Figures
Abstract
The collection of samples of saliva is noninvasive and straightforward, which turns saliva into an ideal fluid for monitoring the adaptive response to training. Here, we investigated the response of the salivary proteins alpha-amylase (sAA), chromogranin A (sCgA), and the concentration of total protein (sTP) as well as salivary nitrite (sNO2) in relation to plasma catecholamines and plasma nitrite (pNO2), respectively. The variation in these markers was compared to the intensity and load of training during a 21-week training season in 12 elite swimmers. Overall, the salivary proteins tracked the concentration of plasma adrenaline and were inversely correlated with the training outcomes. No correlations were observed between sNO2 and pNO2. However, sNO2 correlated positively with the intensity and load of training. We argue that the decrease in sympathetic activity is responsible for the decrease in the concentration of proteins throughout the training season. Furthermore, the increase in nitrite is likely to reflect changes in hemodynamics and regulation of vascular tone. The association of the salivary markers with the training outcomes underlines their potential as noninvasive markers of training status in professional athletes.
Citation: Díaz Gómez MM, Bocanegra Jaramillo OL, Teixeira RR, Espindola FS (2013) Salivary Surrogates of Plasma Nitrite and Catecholamines during a 21-Week Training Season in Swimmers. PLoS ONE 8(5): e64043. https://doi.org/10.1371/journal.pone.0064043
Editor: Dulce Elena Casarini, Federal University of São Paulo (UNIFESP), Escola Paulista de Medicina, Brazil
Received: November 12, 2012; Accepted: April 11, 2013; Published: May 21, 2013
Copyright: © 2013 Díaz Gómez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by grants from the funding agency Fundação de Amparo à Pesquisa do Estado de Minas Gerais FAPEMIG (Protocol # APQ-04655-10). M.M.D.G. and O.L.B.J. received graduate fellowships from Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq and from the international program Programa de Estudante-Convênio de Pós-Graduação PECPG- CAPES, respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Periodization is a structured approach based mainly upon the variation of the volume and the intensity of training. Periodization allows athletes to reach maximal performance at appropriate times by providing the necessary physiological adaptation and recovery [1], [2]. Intense and continuous training can induce changes in a broad series of biochemical parameters such as the release of muscle proteins into the blood and variations in cortisol, urea, iron, catecholamines, and blood counts [3], [4]. These parameters are often used to monitor the physiological response to training. Abnormal levels of skeletal muscle proteins in the blood, for instance, can be interpreted as a signal of muscle damage [5]. High concentrations of cortisol and urea are widely regarded as markers of increased protein turnover [6] whereas decreased levels of iron might compromise performance due to its critical role in the delivery and utilization of oxygen by the active muscle [7]. Finally, variations in catecholamines and leukocytes regularly suggest inadequate recovery from training [5], [6]. However, the quantification of these parameters requires blood sampling and it can be inconvenient for the athletes or pose safety risks. Furthermore, for some people venipuncture is painful and stressful. Consequently, the collection of blood might increase the levels of catecholamines and cortisol, thus invalidating the assay. By contrast, the collection of saliva is noninvasive and straightforward. Therefore, analyzing salivary components is clearly appealing in sports medicine.
Catecholamines occupy critical positions in the regulation of physiological processes during exercise. The concentration of plasma catecholamines rises rapidly during exercise, especially at high intensities. This results in increased cardiac output, vasoconstriction in the non-contracting muscles, stimulation of the sweat glands, transportation of oxygen and energetic substrates to the active muscles, and increased contractility of the skeletal muscles [8]. On the other hand, plasma nitrite (pNO2) is the product of the oxidation of nitric oxide (NO) and is essential for vasodilation in the systemic and renal vasculature. Further, it has been demonstrated that the concentration of pNO2 at rest predicts exercise capacity and is correlated with flow-mediated vasodilation in healthy subjects [9].
Recently, we demonstrated that salivary alpha-amylase (sAA) and salivary nitrite (sNO2) show a proportional response to the variation of the intensity and load of training [10]. Salivary alpha-amylase is the most abundant enzyme in saliva and has digestive and anti-microbial properties [11]. The reasoning behind the use of sAA to monitor training is that sAA is released into the saliva mainly after sympathetic stimulation and thus, is considered a surrogate marker for catecholamines [12]. Considering the role of nitrite in vasodilation, we proposed that sNO2 would show an equivalent response to the intensity of training [10]. However, the levels of nitrite in saliva are substantially higher than in blood due to the reduction of nitrate by oral bacteria [13]. Therefore, the argument that sNO2 is correlated to pNO2 warrants confirmation.
