Study Of The Noninvasive Biomarkers And Their Application In Assessing Of Exercise Performance

When the body is exposed to external environmental stress,it causes a response in the body through specific physiological pathways called the stress response.Exercise as a stress can also cause physiological responses in the neuroendocrine,autonomic nervous system and other pathways.Because sports training can enhance physical fitness,will and improve skills,so sports training activities have also become an important means of military training and selection of military personnel.For example,the Special Forces Assessment and Selection(SFAS)curriculum used by the United States to select Special Forces soldiers is focused on physical challenges(timed weighted road marches,timed runs,obstacle courses,and other physical training),and it is proposed to correlate the levels of exercise-induced physiological stress markers and their changes to analyze training performance,screening predictors,trying to discover individual characteristics related to the behavior and performance of individuals whose mind and body are in a stressful environment,these findings are beneficial to the selection and training of personnel working in military and other highly stressful occupational environments.In China,the selection of special personnel is still mostly organized and implemented by traditional experience,and scientific and systematic theoretical methods are still being explored and established.In a normal state the body is in a delicate internal equilibrium,and when the body is stimulated by external stimuli,the body’s nervous system responds quickly and releases the appropriate hormones to adapt to the current state of stress.In recent years,it has been found that exercise stimulates the activation of various neural pathways in the body,such as the hypothalamic-pituitary-adrenocortical(HPA)axis,sympathetic-adrenal-medullary(SAM)axis and microbial-gut-brain(MGB)axis,resulting in changes in the physiological and biochemical indicators of several systems in the body.Therefore,monitoring of individual physiological status can be applied in the military field,and real-time physiological status monitoring of soldiers can provide predictions about physical health and military mission performance.With the development of modern analytical techniques,metabolomics can be applied to disease diagnosis,physiopathological analysis,etc.These techniques focus mainly on the effects of stress hormones and gut flora metabolites on health and are less common for exercise stress studies and for screening and predictive modeling of biochemical indicators associated with exercise performance.This study focuses on non-invasive biological samples(urine and saliva)from subjects in sports(military training activities),using the solid-phase nanofiber microextraction technique already available in our group,combined with high performance liquid chromatography(HPLC-PAD)and gas chromatography-mass spectrometry(GC-MS)to detect the target substances in the samples,and applying statistical methods to investigate the correlation between potential markers in urine and saliva and sports performance,screen the predictors,and then establish a biochemical detection-based assessment method for soldiers’ military training performance.Forty cadets were recruited,they were 18.79 ± 0.57 years old,172.72 ± 4.82 cm in height and 64.78 ± 9.95 Kg in weight,with 21.70 ± 2.88 Kg/m2 in body mass index;Sixty soldiers were also recruited,they were 20.02 ± 1.62 years old,174.88 ± 5.44 cm in height and 67.63 ± 6.21 Kg in weight,with 22.09 ± 1.50 Kg/m2 in body mass index.All recruits were fitness and without underlying diseases,andtheir daily life and diet were consistent.The study consists of the following main parts.1.Development of a method for the detection of short-chain fatty acids(SCFAs)in urine and analysis of its correlation with human exercise performance.A polyacrylonitrile/poly(3,4-ethylenedioxythiophene)(PAN/PEDOT)nanofiber was synthesized by in situ polymerization and used as the sorbent to treat the samples.Combined with gas chromatography-mass spectrometry(GC-MS),an analysis of urinary SCFAs was developed.The method showed a wide linear range,low limit of detection(LOD),low intra/inter-day error(RSD < 13.7%),and high spiked recoveries(87.5%-104.6%)for the targets,indicating that the established method can meet the requirements for accurate detection of trace SCFAs in urine.Using a common military physical training subject as the experimental paradigm,the correlation between the concentration levels of SCFAs in urine and exercise performance was analyzed before and after the training exercise,using the time taken to complete the task as a proxy for exercise performance.A strong negative correlation(r-0.545 to-0.627)was found between the exercise performance and the concentration increments of acetic acid and butyric acid in the subjects’ urine before after exercise.When the subjects were divided into two groups according to the length of time taken to complete the task,and the receiver operatingcharacteristic curve(ROC curve)was used to predict the exercise performance of soldiers,it was found that the