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Do ACE and CKMM gene variations have potent effects on physical performance in inactive male adolescents?

We studied to ascertain whether the ACE and/or CKMM genotypes independently influence the baseline level of some sport performances in 613 inactive male adolescents (mean ± SD age: 13.24 ± 0.28 years). All DNA samples were extracted and genotyped for ACE I/D and CKMM A/G polymorphisms using a PCR based procedure. One-way analysis of covariance was used to examine the discrepancies in the research phenotypes among various ACE and CKMM polymorphisms. The comparisons of genotype and allele frequencies between adolescents with the best and the worst performances were calculated and analyzed by the Chi square test. All procedures were approved by Medical University Ethics Committee. Written informed consent signed and approved by all subject`s parents were obtained. We observed the effect of the ACE and CKMM polymorphisms on VO (P = 0.001 & P = 0.001 respectively). ACE and CKMM genotypes differed between groups (< 90th vs. ≥ 90) in the multi-stage 20 m shuttle run (P = 0.001 and 0.001). ACE allele frequencies differed between groups (< 90th vs. ≥ 90) in the multi-stage 20-m shuttle run (P = 0.001). This study suggests that the ACE and CKMM polymorphisms influence the endurance performance phenotype in non-trained adolescent males.

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An innovative way to highlight the power of each polymorphism on elite athletes phenotype expression.

The purpose of this study was to determine the probability of soccer players having the best genetic background that could increase performance, evaluating the polymorphism that are considered Performance Enhancing Polymorphism (PEPs) distributed on five genes: PPARα, PPARGC1A, NRF2, ACE e CKMM. Particularly, we investigated how each polymorphism works directly or through another polymorphism to distinguish elite athletes from non-athletic population. Sixty professional soccer players (age 22.5 ± 2.2) and sixty healthy volunteers (age 21.2± 2.3) were enrolled. Samples of venous blood was used to prepare genomic DNA. The polymorphic sites were scanned using PCR-RFLP protocols with different enzyme. We used a multivariate logistic regression analysis to demonstrate an association between the five PEPs and elite phenotype. We found statistical significance in NRF2 (AG/GG genotype) polymorphism/soccer players association (p < 0.05) as well as a stronger association in ACE polymorphism (p =0.02). Particularly, we noticed that the ACE ID genotype and even more the II genotype are associated with soccer player phenotype. Although the other PEPs had no statistical significance, we proved that some of these may work indirectly, amplifying the effect of another polymorphism; for example, seems that PPARα could acts on NRF2 (GG) enhancing the effect of the latter, notwithstanding it had not shown a statistical significance. In conclusion, to establish if a polymorphism can influence the performance, it is necessary to understand how they act and interact, directly and indirectly, on each other.

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Polygenic study of endurance-associated genetic markers ACE I/D, ACTN3 Arg(R)577Ter(X), CKMM A/G NcoI and eNOS Glu(G)298Asp(T) in male Gorkha soldiers.

Gorkhas, a sub-mountainous population of the Himalayan region, are known for strength and bravery. In the present study when "Gorkha" is used without brackets, we are mentioning Gorkhas of Tibeto-Burman origin. Physical capability, strength and endurance are important components of fitness associated with genetic traits. The aim of this study was to examine the endurance potential of male Gorkha soldiers, based on endurance-related genetic markers ACE I/D, ACTN3 Arg (R)577Ter(X), CKMM A/G NcoI and eNOS Glu(G)298Asp(T).

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Optimum polygenic profile to resist exertional rhabdomyolysis during a marathon.

Exertional rhabdomyolysis can occur in individuals performing various types of exercise but it is unclear why some individuals develop this condition while others do not. Previous investigations have determined the role of several single nucleotide polymorphisms (SNPs) to explain inter-individual variability of serum creatine kinase (CK) concentrations after exertional muscle damage. However, there has been no research about the interrelationship among these SNPs. The purpose of this investigation was to analyze seven SNPs that are candidates for explaining individual variations of CK response after a marathon competition (ACE = 287bp Ins/Del, ACTN3 = p.R577X, CKMM = NcoI, IGF2 = C13790G, IL6 = 174G>C, MLCK = C37885A, TNFα = 308G>A).