A series of studies by Chatterton and colleagues in the late 1990 s stimulated considerable interest in sAA as a marker of sympathetic activity [12], [14]. In these studies, it was reported that the levels of sAA increased significantly before parachute jumping [14] and were correlated with plasma noradrenaline (r = .64) and adrenaline (r = .49) after a single bout of exercise [12]. Ever since, a great deal of research has been devoted to investigating changes in the activity of sAA to a broad series of acute exercise protocols. However, none of them investigated further relations between sAA and catecholamines. On the other hand, less attention has been given to sNO2. Few articles have reported changes in sNO2 after single bouts of exercise [15], [16] but only our previous study has shown the response of sNO2, and sAA, to long-term training under resting conditions. In that study, both sAA and sNO2 behaved proportionally, although in opposite directions, to the intensity and load of training. While sAA correlated negatively with the parameters of training, sNO2 correlated positively. Because they reflect the activity of biological systems pivotal to the adaptation to training, sAA and sNO2 could prove to be appealing markers in sports medicine. Further, to be useful, the variation of salivary parameters in response to training should be assessed under resting conditions, as it is done with most of the traditional markers in blood, and that is why longitudinal designs are particularly important.
The current study aimed to extend the previous findings on sAA and sNO2 as markers of the intensity and load of training. We assessed 12 professional swimmers throughout 21 weeks of training. In addition to sAA and sNO2, salivary chromogranin A (sCgA), and salivary total protein (sTP) were quantified in saliva. These parameters were compared to plasma catecholamines and pNO2,. We hypothesized that there would be a strong correlation between salivary proteins and plasma catecholamines and between sNO2 and pNO2. We expected that the variations in these markers would be equivalent to the oscillation of the intensity and load of training.
Methods
Ethics Statement
The subjects were 12 professional swimmers (8 men and 3 women aged 19.3±1.4 years; BMI 24.3±1.7 kg/m2; competition experience; 4.6±2.8 years) different from those who participated in our previous study [10]. None of them smoked, had significant medical or oral health history or was taking regular or incidental medication during the study. One week before the beginning of the collection of the samples, the subjects gave their written informed consent. The experimental protocol was approved by the Institutional Review Board of the Federal University of Uberlandia (Protocol CEP/UFU 483/10).
Design
The subjects were evaluated during their regular training season. The experimental design has been described elsewhere [10]. In sum, the subjects completed nine training sessions per week that included predominantly swimming. The volume, intensity and load during the swimming sessions throughout the 5-month study are shown in Figures 1A and 1B. The intensity of training was established by means of blood lactate measures, with an intensity of 100% corresponding to a swimming velocity at the anaerobic threshold for each individual. Every four weeks during the 5-month season, the subjects attended the laboratory under fasting conditions for collection of saliva and blood. All collection procedures took place at 8 am. On each visit, heart rate (HR) and blood pressure (BP) were recorded. The subjects completed the Profile of Mood States Questionnaire (POMS) immediately before the collection of saliva. The POMS is a 65-item questionnaire measuring tension, depression, anger, confusion, vigor and fatigue on a 5-point Likert scale. Diet logs were kept to ensure consistent caloric and nitrate intake for the 48 hours before each sample collection (Table 1).
Figure 1A shows the variation in the intensity and volume of training. Figure 1B illustrates the variation in the load of training (expressed as a function of volume x intensity).
Sample Collection
Whole saliva was collected with no exogenous stimulation. The saliva was allowed to pool in the mouth and then drooled into pre-weighted collection vials after two minutes. The subjects were asked to refrain from drinking, eating or tooth brushing during the hour prior to the collection of the samples. Immediately after collecting saliva, blood from the antecubital vein (±10 mL) was withdrawn into two EDTA-coated tubes. The samples were obtained by a qualified phlebotomist using standardized venipuncture techniques. The analysis of the blood samples was performed immediately after the collection. The subjects had had their blood routinely sampled prior to this study and none of them reported the procedure as stressful. After the collection of saliva, the samples were stored frozen at −20°C.
Catecholamines
The blood samples were centrifuged at 4°C for 5 min at 5000 rpm and the plasma was separated. The concentration of plasma catecholamines was determined by high performance liquid chromatography with electrochemical detection. The catecholamines were extracted by alumina and eluted by acetic acid. The separation was performed using a Supelcosil LC-18-DB column (Sigma. St. Louis, MO, USA). The mobile phase consisted of 13.8 g of monosodium phosphate, 100 mg of EDTA, 0.2 g of sodium octanesulfonate, and 30 mL of acetonitrile, pH 3.0, delivered at flow rate of 1 mL/min. The electrochemical detector consisted of a triple-electrode system (Electrochemical Detection, Coulochem, ESA, Chelmsford, MA, USA). The concentration of catecholamines in each sample was corrected using 3,4-dihydroxybenzylamine as the internal standard [17]. The assay sensitivity was approximately 10 pg. The intra-assay coefficients of variation were below 10%.
