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Anim Biosci > Volume 30(11); 2017 > Article
Jang, Kim, Kim, Park, Choi, Oh, Song, Kim, and Cho: Analysis of metabolomic patterns in thoroughbreds before and after exercise



Evaluation of exercise effects in racehorses is important in horseracing industry and animal health care. In this study, we compared metabolic patterns between before and after exercise to screen metabolic biomarkers for exercise effects in thoroughbreds.


The concentration of metabolites in muscle, plasma, and urine was measured by 1H nuclear magnetic resonance (NMR) spectroscopy analysis and the relative metabolite levels in the three samples were compared between before and after exercise. Subsequently, multivariate data analysis based on the metabolic profiles was performed using orthogonal partial least square discriminant analysis (OPLS-DA) and variable important plots and t-test was used for basic statistical analysis.


From 1H NMR spectroscopy analysis, 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine. Aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold whereas acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine. The OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine. Statistical analysis showed that after exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased and alanine, glycerol, inosine, lactate, and pyruvate were significantly increased in the urine (p<0.05).


In conclusion, we analyzed integrated metabolic patterns in the muscle, plasma, and urine before and after exercise in racehorses. We found changed patterns of metabolites in the muscle, plasma, and urine of racehorses before and after exercise.


Exercise affects metabolic responses throughout the body [1]. During exercise, muscles generate ATP by using various intramuscular and extramuscular substrates such as creatine phosphate, muscle glycogen, blood-borne glucose, lactate, and free fatty acids. The various substrates for exercise metabolism are dependently determined by exercise intensity and duration as well as training status, dietary manipulation, and other environmental factors [2]. Exercise of maximal intensity increases the amount of lactate derived from the degradation of muscle glycogen, products of adenine nucleotide catabolism, and tricarboxylic acid cycle intermediates related to aerobic energy production [3]; it also promotes glycogenolysis, lipolysis, and ammonia metabolism [4]. Prolonged submaximal intensity exercise improves insulin sensitivity, arterial compliance, and endothelial function [5]; increases lipid catabolism [6]; decreases the catecholamine response [7]; and maintains bone density, skeletal muscle mass, and muscle metabolic capacity during ageing [8].
The equine skeletal muscle displays intrinsic metabolic adaptations based on myofiber structure and function, substrate and by-product transport across the sarcolemma, and coordinated integration of metabolic pathways to produce ATP in response to exercise [9]. Equine muscles store a large amount of glycogen (300 to 650 mol/g dry weight) in fast fibers. The stored glycogen is used as the most important source of energy for muscle contraction during both submaximal (<85% VO2max) and maximal exercise (>85% VO2max) [9]. During prolonged submaximal intensity exercise, lipids also contribute to produce muscle energy with glycogen [10]. After exercise, supplementation of muscle glycogen can slowly take up to 72 h in horses [11]. Previous studies have shown that muscle glycogen supplement after exercise was enhanced by certain processes such as intravenous glucose infusion, oral acetate administration, and rehydration with hypotonic electrolyte solutions in horses [12]. In addition, the buffering capacity that prevents muscle acidosis by lactate is higher in horses than in other species, probably because of high carnosine content [9]. Some studies have also suggested that equine adaptation to exercise could improve both aerobic and anaerobic capacities [9]. However, the mechanism underlying equine metabolism in response to exercise is still unclear.
Recently, multivariate approaches of metabolomic analysis have been used to understand biological mechanisms [13]. With respect to biological endpoints, quantifications of metabolomes could elucidate biological phenomena with other omics studies such as genomics, transcriptomics, and proteomics. For the acquisition of metabolic data, high-resolution 1H or 13C nuclear magnetic resonance (NMR) spectroscopy and mass spectroscopy have been used along with other spectroscopic methodologies [14,15]. The acquired data can be interpreted using multivariate statistical analysis, such as hierarchical cluster analysis, principal component analysis, different types of partial least square analysis, and subsequent modeling with new regression algorithms [16,17].
In this study, we analyzed the metabolic profiles of equine muscle, plasma, and urine before and after exercise by using 1H NMR spectroscopy. On the basis of the analysis results, commonly or specifically expressed metabolites were selected from the muscle, plasma, and urine, and they reflected the effects of exercise. Subsequently, we suggested metabolic pathways related to those metabolites. Our study could contribute to understanding fluctuations in equine metabolism because of exercise.


Horses and ethical statement

Three Thoroughbred were used in this study. The Pusan National University-Institutional Animal Care and Use Committee approved the study design (Approval Number: PNU-2015-0864).

