New Insights into Stress Metabolomics. Looking for new Diagnostic Biomarkers. 

New Insights into Stress Metabolomics. 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Looking for new Diagnostic Biomarkers. Gifty Animwaa Frempong, Guillermina Goni, Mónica Lorenzo-Tejedor, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6503620/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Stress is associated with the onset of various neurological disorders such as depression, PTSD, and anxiety. Despite the extensive research performed, metabolic changes triggered in response to acute psychological stress remain unclear. This study evaluates acute stress biomarkers and its adverse effects in university students through psychometric, biochemical, and metabolomic analyses, implementing Machine Learning on statistical models. In the study, forty participants were subject to relaxation and stress induction using autogenic training and a modified Trier Social Stress Test (TSST-M). Psychometric questionnaires confirmed the achievement of these states, while saliva and blood were sampled for biochemical analyses. Additionally, blood samples were applied to untargeted metabolomic approaches. The results reveal that although most biomarkers showed changes under stress state, the machine learning predictive model successfully identified salivary α-amylase and State-Trait Anxiety Inventory-state (STAI-s) as prominent stress markers. Additionally, several metabolic pathways, including steroid hormone biosynthesis, glycerophospholipid metabolism, linoleic acid metabolism, tyrosine metabolism, and aminoacyl-tRNA biosynthesis, were affected. Alterations of this sort, we conclude, allow us to gain further understanding into the adverse effects systematically associated with stress. In this way, our findings highlight the significant impact of acute mental stress on multiple metabolic pathways directly implicated in stress-related disorders. Biological sciences/Physiology/Neurophysiology Health sciences/Molecular medicine Biological sciences/Neuroscience/Stress and resilience Mental Stress Reactivity Metabolic Responses Biomarkers Metabolomics Trier Social Stress Test Direct Infusion Mass Spectrometry (DI-MS) Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Stress Physiological systems in the body are inherently programmed following rigorous fine-tuning of regulated variables. These variables ​​must be kept within an acceptable dynamic range, known as homeostatic state , which is essential for life and well-being 1 , 2 . However, this optimal balance is constantly challenged by intrinsic and extrinsic adverse forces or stressors . While some stressors, like unexpected events, urgent tasks, traumatic events, and adverse social, economic, and environmental circumstances, often produce psychological effects 3 , 4 others, such as injuries, noise, or exposure to extreme temperatures, could have physical consequences 1 , 2 , 5 . Stressors, when perceived as a threat, lead to a maladaptive stress response or disharmony called distress (popularly referred to as bare ‘stress’). Stress triggers a complex interplay of physiological and behavioral responses aimed at reestablishing homeostasis, hence improving survival chances 1 . This process involves an intricate network engaging the Central Nervous System (CNS) and peripheral organs, leading to the activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis and Sympathetic Nervous System (SNS), followed by the inhibition of the Parasympathetic Nervous System (PSNS) 1 . If this response is not adequate enough to preserve the balance required, an inflammatory response is triggered in an attempt to restore the system to its homeostatic state 6 . These biochemical and physiological