germany) according to a previously described protocol (Kostrzewa-Nowak et al., 2015). The training period was preceded by a week-long familiarization stage, when the examined women exercised 3 times a week for 30 minutes, at an intensity of about 50% of their HRmax. After the week-long familiarization stage, the proper training program started. Training sessions were conducted in a sports hall; a platform was placed in front of the participants on which the instructor presented exercises based on knee bends, lunges, running, skipping and hopping and their combinations creating a choreography set. The individual heart rate (HR) of participants was registered with HR monitors to control the intensity of exercise. The participants were instructed to maintain previously indicated ranges of the HR or relative value of HRmax (maximum heart rate, %). Each training unit consisted of a warm-up (10 min), aerobic exercises which constituted the main part of training (43 min), and stretching and breathing exercises (7 min). Aerobic training included two alternating types of exercises – low and high impact. A low impact style comprised movements with at least one foot on the floor at all times, whereas a high impact style included running, hopping, and jumping with a variety of flight phases (de Angelis, 1998). Music of variable rhythm and tempo was incorporated into both styles. A 12-week program of low-high impact aerobics was divided as follows: (i) 3 weeks including 9 training units, 60 min each, at about 50–60% of HRmax, tempo 135–140 BPM, (ii) 3 weeks comprising 9 training units, 60 min each, at 60–70% of HRmax, tempo 140–152 BPM, (iii) 3 weeks including 9 training units, 60 min each with the intensity of 65–75% of HRmax, tempo 145–158 BPM, and (iv) 3 weeks consisting of 9 training units, 60 min each with an intensity of 65-80% of HRmax, tempo 145–160 BPM. All 36 training units were administered and supervised by the same instructor.
All participants were measured for selected body mass and body composition variables before and after the completion of the 12-week training period. Body mass and body composition were assessed with the bioimpedance method using a Tanita TBF 300M electronic scale (Horton Health Initiatives, USA). The examined variables included: total body mass (kg), fat free mass (FFM, kg), fat mass percentage (FM, %), body mass index (BMI, kg·m-2), and total body water (TBW, kg).
Fasting blood samples were obtained in the morning from the elbow vein. Blood samples from each participant were collected in two tubes. For biochemical analyses, a 4.9 mL S-Monovette tube with ethylenediaminetetraacetic acid (K 3 EDTA; 1.6 mg EDTA/mL blood) and separating gel (SARSTEDT AG & Co., Nümbrecht, Germany) were used. For complete blood count, a 2.6 mL S-Monovette tube with K 3 EDTA (1.6 mg EDTA/mL blood) (SARSTEDT AG & Co., Nümbrecht, Germany) was used. Blood samples for biochemical analyses were centrifuged 300 × g for 15 minutes at room temperature in order to receive blood plasma. Biochemical and hematological analyses were performed before the beginning of the training program and repeated after its completion. The analyses were performed immediately after the blood collection. Complete blood count, including white blood cells (WBC), red blood cells (RBC), haemoglobin (HGB), haematocrit (HTC), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), and total platelet level (PLT) was obtained using a Sysmex K-4500 Haematology Analyzer (TOA SYSMEX, Kobe, Japan). All biochemical analyses were conducted using a Random Access Automatic Biochemical Analyzer for Clinical Chemistry and Turbidimetry A15 (BIO-SYSTEMS S.A., Barcelona, Spain). Blood plasma was used to determine the lipid profile: triglycerides (Tg), cholesterol (Chol), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) concentrations. Plasma Tg and Chol concentrations were determined by a diagnostic colorimetric enzymatic method according to the manufacturer’s protocol (BioMaxima S.A., Lublin, Poland). Manufacturer’s declared intra-assay coefficients of variation (CV) of the method were < 2.5% and < 1.5% for the Tg and Chol determinations, respectively. HDL plasma concentration was determined using a human anti-ß-lipoprotein antibody and colorimetric enzymatic method according to the manufacturer’s protocol (BioMaxima S.A., Lublin, Poland). The manufacturer’s declared intra-assay CV of the method was < 1.5%. Plasma concentrations of LDL were assessed using a direct method according to the manufacturer’s protocol (PZ Cormay S.A., Lomianki, Poland). The manufacturer’s declared intra-assay CV of the method was 4.97%. All procedures were verified using multiparameteric control serum (BIOLABO S.A.S, Maizy, France), as well as control serum of normal level (BioNormL) and high level (BioPathL) lipid profiles (BioMaxima S.A., Lublin, Poland).
The buccal cells donated by the subjects were collected in Resuspension Solution (GenElute Mammalian Genomic DNA Miniprep Kit, Sigma, Germany) using sterile foam-tipped applicators (Puritan, USA). DNA was extracted from the buccal cells using a GenElute Mammalian Genomic DNA Miniprep Kit (Sigma, Germany) according to the manufacturer’s protocol. All samples were genotyped in duplicate using an allelic discrimination assay on a StepOne Real-Time Polymerase Chain Reaction (RT-PCR) instrument (Applied Biosystems, USA).