Sample collection

Blood, muscle, and urine samples were collected from each horse before and after exercise (30 min). Briefly, venous blood samples were collected using a-50 mL syringe and transferred to heparin-containing tubes and centrifuged at 5,000 rpm for 15 min to obtain plasma. The plasma samples were stored at −20°C until NMR sample preparation. For skeletal muscle biopsy, local anesthesia was administered to the gluteus medius, and a biopsy collection syringe was used to obtain the muscle samples before and after exercise. The samples were stored in liquid nitrogen until analysis. Urine was collected from the subjects before and after exercise and centrifuged to remove solids. An 600 μL aliquot of the supernatant was added to a micro centrifuge tube containing 70 μL of D2O solution with 5 mM dextran sulphate sodium (DSS) and 10 mM imidazole. The DSS was used as the qualitative standard for the chemical shift scale. In addition, 30 μL of 0.42% sodium azide was added. The urine samples were stored at −70°C until analysis.

Nuclear magnetic resonance spectroscopy

The skeletal muscle and plasma samples were subjected to 1H NMR spectroscopy analysis. Briefly, 45 μL of the samples was used with 5 μL of deuterium oxide (D2O) containing 20 mM of the reference material trimethylsilylpropionate (TSP); 20 mg of the skeletal muscle samples was analyzed with 25 μL of D2O containing 2 mM of TSP, and 630 μL of the urine samples was mixed with 70 μL of D2O containing 20 mM of TSP before NMR measurement.
We conducted high-resolution magic angle spinning NMR for the skeletal muscle and plasma samples. The spinning rate was 2,050 Hz. To analyze the skeletal muscle, plasma, and urine samples, the Carr-Purcell-Meiboom-Gill pulse sequence was used to remove the water peak and macromolecular peak signal. The acquisition time was 1.704 s, and the relaxation delay was 1.0 s. Each sample was scanned 128 times, and the total analysis time was 8 min and 13 s.
Chenomx NMR Suite 7.1 (Chenomx Inc., Edmonton, AB, Canada) and SIMCAp+12.0 (Umetrics, Umea, Sweden) software were used to minimize the errors of the measured spectrum and statistical analysis, respectively. In this study, we quantified 22 metabolites in the plasma, while 33 metabolites were investigated in the skeletal muscle in both groups. We used TSP as the standard and measured the absolute concentrations of the metabolites to normalize the samples; the relative concentration of each metabolite was measured. The multivariate statistical analysis method was used to calculate the amount of metabolites present in the samples.

Orthogonal partial least square discriminant analysis

All data were converted from the NMR software format to the Microsoft Excel format. One-dimensional NMR spectra data were imported into SIMCA-P (version 12.0, Umetrics Inc., Kinnelon, NJ, USA) for multivariate statistical analysis, to examine intrinsic variations in the data set. These data were scaled using centered scaling prior to the orthogonal partial least square discriminant analysis (OPLS-DA). For the scaling process, the average value of each variable was calculated and then subtracted from the data. OPLS-DA score plots were used to interpret intrinsic variations in the data.

Statistical analysis

Means and standard deviations of the metabolites were calculated using Microsoft Excel. The statistical significance (p<0.05, p<0.01, or p<0.001) of apparent differences in metabolite concentrations before and after exercise was assessed using analysis of variance, followed by the t-test (Prism 5.01, San Diego, CA, USA).


Differentially expressed metabolites and metabolic patterns before and after exercise in horses

The metabolite analyses before and after exercise showed that 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine, respectively. Sixteen metabolites were commonly changed among the muscle, plasma, and urine after exercise, and 11, 3, and 14 metabolites were specifically changed in the muscle, plasma, and urine, respectively, after exercise (Figure 1, Table 1). The relative levels of the metabolites after exercise in the muscle, plasma, and urine were measured and compared with the corresponding levels of before exercise. The results showed that aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold and acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine (Figure 1).

OPLS-DA and variable important plots of the metabolites before and after exercise

OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine (Figure 2A, 2B, 2C). Subsequently, when variable important plots (VIPs) were derived from OPLS-DA for the metabolic patterns before and after exercise, the detected metabolites that contributed to separating the clusters in the respective samples were scored to reflect their priorities (Figure 2D, 2E, 2F). Lactate, creatine, taurine, and cysteine had VIP scores >1 in the muscle; lactate, alanine, glycine, trimethylamine, acetate, and choline had VIP scores >1 in the plasma; and lactate and glycerol had VIP scores >1 in the urine (Table 2).

