Journal of Pharmaceutical and Biomedical Analysis 88 (2014) 441–446
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Short communication
Saliva metabolomics by NMR for the evaluation of sport performance C. Santone a,b,∗,1 , V. Dinallo e,1,2 , M. Paci b,3 , S. D’Ottavio f,4 , G. Barbato b,c,d,∗∗ , S. Bernardini e,2 a
Agricultural Research Council, Research Centre for the Soil-Plant System, Rome, Italy University of Tor Vergata, Department of Chemical Science and Technology, Rome, Italy European Brain Research Institute (EBRI), R. Levi Montalcini Foundation, Rome, Italy d Promedica Bioelectronics srl, Rome, Italy e University of Tor Vergata, Department of Experimental Medicine and Surgery, Rome, Italy f University of Tor Vergata, Department of Systems Medicine, Rome, Italy b c
a r t i c l e
i n f o
Article history: Received 27 June 2013 Received in revised form 16 September 2013 Accepted 19 September 2013 Available online 5 October 2013 Keywords: Intermittent recovery Yo-Yo test Salivary metabolites Physical exercise NMR Metabolomics
a b s t r a c t The paper reports preliminary results of a study in order to verify that saliva is a bio-fluid sensitive to metabolite variations due to stress and fatigue in soccer athletes, and possibly, to identify potential markers of test of performance. Saliva samples of fourteen professional soccer players were collected before and after the stressful physical activity of the level 1 Yo-Yo intermittent recovery test and, also, physiological parameters were evaluated. The NMR spectra of saliva offer a metabolites profiling which was analyzed by Principal Component Analysis as a blind test. The results of NMR pre and post test shows that it was possible to cluster the best and the worst performing athletes and that the role of the actual player may be diagnosed by a different cluster of metabolites profile. Thus saliva can be considered a biofluid metabolically sensitive to the induced physical stress and, in the future, deeper investigated to monitor the performances in athletes. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Many studies have been done to evaluate physical performance in team sports like soccer, basketball and others, all of them involving intermittent demanding physical exercises and the ability to perform intensive exercise after short recovery periods appears to be decisive for the outcome of competition. Much effort of sport physiologists has focused on finding surrogate test exercises which could correlate with the actual performance in competition and predict the progress in improving performances. Yo-Yo
Abbreviations: NMR, nuclear magnetic resonance; PCA, Principal Component Analysis; IR, intermittent recovery; 1D, mono-dimensional. ∗ Corresponding author at: University of Tor Vergata, Department of Chemical Science and Technology, Rome, Italy. Tel.: +39 0672594410; fax: +39 0672594830. ∗∗ Corresponding author at: Promedica Bioelectronics srl, Rome, Italy. Tel.: +39 0672594410; fax: +39 0672594830. E-mail addresses: [email protected] (C. Santone), [email protected] (V. Dinallo), [email protected] (M. Paci), [email protected] (S. D’Ottavio), [email protected] (G. Barbato), [email protected] (S. Bernardini). 1 These authors equally contributed. 2 Tel.: +39 0620902262. 3 Tel.: +39 0672594446; fax: +39 0672594830. 4 Tel.: +39 0620902262. 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.09.021
intermittent recovery test level 1 is a physical test able to evaluate athletes’ ability to repeatedly perform high intensity exercise [1]. It consists of repeated exercise bouts performed at progressively increasing speeds, interspersed with 10 s active rest periods and performed until the subject is exhausted. It was demonstrated that this test may represent a measure of match related physical performance [2]. Apart proteomics approaches [3], studies in the field of sport on the profiling of the metabolites levels in athletes performing this test are not numerous. Studies were presented on single highly diagnostic metabolite, like lactate or phosphocreatine, and some hormones have been dosed through invasive methods (muscle biopsies, blood samples, intra-muscle electrodes, etc.) [4]. All these studies, although give a deep insight into the metabolic energy switch, are scarcely applicable as routine metabolic analysis to everyday sport team life. In fact all biopsies and blood analysis are rather invasive and understandably generally not well tolerated by both team managements and athletes. Furthermore, they give a single measured parameter, important, but that can hardly give a more extensive picture of the complex metabolism of an athlete. To avoid invasive collection methods some studies have been done on urine [5], but in the search for alternative that can correlate with the performance in intermittent sports, we investigated the
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C. Santone et al. / Journal of Pharmaceutical and Biomedical Analysis 88 (2014) 441–446
Fig. 1. Aliphatic region of a 700 MHz 1D 1 H NMR spectrum of human saliva sample pre- (bottom) and post- (top) Yo-Yo IR1 test. Some of the identified meaningful metabolite signals are labeled and the resonance of methyl signal of the internal standard (2-amino-4,6-dimethylpyrimidine) at 2.30 ppm indicated by an arrow. Note that in this plot of the spectra no normalization was taken into account, thus no direct visual comparison of intensities of signals across spectra is accurate.
variation of metabolic content of saliva in the presence of a physical stress that can have a more systematic application in team sports. Metabolic profiling of saliva is only partially explored as potential biomarker with respect than other biofluids such as urine and blood [6,7]. Few articles have described the use of saliva as biofluid for analytical purposes in clinical investigations and in physiological research of sport teams [8] and it was seen that NMR spectroscopy was able to reflect biochemical changes [9,10]. In fact NMR spectroscopy is one of the principal analytical technique for metabolic purpose [11,12] allowing a specific and simultaneous determination of a high number of metabolites. The statistical Principal Component Analysis (PCA) of the data is performed to unravel the meaningful variations. In the present study, the NMR spectroscopy was used to investigate metabolic variations in professional subelite soccer players’ saliva performing Yo-Yo level 1 intermittent recovery test.
2. Materials and methods Subjects: Fourteen elite professional soccer players from the Italian Lega Pro team (C1), with a mean age of 23 ± 3, an average body mass of 72 ± 3.75 kg and an average body height of 1.77 ± 0.03 cm, participated to the study. These belong to team not in the top serie, but an accessible and meaningful sample. None of the athletes was using pharmaceutical or tobacco at the time of the study and all subjects had a controlled diet before the test. They were informed of experimental procedures and gave their consent to the study which was conducted in compliance to ethical principles of the Declaration Helsinki.
