Total Genotype Score Modelling of Polygenic Endurance-Power Profiles in Lithuanian Elite Athletes.
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ABSTRACT:
SUBMITTER: Pranckeviciene E
PROVIDER: S-EPMC8306147 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe C34T genetic polymorphism (rs17602729) in the AMPD1 gene, encoding the skeletal muscle-specific isoform of adenosine monophosphate deaminase (AMPD1), is a common polymorphism among Caucasians that can impair exercise capacity. The aim of the present study was twofold: (1) to determine the C34T AMPD1 allele/genotype frequency distributions in Lithuanian athletes (n = 204, stratified into three groups: endurance, sprint/power and mixed) and compare them with the allele/genotype frequency distributions in randomly selected healthy Lithuanian non-athletes (n = 260) and (2) to compare common anthropometric measurements and physical performance phenotypes between the three groups of athletes depending on their AMPD1 genotype.ResultsThe results of our study indicate that the frequency of the AMPD1 TT genotype was 2.4% in the control group, while it was absent in the athlete group. There were significantly more sprint/power-orientated athletes with the CC genotype (86.3%) compared with the endurance-orientated athletes (72.9%), mixed athletes (67.1%), and controls (74.2%). We determined that the AMPD1 C34T polymorphism is not associated with aerobic muscle performance phenotype (VO2max). For CC genotype the short-term explosive muscle power value (based on Vertical Jump test) of athletes from the sprint/power group was significantly higher than that of the endurance group athletes (P < 0.05). The AMPD1 CC genotype is associated with anaerobic performance (Vertical Jump).ConclusionsThe AMPD1 C allele may help athletes to attain elite status in sprint/power-oriented sports, and the T allele is a factor unfavourable for athletics in sprint/power-oriented sports categories. Hence, the AMPD1 C allele can be regarded as a marker associated with the physical performance of sprint and power. Replications studies are required to confirm this association.
| S-EPMC4032451 | biostudies-literature
Project description:Objective: The manuscript aims to explore the relationship between power performance and SNPs of Chinese elite athletes and to create polygenic models. Methods: One hundred three Chinese elite athletes were divided into the power group (n = 60) and endurance group (n = 43) by their sports event. Best standing long jump (SLJ) and standing vertical jump (SVJ) were collected. Twenty SNPs were genotyped by SNaPshot. Genotype distribution and allele frequency were compared between groups. Additional genotype data of 125 Chinese elite athletes were used to verify the screened SNPs. Predictive and identifying models were established by multivariate logistic regression analysis. Results: ACTN3 (rs1815739), ADRB3 (rs4994), CNTFR (rs2070802), and PPARGC1A (rs8192678) were significantly different in genotype distribution or allele frequency between groups (p < 0.05). The predictive model consisted of ACTN3 (rs1815739), ADRB3 (rs4994), and PPARGC1A (rs8192678), the area under curve (AUC) of which was 0.736. The identifying model consisted of body mass index (BMI), standing vertical jump (SVJ), ACTN3, ADRB3, and PPARGC1A, the area under curve (AUC) of which was 0.854. Based on the two models, nomograms were created to visualize the results. Conclusion: Two models can be used for talent identification in Chinese athletes, among which the predictive model can be used in adolescent athletes to predict development potential of power performance and the identifying one can be used in elite athletes to evaluate power athletic status. These can be applied quickly and visually by using nomograms. When the score is more than the 130 or 148 cutoff, it suggests that the athlete has a good development potential or a high level for power performance.