Nitrite
To avoid the reaction of nitrite with hemoglobin, the blood samples were immediately centrifuged after collection to separate the plasma. Nitrite was determined by acid diazotation [18]. Fifty µL of saliva or plasma were incubated with 50 µL of Griess reagent (1% sulfanilamide in 2.5% H3PO4 and 0.1% N-(1-naphthyl)ethylenediamine dihydrochloride) at room temperature for 10 min. The absorbance was measured at 570 nm using a microplate reader (Molecular Devices, Menlo Park, CA, USA). The content of nitrite was calculated based on a standard curve constructed with NaNO2 at the concentrations of 400, 200, 100, 50, 25, 12.5, 6.25 and 3.12 µM. The assay sensitivity was approximately 125 pmoL. The intra and inter-assay coefficients of variation for duplicate samples was 4.8% and 6.3% and 12.3% and 14.2% for sNO2 and pNO2, respectively.
Salivary Proteins
On the day of analysis, the samples of saliva were thawed and centrifuged at 3000 rpm for 15 minutes. The concentration of the total protein in the samples was determined by the Bradford method [19] using Coomassie Brilliant Blue G-250 (Fisher BioReagents, Fair Lawn, NJ, USA) and bovine serum albumin (Sigma. St. Louis, MO, USA) as the standard protein. The limit of detection of the Bradford method is 1 µg of protein. The intra and inter-assay coefficients of variation for the duplicate samples was 3.2% and 6.1%, respectively. The concentration of the total protein in the samples was used as loading control in western blots. All of the samples from each subject were assayed on the same plate in duplicate. To avoid the possible effects of salivary flow rate on the concentration of proteins, ten micrograms of the total protein from each sample were denatured under reducing conditions and applied on 5–20% SDS–polyacrylamide gradient gels, as previously suggested [20], [21]. The proteins were separated and then transferred onto nitrocellulose membranes in transfer buffer (25 mM Tris, 190 mM glycine, 20% MeOH, pH 7.8–8.4) for two hours at 200 mA and 4°C. The protein transfer was confirmed by visualization with Ponceau. The membranes were blocked for 4 hours at 4°C in blocking buffer (5% non-fat dry milk in PBS w/v). The membranes were then incubated overnight at 4°C with purified polyclonal rabbit anti-human sAA (dilution 1∶5000) (produced in our laboratory) and mouse monoclonal anti-human CgA (dilution 1∶1000) (Millipore, Temecula, CA), respectively. The membranes were subsequently incubated with secondary antibodies for two hours. After the incubations with specific primary and then secondary antibodies, the labeled proteins were detected with ECL reagents and by exposing the developed blots to GE Healthcare films. The densitometrical analysis of the spots was performed using ImageJ (U.S. National Institutes of Health, Bethesda, Maryland, USA) by a researcher who was blinded to the experimental design. The area in pixels of each spot was determined in triplicate, and the means were used for statistical analyses.
Determination of sAA Activity
The samples of saliva were centrifuged at 3000 rpm for 15 min to remove mucins. Ten µL of saliva were diluted (1∶200) in MES buffer (MES 50 mM, NaCl 300 mM, CaCl2 5 mM, KSCN 140 mM, pH 6.3) followed by the addition of 300 µL of pre-heated (37°C) substrate solution (2-chloro-4-nitrophenyl-galactopyranoside maltoside). The optical density was read at 405 nm at one-min intervals during three min at 37°C using a microplate reader (Molecular Devices, Menlo Park, CA, USA). The enzyme activity was determined using the formula: [Absorbance difference per minute × total assay volume (308 ml) × dilution factor (200)]/[millimolar absorptivity of 2-chloro-4-nitrophenyl (12.9) × sample volume (.008 ml) × light path (.97) [10]. The enzyme activity (U/mL) was then multiplied by flow rate (mL/min) to estimate the sAA secretion rate (U/min). The assay sensitivity was approximately 0.4 U/mL. The intra-assay coefficient of variation for duplicate samples was 7.6%.
Statistical Analysis
The data were tested for normality using the Shapiro-Wilk test prior to the analyses. All of the variables were compared by one-way analysis of variance (ANOVA) with repeated measures followed by the Tukey test for multiple comparisons. The relationships between biochemical markers and training outcomes were analyzed using a two-tailed Pearson correlation coefficient. For all of the analyses, significant results were defined at the level of p<0.05. The results shown are means (SD).
