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Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics.

Luck MM, Le Moyec L, Barrey E, Triba MN, Bouchemal N, Savarin P, Robert C - Front Physiol (2015)

Bottom Line: The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001).For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1.The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism.

View Article: PubMed Central - PubMed

Affiliation: Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val D'Essonne Evry, France.

ABSTRACT
Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear Magnetic Resonance (NMR) spectrometry with supervised Orthogonal Projection on Latent Structure (OPLS) statistical analysis. (1)H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race.

No MeSH data available.


Related in: MedlinePlus

(A) Score plot of the best OPLS model computed with the after-exercise samples of the young and experienced horse pair set with age as supervising factor. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for young horses and red for experienced horses. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to an increase in the experienced horses when compared to the young horses in response to endurance exercise. Conversely, negative signals correspond to an increase in the young horses when compared to the experienced horses in response to endurance exercise. The buckets are labeled according to their metabolite assignments according to Figure 1.
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Figure 4: (A) Score plot of the best OPLS model computed with the after-exercise samples of the young and experienced horse pair set with age as supervising factor. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for young horses and red for experienced horses. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to an increase in the experienced horses when compared to the young horses in response to endurance exercise. Conversely, negative signals correspond to an increase in the young horses when compared to the experienced horses in response to endurance exercise. The buckets are labeled according to their metabolite assignments according to Figure 1.

Mentions: An OPLS model was computed with the AE samples of the within matrix of the paired samples from both sets (young and experienced horses) using age as supervising factor. Using the resampling method, the model included the 11 samples from experienced horses and a 1000 combination of 11 samples from young horses. The score plot of the best model (Q2Y = 0.798 and R2Y = 0.896, CV-ANOVA p < 0.001) is presented in Figure 4A. Moreover, good statistical parameters were also obtained for the other models as the median Q2Y was 0.608. These parameters were validated with the permutation test. The loading plot of the best model (Figure 4B) shows that several variables were highly correlated to the effect of age on exercise. Indeed, it appeared that the effect of age on the response to exercise on the metabolome (Table 6) was a stronger signal arising from carbohydrate metabolism (glucose, lactate, creatine) and weaker signals arising from lipid metabolism (choline, alkene, methyl, methylene, methylene-α-ester), amino acid metabolism (glutamate), and glycoprotein metabolism (N-acetyl) for the experienced horses when compared to young horses.


Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics.

Luck MM, Le Moyec L, Barrey E, Triba MN, Bouchemal N, Savarin P, Robert C - Front Physiol (2015)

(A) Score plot of the best OPLS model computed with the after-exercise samples of the young and experienced horse pair set with age as supervising factor. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for young horses and red for experienced horses. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to an increase in the experienced horses when compared to the young horses in response to endurance exercise. Conversely, negative signals correspond to an increase in the young horses when compared to the experienced horses in response to endurance exercise. The buckets are labeled according to their metabolite assignments according to Figure 1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544308&req=5

Figure 4: (A) Score plot of the best OPLS model computed with the after-exercise samples of the young and experienced horse pair set with age as supervising factor. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for young horses and red for experienced horses. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to an increase in the experienced horses when compared to the young horses in response to endurance exercise. Conversely, negative signals correspond to an increase in the young horses when compared to the experienced horses in response to endurance exercise. The buckets are labeled according to their metabolite assignments according to Figure 1.
Mentions: An OPLS model was computed with the AE samples of the within matrix of the paired samples from both sets (young and experienced horses) using age as supervising factor. Using the resampling method, the model included the 11 samples from experienced horses and a 1000 combination of 11 samples from young horses. The score plot of the best model (Q2Y = 0.798 and R2Y = 0.896, CV-ANOVA p < 0.001) is presented in Figure 4A. Moreover, good statistical parameters were also obtained for the other models as the median Q2Y was 0.608. These parameters were validated with the permutation test. The loading plot of the best model (Figure 4B) shows that several variables were highly correlated to the effect of age on exercise. Indeed, it appeared that the effect of age on the response to exercise on the metabolome (Table 6) was a stronger signal arising from carbohydrate metabolism (glucose, lactate, creatine) and weaker signals arising from lipid metabolism (choline, alkene, methyl, methylene, methylene-α-ester), amino acid metabolism (glutamate), and glycoprotein metabolism (N-acetyl) for the experienced horses when compared to young horses.

Bottom Line: The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001).For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1.The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism.

View Article: PubMed Central - PubMed

Affiliation: Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val D'Essonne Evry, France.

ABSTRACT
Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear Magnetic Resonance (NMR) spectrometry with supervised Orthogonal Projection on Latent Structure (OPLS) statistical analysis. (1)H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race.

No MeSH data available.


Related in: MedlinePlus