changes can be consequently used to determine and monitor stress. However, because each individual responds differently according to inherent personality traits along with a myriad of genetic, environmental, and developmental parameters, inter-subject variability is another factor making stress diagnosis and monitoring even more challenging 7 , 8 . Stress is generally classified into three main types: acute, chronic, and negative. Acute stress triggers a time-limited set of cognitive-behavioral and physiological changes as an immediate response to a stressor 1 , 2 . Neuropsychologically, acute stress concomitantly enhances alertness and vigilance. Physiologically, intermediate metabolism is adjusted to increase nutrient levels; increased respiratory and heart rates augment oxygen and cardiac output, supporting cardiovascular tone 1 . The resulting nutrient-enriched blood is redistributed to organs directly involved in stress response orchestration (brain, heart, and skeletal muscles). This comes at the expense of a critical but temporary reduction of blood supply to energy-consuming vegetative functions like digestion, renal and intestinal excretion, reproduction, growth, and immunity 1 , 7 . Chronic stress involves a constant stress stimulus. This can consequently lead to a stage where the body can no longer achieve homeostatic balance, and the individual can no longer deal with the stressors 9 . Negative stress (distress), in turn, 10 yields detrimental effects on several psychological and physiological functions, such as altered cognitive and affective capacities, mental processing, and sleep-arousal cycle disorders, along with simultaneous inhibition of vegetative functions, such as feeding and reproduction. It can also affect gastrointestinal and cardiovascular function, growth, metabolism, reproduction, and immune competence. Individual performance, behavior, and personality development can be equally affected 7 , 9 . Nonetheless, stress reactivity depends on: (i) the type of stressor, for different stressors activate different metabolic pathways; (ii) the intensity and duration of the stressor, such that the higher the degree of stress, the lower specificity of the adaptive response, and (iii) inter-subject variability, considering the manner in which each individual perceives stressors 7 . Psychological Stress and Distress Given its influence on human decision-making, psychological stress (negative stress) represents a major public health concern 11 – 13 . According to the World Health Organization (WHO) 3 , the prevalence of social and medical problems associated with mental stress is globally increasing, also in children, seriously affecting their mental health and well-being. There are many factors contributing to global stress increase. The COVID-19 pandemic, for instance, became a universal stressor centrally involved in a global mental health crisis, since it implied enduring unprecedented short and long-term stressful situations that undermined the mental health of millions 12 , 13 . In any event, and especially when chronic, mental stress exacerbates our susceptibility to several diseases, eventually becoming a common cause of morbidity and mortality 11 . Consequently, mental stress has a visible impact on the Health System, resulting in elevated healthcare costs, invalidity, or productivity loss. In view of this, finding objective and precise diagnostic methods is nowadays a pressing question to be resolved 14 , 15 . Stress Diagnosis To date, stress diagnosis and estimation remain complex and clouded, carrying considerable chances of uncertainty. Current standard diagnostic methods build on validated psychometric questionnaires, tracking stress-induced changes in cognitive and behavioral abilities 