Muscle transcriptome at rest and during recovery from running in elite male ultra-endurance athletes acclimated to a high-carbohydrate versus low-carbohydrate diet
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ABSTRACT:
PROVIDER: PRJNA1139832 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29971500_1.fastq.gz | Fastqsanger.gz | |||
| SRR29971500_2.fastq.gz | Fastqsanger.gz | |||
| SRR29971600_1.fastq.gz | Fastqsanger.gz | |||
| SRR29971600_2.fastq.gz | Fastqsanger.gz | |||
| SRR29971700_1.fastq.gz | Fastqsanger.gz |
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Project description:Low-carbohydrate diets enhance lipid metabolism and decrease reliance on glucose oxidation in athletes, but the associated gene expression patterns remain unclear. To provide mechanistic insight, we investigated the skeletal muscle transcriptome in elite ultra-endurance athletes habitually consuming a high-carbohydrate (HC, n=10, 33±6y, VO2max=63.4±6.2 mL O2•kg-1•min-1) or low-carbohydrate (LC, n=10, 34±7y, VO2max=64.7±3.7 mL O2•kg-1•min-1) diet. Skeletal muscle gene expression was measured at baseline (BL), immediately-post (H0), and 2h (H2) after 3h submaximal treadmill running. Exercise induced a coordinated but divergent expression pattern. LC had higher expression of genes associated with lipid metabolism, particularly at BL. At H2, gene expression patterns were associated with differential pathway activity, including inflammation/immunity, suggesting a diet-specific influence on early muscle recovery. These results indicate that a habitual ketogenic diet leads to differences in resting and exercise-induced skeletal muscle gene expression patterns, underlying our previous findings of differential fuel utilization during exercise in elite male ultra-endurance athletes.
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Project description:BackgroundIt is recommended that endurance athletes consume carbohydrate (CHO) supplements, providing 6-8% CHO concentration, during exercise > 60 minutes to improve athletic performance. Recently research has compared carbohydrate-protein (CHO-P) supplementation to the traditionally used CHO supplementation during endurance exercise, following these supplementation recommendations, in controlled settings, but not under simulated applied conditions such as a field trial. Therefore, the purpose of the present investigation was to test CHO and CHO-P supplementation under applied conditions such that commercially-available isocaloric (CHO-P & double-carbohydrate [CHO-CHO]) and isocarbohydrate (CHO-P & CHO) supplements were compared to a placebo (PLA), within an outdoor running field trial > 60 minutes in order to asses their influence on endurance performance.MethodsTwelve male recreational runners completed four, 19.2 km runs, where they were instructed to run at a pace similar to race pace including a final sprint to the finish, which in this case was the final two laps of the course (1.92 km). Supplementation was provided before the start and in 4 km increments. Performance was measured by time to complete the 19.2 km run and last 1.92 km sprint.ResultsAnalyses found no difference between supplements in time to complete the 19.2 km run (PLA = 88.6 ± 11.6 min, CHO = 89.1 ± 11.3 min, CHO-P = 89.1 ± 11.8 min, CHO-CHO = 89.6 ± 11.9 min) or last 1.92 km sprint to the finish (PLA = 8.3 ± 1.2 min, CHO = 8.2 ± 1.2 min, CHO-P = 8.2 ± 1.2 min, CHO-CHO = 8.4 ± 1.5 min).ConclusionsWhen following recommendation for supplementation within a field trial, commercially available CHO and CHO-P supplements do not appear to enhance performance in male recreational runners.
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Project description:Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.
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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.
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Project description:This study explored the impact of two differing warm-up protocols (involving either resistance exercises or plyometric exercises) on running economy (RE) in healthy recreationally active participants. Twelve healthy university students [three males, nine females, age 20 ± 2 years, maximal oxygen uptake (38.4 ± 6.4 ml min-1 kg-1)] who performed less than 5 h per week of endurance exercise volunteered to participant in this study. All participants completed three different warm-up protocols (control, plyometric, and resistance warm-up) in a counterbalanced crossover design with trials separated by 48 h, using a Latin-square arrangement. Dependent variables measured in this study were RE at four running velocities (7, 8, 9, and 10 km h-1), maximal oxygen uptake; heart rate; respiratory exchange rate; expired ventilation; perceived race readiness; rating of perceived exertion, time to exhaustion and leg stiffness. The primary finding of this study was that the plyometric warm-up improved RE compared to the control warm-up (6.2% at 7 km h-1, ES = 0.355, 9.1% at 8 km h-1, ES = 0.513, 4.5% at 9 km h-1, ES = 0.346, and 4.4% at 10 km h-1, ES = 0.463). There was no statistically significant difference in VO2 between control and resistance warm-up conditions at any velocity. There were also no statistically significant differences between conditions in other metabolic and pulmonary gas exchange variables; time to exhaustion; perceived race readiness and maximal oxygen uptake. However, leg stiffness increased by 20% (P = 0.039, ES = 0.90) following the plyometric warm-up and was correlated with the improved RE at a velocity of 8 km h-1 (r = 0.475, P = 0.041). No significant differences in RE were found between the control and resistance warm-up protocols. In comparison with the control warm-up protocol, an acute plyometric warm-up protocol can improve RE in healthy adults.
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Project description:Total genotype score (TGS) reflects additive effect of genotypes on predicting a complex trait such as athletic performance. Scores assigned to genotypes in the TGS should represent an extent of the genotype's predisposition to the trait. Then, combination of genotypes highly ranks those individuals, who have a trait expressed. Usually, the genotypes are scored by the evidence of a genotype-phenotype relationship published in scientific studies. The scores can be revised computationally using genotype data of athletes, if available. From the available genotype data of 180 Lithuanian elite athletes we created an endurance-mixed-power performance TGS profile based on known ACE rs1799752, ACTN3 rs1815739, and AMPD1 rs17602729, and an emerging MB rs7293 gene markers. We analysed an ability of this TGS profile to stratify athletes according to the sport category that they practice. Logistic regression classifiers were trained to compute the genotype scores that represented the endurance versus power traits in the group of analysed athletes more accurately. We observed differences in TGS distributions in female and male group of athletes. The genotypes with possibly different effects on the athletic performance traits in females and males were described. Our data-driven analysis and TGS modelling tools are freely available to practitioners.
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