The Yo-Yo IR test consists of 2 × 20 m shuttle runs at increasing speeds controlled by audio bleeps from a personal computer. Between each running bout, the subjects have a 10-s period of active recovery. When they are not able to maintain speed and fail to return to the starting position in time twice, the distance covered is recorded and it represents the test result. In this study, level 1, at a starting running speed of 10 km/h was applied after a warmup running. Maximum heart rate was recorded for each subject using a heart rate monitor (VANTAGE NV; Polar Electro, Kempele, Finland) and theoretical VO2 max was calculated. For every athlete two samples were collected one before and one after the test. Saliva samples were collected by 2 min chewing a polyester tampon (Salivette Sarsted, Nürnbrecht, Germany), which was stored in the internal vial of the double-chambered tube, capped and stored at 4 ◦ C until transport to the laboratory. Salivettes were centrifuged at 1500 g for 15 min, 5 l sodium azide (NaN3 ) 1 M was added to each sample and stored at −20 ◦ C. An average volume of 750 ± 200 l was collected per sample. Macromolecules and corpuscles were removed from saliva, centrifuging with Amicon Ultra-4 Centrifugal filter (Millipore) at 4000 rpm, 4 ◦ C for 1 h. pH was measured for all samples and was ranging between 7.3 and 7.6. Sodium azide 5.0 l of (NaN3 ) 1 M were added to the filtrate and stored at −20 ◦ C. Amicon Ultra-4 Centrifugal filters (Millipore) membranes were previously extensively washed to deprive the membrane of the embedded glycerol, the procedure was carried out iteratively until control by NMR spectroscopy of the wash water showed no residual presence of glycerol. This protocol was set after extensive validations tests: a pool of freshly collected saliva (free drooling method) was collected from 10 adult donors, each donating approximately 1.5 ml for three days
C. Santone et al. / Journal of Pharmaceutical and Biomedical Analysis 88 (2014) 441–446
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Fig. 2. 3D score plot of the PCA for matrix . The three components (PC1 , PC2 and PC3 ) shown were able to cluster the pre- (squares) from the post- (circles) Yo-Yo test samples.
consecutively. Collection was made consistently in the morning three hours after the breakfast. All the saliva was pooled (in total about 45 ml) and aliquoted to perform all the validation tests. These tests included: absorbance of proteins, particles and metabolites, stability of metabolites at 1 week and 30 days after freeze drying vs 4 ◦ C temperature storage. NMR spectra were acquired at 298 K on a Bruker Ultrashield spectrometer Avance 700, operating at 1 H frequency of 700.13 MHz, equipped with a 5 mm triple resonance probe with zgradients. Prior to the measurements, saliva samples were thawed and 500 l of each aliquot was mixed with 50 l D2 O and 5 l NaN3 (1 M in D2 O). Two different 1D experiments were carried out for each sample: 1D NOESY 1 H–1 H, mixing time 100 ms, and 1D flip-back with sculpting water suppression pulse sequence [13]. The former spectra was widely used for integrating all signals, while the latter was only used for the quantification of urea whose resonance is highly affected by fast exchange with water protons and required an adhoc acquisition with no saturation on water resonance. The spectra were identically acquired using a total relaxation delay of 14 s, and 16 K complex time points. The relaxation delay and the reading pulse (roughly 54◦ ) were optimized to ensure full relaxation of all the resonances at each scan reproducing the same signal intensity measured with 60 s relaxation delay. The dataset were all identically transformed and zero-filled to 32 K complex, residual water signal was digitally filtered out and integrals were obtained using the XWINNMR software (Bruker, DE). To account for dilution of bulk mass differences between samples due to slight differences in volumes collected (range 550–950 l), individual integral regions were normalized to the total integral region of the spectra, after exclusion of the water resonance and subtraction of the reference
compound integral (389 M) of the reference compound 2-amino4,6-dimethylpyrimidine (Sigma–Aldrich), dissolved in H2 O, and added as internal standard and used also as chemical shift reference (6.61 ppm) [14,15]. Quantification of the actual concentration in the samples of each metabolite was obtained by the integrals normalized to a known added amount with no overlap with the resonance of the sample. In this case normalization appears more easy than in the proteomic case [16]. For metabolites signal assignment, 2D 1 H–1 H correlation spectroscopy (DQF-COSY) double quantum filtered, 2D 1 H–1 H total correlation spectroscopy (TOCSY), 2D 1 H–13 C heteronuclear single quantum coherence (HSQC) and 2D 1 H–13 C HSQC-TOCSY experiments were performed, these latter two were acquired with two different setup, optimized respectively for aliphatic and aromatic CHn groups [17]. All the 2D spectra were recorded on a 10-fold concentrated saliva sample with pH adjusted to 7.4 with concentrated NaOH/HCl. For assignment confirmation sixty different metabolites standard samples were identically dissolved in D2 O with 0.05 M carbonate and 100 mM NaCl buffer and each sample had the pH adjusted to 7.4. With each standard a 2D 1 H–13 C HSQC spectra was recorded, using the spectral conditions previously described. Spectra were transformed using NMRPipe software [18], and analyzed using NMRViewJ (Onemoon Scientific Inc., USA) [19]. Multivariate pattern recognition techniques were used to analyze the NMR data set, since this method is able to handle multiple variables simultaneously and to cope with numerous co-linearities unlike univariate approaches. The analysis was conducted using the Unscrambler X software package (CAMO Software AS, Norway). A total of 108 signals per each spectra, and two physiological variables (heart rate in bpm and total distance in meters) were included as well in the dataset. Data were further categorized per role of the
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Fig. 3. 3D score plot PCA for matrix . Analysing the actual performance in the Yo-Yo test level 1 of the athletes, the three components (PC1 , PC2 and PC4 ) shown were able to cluster the good (squares) from average (circles) and bad (triangles) performers as defined in the text.