16 . Although they are considered highly reliable methods, the interpretation of the questions by the patients and/or the results by the specialist is still highly subjective, thus leading to various biases that can compromise the diagnosis itself 5 , 17 , 18 . In this sense, and despite many efforts, an objective and reliable method for stress diagnosis has not yet been developed. While different biomarkers have been proposed for acute psychological stress determination in the literature, important disparities in the results still exist 19 . Since the distinctive feature of stress response is the activation of SNS and, most importantly, the HPA axis, 20 , 21 , the most promising biomarkers point to metabolites released as a result. Given the multidimensional nature of stress, we submit that determining one or only a few reliable biomarkers for diagnosis is unlikely to be a feasible goal. Reported inconsistencies in the literature may probably be the result of oversimplifying the overall process 22 . To solve this, we propose an omics analysis aiming to identify a significant set of empirically relevant biomarkers, which would result in a more effective approach. In this proposal, metabolomics is presented as the most appropriate strategy 23 , 24 . It involves systematic identification and quantification of the metabolite profile that characterizes the phenotype of an organism in a specific situation. Moreover, metabolomics allows the simultaneous determination of the altered set of metabolites in response to stress, providing a global vision of the metabolic changes arising as a result. Metabolites are the intermediate or end-products of cellular regulatory pathways, and their levels can be regarded as the ultimate response of biological systems to genetic and environmental changes 25 . The present study, integrated into a multidisciplinary project ( ES3-P 19 , 23 , 26 , 27 ) aimed at assessing acute psychological stress, we develop a proposal where the main goal is to determine the metabolomic fingerprint of acute psychological stress in a cohort of volunteeringuniversity students. This would directly contribute to the discovery of new stress biomarkers and help to unveil the molecular basis of adverse outcomes. As a secondary goal, we will analyze the potential utility of diverse biomarkers proposed in the literature and determine how gender differences operate in stress response. RESULTS Participant Characteristics Forty-one healthy young participants were enrolled. One participant opted out, resulting in a final sample size of 40. The group is constituted by young male and female participants in similar proportions (mean age of 22 ± 3.4 years), and a normal Body Mass Index (BMI of 22.4 ± 2.7 kg/m 2 ) according to guidelines established by the WHO 28 . The perceived stress levels measured before administering psychometric tests (Supplementary Table S1 ) showed an average of 49.4 units on a scale from 0 to 100, indicating no to mild stress. Based on habits, the majority of the subjects were non-smokers (85%), occasional consumers of alcoholic beverages (82.5%), and engaged in extracurricular activities (62.5%), mainly practiced sports regularly, learned foreign languages, or engaged in other types of artistic activities. Approximately half of the participants (45%) reported regular coffee consumption. In terms of their social background, most participants lived in urban areas (77.5%), were single (72.5%), and lived with their families (72.5%). With regard to health status, the vast majority of participants did not suffer from chronic diseases (95%) or take medications (75%). However, a small percentage (5%) had chronic diseases such as allergies, migraines, or intestinal reflux, and only 25% were on