Abstract
ackground: Exercise-induced muscle damage (EIMD) is the most common health risk in training. So far, EIMD diagnosis predominantly relies on blood biochemical analysis or medical imaging. EIMD prediction by using saliva shows great prospects in public fitness. Methods: A total of 18 participants performed high-intensity rowing train- ing. Blood biochemical indicator and pain analyses indicated EIMD occurrence. Pseudo- targeted metabolomics techniques were utilized to analyze changes in salivary metabolites after exercise. Results: A total of 43 salivary metabolites significantly increased while 31 salivary metabolites significantly decreased after exercise. The upregulated metabolites were related to hormone secretion, antioxidation, and muscle repair. A partial least squares discriminant analysis model was established, and three potential salivary biomarkers for EIMD prediction were screened. The sensitivity and specificity of single biomarkers achieved more
metabolites after exercise. Results: A total of 43 salivary metabolites significantly increased while 31 salivary metabolites significantly decreased after exercise. The upregulated metabolites were related to hormone secretion, antioxidation, and muscle repair. A partial least squares discriminant analysis model was established, and three potential salivary biomarkers for EIMD prediction were screened. The sensitivity and specificity of single biomarkers achieved more than 88.9% and 94.4% in classification of EIMD occurrence, respectively. The accuracy of classification increased to ~100% with multiple metabolites. Conclusion: Salivary metabolites significantly changed after high-intensity rowing training and EIMD occurrence. Some salivary metabolites exhibited similar trends with blood biochemical indicators. Salivary biomarkers have great prospects in EIMD prediction, and better perfor- mance was achieved with multiple salivary metabolites. Keywords:metabolomics; exercise; muscle damage; blood biochemical analysis; salivary biomarker 1. Introduction Exercise plays a crucial role in maintaining physical health and preventing chronic diseases, effectively improving sleep quality and reducing the incidence of cardiovascu- lar diseases and diabetes [1,2]. The World Health Organization recommends that adults engage in at least 150–300 min of moderate-intensity exercise or at least 75–150 min of high-intensity exercise per week [3]. Additionally, recent studies indicate that short-term high-intensity daily exercise, such as running, fast cycling, basketball, and soccer, is as- sociated with a 22% reduction in mortality risk and a 20% reduction in cancer risk [4–7]. Metabolites2025,15, 405 https://doi.org/10.3390/metabo15060405
Metabolites2025,15, 405 2 of 14 However, high-intensity exercise may also pose health risks [8], the most common of which is exercise-induced muscle damage (EIMD) [9]. High-intensity exercise, especially eccentric exercise, excessively stretches muscle cells, leading to the disruption of sarcomere ultra- structure [10]. Concurrently, increased metabolic stress in high-intensity exercise results in calcium overload and free radical accumulation, further damaging and lysing muscle cells [11]. The most typical symptoms of EIMD include delayed-onset muscle soreness (DOMS) and strength loss, which usually manifest 24 h post-exercise and persist for sev- eral days [12]. Additionally, EIMD is often accompanied by localized inflammation and prolonged training recovery [9]. Severe EIMD may induce rhabdomyolysis, resulting in myoglobinuria, hyperkalemia, hypocalcemia, and disseminated intravascular coagula- tion [13]. Therefore, an early diagnosis of EIMD is of great importance for minimizing exercise-related health risk. So far, the most common method for EIMD diagnosis relies on blood biochemical anal- ysis. Levels of creatine kinase (CK) and lactate dehydrogenase (LDH) in serum obviously increase with the occurrence of EIMD, and they have been used as serum indicators of EIMD [14,15]. The main reason is that when EIMD occurs, especially during high-intensity exercise such as eccentric exercise, muscle cells are excessively stretched, leading to the disruption of sarcomere ultrastructure. Furthermore, EIMD occurrence increases metabolic stress, resulting in calcium overload and free-radical accumulation, further damaging and lysing muscle cells. Therefore, myoglobin, C-reactive protein, cortisol, and testosterone levels in serum are related to EIMD [16]. Therefore, these serum indicators have been used for the auxiliary diagnosis of EIMD. Blood biochemical analysis has been employed in athlete training but is not suitable for public fitness. The main reason is that blood bio- chemical analysis requires professional technicians and biochemical analyzers [17]. Blood sampling also exerts physical and psychological stress, which may interfere with exercise performance [18]. Recently, some studies have reported non-invasive methods for EIMD diagnosis. Med- ical imaging methods, such as ultrasound imaging [19], magnetic resonance imaging [20], optical coherence tomography [21], and infrared observation [22], have been used to moni- tor muscle tissue changes. However, medical imaging equipment is expensive and cannot