Metabolites that responded to exercise

When the levels of the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites were collectively analyzed in the muscle, plasma, and urine, the expressed levels were observed to be significantly changed in the urine after exercise, while no significant differences were detected in the muscle and plasma before and after exercise. After exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased by 38.8%, 44.6%, 19.6%, 22.7%, 33.8%, 30.6%, 37.8%, and 30.8%, respectively, while alanine, glycerol, inosine, lactate, and pyruvate were significantly increased by 436.7%, 2,184.4%, 1,008.8%, 8,347.9%, and 726.5%, respectively, in the urine (p<0.05; Figure 3). With respect to the commonly detected metabolites, alanine, glutamine, lactate, and pyruvate showed significantly different expressions in the urine after exercise (p<0.05; Figure 4); the concentrations of alanine, lactate, and pyruvate in the plasma were significantly higher than in the muscle and urine, whereas the concentration of glutamine was not significantly different between the muscle and plasma (p<0.05; Figure 4).

Enrichment analyses of metabolic pathways that responded to exercise

Enrichment analyses for the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites were performed using MetaboAnalyst 3.0 [18], and 36 pathways were predicted (Table 3).


Metabolic alteration reflects biological responses to various genetic, transcriptiomic, proteomic, and environmental influences [1921]. Many metabolic studies have applied to characterize metabolic patterns derived from altered gene function in plants [22,23], explore microbial metabolism [24], assess drug toxicity [25] and diagnostic applications [26], and discover biomarkers for animal health and disease [21,27,28]. Therefore, metabolic biomarkers are regarded as a promising tool for improving animal health and welfare.
Since domestication, horses have been selected for superior athletic traits related to strength, endurance, and speed. In particular, racehorses have undergone artificial structural and functional adaptations for athletic performances. As a result, racehorses developed maximal aerobic capacity, intramuscular energy stores, mitochondrial volume in the muscle, and oxygen-carrying capacity in the blood [29]. From the unique physiological properties, most of the metabolic studies on exercising horses focused on glycogen stores, whereas only a few studies have addressed muscle triglyceride or protein stores. During intensive short-term exercise, muscle glycogen stores may be depleted by 20% to 35%, and prolonged exercise results in a decline in muscle glycogen by 50% to 100% [30]. However after cessation of exercise, the rate of glycogen repletion is much lower in horses than in other animal species and human athletes [31]. In addition, exercise induces changes in the amino acid profile in the blood and muscle. An increase in branched-chain amino acids, such as leucine, isoleucine, and valine, has been observed during prolonged sub-maximal exercise in horses [32], and it may have been due to increased output by the liver in which proteolysis has been shown to accelerate during exercise [33]. Furthermore, certain amino acids are believed to be oxidized for energy production in the muscle [34], although the contribution of proteins to energy expenditure in horses during exercise is still unknown. Recently, exercise in young horses was associated with lipid metabolism, including choline and glycerol; carbohydrate metabolism, including lactate, fumarate, and glucose; and amino acid metabolism, including creatine, creatinine, phenylalanine, tyrosine, and glutamate [35]. Collectively, our results showed the consistency of the differentially expressed metabolites in relation with the enrichment analysis of the metabolic pathways. We also suggested additional metabolic changes during equine exercise.
Alanine, glutamine, lactate, and pyruvate, which were commonly detected among the muscle, plasma, and urine, showed significantly different expressions in the urine after exercise (p< 0.05). During exercise, muscle glycogen, which is a primary energy source, is sequentially processed to pyruvate and pyruvate and can be used to produce ATP aerobically or anaerobically through glycolysis [36]. When the muscle cannot use enough oxygen for aerobic glycolysis at high-exercise intensities, anaerobic glycolysis produces ATP in the cytosol of the muscle by the incomplete breakdown of glucose into lactate [36]. Subsequently, muscle lactate is excreted into the blood for the balance of production rate and removal [37]. Once in the bloodstream, lactate can be taken up by exercising or non-exercising skeletal muscles, kidney, or liver, where it is converted to pyruvate for gluconeogenesis [38]. Concurrently, when muscles degrade amino acids for energy needs, the resulting nitrogen is transaminated to pyruvate to produce alanine. This alanine is transported to the liver, where nitrogen enters the urea cycle and pyruvate is used to produce glucose [39]. In addition, glutamine is primarily synthesized from glutamate and glutamic acid in the skeletal muscle. Glutamine is considered important for the maintenance of the renal tubules, contributing to the healthy functioning of the kidneys. Glutamine in the kidneys contributes to the elimination of acids from the blood, and it is lysed to glutamate, aspartate, pyruvate, lactate, alanine, and citrate through a series of metabolic reactions [40]. Collectively, we suggest that the fluctuations in alanine, glutamine, lactate, and pyruvate are potentially associated with exercise in the muscle, blood, and urine of Thoroughbred horses (Figure 4). The balances of these metabolites in equine biofluid could be utilized as an effective indicator of feeding and management to maintain optimal racing performance.
In conclusion, we first tried to analyze the integrated metabolic patterns and enrichment of metabolic pathways in the muscle, plasma, and urine of racehorses before and after exercise. Our results could contribute to understanding metabolic regulation and development of metabolic markers for equine exercise.