prescribed medications (mainly contraceptives, antihistamines, and antiasthmatics), which did not hinder the measurement sessions. Stress Evaluation and Measurement Psychometric tests Scores for STAI-s, VAS, and SSC showed statistically significant increases between RS and SS (Table 1 ), thus confirming that the participants had become stressed after applying the TSST-M test. The PSS and STAI-t tests did not show significant variation between the states. This reflects coherence in the evaluation since these questionnaires indicate one’s predisposition (trait) to respond to stressful situations, but do not evaluate the subject’s current state. Biochemical variables Statistically significant increases in the biochemical stress markers ΔAA sl , ΔFR sl , Cp pl , and Pr pl were observed between sessions. In contrast, the levels of ΔCr sl and Glu sr did not change significantly after the stressor was applied (Table 1 ). Sex-based disparities were observed in Cp pl and Glu sr , with comparatively lower levels in females (Table 1 ). It is worth mentioning that all variables were within the clinically accepted normal range. Table 1 Inter-subject median and median absolute deviation (MAD) of stress markers. All Female Male Stress markers Relax session Stress session Relax session Stress session Relax session Stress session Psychometric variables PSS (0–40) 21.0 ± 2.2 20.0 ± 3.0 21.67 ± 1.5 21.5 ± 3.7 21.5 ± 3.7 19.5 ± 3.7 STAI-s (0–80) 15.5 ± 6.7 23.0 ± 8.9 ** 16.0 ± 8.9 24.0 ± 8.2 14.0 ± 4.5 20.0 ± 8.2 STAI-t (0–60) 20.5 ± 9.6 19.5 ± 8.9 24.0 ± 12.6 21.5 ± 12.6 18.5 ± 8.2 18.5 ± 3.7 SSC (0–80) 17.5 ± 10.4 27.5 ± 18.5 ** 19.0 ± 12.6 32.5 ± 15.6 17.0 ± 9.64 23.0 ± 18.5 VAS (0-100) 30.0 ± 18.5 50.0 ± 29.7 ** 35.0 ± 22.2 50.0 ± 29.7 30.0 ± 25.9 50.0 ± 29.7 Biochemical Parameters Cp pl (pmol/L) a 5.9 ± 2.6 6.2 ± 2.9 * 3.7 ± 1.6 3.6 ± 1.8 7.0 ± 3.6 8.5 ± 4.2 Osm pl (mOsm/L) 303.0 ± 3.0 304.0 ± 4.0 303.0 ± 5.9 299.0 ± 2.9 304.0 ± 2.9 306.0 ± 5.2 Pr pl (ng/ml) 7.7 ± 1.7 8.3 ± 2.1 * 7.9 ± 2.5 8.9 ± 2.7 7.1 ± 2.1 7.6 ± 2.8 ΔCr sl (ng/ml) -0.06 ± 0.03 -0.04 ± 0.03 -0.03 ± 0.04 -0.03 ± 0.04 -0.06 ± 0.03 -0.06 ± 0.04 ΔAA sl (U/ml) 2.2 ± 18.2 45.3 ± 28.2 ** -2.2 ± 44.8 64.4 ± 35.3 2.3 ± 26.7 31.8 ± 22.8 Glu sr (ng/ml) a 91.0 ± 3.0 88.0 ± 5.0 89.0 ± 5.9 86.0 ± 5.9 91.0 ± 4.4 88.5 ± 5.9 ΔFR sl (ml/min) -0.1 ± 0.4 -0.1 ± 0.2 * -0.05 ± 0.5 -0.1 ± 0.2 -0.05 ± 0.4 -0.1 ± 0.1 The variations in psychometric variables and biochemical variables between RS and SS were analysed using Wilcoxon Signed-Rank Test at a significance level of α = 5%. Marked features show significant differences between sessions; *p-values < 0.05, **p-values < 0.001. a : statistically significant differences between sexes (p-value < 0.05). Correlations among the studied variables Our findings (Fig. 1 ) indicated a significant positive correlation (r) between VAS and ΔAA sl (r = 0.351, p < 0.01) and a significant negative correlation (r) between VAS and ΔFR sl (r = -0.277, p < 0.01). In addition, a positive association was observed among all psychometric variables, whereas a much less significant association (r) for VAS and PSS (r = 0.198, p = 0.078). The correlation (r) between ΔFR sl and ΔAA sl was negative (r = -0.387, p < 0.01). In contrast, no association (r) was observed between ΔAA sl and ΔCr sl . Stress Reference Scale (SRS) To build the SRS, psychometric and biochemical variables that were statistically significant in differentiating RS and SS states were included. The results of the PCA with n = 80 (40 RS and 40 SS) and seven dimensions are shown in Table 2 . The first four components exhibited eigenvalues greater than 0.7 and explained 84% of the total variance. The loading vectors (correlation coefficient scores) of each component allowed for the interpretation