exerts physical and psychological stress, which may interfere with exercise performance [18]. Recently, some studies have reported non-invasive methods for EIMD diagnosis. Med- ical imaging methods, such as ultrasound imaging [19], magnetic resonance imaging [20], optical coherence tomography [21], and infrared observation [22], have been used to moni- tor muscle tissue changes. However, medical imaging equipment is expensive and cannot be easily operated. Compared to medical imaging, electromyographic signals are easier to measure and are associated with EIMD [23], but specific electromyographic indicators have not yet been reported. Self-diagnosis of EIMD is achieved using the combination of muscle strength assessment [24], but the significant decrease in muscle strength typically lags 24 h behind EIMD onset [25]. Therefore, further research is required to develop non-invasive methods for the early diagnosis of EIMD. Saliva has been utilized for non-invasive disease diagnosis. Currently, saliva is mainly used in the diagnosis of oral diseases. Numerous micro RNAs, such as miR-200a and miR-31, have been employed in diagnosing oral squamous cell carcinoma (OSCC) [26]. Additionally, a recent study identified 25 salivary metabolites as potential biomarkers of OSCC [27]. Metabolites in saliva, such as butyrate, have been used to diagnose orthodonti- cally induced external apical root resorption [28], while TLR4 mRNA has been applied for diagnosing periodontitis [29]. Furthermore, recent research has also reported the associa- tion of salivary metabolites with non-oral diseases. For instance, higher cortisol levels have been observed in the saliva of Parkinson’s disease patients [30]. Lower prolactin levels have been found in the saliva of lung and prostate cancer patients [31,32]. In diabetic patients, significantly elevated levels of salivary amylase have been observed [33]. Consequently, recent studies have focused on using salivary biomarkers for the diagnosis of non-oral diseases. Shu et al. reported that fucosylated N-/O-linked glycans serve as a biomarker for gastric cancer [34], and salivary pepsin has been utilized for diagnosing gastroesophageal
Metabolites2025,15, 405 3 of 14 reflux disease and laryngopharyngeal reflux [35–38]. Considering that various disease biomarkers are present in saliva, it is reasonable that biomarkers of EIMD might also be present in saliva. A series of studies have demonstrated that exercise induces changes in salivary metabo- lites. Alzharani et al. reported that two days of soccer training resulted in significant changes in 27 salivary metabolites, with 22 being upregulated and 5 being downregu- lated [39]. Ra et al. further elucidated the physiological functions of metabolite changes [40]. In this study, three days of soccer training led to the significant upregulation of 10 salivary metabolites. These metabolites were mainly involved in glucose synthesis and the tricar- boxylic acid cycle, supporting higher energy metabolism. In fact, short-term high-intensity exercise also obviously changed salivary metabolites. For instance, Pitti et al. found sig- nificant changes in 17 salivary metabolites after a soccer match (~90 min), with 14 being upregulated and 3 downregulated. Moreover, the trend of salivary metabolite changes varied with training methods. Compared to outfield players, goalkeepers exhibited unique changes in salivary metabolites post-match, e.g., an increase in formate and a decrease in isocaproate [41]. Notably, different from outfield player training, goalkeeper training involves rapid-response activities such as jumping, diving, and falling, which entails higher risks of muscle damage [42,43]. Therefore, unique metabolite changes in goalkeeper train- ing might be related to EIMD. However, EIMD onset was not analyzed in the above studies, and salivary biomarker screening of EIMD has not been reported. Therefore, this study aimed to clarify salivary metabolite changes after high-intensity exercise and screen potential salivary biomarkers for EIMD prediction. First, 18 untrained young male participants were recruited and subjected to high-intensity rowing exercise. Then, EIMD occurrence was determined by blood biochemical analysis. Following that, quasi-targeted metabolomics was employed to analyze the changes in and characteristics of salivary metabolites immediately after exercise, elucidating differential metabolites and metabolic pathways. The metabolites strongly correlated with blood biochemical indicators were also identified. Finally, potential salivary biomarkers for EIMD prediction were screened, and the performances of multiple salivary metabolites in EIMD prediction were evaluated.