| S-EPMC8532995 | biostudies-literature
Project description:Due to the high-velocity collision-based nature of elite rugby league and union, the risk of sustaining a concussion is high. Occurrence of and outcomes following a concussion are probably affected by the interaction of multiple genes in a polygenic manner. This study investigated whether suspected concussion-associated polygenic profiles of elite rugby athletes differed from non-athletes and between rugby union forwards and backs. We hypothesised that a total genotype score (TGS) using eight concussion-associated polymorphisms would be higher in elite rugby athletes than non-athletes, indicating selection for protection against incurring or suffering prolonged effects of, concussion in the relatively high-risk environment of competitive rugby. In addition, multifactor dimensionality reduction was used to identify genetic interactions. Contrary to our hypothesis, TGS did not differ between elite rugby athletes and non-athletes (p ≥ 0.065), nor between rugby union forwards and backs (p = 0.668). Accordingly, the TGS could not discriminate between elite rugby athletes and non-athletes (AUC ~0.5), suggesting that, for the eight polymorphisms investigated, elite rugby athletes do not have a more ‘preferable’ concussion-associated polygenic profile than non-athletes. However, the COMT (rs4680) and MAPT (rs10445337) GC allele combination was more common in rugby athletes (31.7%; p < 0.001) and rugby union athletes (31.8%; p < 0.001) than non-athletes (24.5%). Our results thus suggest a genetic interaction between COMT (rs4680) and MAPT (rs10445337) assists rugby athletes in achieving elite status. These findings need exploration vis-à-vis sport-related concussion injury data and could have implications for the management of inter-individual differences in concussion risk.
| S-EPMC9141383 | biostudies-literature
Project description:Many empirical and descriptive models have been proposed since the beginning of the 20th century. In the present study, the power-law (Kennelly) and logarithmic (Péronnet-Thibault) models were compared with asymptotic models such as 2-parameter hyperbolic models (Hill and Scherrer), 3-parameter hyperbolic model (Morton), and exponential model (Hopkins). These empirical models were compared from the performance of 6 elite endurance runners (P. Nurmi, E. Zatopek, J. Väätäinen, L. Virén, S. Aouita, and H. Gebrselassie) who were world-record holders and/or Olympic winners and/or world or European champions. These elite runners were chosen because they participated several times in international competitions over a large range of distances (1500, 3000, 5000, and 10000 m) and three also participated in a marathon. The parameters of these models were compared and correlated. The less accurate models were the asymptotic 2-parameter hyperbolic models but the most accurate model was the asymptotic 3-parameter hyperbolic model proposed by Morton. The predictions of long-distance performances (maximal running speeds for 30 and 60 min and marathon) by extrapolation of the logarithmic and power-law models were more accurate than the predictions by extrapolation in all the asymptotic models. The overestimations of these long-distance performances by Morton's model were less important than the overestimations by the other asymptotic models.
| S-EPMC6192093 | biostudies-literature
Project description:PurposeThis longitudinal study aimed at comparing heart rate variability (HRV) in elite athletes identified either in 'fatigue' or in 'no-fatigue' state in 'real life' conditions.Methods57 elite Nordic-skiers were surveyed over 4 years. R-R intervals were recorded supine (SU) and standing (ST). A fatigue state was quoted with a validated questionnaire. A multilevel linear regression model was used to analyze relationships between heart rate (HR) and HRV descriptors [total spectral power (TP), power in low (LF) and high frequency (HF) ranges expressed in ms(2) and normalized units (nu)] and the status without and with fatigue. The variables not distributed normally were transformed by taking their common logarithm (log10).Results172 trials were identified as in a 'fatigue' and 891 as in 'no-fatigue' state. All supine HR and HRV parameters (Beta±SE) were significantly different (P<0.0001) between 'fatigue' and 'no-fatigue': HRSU (+6.27±0.61 bpm), logTPSU (-0.36±0.04), logLFSU (-0.27±0.04), logHFSU (-0.46±0.05), logLF/HFSU (+0.19±0.03), HFSU(nu) (-9.55±1.33). Differences were also significant (P<0.0001) in standing: HRST (+8.83±0.89), logTPST (-0.28±0.03), logLFST (-0.29±0.03), logHFST (-0.32±0.04). Also, intra-individual variance of HRV parameters was larger (P<0.05) in the 'fatigue' state (logTPSU: 0.26 vs. 0.07, logLFSU: 0.28 vs. 0.11, logHFSU: 0.32 vs. 0.08, logTPST: 0.13 vs. 0.07, logLFST: 0.16 vs. 0.07, logHFST: 0.25 vs. 0.14).ConclusionHRV was significantly lower in 'fatigue' vs. 'no-fatigue' but accompanied with larger intra-individual variance of HRV parameters in 'fatigue'. The broader intra-individual variance of HRV parameters might encompass different changes from no-fatigue state, possibly reflecting different fatigue-induced alterations of HRV pattern.