Results
Markers of Autonomic Activity
Figures 2A and 2B show the response to training of the markers of autonomic activity. We noted significant differences in adrenaline [F (4, 9) = 5.26, p = 0.019], noradrenaline [F (4, 9) = 7.47, p = 0.002] and dopamine [F (4, 9) = 6.76, p = 0.004]. The concentration of adrenaline and dopamine decreased up to the middle of the training season and subsequently increased towards the baseline levels. On the other hand, noradrenaline oscillated with each month of training. Similar results to those of adrenaline were observed for the salivary surrogate markers. The activity of sAA [F (4, 12) = 12.43, p = 0.019], as well as its concentration [F (4, 11) = 11.15 p<0.0001], sCgA [F (4, 10) = 25.03, p<0.0001], and sTP [F (4,9) = 18.24, p<0.001] declined up to the middle of the season with following increases by the end (Figures 2 and 3). No differences in salivary flow rate, HR or BP were observed in response to training (Figure 4 and Table 2).
Figure 2A shows the variation in adrenaline and noradrenaline in response to training. Adrenaline: • Different from Training week 16. *Different from Training Week 21. Noradrenaline: δ Different from Training Week 11. σ Different from Training Week 21. Figure 2B shows the variation in dopamine. *Different from Training Week 16. Figure 2C shows the response of sAA activity vs. concentration. sAA activity: *Different from Training Week 11. sAA concentration: • Different from Training Week 21. Figure 2D shows the variation in sCgA and sTP in response to training. sTP: *Different from Training Week 11. sCgA: • Different from training week 6. δ Different from training week 11. σ Different from Training Week 16.
No difference in salivary flow was observed throughout the training season.
Nitrite
As expected, the concentration of nitrite in saliva was greater than in plasma. The levels of both sNO2 [F (4, 11) = 3.59 p<0.05] and pNO2 [F (4, 12) = 6,42 p = 0.027] varied significantly in response to training. However, no significant correlation was found between the two (Figure 5).
*Different from Training Week 21 (pNO2).
Correlation between Biochemical Markers and Training Outcomes
The Pearson correlation coefficients between the biochemical markers and the training outcomes are shown in Table 3. Throughout the training season, the variation in sAA (both the activity and the concentration) showed a strong positive correlation with adrenaline whereas sCgA and sPT correlated modestly. Only sTP correlated positively with dopamine. We found no significant correlations between the salivary proteins and noradrenaline. Interestingly, we observed a strong positive correlation between the activity and the concentration of sAA [r(12) = 0.75, p<0.05]. Furthermore, adrenaline, dopamine and the salivary proteins correlated negatively with the intensity and load of training. On the other hand, sNO2 but not pNO2 correlated positively with the training outcomes.
Discussion
Consistent with our original hypothesis, we found strong and modest correlations between the salivary proteins and adrenaline. Further, the salivary proteins and sNO2 showed a proportional response to the intensity and load of training. However, contrary to our preliminary hypothesis, sNO2 was not associated to pNO2 and the latter did not predict the training outcomes.
It is well documented that training modulates autonomic activity. The adaptations associated with long-term training include a decrease in sympathetic activity and an increase in parasympathetic drive [22]. Several reports have found lower resting levels of adrenaline after long-term training in humans [23]–[26]. In these studies, the decrease in adrenaline appears to be more related to the intensity of exercise than to the duration. In our study, catecholamines varied significantly in response to training, and adrenaline was strongly correlated with the load of training.
Although it would be expected that as direct markers of autonomic activity catecholamines, HR and BP displayed similar patterns during training, it is worth mentioning that the autonomic regulation is a tissue-specific and intricate process. Most of the mechanisms linked to the exercise-induced bradycardia, for instance, are thought to be a consequence of increases in vagal tone and a reduction in intrinsic HR. However, a reduction in sympathetic tone is considered to have little effect on the lower HR observed in trained subjects [26], [27]. We failed to observe significant changes in HR and BP. This may be attributed to a “ceiling” effect of training considering the experience of the subjects as competitive athletes, or to the little effect of catecholamines in decreasing sympathetic activity in the heart and blood vessel.