blood biochemical analysis. Following that, quasi-targeted metabolomics was employed to analyze the changes in and characteristics of salivary metabolites immediately after exercise, elucidating differential metabolites and metabolic pathways. The metabolites strongly correlated with blood biochemical indicators were also identified. Finally, potential salivary biomarkers for EIMD prediction were screened, and the performances of multiple salivary metabolites in EIMD prediction were evaluated. This study first reveals salivary metabolite variation after high-intensity rowing train- ing and proposes potential biomarkers for EIDM prediction. The results not only contribute to the interpretation of metabolite variation after high-intensity exercise but also provide a basis for the development of point-of-care device for EIMD prediction. 2. Material and Methods 2.1. Subject A total of 18 healthy young male participants were recruited for this study. All participants were university students with a mean age of 23 years and an average BMI of 22.9±1.8 kg·m −2 (Table). A questionnaire confirmed that none of the participants had regular exercise habits or previous experience with rowing training. To minimize exercise-related risks, all participants were free from cardiovascular and musculoskeletal diseases. To prevent the influence of oral diseases on salivary metabolites, none of the participants had periodontal diseases or other oral conditions, as determined by self-report. Additionally, none of the participants experienced bacterial or viral infections during the experiment because infection may induce pathological interference with CK levels. At rest, all participants exhibited normal levels of blood indicators of EIMD (CK: 55–170 U/L, LDH: 125–220 U/L). All the participants provided written informed consent before exercise, and approval was obtained from the ethics committee of the Beijing Institute of Technology [BIT-EC-H-2022143, approved on 11 August 2022].
Metabolites2025,15, 405 4 of 14 Table 1.Physical indicators of participants. Physical Indicators Range Average Value ±Standard Error Height/cm 168–186 177.6 ±4.6 Weight/kg 58–86 72.3 ±7.0 BMI/kg·m −2 20.3–27.1 22.9 ±1.8 2.2. Exercise Protocol All participants were required to engage in rowing exercise using a magnetic resistance rowing machine (MRH3208A, Mobifitness Co., Shanghai, China). The maximum resistance was 32, and a high resistance of 30 was used in this study. The training goal was a total rowing distance of 3.0 km, requiring approximately 600 cycles of pulling and releasing. After every 200 m of rowing, the resistance was adjusted to level 1 for 30 s to provide a quick rest. During the rowing exercise, all the participants were asked to perform rapid pulling and slow releasing because releasing involves more eccentric exercise. Continuous encouragement was provided, and all the participants completed the set goal. Upon achieving the set goal, participants were asked to perform a vertical-jump, and the height of the jump was recorded to evaluate strength loss. DOMS was measured using the visual analog scale (VAS) at 24 h after exercise (Short for 24 h Post-Ex). Subjects were asked to mark a point on a VAS of 100 mm in length, where 0 mm signified “no pain” and 100 mm signified “extremely painful”. 2.3. Sample Collection and Preservation Blood and saliva samples were collected from participants at two time points, in- cluding at rest (Pre-Ex) and immediately after exercise (Post-Ex). To avoid interference from blood sampling with exercise, the two sampling steps were not performed on the same day, but on two consecutive days. On the first day, participants were asked to have a standard breakfast at 8:00 am. Then, the participants rested for 1 h, and blood and saliva were sampled at around 9:00 am. On the second day, the participants had the same breakfast at 8:00 am. Then, the participants were asked to rest for 30 min and to perform rowing exercise. Blood and saliva samples were collected immediately after the exercise session at approximately 9:00 am. Blood sampling was conducted at the Beijing Institute of Technology Hospital,