| S-EPMC3741143 | biostudies-literature
Project description:We assessed whether blood lipid metabolites and their changes associate with various cardiometabolic, endocrine, bone- and energy-related comorbidities of Relative Energy Deficiency in Sport (RED-S) in female elite endurance athletes. Thirty-eight Scandinavian female elite athletes underwent a day-long exercise test. Five blood samples were obtained during the day - at fasting state and before and after two standardized exercise tests. Clinical biomarkers were assessed at fasting state, while untargeted lipidomics was undertaken using all blood samples. Linear and logistic regression was used to assess associations between lipidomic features and clinical biomarkers. Overrepresentations of findings with P < 0.05 from these association tests were assessed using Fisher's exact tests. Self-organizing maps and a trajectory clustering algorithm were utilized to identify informative clusters in the population. Twenty associations PFDR < 0.05 were detected between lipidomic features and clinical biomarkers. Notably, cortisol demonstrated an overrepresentation of associations with P < 0.05 compared to other traits (PFisher = 1.9×10-14). Mean lipid trajectories were created for 201 named features for the cohort and subsequently by stratifying participants by their energy availability and menstrual dysfunction status. This exploratory analysis of lipid trajectories indicates that participants with menstrual dysfunction might have decreased adaptive response to exercise interventions.
| S-EPMC7012926 | biostudies-literature
Project description:The genetic profile that is needed to identify talents has been studied extensively in recent years. The main objective of this investigation was to approach, for the first time, the study of genetic variants in several polygenic profiles and their role in elite endurance and professional football performance by comparing the allelic and genotypic frequencies to the non-athlete population. In this study, genotypic and allelic frequencies were determined in 452 subjects: 292 professional athletes (160 elite endurance athletes and 132 professional football players) and 160 non-athlete subjects. Genotyping of polymorphisms in liver metabolisers (CYP2D6, GSTM1, GSTP and GSTT), iron metabolism and energy efficiency (HFE, AMPD1 and PGC1a), cardiorespiratory fitness (ACE, NOS3, ADRA2A, ADRB2 and BDKRB2) and muscle injuries (ACE, ACTN3, AMPD1, CKM and MLCK) was performed by Polymerase Chain Reaction-Single Nucleotide Primer Extension (PCR-SNPE). The combination of the polymorphisms for the "optimal" polygenic profile was quantified using the genotype score (GS) and total genotype score (TGS). Statistical differences were found in the genetic distributions between professional athletes and the non-athlete population in liver metabolism, iron metabolism and energy efficiency, and muscle injuries (p<0.001). The binary logistic regression model showed a favourable OR (odds ratio) of being a professional athlete against a non-athlete in liver metabolism (OR: 1.96; 95% CI: 1.28-3.01; p = 0.002), iron metabolism and energy efficiency (OR: 2.21; 95% CI: 1.42-3.43; p < 0.001), and muscle injuries (OR: 2.70; 95% CI: 1.75-4.16; p < 0.001) in the polymorphisms studied. Genetic distribution in professional athletes as regards endurance (professional cyclists and elite runners) and professional football players shows genetic selection in these sports disciplines.
| S-EPMC9480996 | biostudies-literature
Project description:In recent years, numerous prognostic models have been developed to predict VO2max. Nevertheless, their accuracy in endurance athletes (EA) stays mostly unvalidated. This study aimed to compare predicted VO2max (pVO2max) with directly measured VO2max by assessing the transferability of the currently available prediction models based on their R2, calibration-in-the-large, and calibration slope. 5,260 healthy adult EA underwent a maximal exertion cardiopulmonary exercise test (CPET) (84.76% male; age 34.6±9.5 yrs.; VO2max 52.97±7.39 mL·min-1·kg-1, BMI 23.59±2.73 kg·m-2). 13 models have been selected to establish pVO2max. Participants were classified into four endurance subgroups (high-, recreational-, low- trained, and "transition") and four age subgroups (18-30, 31-45, 46-60, and ≥61 yrs.). Validation was performed according to TRIPOD guidelines. pVO2max was low-to-moderately associated with direct CPET measurements (p>0.05). Models with the highest accuracy were for males on a cycle ergometer (CE) (Kokkinos R2 = 0.64), females on CE (Kokkinos R2 = 0.65), males on a treadmill (TE) (Wasserman R2 = 0.26), females on TE (Wasserman R2 = 0.30). However, selected models underestimated pVO2max for younger and higher trained EA and overestimated for older and lower trained EA. All equations demonstrated merely moderate accuracy and should only be used as a supplemental method for physicians to estimate CRF in EA. It is necessary to derive new models on EA populations to include routinely in clinical practice and sports diagnostic.