athlete with A = attack, C = midfield, D = defense, and according to the expected response to the Yo-Yo test data were also categorized in “good”, “normal” and “poor” performer. Data were mean centered and autoscaled, dividing the mean centered data by the square root of the standard deviation of the variable thus giving origin to a dimensionless data, prior to analysis [20]. Principal Component Analysis (PCA) was performed on the resulting data matrix. Briefly PCA is an unsupervised technique that describes observables (i.e. integrals of the NMR signal from saliva) with regards to one or more latent variables termed principal components (PCs), which are linear combinations of the original variables. The technique generates a set of PCs and the variation not captured in the first PC forms the residual through which a second uncorrelated component is calculated, and to ensure uncorrelation the condition that the PC2 should be orthogonal to the PC1 the process identifies new PC orthogonal axis, until reaching the set threshold of explained variation (>95% in our case). Each observation is then assigned a score along each PC. The weight given to each original NMR integral within any PC describes how influential that particular variable is and the correlation of the variable to others. The determination of which of the original variables (metabolites or their combination) are responsible for this separation the analysis of the loading scores of the PCs should be made [21]. To choose the standards acquisition conditions pH measurements on saliva samples and conductivity tests were done and it was seen that a buffer containing 50 mM CO3 2− /HCO3 − at pH 7.4 and 100 mM NaCl was a suitable buffer reproducing the magnetic susceptivity characteristics of saliva relevant for this study. All steps were tested and eventual leakage (adsorption into Salivette, sample degradation and contaminations) of metabolite concentration were minimized; i.e. it was verified if 4 ◦ C was not a
suitable condition of storage for more than a week since producing changes in the sample condition as shifts or changes of relative intensity. On the contrary, no such effect was visualized from the frozen sample. Per each athlete 2 samples were collected, one pre and one post-test.
3. Results and discussion The setup of the sampling protocol, the control that samples were stable for one week and one month and the comparison to freshly prepared samples, as reported in Section 2, were performed. The assignment of the signals was obtained using combined strategies as reported in Section 2. As example, in supplementary material is shown a region of the COSY-DQF spectra with the correlations identified for seven metabolites: alanine, aspartate, glycerol, glucose, histidine, lactate and leucine. The assignment of the signals was performed using a combined strategy: by the Human Metaboloma Database (HMDB) [22] and the literature available [23] and using single compounds standard samples by superposing spectra of the saliva sample and each of the 60 standard. In supplementary materials it is shown an enlarged view of the superposition of a region of the HSQCs spectra of the saliva and 5 different standards. Finally, for ambiguous cases, after the acquisition of the spectra, the standard was added directly to the saliva sample evidencing thus the increase in intensity of the actual metabolite signals (data not shown). Spectra were acquired at pulse length and relaxation conditions to ensure that all metabolite spectral lines and the reference compound resonances where consistently and completely relaxed at each scan as reported in Materials and methods.
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Fig. 4. 3D score plot PCA for matrix . Analysing the different performances in the Yo-Yo test level 1 according to the player role, the three components (PC2 , PC3 and PC4 ) shown were able to cluster the defense (squares) and midfield (triangles) from attack (circles) as defined in the text.
After normalization procedure, the concentration of metabolites in the sample was quantified with respect to the known concentration of reference. In Fig. 1 is shown, for the same athlete, the aliphatic region of a 1D spectra of the sample pre (bottom) and post (top) level 1 YoYo test. Resonances are labeled from several meaningful signals. A total of 108 signals were chosen for integration all over the spectra, interval of integration were chosen in such a way that extreme limits of integration were always positioned at a minima in the spectra. Integrals were thus encompassing single signals as well as small groups of degenerate signals. The performance of the 14 athletes in the level 1 Yo-Yo test resulted in an average run of 1731 ± 380 m. Then three performance categories were arbitrarily established in order to analyze the result of the Yo-Yo test: Bad, Normal and Good Performers. The Normal Performers were defined as the athletes who covered a distance within the range of one standard deviation around the average (±1/2 SD), thus the Bad Performers covered less then 1541 m, while the Good Performers covered more 1921 m. Besides the obvious differences in the integral values among samples from different athletes, which was a rescaling problem [20], the major effort was to perform the identification of the meaningful metabolic variations due to the Yo-Yo test performance. In fact several metabolites produced more than one signal, presenting a certain degree of overlap, we identified signals strictly correlated belonging to the same molecule. These resonances identified by their metabolite assignment, and subjected to auto-correlation analysis, (R2 > 0.92) were eliminated. This filtering step reduced the integral data from the original 108 signals to 74, resulting in data matrix constituted of 28 rows (samples pre and post Yo-Yo) × 74 columns (presumably unique metabolites).