We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.


This study was supported by grants from the Next Generation Bio Green 21 Program (No. PJ01117301, PJ01104401), Rural Development Administration, Republic of Korea.

Figure 1
Metabolic clustering (left) and heatmap analysis of the differentially expressed metabolites (right) among the muscle, plasma, and urine.
Figure 2
Analysis of the metabolic patterns in equine muscle, plasma, and urine before and after exercise. Orthogonal partial least square discriminant analysis (OPLS-DA) (R2X: 0.977; R2Y: 0.852; Q2: −0.142) (A) and variable importance plots (VIPs) for the muscle (D). OPLS-DA (R2X: 0.889; R2Y: 0.883; Q2: −1.33) (B) and VIPs for the plasma (E). OPLS-DA (R2X: 0.987; R2Y: 1; Q2: 0.971) (C) and VIPs for the urine (F).
Figure 3
On the basis of the differentially expressed (fold change >2 or <0.5) or high-variable importance plots (VIPs)-score (VIP >1) metabolites, concentration of the metabolites in the urine before and after exercise. Error bars are expressed as standard deviation; * p<0.05; ** p<0.01; *** p<0.001.
Figure 4
The metabolic cycles for alanine, glutamine, lactate, and pyruvate from the muscle to the kidney, and the concentrations of alanine, glutamine, lactate, and pyruvate in the muscle, plasma, and urine before and after exercise.
Table 1
Metabolic clustering among the muscle, plasma, and urine
Clustering Total Metabolites
Muscle only 11 Anserine, aspartate, betaine, carnitine, cysteine, ethanol, fumarate, o-phosphocholine, o-phosphoethanolamine, serine, sn-glycero-3-phosphocholine
Plasma only 3 Formate, histidine, propionate
Urine only 13 Acetoacetate, allantoin, benzoate, citrate, citrulline, glutarate, hippurate, homocitrulline, inosine, methylsuccinate, phenylacetylglycine, trigonelline, trimethylamine n-oxide
Muscle and plasma 5 Choline, glycine, myo-inositol, phenylalanine, proline
Plasma and urine 1 Trimethylamine
Urine and muscle 3 Arginine, glucose, glycerol
Muscle, plasma, and urine 16 Lactate, creatine, taurine, glutamine, methionine, threonine, pyruvate, succinate, leucine, valine, isoleucine, glutamate, alanine, acetate, tyrosine, lysine
Table 2
VIP scores show the list of metabolites that contributed to the separation of the clustering in the muscle (R2X: 0.977; R2Y: 0.852; Q2: −0.142), plasma (R2X: 0.889; R2Y: 0.883; Q2: −1.33), and urine (R2X: 0.987; R2Y: 1; Q2: 0.971) before and after exercise
Table 3
List of metabolic pathways obtained using enrichment analysis for the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites
Related metabolic pathway Total Expected Hits Raw p Holm p FDR
Protein biosynthesis 19 0.622 5 0.000217 0.0174 0.0113
Urea cycle 20 0.655 5 0.000283 0.0224 0.0113
Glycine, serine and threonine metabolism 26 0.851 5 0.00105 0.0817 0.0279
Ammonia recycling 18 0.589 4 0.00204 0.157 0.0408
Arginine and proline metabolism 26 0.851 4 0.00832 0.632 0.133
Pyruvate metabolism 20 0.655 3 0.0246 1 0.328
Betaine metabolism 10 0.327 2 0.0395 1 0.4
Methionine metabolism 24 0.785 3 0.04 1 0.4
Aspartate metabolism 12 0.393 2 0.0556 1 0.495
Biotin metabolism 4 0.131 1 0.125 1 0.999
Alanine metabolism 6 0.196 1 0.181 1 1
Taurine and hypotaurine metabolism 7 0.229 1 0.208 1 1
Gluconeogenesis 27 0.884 2 0.22 1 1
Cysteine metabolism 8 0.262 1 0.235 1 1
Malate-aspartate shuttle 8 0.262 1 0.235 1 1
Butyrate metabolism 9 0.295 1 0.26 1 1
Glutathione metabolism 10 0.327 1 0.284 1 1
Ketone body metabolism 10 0.327 1 0.284 1 1
Glucose-alanine cycle 12 0.393 1 0.331 1 1
Beta-alanine metabolism 13 0.425 1 0.353 1 1
Phenylalanine and tyrosine metabolism 13 0.425 1 0.353 1 1
Lysine degradation 13 0.425 1 0.353 1 1
Glycerolipid metabolism 13 0.425 1 0.353 1 1
Purine metabolism 45 1.47 2 0.44 1 1
Propanoate metabolism 18 0.589 1 0.454 1 1
Glutamate metabolism 18 0.589 1 0.454 1 1
Phospholipid biosynthesis 19 0.622 1 0.472 1 1
Insulin signalling 19 0.622 1 0.472 1 1
Bile acid biosynthesis 49 1.6 2 0.485 1 1
Glycolysis 21 0.687 1 0.507 1 1
Porphyrin metabolism 22 0.72 1 0.524 1 1
Citric acid cycle 23 0.753 1 0.54 1 1
Galactose metabolism 25 0.818 1 0.57 1 1
Valine, leucine and isoleucine degradation 36 1.18 1 0.706 1 1
Pyrimidine metabolism 36 1.18 1 0.706 1 1
Tyrosine metabolism 38 1.24 1 0.726 1 1