area under the ROC curve(AUC)of acetic acid concentration increment was 0.736 and 0.679 for the trainees and soldiers,respectively.The AUCfor butyric acid was 0.828 and 0.728 for the trainees and soldiers,respectively,indicating that the AUC showed good predictive effect when used as a judgment classifier in the exercise performance prediction model.2.Detection of representative biochemical indicators of the HPA axis and SAM system in urine and their correlation with exercise performanceThe hormones cortisol and cortisone associated with the HPA axis,and epinephrine and norepinephrine associated with the SAM system in the urine of subjects before and after exercise were measured and statistically analyzed using the relevant hormone analysis methods available in the group.The ratio of cortisol to cortisone(C/Cn)and the ratio ofepinephrine to norepinephrine(E/NE)in morning urine were found to be moderately correlated with exercise performance.Pearson correlation coefficients for C/Cn values were-0.452 and-0.504 in the trainees and soldiers,respectively,while for E/NE values were 0.597 and 0.762,respectively.When ROC curves were used to predict the individuals’ exercise performance,the AUCs calculated from C/Cn values were 0.736 and 0.796 for the trainees and soldiers,respectively,and for E/NE values were 0.760 and 0.849,respectively.The study showed that C/Cn and E/NE values were more effective in the exercise performance prediction model.Using the salivary amylase tester developed by the group,the α-amylase activity in the saliva of the subjects before and after exercise was measured and statistically analyzed.The increment of salivary α-amylase activity before and after exercise was found to have a moderate positive correlation with exercise performance.The pearson correlation coefficients were 0.583 and 0.569 for the trainees and the soldiers,respectively.When the ROC curves were used to predict the performance of the subjects,the AUCs were 0.844 and 0.604 for the trainees and the soldiers,respectively.It was shown that as a sympathetic marker,incremental alphaamylase activity can also be used as a predictor of exercise performance and has better results in the prediction of exercise performance.3.The correlation between physiological indicators and human exercise performance and its association with biochemical indicatorsHeart rate and heart rate variability indicators were collected from soldiers during training using POLAR Team2 team heart rate telemetry and were correlated with soldiers’ exercise performance.The results showed that resting heart rate was weakly correlated with exercise performance in the trainees(Pearson correlation coefficient r = 0.251)and positively correlated with log pre-exercise amylase activity(Pearson correlation coefficient r = 0.560).Heart rate variability frequency domain indicatorlow frequency mean was weakly correlated with exercise performance in the trainees(Pearson correlation coefficient r =-0.227),while heart rate variability frequency domain indicator-high frequency mean was moderately correlated with exercise performance in the trainees(Pearson correlation coefficient r =-0.474);correlating low and high frequency ratio was moderately correlated with exercise performance(Pearson’s correlation coefficient r = 0.312).When the high-frequency mean was used to predict exercise performance in the trainees using the ROC curve,the AUC was found to be 0.647,indicating the accuracy of using the high-frequency mean in predicting exercise performance in the population,and suggesting that physiological and biochemical indicators can be used to complement each other in exercise performance assessment studies.4.The establishment of the prediction modelResting heart rate(HR),α-amylase activity(α-amylase),epinephrine-tonorepinephrine ratio(E/NE),HF mean of heart rate variability(HF),and cortisol/cortisone ratio(C/Cn)were included as variables,and time to complete training(Time)was included as the dependent variable in the stratified regression analysis.The results showed a statistically significant model(P < 0.001)with a linear correlation between the dependent and independent variables,indicating that the inclusion of these independent variables helped to predict the dependent variable compared to the null model.The final equation of the exercise performance prediction model obtained wasexercise time spent(Time)= 1443.464 + 4.042* HR + 8.809*(α-amylase)-28.427*(HF)+ 9.607*(E/NE)-239.906*(C/Cn).The combined multimetric prediction model developed above was able to make preliminary predictions of the soldiers’ exercise performance.Due to the limitations of the research samples and time,the number of subjects in this study was insufficient and the subjects were all male,which leads to large deviations in the statistical analysis results.It also remains to be veriedwhtherthe study results can be adapted to the whole population(including women).In addition,the secretion of body hormones can be affected by diet structure,psychological fluctuations,temperature changes and air quality,so scientific long-term follow-up tests should be conducted to investigate the extent to which these variables have an impact on exercise stress.