In the PCA analysis a total of four PCs accounted for >96% of the total variance observed within the data matrix (data not shown). The meaningful variations in metabolite concentrations pre and post Yo-Yo test are evidenced in Fig. 2, where the score is shown in a 3D-plot for the PC1, PC2 and PC3. It is sufficient to distinguish arising from the pre-Yo-Yo from the post Yo-Yo. As supplementary material the corresponding loading plots are available on request by the authors, where the metabolites which are mostly affecting the PCs may be identified. For PC1 the most relevant metabolites are urea and glucose, for PC2 lactate, citrate and acetate, while for PC3 glycerol, glutamate, leucine, alanine and lysine (in supplementary material the table of the identified metabolites in this study). The next relevant question to tackle was if there was any correlation within the variation of metabolites concentrations and the performance offered in the Yo-Yo test by the athletes. Therefore, the data matrix was transformed considering the ratios of the integrals post/pre Yo-Yo test, reducing the number of rows to 14 one per athlete, and at this point heart rate (in bpm) and the total distance output of the Yo-Yo test were added as variables to the data matrix. A total of six PCs made on 14 samples per 76 variables were sufficient to account for slightly more than 95% of the total variance. To evidence the metabolites which may be diagnostic for the performance in the Yo-Yo test the performance was classified in good, normal and bad as reported in Section 2. In the Fig. 3 is shown in a 3D-plot for the PC1, PC2 and PC4 (note these are PC of matrix, thus different from those of matrix ). These axes are sufficient to distinguish and cluster the data according to the performance category. For PC1 more than 20 metabolites fall in the meaningful region of the loading plots, in supplementary material (available on request by the authors) citing among them creatine, lysine, leucine, choline,
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inositol, tyrosine, glutamine, aspartate, etc. for PC2 this number is greatly reduced, the relevant ones being only 8, and among them we identified glucose, arginine, acetate, citrate and alanine (in supplementary material the table of the identified metabolites in this study). For PC4 we identified lactate and glutamate. To identify the metabolites which may be diagnostic of the athlete role we then analyzed the matrix categorizing the performance in the attack, middle-field and defense categories, following the rationale mentioned in Section 2. Fig. 4 is shown in a 3D-plot for the PC2, PC3 and PC4. Actually axes PC1 and PC3 were already sufficient to segregate the attack role with respect to the middlefield and defense roles. However it was not possible to completely unravel the latter two since at least one defense player was always interleaving with the data of the middle-field role. The combination shown in Fig. 4 gives the best possible separation with the data we collected. For the PC3 the corresponding loading plot shown in supplementary material (available on request by the authors), identifies about 5 relevant signals, among them we identified the ornithine, inositol and leucine signals. Taken together the results obtained indicate that the NMR metabolic approach on saliva by Yo-Yo test appears meaningful. On the other hand, Yo-Yo test is a rather well established, for which several physiological comparative studies have also been published and it has also been shown that the performance of this test may be significantly different for players in different roles in this sport [1]. The result of the level 1 Yo-Yo test of the team in study was compared with the published results for professional soccer teams. The average result of the team was 1731 ± 380 m and it was reported that the average result for an homogeneously trained sub-elite team is 1867 ± 72 m [1]. The discrepancy and the wider standard deviation is due to the fact that our test was performed at the start of the pre-seasonal preparation and we arbitrarily established three categories of performance: good, average and bad (as reported in Section 2). Some metabolites could be identified in saliva (table in supplementary material), whose concentration variation was correlated with the performance in the level 1 Yo-Yo test. Particularly, we cluster the good performers and the bad performers from the average performing ones. As shown in Fig. 3, it is possible to segregate by performance the athletes by using just three axis, PC1, PC2 and PC4. Among the meaningful metabolites variation by the loading plots (available on request by the authors) which were identified for PC1 we pin-point tyrosine, inositol, creatine, lysine; for PC2 citrate, glucose, acetate and arginine, for PC4 lactate and glutamate. These results seem a promising good starting point to potentially identify good from bad performers. In Fig. 4 the data were categorized by player role, using three categories attack, defense and middle-field. The result is that three axis were enough to give the best separation among categories, namely PC2, PC3 and PC4. As it is clearly seen, the attack players could be easily segregated from the other two categories, while segregation of middle-field and defense players is not as effective, in fact at least one player per each category is interspersed with the other category. Among the meaningful metabolites variations (also visible in loading plots available on request by the authors) which were identified for PC2 we pin-point glucose, citrate and acetate; for PC3 leucine, ornithine, myo-inositol, for PC4 lactate and glutamate. The reliability of this result led to consider them preliminary for the difficulty to find numerous subjects for these study and for the time required for the NMR determination. In fact few attack players (n = 2) were available for this test, while the middle-field and the defense (n = 6) both were comparable in numbers. Further validation studies are required in the next future but as a first conclusion saliva resulted to be a promising bio-fluid sensitive to athletes performances. The physiological basis for the complete interpretation of these results
requires further investigations in the future. In fact a simple correlation among the variations of metabolite concentrations from plasma, muscle tissue biopsies and saliva is unlikely. Acknowledgments We wish to acknowledge Dr. Gaetano Giubila for his contribution and Fabio Bertocchi for its technical assistance in the NMR laboratory. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jpba.2013.09.021. References [1] J. Bangsbo, F.M. Iaia, P. Krustrup, The Yo-Yo intermittent recovery test: useful tool for evaluation of physical performance in intermittent sports, Sports Med. 38 (2008) 37–51. [2] P. Krustrup, M. Mohr, T. Amstrup, T. Rysgaard, J. Johansen, A. Steensberg, P.K. Pedersen, J. Bangsbo, The Yo-Yo intermittent recovery test: physiological response, reliability, and validity, Med. Sci. Sports Exerc. 35 (2003) 697–705. [3] G. Banfi, A. Colombini, G. Lombrardi, A. Lubkowska, Metabolic markers in sports medicine, Adv. Clin. Chem. 56 (2012) 1–54. [4] P. Krustrup, M. Mohr, A. Steensberg, J. Bencke, M. Kiaer, J. Bangsbo, Muscle and blood metabolites during a soccer game: implications for sprint performance, Med. Sci. Sports Exerc. 8 (2006) 1165–1174. [5] C. Enea, F. Seguin, J. Petitpas-Mulliez, N. Boildieu, N. Boisseau, N. Delpech, V. Diaz, M. Eugène, B. Dugué, 1 H NMR-based metabolomics approach for exploring urinary metabolome modifications after acute and chronic physical exercise, Anal. Bioanal. Chem. 396 (2010) 1167–1176. [6] H.J. Issaq, O. Nativ, T. Waybright, Detection of bladder cancer in human urine by metabolic profiling using high performance liquid chromatography/mass spectrometry, J. Urol. 179 (2008) 2422–2426. [7] S. Kochhar, D.M. Jacobs, Z. Ramadan, F. Berruex, A. Fuerholz, L.B. Fay, Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics, Anal. Biochem. 