Raw p, raw p value; FDR, false discovery rate.


1. Ball D. Metabolic and endocrine response to exercise: sympathoadrenal integration with skeletal muscle. J Endocrinol 2015; 224:R79–95.
crossref pmid
2. Hargreaves M. Skeletal muscle metabolism during exercise in humans. Clin Exp Pharmacol Physiol 2000; 27:225–8.
crossref pmid
3. Van Hall G, Jensen-Urstad M, Rosdahl H, et al. Leg and arm lactate and substrate kinetics during exercise. Am J Physiol Endocrinol Metab 2003; 284:E193–205.
crossref pmid
4. Lewis GD, Farrell L, Wood MJ, et al. Metabolic signatures of exercise in human plasma. Sci Transl Med 2010; 2:33ra7
crossref pmid pmc
5. Pierce GL, Donato AJ, LaRocca TJ, et al. Habitually exercising older men do not demonstrate age-associated vascular endothelial oxidative stress. Aging Cell 2011; 10:1032–7.
crossref pmid pmc
6. Henderson GC, Alderman BL. Determinants of resting lipid oxidation in response to a prior bout of endurance exercise. J Appl Physiol 2014; 116:95–103.
crossref pmid
7. Kjaer M, Galbo H. Effect of physical training on the capacity to secrete epinephrine. J Appl Physiol 1988; 64:11–6.
crossref pmid
8. Olesen J, Gliemann L, Bienso R, et al. Exercise training, but not resveratrol, improves metabolic and inflammatory status in skeletal muscle of aged men. J Physiol 2014; 592:1873–86.
crossref pmid pmc
9. Rivero JL, Hill EW. Skeletal muscle adaptations and muscle genomics of performance horses. Vet J 2016; 209:5–13.
crossref pmid
10. Geor RJ, Hinchcliff KW, McCutcheon LJ, Sams RA. Epinephrine inhibits exogenous glucose utilization in exercising horses. J Appl Physiol 2000; 88:1777–90.
crossref pmid
11. Brojer J, Holm S, Jonasson R, Hedenstrom U, Essen-Gustavsson B. Synthesis of proglycogen and macroglycogen in skeletal muscle of standardbred trotters after intermittent exercise. Equine Vet J Suppl 2006; 38:335–9.
12. Waller AP, Heigenhauser GJ, Geor RJ, Spriet LL, Lindinger MI. Fluid and electrolyte supplementation after prolonged moderate-intensity exercise enhances muscle glycogen resynthesis in Standardbred horses. J Appl Physiol 2009; 106:91–100.
crossref pmid
13. Jang HJ, Kim JW, Ryu SH, et al. Metabolic profiling of antioxidant supplement with phytochemicals using plasma 1H NMR-based metabolomics in humans. J Funct Foods 2016; 24:112–21.
14. Barding GA, Salditos R, Larive CK. Quantitative NMR for bioanalysis and metabolomics. Anal Bioanal Chem 2012; 404:1165–79.
crossref pmid
15. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006; 7:142
crossref pmid pmc
16. Zang Q, Keire DA, Wood RD, et al. Class modeling analysis of heparin 1H NMR spectral data using the soft independent modeling of class analogy and unequal class modeling techniques. Anal Chem 2011; 83:1030–9.
crossref pmid
17. Kim KB, Um SY, Chung MW, et al. Toxicometabolomics approach to urinary biomarkers for mercuric chloride (HgCl2)-induced nephrotoxicity using proton nuclear magnetic resonance (1H NMR) in rats. Toxicol Appl Pharmacol 2010; 249:114–26.
crossref pmid
18. Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res 2015; 43:W251–7.
crossref pmid pmc pdf
19. German JB, Hammock BD, Watkins SM. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics 2005; 1:3–9.
crossref pmid pmc
20. Oresic M, Vidal-Puig A, Hanninen V. Metabolomic approaches to phenotype characterization and applications to complex diseases. Expert Rev Mol Diagn 2006; 6:575–85.
crossref pmid
21. Moore RE, Kirwan J, Doherty MK, Whitfield PD. Biomarker discovery in animal health and disease: the application of post-genomic technologies. Biomark Insights 2007; 2:185–96.
crossref pmid pmc
22. Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol 2003; 54:669–89.
crossref pmid
23. Schauer N, Fernie AR. Plant metabolomics: towards biological function and mechanism. Trends Plant Sci 2006; 11:508–16.
crossref pmid
24. Kell DB. Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol 2004; 7:296–307.
crossref pmid
25. Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002; 1:153–61.
crossref pmid
26. Brindle JT, Antti H, Holmes E, et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med 2002; 8:1439–44.
crossref pmid
27. Whitfield PD, Noble PJM, Major H, et al. Metabolomics as a diagnostic tool for hepatology: validation in a naturally occurring canine model. Metabolomics 2005; 1:215–25.
28. Dumas ME, Canlet C, Vercauteren J, Andre F, Paris A. Homeostatic signature of anabolic steroids in cattle using 1H-13C HMBC NMR metabonomics. J Proteome Res 2005; 4:1493–502.
crossref pmid
29. Evans DL. Physiology of equine performance and associated tests of function. Equine Vet J 2007; 39:373–83.
crossref pmid
30. Hodgson DR, Rose RJ. The athletic horse Principles and practice of equine sports medicine. Philadelphia, PA, USA: Saunders; 1994.