of the type of information collected by each component (Table 2 ). Thus, the first component mainly collected information corresponding to the psychometric tests, while the second component was positively associated with ΔFR sl and negatively with ΔAA sl . The third component had the highest scores for Cp pl, and the fourth had a strong positive correlation with Pr pl . Together, these components provide information on the different aspects (factors) involved in responses to acute psychological stress. The proposed SRS is expressed as Eq. (1): $$\:\begin{array}{c}SRS=\left(0.15*{STAI}_{s}+0.14*VAS+0.14*SSC+0.12*{AA}_{sl}+0.11*{FR}_{sl}+0.19*Cp+0.15*Pr\right)\#\left(1\right)\end{array}$$ Our findings indicated that SRS scores were significantly higher in SS than in RS (p = 1.299e-05). In addition, no significant sex-based variation was observed in SRS scores. Table 2 Principal Components Analysis (PCA) summary with eigenvalues, explained variances, and weights of the proposed SRS reference scale. PCA Component Weight (%) Variables 1 2 3 4 Pr pl 0.2466550 0.00162197 0.57448912 0.776963442 * 15 AA s 0.4094267 -0.74777448 * 0.22566106 -0.143047078 12 STAI-s 0.8509134 * 0.38870408 -0.10066183 0.005090755 15 SSC 0.8341677 * 0.30798238 -0.04963621 -0.004881756 14 VAS 0.8367070 * -0.01633558 -0.19598637 -0.094137137 14 FR s -0.3964135 0.71681296 * 0.20191654 -0.086553101 11 Cp pl 0.1713332 0.09291078 0.79956896 * -0.518754316 19 Eigen value 2.5349358 1.3278334 1.1120487 0.9096437 Variance (%) 36.213368 18.969049 15.886410 12.994910 Cum. variance (%) 36.21337 55.18242 71.06883 84.06374 Variance expl. (%) 43 23 19 15 100 Cum. variance: Cumulative variance; Variance expl.: Percentage of variance explained, proportional to the total variance explained by the four components. *variables with the highest weights in each component. Machine Learning: Decision Tree and Statistical Models Models created to predict whether an individual is stressed or relaxed provided similar results, indicating their robustness. Decision tree, bagging decision tree, and logistic regression models revealed that the most important variables for the prediction of acute psychological stress were ΔAA sl and STAI-s, whereas the random forest models indicated ΔFR sl as an additional predictor of acute stress (Fig. 2 ). The predictive accuracy of the decision tree model was 65.21%, while the random forest and logistic regression models had accuracies of 73.91% and an area under the receiver operating curves (ROC) of 0.84 and 0.85, respectively. Metabolomic Analyses Raw DIMS profiles showed approximately 1500 signals for each mode (ESI (+) and ESI (-)). After data curation, the features that remained were passed on for subsequent statistical analysis. PCA plots revealed a clear separation between blood metabolites for RS and SS (Fig. 3 ) for both ESI (+) and ESI (-), suggesting a clear influence of acute psychological stress on the blood metabolome. The loading diagram for both modes showed that the number of potential biomarkers in SS was significantly larger than that in RS (Supplementary Fig. S2). PLS-DA models built with ESI (+) and ESI (-) data provided good clustering of the samples and displayed a clear classification of each state. For ESI (+) mode, the model provided good explained variance (R 2 ) and predictive variance (Q 2 ) parameters with values of 0.8 and 0.259, respectively. Differential metabolites, with a Variable Importance in Projection (VIP) score > 2 29 , and variation coefficients (CV%) below 20%, to avoid subjectivity in the selection process, both for RS and SS in ESI (+), are shown in Table 3 . Most of the signals obtained in ESI (+) mode showed significantly altered blood levels (p < 0.05) of various amino acids and