| S-EPMC9876283 | biostudies-literature
Project description:most of the research concerning the influence of genetics on endurance performance has been carried out by investigating target genes separately. However, endurance performance is a complex trait that can stem from the interaction of several genes. The objective of this study was to compare the frequencies of polymorphisms in target genes involving cardiorespiratory functioning in elite endurance athletes vs. non-athlete controls. genotypic frequencies were determined in 123 elite endurance athletes and in 122 non-athletes. Genotyping of ACE (rs4340), NOS3 (rs2070744 and rs1799983), ADRA2a (rs1800544 and rs553668), ADRB2 (rs1042713 and rs1042714), and BDKRB2 (rs5810761) was performed by polymerase chain reaction. The total genotype score (TGS: from 0 to 100 arbitrary units; a.u.) was calculated from the genotype score in each polymorphism. the mean TGS in non-athletes (47.72 ± 11.29 a.u.) was similar to elite endurance athletes (46.54 ± 11.32 a.u., p = 0.415). The distribution of TGS frequencies were also similar in non-athletes and elite endurance athletes (p = 0.333). There was no TGS cut-off point to discriminate being elite endurance athletes. the genetic profile in the selected genes was similar in elite endurance athletes and in controls, suggesting that the combination of these genes does not determine endurance performance.
| S-EPMC8391315 | biostudies-literature
Project description:Although the oral microbiota is known to play a crucial role in human health, there are few studies of diet x oral microbiota interactions, and none in elite athletes who may manipulate their intakes of macronutrients to achieve different metabolic adaptations in pursuit of optimal endurance performance. The aim of this study was to investigate the shifts in the oral microbiome of elite male endurance race walkers from Europe, Asia, the Americas and Australia, in response to one of three dietary patterns often used by athletes during a period of intensified training: a High Carbohydrate (HCHO; n = 9; with 60% energy intake from carbohydrates; ~8.5 g kg-1 day-1 carbohydrate, ~2.1 g kg-1 day-1 protein, 1.2 g kg-1 day-1 fat) diet, a Periodised Carbohydrate (PCHO; n = 10; same macronutrient composition as HCHO, but the intake of carbohydrates is different across the day and throughout the week to support training sessions with high or low carbohydrate availability) diet or a ketogenic Low Carbohydrate High Fat (LCHF; n = 10; 0.5 g kg-1 day-1 carbohydrate; 78% energy as fat; 2.1 g kg-1 day-1 protein) diet. Saliva samples were collected both before (Baseline; BL) and after the three-week period (Post treatment; PT) and the oral microbiota profiles for each athlete were produced by 16S rRNA gene amplicon sequencing. Principal coordinates analysis of the oral microbiota profiles based on the weighted UniFrac distance measure did not reveal any specific clustering with respect to diet or athlete ethnic origin, either at baseline (BL) or following the diet-training period. However, discriminant analyses of the oral microbiota profiles by Linear Discriminant Analysis (LDA) Effect Size (LEfSe) and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) did reveal changes in the relative abundance of specific bacterial taxa, and, particularly, when comparing the microbiota profiles following consumption of the carbohydrate-based diets with the LCHF diet. These analyses showed that following consumption of the LCHF diet the relative abundances of Haemophilus, Neisseria and Prevotella spp. were decreased, and the relative abundance of Streptococcus spp. was increased. Such findings suggest that diet, and, in particular, the LCHF diet can induce changes in the oral microbiota of elite endurance walkers.
| S-EPMC6471070 | biostudies-literature