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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 OPLS model computed with before (BE) and after (AE) exercise samples from the same finisher horse. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for BE and red for AE. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites present at increased concentrations at AE. Conversely, negative signals correspond to metabolites present at increased concentrations at AE. The buckets are labeled according to their metabolite assignments according to Figure 1. (C) Projection of the sample spectra that are not in the model (A). Each new spectrum was projected in the score plot using the previously constructed model (A) to enable prediction of BE or AE spectra. The AUROC (not shown) was equal to 1 and the optimal cutoff value was equal to 0.43.
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Figure 3: (A) Score plot of the OPLS model computed with before (BE) and after (AE) exercise samples from the same finisher horse. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for BE and red for AE. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites present at increased concentrations at AE. Conversely, negative signals correspond to metabolites present at increased concentrations at AE. The buckets are labeled according to their metabolite assignments according to Figure 1. (C) Projection of the sample spectra that are not in the model (A). Each new spectrum was projected in the score plot using the previously constructed model (A) to enable prediction of BE or AE spectra. The AUROC (not shown) was equal to 1 and the optimal cutoff value was equal to 0.43.

Mentions: The second OPLS model was computed with the 92 samples of the young horses pair set (i.e., finisher young horses taken both at BE and AE) in order to detect the effect of the 90 km ride. The score plot of the model is presented in Figure 3A. Good statistical performance was obtained for this model as R2Y = 0.947, Q2Y = 0.856 and the CV-ANOVA p-value was below 0.001. The fit and predictability parameters were validated with the permutation test. Several variables were highly correlated to the sampling time (BE or AE). These are presented in the loading plot (Figure 3B). The 90 km ride effects on the metabolome, demonstrated in the second model, were similar to the ones of the ≤90 km endurance exercise effects shown in the first model. These results are shown in Table 2.


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 OPLS model computed with before (BE) and after (AE) exercise samples from the same finisher horse. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for BE and red for AE. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites present at increased concentrations at AE. Conversely, negative signals correspond to metabolites present at increased concentrations at AE. The buckets are labeled according to their metabolite assignments according to Figure 1. (C) Projection of the sample spectra that are not in the model (A). Each new spectrum was projected in the score plot using the previously constructed model (A) to enable prediction of BE or AE spectra. The AUROC (not shown) was equal to 1 and the optimal cutoff value was equal to 0.43.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: (A) Score plot of the OPLS model computed with before (BE) and after (AE) exercise samples from the same finisher horse. Tpred represents the predictive axis and Torth, the orthogonal axis. Each dot corresponds to a spectrum, colored in blue for BE and red for AE. (B) Loading plot of the score plot predictive axis. The metabolite correlations are represented by the color scale. Positive signals correspond to metabolites present at increased concentrations at AE. Conversely, negative signals correspond to metabolites present at increased concentrations at AE. The buckets are labeled according to their metabolite assignments according to Figure 1. (C) Projection of the sample spectra that are not in the model (A). Each new spectrum was projected in the score plot using the previously constructed model (A) to enable prediction of BE or AE spectra. The AUROC (not shown) was equal to 1 and the optimal cutoff value was equal to 0.43.
Mentions: The second OPLS model was computed with the 92 samples of the young horses pair set (i.e., finisher young horses taken both at BE and AE) in order to detect the effect of the 90 km ride. The score plot of the model is presented in Figure 3A. Good statistical performance was obtained for this model as R2Y = 0.947, Q2Y = 0.856 and the CV-ANOVA p-value was below 0.001. The fit and predictability parameters were validated with the permutation test. Several variables were highly correlated to the sampling time (BE or AE). These are presented in the loading plot (Figure 3B). The 90 km ride effects on the metabolome, demonstrated in the second model, were similar to the ones of the ≤90 km endurance exercise effects shown in the first model. These results are shown in Table 2.

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