related metabolites (serine, indole, alanine, phenylalanine, valine, histidine, N-acetyl glutamine), altered sterols and steroid hormone biosynthesis (hydrocortisone, aldosterone, corticosterone, 11-deoxycorticosterone (DOC), progesterone, pregnenolone, cholesterol, 17α-hydroxypregnenolone, 11-deoxycortisol, 17-deoxycortisol, 17β-oestradiol, oestrone), and catecholamine neurotransmitters (dopamine, norepinephrine, epinephrine) (Table 3 ). The remaining significantly altered metabolites in SS corresponded largely to fatty acids and cellular membrane components (isobutyrate, choline, glycerophosphocholine, lyso-phosphatidylcholine (LPC)), sucrose sugar, and changes in muscle-related metabolites (creatine and carnitine). Nonetheless, the most predominant metabolites in RS included tyrosine, tryptophan, and its derivatives (the neurotransmitter serotonin, the neurotoxin quinolinic acid, and the hormone melatonin), derivatives of nitrogenous bases of nucleic acids (hypoxanthine and 2,4-dihydroxypyrimidine), and derivate of B3 vitamin N-methylnicotinamide (NMN) (Table 3 ). Analysis of blood samples in ESI (-) mode showed a comparable R 2 of 0.84, but a comparatively lower Q 2 of 0.04. The significant signals obtained in this mode were identified as fatty acids and phospholipids (Table 4 ), suggesting that stress leads to a substantial alteration of the lipid profile. Subsequent pathway analysis revealed many metabolic pathways that were significantly altered by acute mental stress. These included steroid hormone biosynthesis (p = 1.09E-07), glycerophospholipid metabolism (p = 4.03E-04), linoleic acid metabolism (p = 3.27E-03), aminoacyl-tRNA biosynthesis (p = 1.09E-02), and tyrosine metabolism (p = 4.14E-02) (Fig. 4 ). Table 3 Significantly differential metabolites determined using positive ion mode (ESI (+)) after relax session (RS) and after stress induction (SS). Predominant metabolites in SS Formula m /z [M + H] + Δm (ppm) p -value CV (%) VIP Hydrocortisone a C 21 H 30 O 5 363.4653 -7.3 1.8·10 − 2 6.2 2.18 Aldosterone a C 21 H 28 O 5 361.4485 1.8 2.6·10 − 3 7.6 2.09 Corticosterone a C 21 H 30 O 4 347.2245 6.6 2.9·10 − 2 5.3 2.05 DOC a C 21 H 30 O 3 331.2253 -6.0 3.1·10 − 4 6.5 2.10 Progesterone (P4) a C 21 H 30 O 2 315.2314 -3.2 4.1·10 − 2 9.7 2.68 Pregnenolone (P5) a C 21 H 32 O 2 317.2498 5.7 4.0·10 − 2 7.8 2.09 Cholesterol a C 27 H 46 O 387.3598 -7.2 5.1·10 − 3 4.4 2.01 17-OHP a C 21 H 32 O 3 333.2403 -7.8 1.1·10 − 3 6.3 2.62 11-deoxycortisol a C 21 H 30 O 4 347.2257 10.1 2.1·10 − 2 7.3 2.09 17-deoxycortisol a C 21 H 30 O 4 347.2257 10.1 2.1·10 − 2 7.3 2.09 17β-oestradiol a C 18 H 24 O 2 273.1878 8.8 1.7·10 − 2 11.2 2.36 Oestrone (E1) a C 18 H 22 O 2 271.1706 2.9 8.0·10 − 3 12.3 3.01 Sucrose C 12 H 22 O 11 342.29648 2.05 4.4·10 − 2 2.9 2.71 Serine a C 3 H 7 NO 3 106.0514 9.4 3.2·10 − 3 7.2 2.41 Indole a C 8 H 7 N 118.0670 11.8 2.9·10 − 2 5.8 2.34 Alanine C 3 H 7 NO 2 89.09318 8.32 6.1·10 − 3 3.1 2.53 Phenylalanine a C 9 H 11 NO 2 166.0858 -6.0 1.7·10 − 2 5.1 2.42 Dopamine a C 8 H 11 NO 2 154.0857 -7.1 9.4·10 − 3 5.3 2.37 Isobutyrate C 4 H 7 O 2 87.0971 -3.21 2.63·10 − 2 4.2 2.57 Norepinephrine a C 8 H 11 NO 3 170.0826 5.3 2.4·10 − 2 5.8 2.27 Epinephrine a C 9 H 13 NO 3 184.0959 -7.6 8.1·10 − 3 6.0 2.35 Choline a C 5 H 13 NO 103.1628 -15.0 3.4·10 − 2 8.2 2.81 Valine a C 5 H 11 NO 2 117.1463 -7.5 1.5·10 − 3 6.4 2.31 Creatine a C 4 H 9 N 3 O 2 131.1331 -17.1 4.1·10 − 2 10.0 2.03 Histidine a C 6 H 9 N 3 O 2 155.1545 -12.2 1.7·10 − 3 5.4 2.07 Carnitine a C 7 H 15 NO 3 161.1989 -11.8 3.5·10 − 3 9.4 2.24 NAG a C 7 H 12 N 2 O 4 188.1811 -10.8 1.8·10 − 2 7.9 2.13 GPCh a C 8 H 20 NO 6 P 257.2212 -9.5 2.5·10 − 2 9.8 2.19 LPC (18:1) a C 26 H 52 NO 7 P 521.6673 12.6 1.4·10 − 3 8.5 2.28 LPC (18:0) a C 26 H 54 NO 7 P 523.6832 -11.2 3.1·10 − 3 6.4 2.11 Predominant metabolites in RS Formula m / z [M + H] + Δm (ppm) p -value CV (%) VIP L-Tryptophan a C 11 H 12 N 2 O 2 205.0967 -4.9 4.10·10 − 3 5. 