It is interesting to note that the salivary proteins displayed a similar pattern to adrenaline and significantly correlated with the intensity and load of training. As mentioned above, salivary proteins are released into saliva mainly after sympathetic stimulation [28]. Therefore, a decline in the levels of proteins in response to training is expected. Chatterton et al., reported correlations of.64 and.49 between sAA, adrenaline and noradrenaline, respectively, after 10-min intervals of walking, jogging and running [29]. None of the salivary proteins assessed in our study correlated with noradrenaline. Furthermore, although significantly different throughout the training season, noradrenaline failed to correlate with the training outcomes. Under resting conditions, most of the noradrenaline found in plasma is the result of spillover from the sympathetic nerve terminals with a small proportion coming from the adrenal gland [30]. We attribute the lack of correlation between the salivary proteins and noradrenaline in our study to the fact that the subjects were assessed under resting conditions and not after acute submaximal exercise.
Salivary alpha-amylase is the most abundant enzyme in saliva. It is released mainly from the parotid and the submandibular glands and has digestive and anti-fungal properties [11]. Thus far, most of the studies on the sAA response to exercise have incorporated kinetic assays with only a handful of them assessing the concentration of the enzyme [20], [21]. It has been suggested that post-translational modifications such as glycosylations, or the formation of protein complexes between sAA and mucins might affect amylolytic function [31]. Therefore, the concentration of sAA rather than its activity would be a more appropriate marker of autonomic drive. No other study has investigated this association in response to training. Here, we observed a strong correlation between the two (r = 0.75). Additionally, both the concentration and the activity of sAA correlated significantly with the intensity and load of training. It remains to be seen whether other populations, such as type II diabetics or immunocompromised patients show a corresponding response between the activity and the concentration of sAA during exercise.
Salivary chromogranin A is secreted into saliva from the submandibular gland. As a surrogate marker of autonomic activity, sCgA shares the same rationale that sAA. Both are secreted into saliva mainly after sympathetic innervation to the salivary glands. Most studies agree on increments in sCgA in response to exercise. Salivary chromogranin A has been shown to increase proportionally to the intensity of exercise [20], [32] and to correlate with the double product of HR and BP (r = 0.89) as well as the rate of perceived exertion (r = 0.82) during a maximal exercise test [33]. In our study sCgA correlated positively with adrenaline and these two correlated negatively with the intensity and load of training. Our data are in agreement with previous studies showing equivalent responses of sAA and sCgA to acute maximal exercise [20], [21], [32], exposure to microgravity [34] and adverse psychological stimuli [35]. The study that observed a significant increase in sCgA but not sAA in response to exercise did show that both peaked at the end of the exercise and returned to baseline levels 30 min thereafter [33].
Only recently has research identified sTP as a prospective marker in sports medicine. A previous report, for instance, indicates that sTP could predict dehydration after exercise [36]. We have previously proposed that sTP is an ideal marker of autonomic activity [21] and some studies have shown parallel responses between sTP and other salivary proteins during exercise [20], [37]. Here, sTP correlated with adrenaline and the training outcomes. As with sAA and sCgA, the concentration of sTP in saliva rises after innervation to the glands. However, unlike the former proteins, the quantification of sTP is straightforward and inexpensive.
The two major pathways for the production of NO in the body are the arginine/NOS and the nitrate-nitrite-NO pathway. Thus, nitrate and nitrite are important alternative sources of NO and are involved in vascular processes. Nitric oxide is an essential regulator of metabolism and vascular tone. In the striated muscle, NO increases insulin sensitivity, stimulates lipid oxidation and reduces contractility. In the blood vessel, it is known to inhibit contraction, platelet aggregation and promote vasodilation [38]. Here, we have shown that the resting levels of both sNO2 and pNO2 increase in response to long-term training. However, the variation in one of them does not reflect changes in the other. Dietary nitrate is absorbed in the small intestine and concentrated in the salivary glands. Roughly 5% of the ingested nitrate is reduced to nitrite in the oral cavity by commensal bacteria. Consequently, the levels of nitrate in saliva are approximately 10-fold higher than in plasma [39]. On the other hand, only a fraction of plasma nitrate originates from the NOS pathway [40]. Other groups have presented evidence suggesting that nitrite can be transformed to NO either spontaneously in the blood, or enzymatically in the endothelial cells [41], [42]. Under hypoxic and acidic conditions, such as exercise, nitrite is converted to NO increasing blood flow [43]. This would constitute a NO-scavenging mechanism whereby erythrocytes are unable to form nitrate and methemoglobin. Thus, the levels of sNO2 may be more related to plasma nitrate than nitrite. Despite the lack of association between sNO2 and pNO2, the former did correlate with the training outcomes and constitutes an attractive marker of the load of training in sports medicine.