Allele frequencies were determined by gene counting. A chi-square test was used to examine the Hardy-Weinberg equilibrium. To determine the influence of the near
The near MC4R genotypes and response to training (two-way mixed ANOVA) Genotyp e x Training Mean ± standard deviation; p values for main effects (genotype and training) and genotype x training interaction; d – effect size; BMI – body mass index; FM – fat mass percentage; FFM – fat free mass; TBW – total body water; Chol – cholesterol; Tg – triglycerides; HDL – high-density lipoprotein; LDL – low-density lipoprotein
Variable CC+CT (n = 78) TT (n = 123) Genotype Training Before training After training Before training After training Body mass (kg) 60.6 ± 8.3 59.6 ± 8.0 60.8±7.4 60.1±7.4 d = −0.65 d = −0.41 BMI (kg·m−2) 21.5 ± 2.3 21.2 ± 2.3 21.7±2.4 21.5±2.4 d = −0.66 d = −0.37 FM (%) 23.9 ± 6.0 22.2 ± 6.3 24.0±5.2 22.7±5.3 d = −0.80 d = −0.53 FFM (kg) 45.6 ± 3.5 46.1 ± 3.4 45.8±3.1 46.2±3.2 d =0.48 d = 0.27 TBW (kg) 33.4 ± 2.6 33.3 ± 4.3 33.6±2.6 33.9±2.4 d = −0.01 d = 0.20 Chol (mg/dl) 167 ± 24 168 ± 30 170±27 168±26 d =0.04 d = −0.09 Tg (mg/dl) 76.4 ± 25.8 85.0 ± 38.5 80.0±34.5 83.3±31.0 d =0.24 d = 0.11 HDL (mg/dl) 66.1 ± 13.7 61.0 ± 13.3 64.0±12.2 60.2±12.2 d = −0.44 d = −0.38 LDL (mg/dl) 86.1 ± 21.0 101.8 ± 101.6 89.5±21.5 90.7±22.4 d = 0.16 d = 0.07 Glucose (mg/dl) 79.8 ± 11.0 76.8 ± 10.5 76.81± 9.2 74.80± 10.1 d = −0.24 d = −0.22
The near MC4R genotypes and response to training (two-way mixed ANOVA)
Mean ± standard deviation; p values for main effects (genotype and training) and genotype x training interaction; d – effect size; BMI – body mass index; FM – fat mass percentage; FFM – fat free mass; TBW – total body water; Chol – cholesterol; Tg – triglycerides; HDL – high-density lipoprotein; LDL – low-density lipoprotein
Recently, in two GWAS, the common polymorphism rs17782313 near
Previous studies confirm a strong association between the polymorphism near
On the other hand, we identified a statistically significant association between the glucose level and the genotype. Participants with CC and CT genotypes had a higher glucose level during the entire study period compared with the TT genotype. Our results showed that 6.5% of individuals with the risk genotype, but no one with the TT genotype, had fasting hyperglycemia. Their glucose level ranged from 106 to 118 mg/dl. Pre-diabetes, based on glycaemic variables above normal, but below diabetes thresholds, constitutes a high-risk state for diabetes with high conversion rates and there is accumulating evidence implying that damage on kidneys and nerves already exists at the pre-diabetic stage (Tabak et al., 2012). Interestingly, after the 12 week training program their glucose level was normal, and it ranged from 85 to 93 mg/dl. Although, this change was not statistically significant, it shows a medically important trend, which needs further experimental investigations. Our results may suggest that SNP rs17782313 influences the regulatory region of the
Chambers et al. (2008) confirmed a relationship between the glucose metabolism and SNPs localized near the
In the present study, we did not observe the near
Our results may be supported by the previous study which did not show an association between the polymorphism and selected body composition measurements in 242 participants undergoing a 9-month lifestyle intervention (Haupt et al., 2009). In a study performed on 111,421 adults of European descent, Ahmad et al. (2013) analyzed 12 loci connected with obesity-related traits and also did not reveal evidence of the polymorphism rs17782313 x physical activity interactions, although the impact of
The study has several limitations that should be mentioned. The failure to detect a gene x physical activity interaction in our study may reflect the influence of population-specific characteristics such as high overall physical activity levels and relatively low body mass in the studied population, a small sample size or the effect of age (Li et al. 2010). In addition, obesity is a polygenic trait, as a result the genetic marker analysed independently is likely to make only a limited contribution to the obesity phenotype: it seems more likely that such status depends on the simultaneous presence of multiple such variants.
In summary, we identified a statistically significant relationship between the glucose level and genotype, what is an important observation from a medical point of view. Participants with CC and CT genotypes had a higher glucose level during the entire study period compared with the TT genotype. However, we did not confirm the effect of the near
The near MC4R genotypes and response to training (two-way mixed ANOVA)
|Variable||CC+CT (n = 78)||TT (n = 123)||Genotype||Training|
|Before training||After training||Before training||After training|
|Body mass (kg)||60.6 ± 8.3||59.6 ± 8.0||60.8±7.4||60.1±7.4|
|d = −0.65||d = −0.41|
|BMI (kg·m−2)||21.5 ± 2.3||21.2 ± 2.3||21.7±2.4||21.5±2.4|
|d = −0.66||d = −0.37|
|FM (%)||23.9 ± 6.0||22.2 ± 6.3||24.0±5.2||22.7±5.3|
|d = −0.80||d = −0.53|
|FFM (kg)||45.6 ± 3.5||46.1 ± 3.4||45.8±3.1||46.2±3.2|
|d =0.48||d = 0.27|
|TBW (kg)||33.4 ± 2.6||33.3 ± 4.3||33.6±2.6||33.9±2.4|
|d = −0.01||d = 0.20|
|Chol (mg/dl)||167 ± 24||168 ± 30||170±27||168±26|
|d =0.04||d = −0.09|
|Tg (mg/dl)||76.4 ± 25.8||85.0 ± 38.5||80.0±34.5||83.3±31.0|
|d =0.24||d = 0.11|
|HDL (mg/dl)||66.1 ± 13.7||61.0 ± 13.3||64.0±12.2||60.2±12.2|
|d = −0.44||d = −0.38|
|LDL (mg/dl)||86.1 ± 21.0||101.8 ± 101.6||89.5±21.5||90.7±22.4|
|d = 0.16||d = 0.07|
|Glucose (mg/dl)||79.8 ± 11.0||76.8 ± 10.5||76.81± 9.2||74.80± 10.1|
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