31. Vervuert I. Energy metabolism of the performance horse. In : Proceedings of the 5th European Equine Nutrition & Health Congress 2011; 2011 Apr 15–16; Waregem, Belgium: The Scientific Committee of the European Equine Health and Nutrition Congress; 2011.

32. Vervuert I, Coenen M, Watermülder E. Metabolic responses to oral tryptophan supplementation before exercise in horses. J Anim Physiol Anim Nutr (Berl) 2005; 89:140–5.
crossref pmid
33. Dohm GL, Tabscott EB, Kasperek GJ. Protein degradation during exercise and recovery. Med Sci Sports Exerc Suppl 1987; 189:166–71.
34. Strüder KH, Hollmann W, Platen P, et al. Alterations in plasma free tryptophan and large neutral amino acids do not affect perceived exertion and prolactin during 90 min of treadmill exercise. Int J Sports Med 1996; 17:73–9.
crossref pmid pdf
35. Luck MM, Le Moyec L, Barrey E, et al. Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics. Front Physiol 2015; 6:198
crossref pmid pmc
36. Adeva-Andany M, Lopez-Ojen M, Funcasta-Calderon R, et al. Comprehensive review on lactate metabolism in human health. Mitochondrion 2014; 17:76–100.
crossref pmid
37. Hubbard JL. The effect of exercise on lactate metabolism. J Physiol 1973; 231:1–18.
crossref pmid pmc
38. Katz J, Tayek JA. Recycling of glucose and determination of the Cori Cycle and gluconeogenesis. Am J Physiol 1999; 277:E401–7.
crossref pmid
39. Shulman GI. Cellular mechanisms of insulin resistance in humans. Am J Cardiol 1999; 84:3J–10J.
crossref pmid
40. Leguina-Ruzzi A. Therapeutic targets of glutamine in parenteral nutrition: a medical science review. Int J Prev Treat 2015; 4:34–9.

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