For a better understanding of the physiological impact of training, we included analysis of glucose, triglycerides, cholesterol, cortisol, urea, iron, lactate dehydrogenase, creatine kinase and complete blood counts (data not shown). These markers were evaluated at the same points than nitrite, catecholamines and the salivary proteins. Additionally, the subjects answered the POMS immediately before the collection of the samples. Glucose, triglycerides, and cholesterol were selected to monitor substantial changes and/or compliance with the diet before each collection point. Because there were no differential changes in any of these parameters and the diet was standardized 48 hours before collecting the samples, we are certain that the variation in catecholamines, salivary proteins and nitrite was due to the oscillation of the training variables. The lack of any significant difference in these parameters during the training season is broadly consistent with several studies in professional athletes which have reported no effect of training in metabolic, hormonal or immunological markers when the periodization of training is appropriate [44]–[47]. It is important to point out that the parameters above have been shown to vary mainly in stages of overreaching and overtraining, which frequently imply grueling loads of training. However, there is no reason to expect any change in these markers when the load of training is properly tolerated. Regarding the female athletes in our study, no difference has been observed in the levels of salivary proteins between men and women under resting conditions [48], [49] or in response to adverse stimuli [50]. Moreover, although we did not control for the phase of the menstrual cycle, it is well documented that the variation of sex hormones during the menstrual cycle does not influence such parameters [51]–[54].
In conclusion, sAA (both the activity and the concentration), sCgA and sTP track the concentration of plasma adrenaline in response to training. Given the simplicity for the quantification of sTP, we propose that sTP be used instead of sAA and sCgA as a marker of autonomic activity. Salivary NO2 does not reflect changes in pNO2. However, sNO2 does predict training outcomes and along with the salivary proteins represent an attractive marker of training status in professional athletes.
Author Contributions
Conceived and designed the experiments: MD. Performed the experiments: MD OB RT. Analyzed the data: MD OB. Wrote the paper: MD. Revised and approved the final version of the manuscript: MD OB RT FE.
References
- 1. Stone MH (1990) Muscle conditioning and muscle injuries. Med Sci Sports Exerc 22: 457–462.
- 2. Gabbett TJ (2004) Reductions in pre-season training loads reduce training injury rates in rugby league players. Br J Sports Med 38: 743–749 .
- 3. Banfi G, Colombini A, Lombardi G, Lubkowska A (2012) Metabolic markers in sports medicine. Adv Clin Chem 56: 1–54.
- 4. Rønsen O, Børsheim E, Bahr R, Klarlund pedersen B, Haug E, et al. (2004) Immuno-endocrine and metabolic responses to long distance ski racing in world-class male and female cross-country skiers. Scandinavian Journal of Medicine & Science in Sports 14: 39–48 .
- 5. Millet GY, Tomazin K, Verges S, Vincent C, Bonnefoy R, et al. (2011) Neuromuscular consequences of an extreme mountain ultra-marathon. PLoS ONE 6: e17059 .
- 6. Hug M, Mullis PE, Vogt M, Ventura N, Hoppeler H (2003) Training modalities: over-reaching and over-training in athletes, including a study of the role of hormones. Best Pract Res Clin Endocrinol Metab 17: 191–209.
- 7. Peeling P, Dawson B, Goodman C, Landers G, Trinder D (2008) Athletic induced iron deficiency: new insights into the role of inflammation, cytokines and hormones. European Journal of Applied Physiology 103: 381–391 .
- 8. Zouhal H, Jacob C, Delamarche P, Gratas-Delamarche A (2008) Catecholamines and the effects of exercise, training and gender. Sports Med 38: 401–423.
- 9.
Rassaf T, Lauer T, Heiss C, Balzer J, Mangold S, et al.. (2007) Nitric oxide synthase-derived plasma nitrite predicts exercise capacity. Br J Sports Med 41: 669–673; discussion 673. doi:10.1136/bjsm.2007.035758.
- 10.
Diaz MM, Bocanegra OL, Teixeira RR, Soares SS, Espindola FS (2012) Salivary Nitric Oxide and Alpha-Amylase as Indexes of Training Intensity and Load. Int J Sports Med. Available: http://www.ncbi.nlm.nih.gov/pubmed/22960992. Accessed 19 September 2012.
- 11. Scannapieco FA, Torres G, Levine MJ (1993) Salivary alpha-amylase: role in dental plaque and caries formation. Crit Rev Oral Biol Med 4: 301–307.
- 12. Chatterton RT Jr, Vogelsong KM, Lu YC, Ellman AB, Hudgens GA (1996) Salivary alpha-amylase as a measure of endogenous adrenergic activity. Clin Physiol 16: 433–448.
- 13. Archer DL (2002) Evidence that ingested nitrate and nitrite are beneficial to health. J Food Prot 65: 872–875.
- 14. Chatterton RT Jr, Vogelsong KM, Lu YC, Hudgens GA (1997) Hormonal responses to psychological stress in men preparing for skydiving. J Clin Endocrinol Metab 82: 2503–2509.
