Dataset Information

PPAR? Promotes Running Endurance by Preserving Glucose.

ABSTRACT: Management of energy stores is critical during endurance exercise; a shift in substrate utilization from glucose toward fat is a hallmark of trained muscle. Here we show that this key metabolic adaptation is both dependent on muscle PPAR? and stimulated by PPAR? ligand. Furthermore, we find that muscle PPAR? expression positively correlates with endurance performance in BXD mouse reference populations. In addition to stimulating fatty acid metabolism in sedentary mice, PPAR? activation potently suppresses glucose catabolism and does so without affecting either muscle fiber type or mitochondrial content. By preserving systemic glucose levels, PPAR? acts to delay the onset of hypoglycemia and extends running time by ?100 min in treated mice. Collectively, these results identify a bifurcated PPAR? program that underlies glucose sparing and highlight the potential of PPAR?-targeted exercise mimetics in the treatment of metabolic disease, dystrophies, and, unavoidably, the enhancement of athletic performance.

SUBMITTER: Fan W

PROVIDER: S-EPMC5492977 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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PPARδ Promotes Running Endurance by Preserving Glucose.

Fan Weiwei W Waizenegger Wanda W Lin Chun Shi CS Sorrentino Vincenzo V He Ming-Xiao MX Wall Christopher E CE Li Hao H Liddle Christopher C Yu Ruth T RT Atkins Annette R AR Auwerx Johan J Downes Michael M Evans Ronald M RM

Cell metabolism 20170501 5

Management of energy stores is critical during endurance exercise; a shift in substrate utilization from glucose toward fat is a hallmark of trained muscle. Here we show that this key metabolic adaptation is both dependent on muscle PPARδ and stimulated by PPARδ ligand. Furthermore, we find that muscle PPARδ expression positively correlates with endurance performance in BXD mouse reference populations. In addition to stimulating fatty acid metabolism in sedentary mice, PPARδ activation potently ...[more]

PMID: 28467934

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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.

Project description:Subjects Eleven male volunteers (mean age 42.3 + 7.4 years; weight 73.9 + 7.7 kg, height 176.9 + 4.0 cm, body mass index (BMI) 23.6 + 2.1 kg/m2 and body fat: 17.7 + 4.3%) participated in this experiment. They were recruited among experienced ultra-endurance runners and all of them had run at least a race longer than 24 h or 4100 km. On average, they had 15.3 + 7.1 years of training history in running and 7.1 + 4.4 years of ultra-endurance experience. They reported to run an average of 80.5 + 11.7 km/week. Written informed consent was obtained from the subjects. The study was conducted according to the Declaration of Helsinki. The protocol has been approved by the local ethics committee (Comité de Protection des Personnes Sud-Est 1, France) and registered in http:// clinicaltrial.gov (# NCT 00428779). Procedures The subjects visited the laboratory twice, with each session separated by 3–4 weeks. All subjects had run at least three times on a motorized treadmill before taking part in the experiment. In the first session, body mass, height and percentage of body fat (measurements of skin-fold thickness) were measured. The subjects performed a maximal test on a motorized treadmill (Gymrol S2500, HEF Tecmachine, Andrezieux-Boutheon, France), to determine the anaerobic threshold VO2max and the velocity associated with VO2max. The initial velocity was set at 10 km/h and the first stage lasted 6 min. Then, the test consisted of a maximal discontinuous incremental test (slope 50%), where the speed was progressively increased by 1.5 km/h every 3 min, followed by 1 min of rest for the collection of blood samples from the finger tips. The second session consisted of the 24-h treadmill ultra-endurance running protocol (24TR). Subjects were asked to refrain from strenuous exercise for a week before the 24TR. The day of the experiment, all subjects ate the same lunch at noon. About 2 h before starting, a muscle biopsy was taken. Pre-exercise muscle biopsy samples (120 mg) were collected under local anesthesia from the superficial portion of the left vastus lateralis muscle using a percutaneous technique [Henriksson (1979) Acta Neurol Scand 59: 317–323]. A large well-organized fascicle of fibers was oriented and included in an embedding medium (Cryomount; Histolab, Göteborg, Sweden), frozen in isopentane, cooled to its freezing point in liquid nitrogen and stored in liquid nitrogen until further cryostat sectioning. The remaining pieces of the sample were rapidly frozen and stored in liquid nitrogen until protein analyses were performed. After the muscle biopsy procedure, the subjects rested for approximately 2 h and then they were asked to start the 24TR. Ten minutes after the start of the 24TR, subjects were asked to run 4 min at 8 km/h for measurements of running economy (RE). A slow speed was chosen for RE because the average speed over the 24TR was 7.9 + 1.0 km/h, and 8 km/h is representative of the pace over such an extreme exercise [Millet et al. (2009) Scand J Med Sci Sports 21: 54-61]. The food and water intakes during the 24TR were managed by the subjects as in a normal race. Post-exercise muscle biopsy samples (120 mg) were then collected as described above.

Dataset Information

PPAR? Promotes Running Endurance by Preserving Glucose.

Publications

PPARδ Promotes Running Endurance by Preserving Glucose.

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