were sampled at around 9:00 am. On the second day, the participants had the same breakfast at 8:00 am. Then, the participants were asked to rest for 30 min and to perform rowing exercise. Blood and saliva samples were collected immediately after the exercise session at approximately 9:00 am. Blood sampling was conducted at the Beijing Institute of Technology Hospital, and the blood samples were preserved in serum separation tubes and analyzed on the same day. Saliva samples were self-collected by participants using saliva collection devices, and the pretreated saliva samples were stored at−80 ◦ C for metabolite analysis. 2.4. Analysis of Blood Biochemical Markers and Salivary Metabolites Two biochemical indicators of EIMD, including CK and LDH, were analyzed at Bei- jing Dian Diagnostics Laboratory. CK catalyzed the conversion of creatine and ATP to phosphocreatine and ADP, and the activity was quantitatively determined by measur- ing the rate of ADP production. LDH catalyzed the conversion of lactate and oxidized nicotinamide adenine dinucleotide (NAD + ) to pyruvate and reduced nicotinamide adenine dinucleotide (NADH), and the activity was quantitatively determined by measuring the rate of NADH production. Salivary metabolites were analyzed using quasi-targeted metabolomics with broad detection range and high accuracy. First, the saliva samples were centrifuged to remove cellular debris and impurities. Then, methanol was used to remove proteins in the saliva samples, and the metabolites were extracted and concentrated by lyophilization. Finally, liquid chromatography–mass spectrometry was utilized to measure metabolites. Metabo- lite detection was performed using the multiple reaction monitoring mode with a mass
Metabolites2025,15, 405 5 of 14 spectrometer (QTRAP 6500+, AB SCIEX, Marlborough, MA, USA) based on the Novogene database. Metabolite qualification was achieved by assessing Q3, while qualitative analysis was based on the retention time, Q1/Q3 ion pair information, and secondary spectral data. 2.5. Bioinformatics Analysis The significance of blood indicator changes after exercise was analyzed by using the paired t-test function in Prism (version 10.2, GraphPad Software, USA). Salivary metabo- lite concentration was standardized before bioinformatics analysis. The standardization was performed by the ratio of metabolite concentration to creatinine concentration, which helped to mitigate the influence of salivary hydration status on metabolite concentra- tions [41]. Bioinformatics analysis was performed using the MetaboAnalyst 6.0 platform (www. metaboanalyst.ca as follows: (i)The overall changes in salivary metabolites after exercise were examined by using principal component analysis (PCA) and hierarchical clustering heatmaps. PCA was used to identify patterns of variation among samples. Hierarchical clustering showed the relationships between different samples and metabolites based on the similarity in metabolite profiles. (ii)The salivary metabolites strongly correlated with CK and LDH were identified by using the pattern search module, and they are two important blood biochemical indicators of EIMD. The high correlation between salivary metabolites and CK/LDH was significant for understanding the physiological response to exercise, developing non-invasive diagnostic methods for EIMD, and exploring the body’s metabolic adaptation mechanisms. (iii) t- test. The statistical test helped to determine which metabolites had significant changes between the Pre-Ex and Post-Ex groups. The enriched metabolic pathways were revealed by using the Kyoto Encyclopedia of Genes and Genomes (KEGG) module. KEGG is a comprehensive database that integrates genomic, chemical, and systemic functional information, which helped us to understand the biological functions and pathways associated with the differential metabolites. (iv)A discriminant model for classification was established using partial least squares discriminant analysis (PLS-DA). The potential salivary biomarkers for EIMD predic- tion were screened by the operating characteristic curve (ROC). Based on the area under the curve (AUC) ranking, potential salivary biomarkers for EIMD prediction were screened. The prediction performances of multiple salivary metabolites were evaluated by using random forest, which contributed to
for classification was established using partial least squares discriminant analysis (PLS-DA). The potential salivary biomarkers for EIMD predic- tion were screened by the operating characteristic curve (ROC). Based on the area under the curve (AUC) ranking, potential salivary biomarkers for EIMD prediction were screened. The prediction performances of multiple salivary metabolites were evaluated by using random forest, which contributed to determining the optimal number of metabolites for more accurate EIMD prediction. 3. Results 3.1. Blood Biochemical Indicators Analyses In this study, 18 young male participants without regular exercise experience were asked to perform rowing exercise with high resistance, and all the participants success- fully completed the set goal of exercise. CK levels increased from 98.1±19.4 U/L to141.2±62.4 U/L with apvalue of 0.004. Correspondingly, LDH levels rose from 149.8±17.3 U/L to 162.8±17.4 U/L with apvalue of 0.0108 (Figurea,b). All the participants reported DOMS one day after training with a VAS value of ~50 mm, with a pvalue of 0.0003 (Figurec).