- 15. Rahman ZA, Abdullah N, Singh R, Sosroseno W (2010) Effect of acute exercise on the levels of salivary cortisol, tumor necrosis factor-alpha and nitric oxide. J Oral Sci 52: 133–136.
- 16. Panossian AG, Oganessian AS, Ambartsumian M, Gabrielian ES, Wagner H, et al. (1999) Effects of heavy physical exercise and adaptogens on nitric oxide content in human saliva. Phytomedicine 6: 17–26.
- 17. Eisenhofer G, Goldstein DS, Stull R, Keiser HR, Sunderland T, et al. (1986) Simultaneous liquid-chromatographic determination of 3,4-dihydroxyphenylglycol, catecholamines, and 3,4-dihydroxyphenylalanine in plasma, and their responses to inhibition of monoamine oxidase. Clinical Chemistry 32: 2030–2033.
- 18. Tsikas D (2007) Analysis of nitrite and nitrate in biological fluids by assays based on the Griess reaction: appraisal of the Griess reaction in the L-arginine/nitric oxide area of research. J Chromatogr B Analyt Technol Biomed Life Sci 851: 51–70 .
- 19. Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72: 248–254.
- 20.
Bocanegra OL, Diaz MM, Teixeira RR, Soares SS, Espindola FS (2012) Determination of the lactate threshold by means of salivary biomarkers: chromogranin A as novel marker of exercise intensity. European Journal of Applied Physiology. Available: http://www.ncbi.nlm.nih.gov/pubmed/22227853. Accessed 30 January 2012.
- 21.
Diaz MM, Bocanegra OL, Teixeira RR, Soares SS, Espindola FS (2012) Response of Salivary Markers of Autonomic Activity to Elite Competition. International journal of sports medicine. Available: http://www.ncbi.nlm.nih.gov/pubmed/22581684. Accessed 2 July 2012.
- 22. Mueller PJ (2007) Exercise training and sympathetic nervous system activity: evidence for physical activity dependent neural plasticity. Clin Exp Pharmacol Physiol 34: 377–384 .
- 23. Winder WW, Hagberg JM, Hickson RC, Ehsani AA, McLane JA (1978) Time course of sympathoadrenal adaptation to endurance exercise training in man. J Appl Physiol 45: 370–374.
- 24. Winder WW, Hickson RC, Hagberg JM, Ehsani AA, McLane JA (1979) Training-induced changes in hormonal and metabolic responses to submaximal exercise. J Appl Physiol 46: 766–771.
- 25. Lacour JR, Pequignot JM, Geyssant A, Coudert J, Peyrin L (1982) [Effect of training on plasma levels of catecholamines on the course of submaximal exercise]. J Physiol (Paris) 78: 838–842.
- 26.
Mazon J, Gastaldi A, Di Sacco T, Cozza I, Dutra S, et al.. (2011) Effects of training periodization on cardiac autonomic modulation and endogenous stress markers in volleyball players. Scandinavian Journal of Medicine & Science in Sports: no–no. doi:10.1111/j.1600-0838.2011.01357.x.
- 27. Smith ML, Hudson DL, Graitzer HM, Raven PB (1989) Exercise training bradycardia: the role of autonomic balance. Med Sci Sports Exerc 21: 40–44.
- 28. Proctor GB, Carpenter GH (2007) Regulation of salivary gland function by autonomic nerves. Auton Neurosci 133: 3–18 .
- 29. Chatterton RT Jr, Vogelsong KM, Lu YC, Ellman AB, Hudgens GA (1996) Salivary alpha-amylase as a measure of endogenous adrenergic activity. Clin Physiol 16: 433–448.
- 30. Goldstein DS, Eisenhofer G, Kopin IJ (2003) Sources and significance of plasma levels of catechols and their metabolites in humans. J Pharmacol Exp Ther 305: 800–811 .
- 31. Mandel AL, Peyrot des Gachons C, Plank KL, Alarcon S, Breslin PAS (2010) Individual differences in AMY1 gene copy number, salivary α-amylase levels, and the perception of oral starch. PLoS ONE 5: e13352 .
- 32. Allgrove JE, Gomes E, Hough J, Gleeson M (2008) Effects of exercise intensity on salivary antimicrobial proteins and markers of stress in active men. J Sports Sci 26: 653–661 .
- 33. Gallina S, Di Mauro M, D’Amico MA, D’Angelo E, Sablone A, et al. (2011) Salivary chromogranin A, but not α-amylase, correlates with cardiovascular parameters during high-intensity exercise. Clin Endocrinol (Oxf) 75: 747–752 .