352 (2006) 274–281. [8] E. Papacosta, G.P. Nassis, Saliva as a tool for monitoring steroid, peptide and immune markers in sport and exercise science, J. Sci. Med. Sport. 14 (2011) 424–434. [9] H.C. Bertram, N. Eggers, N. Eller, Potential of human saliva for nuclear magnetic resonance-based metabolomics and for health-related biomarker identification, Anal. Chem. 81 (2009) 9188–9193. [10] I. Takeda, C. Stretch, P. Barnaby, Understanding the human salivary metabolome, NMR Biomed. 22 (2009) 577–584. [11] H.C. Bertram, A. Malmendal, B.O. Petersen, Effect of magnetic field strength on NMR-based metabonomic human urine data. Comparative study of 250, 400, 500, and 800 MHz, Anal. Chem. 79 (2007) 7110–7115. [12] J.C. Lindon, E. Holmes, J.K. Nicholson, Metabonomics techniques and applications to pharmaceutical research & development, Pharm. Res. 23 (2006) 1075–1088. [13] T.L. Hwang, A.J. Shaka, Water suppression that works. Excitation sculpting using arbitrary waveforms and pulsed field gradients, J. Magn. Reson. 112 (1995) 275–279. [14] E. Holmes, A.W. Nicholls, J.C. Lindon, Development of a model for classification of toxin-induced lesions using 1 H NMR spectroscopy of urine combined with pattern recognition, NMR Biomed. 11 (1998) 235–244. [15] M. Spraul, P. Neidig, U. Klauck, Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples, J. Pharm. Biomed. Anal. 12 (1994) 1215–1225. [16] H. Zauber, V. Schüler, W. Schulze, Systematic evaluation of reference protein normalization in proteomic experiments, Front. Plant Sci. (2013) 4–25. [17] J. Schleucher, M. Schwendinger, M. Sattler, A general enhancement scheme in heteronuclear multidimensional NMR employing pulsed field gradients, J. Biomol. NMR 4 (1994) 301–306. [18] F. Delaglio, S. Grzesiek, G.W. Vuister, G. Zhu, J. Pfeifer, A. Bax, NMRPipe: a multidimensional spectral processing system based on UNIX pipes, J. Biomol. NMR 6 (1995) 277–293. [19] B.A. Johnson, Using NMR view to visualize and analyze the NMR spectra of macromolecules, Methods Mol. Biol. 278 (2004) 313–352. [20] R.A. Van der Berg, H.C.J. Hoefsloot, J.A. Westerhuis, A.K. Smilde, M.J. Van der Werf, Centering, scaling, and transformations: improving the biological information content of metabolomics data, BMC Genomics 7 (2006) 142–157. [21] J.E. Jackson, A User’s Guide to Principal Component, John Wiley & Sons, Inc., New York, 1991. [22] Human Metabolome Database (HMDB), 2011, http://www.hmdb.ca/ [23] T.W. Fan, Metabolite profiling by one and two-dimensional NMR analysis of complex mixtures, Prog. Nucl. Magn. Reson. Spectrosc. 28 (1996) 161–219.
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Short communication
Saliva metabolomics by NMR for the evaluation of sport performance C. Santone a,b,∗,1 , V. Dinallo e,1,2 , M. Paci b,3 , S. D’Ottavio f,4 , G. Barbato b,c,d,∗∗ , S. Bernardini e,2 a
Agricultural Research Council, Research Centre for the Soil-Plant System, Rome, Italy University of Tor Vergata, Department of Chemical Science and Technology, Rome, Italy European Brain Research Institute (EBRI), R. Levi Montalcini Foundation, Rome, Italy d Promedica Bioelectronics srl, Rome, Italy e University of Tor Vergata, Department of Experimental Medicine and Surgery, Rome, Italy f University of Tor Vergata, Department of Systems Medicine, Rome, Italy b c
a r t i c l e
i n f o
Article history: Received 27 June 2013 Received in revised form 16 September 2013 Accepted 19 September 2013 Available online 5 October 2013 Keywords: Intermittent recovery Yo-Yo test Salivary metabolites Physical exercise NMR Metabolomics
a b s t r a c t The paper reports preliminary results of a study in order to verify that saliva is a bio-fluid sensitive to metabolite variations due to stress and fatigue in soccer athletes, and possibly, to identify potential markers of test of performance. Saliva samples of fourteen professional soccer players were collected before and after the stressful physical activity of the level 1 Yo-Yo intermittent recovery test and, also, physiological parameters were evaluated. The NMR spectra of saliva offer a metabolites profiling which was analyzed by Principal Component Analysis as a blind test. The results of NMR pre and post test shows that it was possible to cluster the best and the worst performing athletes and that the role of the actual player may be diagnosed by a different cluster of metabolites profile. Thus saliva can be considered a biofluid metabolically sensitive to the induced physical stress and, in the future, deeper investigated to monitor the performances in athletes. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Many studies have been done to evaluate physical performance in team sports like soccer, basketball and others, all of them involving intermittent demanding physical exercises and the ability to perform intensive exercise after short recovery periods appears to be decisive for the outcome of competition. Much effort of sport physiologists has focused on finding surrogate test exercises which could correlate with the actual performance in competition and predict the progress in improving performances. Yo-Yo
Abbreviations: NMR, nuclear magnetic resonance; PCA, Principal Component Analysis; IR, intermittent recovery; 1D, mono-dimensional. ∗ Corresponding author at: University of Tor Vergata, Department of Chemical Science and Technology, Rome, Italy. Tel.: +39 0672594410; fax: +39 0672594830. ∗∗ Corresponding author at: Promedica Bioelectronics srl, Rome, Italy. Tel.: +39 0672594410; fax: +39 0672594830. E-mail addresses: [email protected] (C. Santone), [email protected] (V. Dinallo), [email protected] (M. Paci), [email protected] (S. D’Ottavio), [email protected] (G. Barbato), [email protected] (S. Bernardini). 1 These authors equally contributed. 2 Tel.: +39 0620902262. 3 Tel.: +39 0672594446; fax: +39 0672594830. 4 Tel.: +39 0620902262. 0731-7085/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jpba.2013.09.021
intermittent recovery test level 1 is a physical test able to evaluate athletes’ ability to repeatedly perform high intensity exercise [1]. It consists of repeated exercise bouts performed at progressively increasing speeds, interspersed with 10 s active rest periods and performed until the subject is exhausted. It was demonstrated that this test may represent a measure of match related physical performance [2]. Apart proteomics approaches [3], studies in the field of sport on the profiling of the metabolites levels in athletes performing this test are not numerous. Studies were presented on single highly diagnostic metabolite, like lactate or phosphocreatine, and some hormones have been dosed through invasive methods (muscle biopsies, blood samples, intra-muscle electrodes, etc.) [4]. All these studies, although give a deep insight into the metabolic energy switch, are scarcely applicable as routine metabolic analysis to everyday sport team life. In fact all biopsies and blood analysis are rather invasive and understandably generally not well tolerated by both team managements and athletes. Furthermore, they give a single measured parameter, important, but that can hardly give a more extensive picture of the complex metabolism of an athlete. To avoid invasive collection methods some studies have been done on urine [5], but in the search for alternative that can correlate with the performance in intermittent sports, we investigated the
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Fig. 1. Aliphatic region of a 700 MHz 1D 1 H NMR spectrum of human saliva sample pre- (bottom) and post- (top) Yo-Yo IR1 test. Some of the identified meaningful metabolite signals are labeled and the resonance of methyl signal of the internal standard (2-amino-4,6-dimethylpyrimidine) at 2.30 ppm indicated by an arrow. Note that in this plot of the spectra no normalization was taken into account, thus no direct visual comparison of intensities of signals across spectra is accurate.