Metabolites2025,15, 405 6 of 14 Figure 1.Serum indicators of EIMD and DOMS before and after rowing exercise ((a) CK; (b) LDH; (c) VAS score). (* indicatesp< 0.05, while ** indicatesp< 0.01). 3.2. Changes in Salivary Metabolites After High-Intensity Rowing Training A total of 594 metabolites were revealed in 36 saliva samples, indicating that more metabolites were observed in this study [39]. All the 36 saliva samples were visualized by PCA analysis, and three principal components collectively accounted for 89.7% of metabolite variation (Figurea,b). In the PCA score plot (Figureb), the green ellipse represents the samples of Pre-Ex, while the red ellipse delineates Immediate Post-Ex, demonstrating clear separation between groups. Notably, 18 samples in the Pre-Ex group are clustered in one region, while 18 samples in the Post-Ex group are clustered in another region (Figureb). Similar results were observed by cluster heatmap analysis (Figurec). All the 36 samples are clustered into two groups, which is completely consistent with the experimental grouping. Among these metabolites, the changes in some metabolites exhibit high correlation with CK and LDH (Figure). Erucamide is positively correlated with CK, while Tris(2-carboxyethyl)phosphine is negatively correlated with CK (Figurea). Hexadecanamide is positively correlated to LDH, while 3 ′ -Adenylic acid is negatively correlated to LDH (Figurea). A total of 74 metabolites exhibited significant changes immediately after exercise, with 43 metabolites being upregulated and 31 metabolites being downregulated (Figurea,b ). According to z-scores, the top 10 upregulated metabolites were Tetradecanamide, Oleamide, Hexadecanamide, Feruloyl Putrescine, Metanephrine, Diflunisal, Gondoic Acid, Niflu- mic Acid, 10E,12Z-Octadecadienoic Acid, and Erucamide. According to z-scores, the top 10 downregulated metabolites were Tris(2-carboxyethyl)phosphine, Pyrroloquino- line Quinone, Lipoic Acid, Nicotinamide Mononucleotide, Adenine, S-Methyl-L-cysteine, Dopamine, UMP, Oxypurinol, and Isophorone. Differential metabolites were due to the metabolic pathway changes after exercise, and they were illustrated by KEGG enrichment analysis (Figurec,d). The downregulated pathways were mainly related to carbohydrate metabolism. Similarly, pentose phosphate metabolism, starch and sucrose metabolism, and glycogen biosynthesis were significantly downregulated. In comparison, upregulated pathways were related to hormone secre- tion, antioxidation, and muscle repair. Two Methyluric acids in caffeine metabolism were upregulated. The biosynthesis of unsaturated fatty acids
and they were illustrated by KEGG enrichment analysis (Figurec,d). The downregulated pathways were mainly related to carbohydrate metabolism. Similarly, pentose phosphate metabolism, starch and sucrose metabolism, and glycogen biosynthesis were significantly downregulated. In comparison, upregulated pathways were related to hormone secre- tion, antioxidation, and muscle repair. Two Methyluric acids in caffeine metabolism were upregulated. The biosynthesis of unsaturated fatty acids (UFAs) was upregulated. Addi- tionally, the biosynthesis of terpene quinones was significantly increased. Phenylalanine, tyrosine, and tryptophan biosynthesis was markedly upregulated after exercise. Simultane-
Description
This study analyzes salivary metabolite changes after high-intensity rowing training and identifies potential biomarkers for EIMD prediction.