Dataset Information

Metabolic Predictors of Equine Performance in Endurance Racing.

ABSTRACT: Equine performance in endurance racing depends on the interplay between physiological and metabolic processes. However, there is currently no parameter for estimating the readiness of animals for competition. Our objectives were to provide an in-depth characterization of metabolic consequences of endurance racing and to establish a metabolic performance profile for those animals. We monitored metabolite composition, using a broad non-targeted metabolomics approach, in blood plasma samples from 47 Arabian horses participating in endurance races. The samples were collected before and after the competition and a total of 792 metabolites were measured. We found significant alterations between before and after the race in 417 molecules involved in lipids and amino acid metabolism. Further, even before the race starts, we found metabolic differences between animals who completed the race and those who did not. We identified a set of six metabolite predictors (imidazole propionate, pipecolate, ethylmalonate, 2R-3R-dihydroxybutyrate, ?-hydroxy-isovalerate and X-25455) of animal performance in endurance competition; the resulting model had an area under a receiver operating characteristic (AUC) of 0.92 (95% CI: 0.85-0.98). This study provides an in-depth characterization of metabolic alterations driven by endurance races in equines. Furthermore, we showed the feasibility of identifying potential metabolic signatures as predictors of animal performance in endurance competition.

SUBMITTER: Halama A

PROVIDER: S-EPMC7912089 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Metabolic Predictors of Equine Performance in Endurance Racing.

Halama Anna A Oliveira Joao M JM Filho Silvio A SA Qasim Muhammad M Achkar Iman W IW Johnson Sarah S Suhre Karsten K Vinardell Tatiana T

Metabolites 20210131 2

Equine performance in endurance racing depends on the interplay between physiological and metabolic processes. However, there is currently no parameter for estimating the readiness of animals for competition. Our objectives were to provide an in-depth characterization of metabolic consequences of endurance racing and to establish a metabolic performance profile for those animals. We monitored metabolite composition, using a broad non-targeted metabolomics approach, in blood plasma samples from 4 ...[more]

PMID: 33572513

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Project description:Maternal aging has negative influences on the development and racing performance of their offspring in racehorses. However, the mechanism by which pregnancy at old age reduces the race performance of the offspring is unknown. Here, two hypotheses were posited: 1) Foals born to older mares are more likely to have muscular, skeletal, and cognitive disadvantages (direct effects). 2) Foals born to older mares are more likely to be affected by non-physiological factors correlating with the mare's age, such as the quality of sires (e.g. low-quality sires are likely to be chosen as partners of older broodmares). To test these hypotheses, the effect of the broodmare's age on the offspring's racing performance was examined, while controlling for the effects of the stallion's quality, age, and ID, offspring's sex, trainer, and the location of the training center. Information of racehorses registered to the Japan Racing Association were collected from the Japan Bloodhorse Breeders' Association website. Overall, results showed that the racing performance of horses born from older mares was lower than that of horses born from younger mares. However, generalized linear mixed models (GLMM) indicated that the quality of sires was significantly associated with the offspring's racing performance, rather than the broodmare's age itself. Furthermore, the age of broodmares was negatively correlated with the quality of sires, although the variance inflation factor was low. Therefore, the effect of maternal aging was negligible or only limited, and rather, the sire's quality had an important influence on the offspring's racing performance. Low quality sires, or cheap stallions in other words, are likely to be chosen as partners of older blood-mares, which may have reproductive risks such as lower fertility and higher rate of miscarriages. The present study suggests that the conventional belief that racehorses born from older mares show lower performance may not always be accurate.

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.

Dataset Information

Metabolic Predictors of Equine Performance in Endurance Racing.

Publications

Metabolic Predictors of Equine Performance in Endurance Racing.

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