variation of metabolic content of saliva in the presence of a physical stress that can have a more systematic application in team sports. Metabolic profiling of saliva is only partially explored as potential biomarker with respect than other biofluids such as urine and blood [6,7]. Few articles have described the use of saliva as biofluid for analytical purposes in clinical investigations and in physiological research of sport teams [8] and it was seen that NMR spectroscopy was able to reflect biochemical changes [9,10]. In fact NMR spectroscopy is one of the principal analytical technique for metabolic purpose [11,12] allowing a specific and simultaneous determination of a high number of metabolites. The statistical Principal Component Analysis (PCA) of the data is performed to unravel the meaningful variations. In the present study, the NMR spectroscopy was used to investigate metabolic variations in professional subelite soccer players’ saliva performing Yo-Yo level 1 intermittent recovery test.
2. Materials and methods Subjects: Fourteen elite professional soccer players from the Italian Lega Pro team (C1), with a mean age of 23 ± 3, an average body mass of 72 ± 3.75 kg and an average body height of 1.77 ± 0.03 cm, participated to the study. These belong to team not in the top serie, but an accessible and meaningful sample. None of the athletes was using pharmaceutical or tobacco at the time of the study and all subjects had a controlled diet before the test. They were informed of experimental procedures and gave their consent to the study which was conducted in compliance to ethical principles of the Declaration Helsinki.
The Yo-Yo IR test consists of 2 × 20 m shuttle runs at increasing speeds controlled by audio bleeps from a personal computer. Between each running bout, the subjects have a 10-s period of active recovery. When they are not able to maintain speed and fail to return to the starting position in time twice, the distance covered is recorded and it represents the test result. In this study, level 1, at a starting running speed of 10 km/h was applied after a warmup running. Maximum heart rate was recorded for each subject using a heart rate monitor (VANTAGE NV; Polar Electro, Kempele, Finland) and theoretical VO2 max was calculated. For every athlete two samples were collected one before and one after the test. Saliva samples were collected by 2 min chewing a polyester tampon (Salivette Sarsted, Nürnbrecht, Germany), which was stored in the internal vial of the double-chambered tube, capped and stored at 4 ◦ C until transport to the laboratory. Salivettes were centrifuged at 1500 g for 15 min, 5 l sodium azide (NaN3 ) 1 M was added to each sample and stored at −20 ◦ C. An average volume of 750 ± 200 l was collected per sample. Macromolecules and corpuscles were removed from saliva, centrifuging with Amicon Ultra-4 Centrifugal filter (Millipore) at 4000 rpm, 4 ◦ C for 1 h. pH was measured for all samples and was ranging between 7.3 and 7.6. Sodium azide 5.0 l of (NaN3 ) 1 M were added to the filtrate and stored at −20 ◦ C. Amicon Ultra-4 Centrifugal filters (Millipore) membranes were previously extensively washed to deprive the membrane of the embedded glycerol, the procedure was carried out iteratively until control by NMR spectroscopy of the wash water showed no residual presence of glycerol. This protocol was set after extensive validations tests: a pool of freshly collected saliva (free drooling method) was collected from 10 adult donors, each donating approximately 1.5 ml for three days
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Fig. 2. 3D score plot of the PCA for matrix . The three components (PC1 , PC2 and PC3 ) shown were able to cluster the pre- (squares) from the post- (circles) Yo-Yo test samples.