- 34. Rai B, Kaur J (2011) Salivary stress markers and psychological stress in simulated microgravity: 21 days in 6° head-down tilt. J Oral Sci 53: 103–107.
- 35. Filaire E, Dreux B, Massart A, Nourrit B, Rama LM, et al. (2009) Salivary alpha-amylase, cortisol and chromogranin A responses to a lecture: impact of sex. Eur J Appl Physiol 106: 71–77 .
- 36. Walsh NP, Montague JC, Callow N, Rowlands AV (2004) Saliva flow rate, total protein concentration and osmolality as potential markers of whole body hydration status during progressive acute dehydration in humans. Arch Oral Biol 49: 149–154.
- 37. Bortolini MJS, De Agostini GG, Reis IT, Lamounier RPMS, Blumberg JB, et al. (2009) Total protein of whole saliva as a biomarker of anaerobic threshold. Res Q Exerc Sport 80: 604–610.
- 38. Levine AB, Punihaole D, Levine TB (2012) Characterization of the role of nitric oxide and its clinical applications. Cardiology 122: 55–68 .
- 39. McKnight GM, Smith LM, Drummond RS, Duncan CW, Golden M, et al. (1997) Chemical synthesis of nitric oxide in the stomach from dietary nitrate in humans. Gut 40: 211–214.
- 40. Dejam A, Hunter CJ, Pelletier MM, Hsu LL, Machado RF, et al. (2005) Erythrocytes are the major intravascular storage sites of nitrite in human blood. Blood 106: 734–739 .
- 41. Millar TM, Stevens CR, Benjamin N, Eisenthal R, Harrison R, et al. (1998) Xanthine oxidoreductase catalyses the reduction of nitrates and nitrite to nitric oxide under hypoxic conditions. FEBS Lett 427: 225–228.
- 42. Hunt JF, Fang K, Malik R, Snyder A, Malhotra N, et al. (2000) Endogenous airway acidification. Implications for asthma pathophysiology. Am J Respir Crit Care Med 161: 694–699.
- 43. Gladwin MT, Shelhamer JH, Schechter AN, Pease-Fye ME, Waclawiw MA, et al. (2000) Role of circulating nitrite and S-nitrosohemoglobin in the regulation of regional blood flow in humans. Proc Natl Acad Sci USA 97: 11482–11487 .
- 44. Meyer T, Meister S (2011) Routine Blood Parameters in Elite Soccer Players. International Journal of Sports Medicine 32: 875–881 .
- 45. Banfi G, Del Fabbro M, Mauri C, Corsi MM, Melegati G (2006) Haematological parameters in elite rugby players during a competitive season. Clin Lab Haematol 28: 183–188 .
- 46. Mujika I, Chatard JC, Geyssant A (1996) Effects of training and taper on blood leucocyte populations in competitive swimmers: relationships with cortisol and performance. Int J Sports Med 17: 213–217 .
- 47. Ostojic SM, Ahmetovic Z (2009) Indicators of iron status in elite soccer players during the sports season. International Journal of Laboratory Hematology 31: 447–452 .
- 48. Rohleder N, Nater UM, Wolf JM, Ehlert U, Kirschbaum C (2004) Psychosocial stress-induced activation of salivary alpha-amylase: an indicator of sympathetic activity? Ann N Y Acad Sci 1032: 258–263 .
- 49. Rantonen PJ, Meurman JH (2000) Correlations between total protein, lysozyme, immunoglobulins, amylase, and albumin in stimulated whole saliva during daytime. Acta Odontol Scand 58: 160–165.
- 50. Harm DL, Schlegel TT (2002) Predicting motion sickness during parabolic flight. Auton Neurosci 97: 116–121.
- 51. Tenovuo J, Laine M, Söderling E, Irjala K (1981) Evaluation of salivary markers during the menstrual cycle: peroxidase, protein, and electrolytes. Biochem Med 25: 337–345.
- 52.
Bhoola KD, Matthews RW, Roberts F (1978) A survey of salivary kallikrein and amylase in a population of schoolgirls, throughout the menstrual cycle. Clin Sci Mol Med Suppl 55: 561–566.
- 53. D’Alessandro S, Curbelo HM, Tumilasci OR, Tessler JA, Houssay AB (1989) Changes in human parotid salivary protein and sialic acid levels during pregnancy. Arch Oral Biol 34: 829–831.
- 54. Laine M, Pienihäkkinen K, Ojanotko-Harri A, Tenovuo J (1991) Effects of low-dose oral contraceptives on female whole saliva. Arch Oral Biol 36: 549–552.