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[Association of CKMM gene A/G polymorphism and athletic performance of uyghurnationality].

Discusses the distributive characters of the Creatine Kinase MM (CKMM) gene A/G Polymorphism in XinjiangUyghur, One hundred and fourtheen athletes and 441 general population of Uyghur were involved in the study.

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Biomarker evaluation of skeletal muscle toxicity following clofibrate administration in rats.

The use of sensitive biomarkers to monitor skeletal muscle toxicity in preclinical toxicity studies is important for the risk assessment in humans during the development of a novel compound. Skeletal muscle toxicity in Sprague Dawley Rats was induced with clofibrate at different dose levels for 7 days to compare standard clinical pathology assays with novel skeletal muscle and cardiac muscle biomarkers, gene expression and histopathological changes. The standard clinical pathology assays aspartate aminotransferase (AST), alanine aminotransferase (ALT), and creatine kinase (CK) enzyme activity were compared to novel biomarkers fatty acid binding protein 3 (Fabp3), myosin light chain 3 (Myl3), muscular isoform of CK immunoreactivity (three isoforms CKBB, CKMM, CKMB), parvalbumin (Prv), skeletal troponin I (sTnI), cardiac troponin T (cTnT), cardiac troponin I (cTnI), CKMM, and myoglobin (Myo). The biomarker elevations were correlated to histopathological findings detected in several muscles and gene expression changes. Clofibrate predominantly induced skeletal muscle toxicity of type I fibers of low magnitude. Useful biomarkers for skeletal muscle toxicity were AST, Fabp3, Myl3, (CKMB) and sTnI. Measurements of CK enzyme activity by a standard clinical assay were not useful for monitoring clofibrate-induced skeletal muscle toxicity in the rat at the doses used in this study.

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Evaluation of a 7-Gene Genetic Profile for Athletic Endurance Phenotype in Ironman Championship Triathletes.

Polygenic profiling has been proposed for elite endurance performance, using an additive model determining the proportion of optimal alleles in endurance athletes. To investigate this model's utility for elite triathletes, we genotyped seven polymorphisms previously associated with an endurance polygenic profile (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170bp/985+185bp, HFE His63Asp, GDF8 Lys153Arg and PPARGC1A Gly482Ser) in a cohort of 196 elite athletes who participated in the 2008 Kona Ironman championship triathlon. Mean performance time (PT) was not significantly different in individual marker analysis. Age, sex, and continent of origin had a significant influence on PT and were adjusted for. Only the AMPD1 endurance-optimal Gln allele was found to be significantly associated with an improvement in PT (model p = 5.79 x 10-17, AMPD1 genotype p = 0.01). Individual genotypes were combined into a total genotype score (TGS); TGS distribution ranged from 28.6 to 92.9, concordant with prior studies in endurance athletes (mean±SD: 60.75±12.95). TGS distribution was shifted toward higher TGS in the top 10% of athletes, though the mean TGS was not significantly different (p = 0.164) and not significantly associated with PT even when adjusted for age, sex, and origin. Receiver operating characteristic curve analysis determined that TGS alone could not significantly predict athlete finishing time with discriminating sensitivity and specificity for three outcomes (less than median PT, less than mean PT, or in the top 10%), though models with the age, sex, continent of origin, and either TGS or AMPD1 genotype could. These results suggest three things: that more sophisticated genetic models may be necessary to accurately predict athlete finishing time in endurance events; that non-genetic factors such as training are hugely influential and should be included in genetic analyses to prevent confounding; and that large collaborations may be necessary to obtain sufficient sample sizes for powerful and complex analyses of endurance performance.

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