consecutively. Collection was made consistently in the morning three hours after the breakfast. All the saliva was pooled (in total about 45 ml) and aliquoted to perform all the validation tests. These tests included: absorbance of proteins, particles and metabolites, stability of metabolites at 1 week and 30 days after freeze drying vs 4 ◦ C temperature storage. NMR spectra were acquired at 298 K on a Bruker Ultrashield spectrometer Avance 700, operating at 1 H frequency of 700.13 MHz, equipped with a 5 mm triple resonance probe with zgradients. Prior to the measurements, saliva samples were thawed and 500 l of each aliquot was mixed with 50 l D2 O and 5 l NaN3 (1 M in D2 O). Two different 1D experiments were carried out for each sample: 1D NOESY 1 H–1 H, mixing time 100 ms, and 1D flip-back with sculpting water suppression pulse sequence [13]. The former spectra was widely used for integrating all signals, while the latter was only used for the quantification of urea whose resonance is highly affected by fast exchange with water protons and required an adhoc acquisition with no saturation on water resonance. The spectra were identically acquired using a total relaxation delay of 14 s, and 16 K complex time points. The relaxation delay and the reading pulse (roughly 54◦ ) were optimized to ensure full relaxation of all the resonances at each scan reproducing the same signal intensity measured with 60 s relaxation delay. The dataset were all identically transformed and zero-filled to 32 K complex, residual water signal was digitally filtered out and integrals were obtained using the XWINNMR software (Bruker, DE). To account for dilution of bulk mass differences between samples due to slight differences in volumes collected (range 550–950 l), individual integral regions were normalized to the total integral region of the spectra, after exclusion of the water resonance and subtraction of the reference
compound integral (389 M) of the reference compound 2-amino4,6-dimethylpyrimidine (Sigma–Aldrich), dissolved in H2 O, and added as internal standard and used also as chemical shift reference (6.61 ppm) [14,15]. Quantification of the actual concentration in the samples of each metabolite was obtained by the integrals normalized to a known added amount with no overlap with the resonance of the sample. In this case normalization appears more easy than in the proteomic case [16]. For metabolites signal assignment, 2D 1 H–1 H correlation spectroscopy (DQF-COSY) double quantum filtered, 2D 1 H–1 H total correlation spectroscopy (TOCSY), 2D 1 H–13 C heteronuclear single quantum coherence (HSQC) and 2D 1 H–13 C HSQC-TOCSY experiments were performed, these latter two were acquired with two different setup, optimized respectively for aliphatic and aromatic CHn groups [17]. All the 2D spectra were recorded on a 10-fold concentrated saliva sample with pH adjusted to 7.4 with concentrated NaOH/HCl. For assignment confirmation sixty different metabolites standard samples were identically dissolved in D2 O with 0.05 M carbonate and 100 mM NaCl buffer and each sample had the pH adjusted to 7.4. With each standard a 2D 1 H–13 C HSQC spectra was recorded, using the spectral conditions previously described. Spectra were transformed using NMRPipe software [18], and analyzed using NMRViewJ (Onemoon Scientific Inc., USA) [19]. Multivariate pattern recognition techniques were used to analyze the NMR data set, since this method is able to handle multiple variables simultaneously and to cope with numerous co-linearities unlike univariate approaches. The analysis was conducted using the Unscrambler X software package (CAMO Software AS, Norway). A total of 108 signals per each spectra, and two physiological variables (heart rate in bpm and total distance in meters) were included as well in the dataset. Data were further categorized per role of the
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Fig. 3. 3D score plot PCA for matrix . Analysing the actual performance in the Yo-Yo test level 1 of the athletes, the three components (PC1 , PC2 and PC4 ) shown were able to cluster the good (squares) from average (circles) and bad (triangles) performers as defined in the text.
athlete with A = attack, C = midfield, D = defense, and according to the expected response to the Yo-Yo test data were also categorized in “good”, “normal” and “poor” performer. Data were mean centered and autoscaled, dividing the mean centered data by the square root of the standard deviation of the variable thus giving origin to a dimensionless data, prior to analysis [20]. Principal Component Analysis (PCA) was performed on the resulting data matrix. Briefly PCA is an unsupervised technique that describes observables (i.e. integrals of the NMR signal from saliva) with regards to one or more latent variables termed principal components (PCs), which are linear combinations of the original variables. The technique generates a set of PCs and the variation not captured in the first PC forms the residual through which a second uncorrelated component is calculated, and to ensure uncorrelation the condition that the PC2 should be orthogonal to the PC1 the process identifies new PC orthogonal axis, until reaching the set threshold of explained variation (>95% in our case). Each observation is then assigned a score along each PC. The weight given to each original NMR integral within any PC describes how influential that particular variable is and the correlation of the variable to others. The determination of which of the original variables (metabolites or their combination) are responsible for this separation the analysis of the loading scores of the PCs should be made [21]. To choose the standards acquisition conditions pH measurements on saliva samples and conductivity tests were done and it was seen that a buffer containing 50 mM CO3 2− /HCO3 − at pH 7.4 and 100 mM NaCl was a suitable buffer reproducing the magnetic susceptivity characteristics of saliva relevant for this study. All steps were tested and eventual leakage (adsorption into Salivette, sample degradation and contaminations) of metabolite concentration were minimized; i.e. it was verified if 4 ◦ C was not a
suitable condition of storage for more than a week since producing changes in the sample condition as shifts or changes of relative intensity. On the contrary, no such effect was visualized from the frozen sample. Per each athlete 2 samples were collected, one pre and one post-test.
3. Results and discussion The setup of the sampling protocol, the control that samples were stable for one week and one month and the comparison to freshly prepared samples, as reported in Section 2, were performed. The assignment of the signals was obtained using combined strategies as reported in Section 2. As example, in supplementary material is shown a region of the COSY-DQF spectra with the correlations identified for seven metabolites: alanine, aspartate, glycerol, glucose, histidine, lactate and leucine. The assignment of the signals was performed using a combined strategy: by the Human Metaboloma Database (HMDB) [22] and the literature available [23] and using single compounds standard samples by superposing spectra of the saliva sample and each of the 60 standard. In supplementary materials it is shown an enlarged view of the superposition of a region of the HSQCs spectra of the saliva and 5 different standards. Finally, for ambiguous cases, after the acquisition of the spectra, the standard was added directly to the saliva sample evidencing thus the increase in intensity of the actual metabolite signals (data not shown). Spectra were acquired at pulse length and relaxation conditions to ensure that all metabolite spectral lines and the reference compound resonances where consistently and completely relaxed at each scan as reported in Materials and methods.
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Fig. 4. 3D score plot PCA for matrix . Analysing the different performances in the Yo-Yo test level 1 according to the player role, the three components (PC2 , PC3 and PC4 ) shown were able to cluster the defense (squares) and midfield (triangles) from attack (circles) as defined in the text.
After normalization procedure, the concentration of metabolites in the sample was quantified with respect to the known concentration of reference. In Fig. 1 is shown, for the same athlete, the aliphatic region of a 1D spectra of the sample pre (bottom) and post (top) level 1 YoYo test. Resonances are labeled from several meaningful signals. A total of 108 signals were chosen for integration all over the spectra, interval of integration were chosen in such a way that extreme limits of integration were always positioned at a minima in the spectra. Integrals were thus encompassing single signals as well as small groups of degenerate signals. The performance of the 14 athletes in the level 1 Yo-Yo test resulted in an average run of 1731 ± 380 m. Then three performance categories were arbitrarily established in order to analyze the result of the Yo-Yo test: Bad, Normal and Good Performers. The Normal Performers were defined as the athletes who covered a distance within the range of one standard deviation around the average (±1/2 SD), thus the Bad Performers covered less then 1541 m, while the Good Performers covered more 1921 m. Besides the obvious differences in the integral values among samples from different athletes, which was a rescaling problem [20], the major effort was to perform the identification of the meaningful metabolic variations due to the Yo-Yo test performance. In fact several metabolites produced more than one signal, presenting a certain degree of overlap, we identified signals strictly correlated belonging to the same molecule. These resonances identified by their metabolite assignment, and subjected to auto-correlation analysis, (R2 > 0.92) were eliminated. This filtering step reduced the integral data from the original 108 signals to 74, resulting in data matrix constituted of 28 rows (samples pre and post Yo-Yo) × 74 columns (presumably unique metabolites).
In the PCA analysis a total of four PCs accounted for >96% of the total variance observed within the data matrix (data not shown). The meaningful variations in metabolite concentrations pre and post Yo-Yo test are evidenced in Fig. 2, where the score is shown in a 3D-plot for the PC1, PC2 and PC3. It is sufficient to distinguish arising from the pre-Yo-Yo from the post Yo-Yo. As supplementary material the corresponding loading plots are available on request by the authors, where the metabolites which are mostly affecting the PCs may be identified. For PC1 the most relevant metabolites are urea and glucose, for PC2 lactate, citrate and acetate, while for PC3 glycerol, glutamate, leucine, alanine and lysine (in supplementary material the table of the identified metabolites in this study). The next relevant question to tackle was if there was any correlation within the variation of metabolites concentrations and the performance offered in the Yo-Yo test by the athletes. Therefore, the data matrix was transformed considering the ratios of the integrals post/pre Yo-Yo test, reducing the number of rows to 14 one per athlete, and at this point heart rate (in bpm) and the total distance output of the Yo-Yo test were added as variables to the data matrix. A total of six PCs made on 14 samples per 76 variables were sufficient to account for slightly more than 95% of the total variance. To evidence the metabolites which may be diagnostic for the performance in the Yo-Yo test the performance was classified in good, normal and bad as reported in Section 2. In the Fig. 3 is shown in a 3D-plot for the PC1, PC2 and PC4 (note these are PC of matrix, thus different from those of matrix ). These axes are sufficient to distinguish and cluster the data according to the performance category. For PC1 more than 20 metabolites fall in the meaningful region of the loading plots, in supplementary material (available on request by the authors) citing among them creatine, lysine, leucine, choline,
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inositol, tyrosine, glutamine, aspartate, etc. for PC2 this number is greatly reduced, the relevant ones being only 8, and among them we identified glucose, arginine, acetate, citrate and alanine (in supplementary material the table of the identified metabolites in this study). For PC4 we identified lactate and glutamate. To identify the metabolites which may be diagnostic of the athlete role we then analyzed the matrix categorizing the performance in the attack, middle-field and defense categories, following the rationale mentioned in Section 2. Fig. 4 is shown in a 3D-plot for the PC2, PC3 and PC4. Actually axes PC1 and PC3 were already sufficient to segregate the attack role with respect to the middlefield and defense roles. However it was not possible to completely unravel the latter two since at least one defense player was always interleaving with the data of the middle-field role. The combination shown in Fig. 4 gives the best possible separation with the data we collected. For the PC3 the corresponding loading plot shown in supplementary material (available on request by the authors), identifies about 5 relevant signals, among them we identified the ornithine, inositol and leucine signals. Taken together the results obtained indicate that the NMR metabolic approach on saliva by Yo-Yo test appears meaningful. On the other hand, Yo-Yo test is a rather well established, for which several physiological comparative studies have also been published and it has also been shown that the performance of this test may be significantly different for players in different roles in this sport [1]. The result of the level 1 Yo-Yo test of the team in study was compared with the published results for professional soccer teams. The average result of the team was 1731 ± 380 m and it was reported that the average result for an homogeneously trained sub-elite team is 1867 ± 72 m [1]. The discrepancy and the wider standard deviation is due to the fact that our test was performed at the start of the pre-seasonal preparation and we arbitrarily established three categories of performance: good, average and bad (as reported in Section 2). Some metabolites could be identified in saliva (table in supplementary material), whose concentration variation was correlated with the performance in the level 1 Yo-Yo test. Particularly, we cluster the good performers and the bad performers from the average performing ones. As shown in Fig. 3, it is possible to segregate by performance the athletes by using just three axis, PC1, PC2 and PC4. Among the meaningful metabolites variation by the loading plots (available on request by the authors) which were identified for PC1 we pin-point tyrosine, inositol, creatine, lysine; for PC2 citrate, glucose, acetate and arginine, for PC4 lactate and glutamate. These results seem a promising good starting point to potentially identify good from bad performers. In Fig. 4 the data were categorized by player role, using three categories attack, defense and middle-field. The result is that three axis were enough to give the best separation among categories, namely PC2, PC3 and PC4. As it is clearly seen, the attack players could be easily segregated from the other two categories, while segregation of middle-field and defense players is not as effective, in fact at least one player per each category is interspersed with the other category. Among the meaningful metabolites variations (also visible in loading plots available on request by the authors) which were identified for PC2 we pin-point glucose, citrate and acetate; for PC3 leucine, ornithine, myo-inositol, for PC4 lactate and glutamate. The reliability of this result led to consider them preliminary for the difficulty to find numerous subjects for these study and for the time required for the NMR determination. In fact few attack players (n = 2) were available for this test, while the middle-field and the defense (n = 6) both were comparable in numbers. Further validation studies are required in the next future but as a first conclusion saliva resulted to be a promising bio-fluid sensitive to athletes performances. The physiological basis for the complete interpretation of these results
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