3 2.56 Serotonin a C 10 H 12 N 2 O 177.1039 6.8 1.9·10 − 2 5.6 2.18 Melatonin a C 13 H 16 N 2 O 2 233.1270 -8.6 3.0·10 − 2 6.8 2.41 Tyrosine C 9 H 11 N 1 O 3 181.1885 -2.15 5.2·10 − 2 4.1 2.75 Aminoethanol C 2 H 7 NO 61.0831 3.40 3.15·10 − 3 3.9 2.05 Hypoxanthine C 5 H 4 N 4 O 136.1115 2.95 5.27·10 − 3 2.7 2.98 Quinolinic acid C 7 H 5 NO 4 167.1189 -3.04 25.0·10 − 2 3.2 2.43 2, 4- dihydroxypyrimidine C 4 H 6 N 2 O 98.1032 -5.53 7.35·10 − 3 5.0 2.12 N-Methylnicotinamide C 7 H 8 N 2 O 136.1512 -3.95 2.90·10 − 2 4.5 2.31 MS/MS: Tandem Mass Spectrometry data, and elucidation of fragmentation patterns for each m/z, which confirms unequivocal structural and chemical characterization in all the cases. p -value was calculated by T-test analysis for each of the m/z / intensity relations, considering significant values of p ≤ 0.05. Δm : mass error expressed in ppm. CV: coefficient of variation was considered values < 20% to obtain a method with good reproducibility. VIP: variable importance in projection was set up at a minimum value of 2 to ensure selection of predominant m/z in each group. DOC: 11-deoxycorticosterone; 17-OHP: 17α-hydroxypregnenolone; NAG: N-acetyl glutamine; GPCh: glycerophosphocholine; LPC: lysophosphatidylcholine. a : previously published in a preliminary report by Lorenzo-Tejedor et al . 23 Table 4 Significantly differential metabolites identified after stress induction using negative mode (ESI (-)) Predominant metabolites in SS Formula MS/MS product ions m/z Δm (ppm) p -value CV (%) VIP Caprylic acid C 8 H 16 O 2 143.10 (− H+) -6.1 3.7·10 − 2 5.2 2.01 Capric acid C 10 H 20 O 2 171.10 (− H+) -9.8 3.3·10 − 3 9.2 2.45 Linoleic acid C 18 H 32 O 2 279.20 (− H+) -5.7 2.3·10 − 2 2.4 2.80 DHA C 22 H 32 O 2 327.20 (− H+) 3.2 5.0·10 − 4 7.0 2.32 LPC (20:5) C 28 H 48 NO 7 P 359.26, 184.07, 104.10, 86.09 -4.3 3.1·10 − 2 10.0 2.45 PPE (16:0/22:6) C 43 H 74 NO 7 P 746.50 (− H+), 327.23, 196.07 -7.6 2.6·10 − 2 6.5 2.96 PPE (18:1/20:4) C 43 H 76 NO 7 P 748.50 (− H+), 303.30, 196.10 5.2 5.4·10 − 3 8.1 2.06 PPE (18:0/20:4) C 43 H 78 NO 7 P 750.50 (− H+), 303.20, 196.10 -9.2 2.2·10 − 3 3.4 2.32 PPE (18:0/22:6) C 45 H 78 NO 7 P 774.50 (− H+), 327.20, 196.10 8.5 1.9·10 − 2 9.7 2.47 PC (16:0/20:5) C 44 H 78 NO 8 P 313.20, 359.30, 184.10, 104.10, 86.0 -6.1 2.0·10 − 2 4.3 2.65 PPC (16:0/22:6) C 46 H 80 NO 7 P 387.20, 184.0, 104.10, 86.0 -8.5 2.2·10 − 2 6.2 2.15 PPC (18:1/22:6) C 48 H 82 NO 7 P 385.20, 184.0, 104.10, 86.0 5.5 6.3·10 − 3 7.9 2.98 PC (18:1/20:4) C 46 H 82 NO 8 P 339.20, 361.0, 184.0, 104.10,86.0 -6.8 2.6·10 − 2 5.8 2.50 PC (18:0/22:6) C 48 H 84 NO 8 P 341.0, 38.0, 184.0, 104.10, 86.0 11.4 3.0·10 − 2 11.5 2.21 MS/MS (Tandem Mass Spectrometry) data, and elucidation of fragmentation patterns for each m/z which confirms unequivocal structural and chemical characterization in all the cases; p -value was calculated by T-test analysis for each of the m/z / intensity relations and considering significant values of p ≤ 0.05; Δm is the mass error expressed in ppm; CV: coefficient of variation were considered values < 20% to obtain a method with good reproducibility; VIP: variable importance in projection was set up at a minimum value of 2 to ensure selection of predominant m/z in each group. DHA: docosahexaenoic acid; LPC: lyso-phosphatidylcholine; PPE: ethanolamine-plasmalogen; PC: phosphocholine; PPC: choline-plasmalogen. DISCUSSION In this study, a modified form of the Trier Social Stress Test (TSST-M) was used to induce acute stress in university students. We found significant differences between RS and SS in psychometric tests (STAI-s, VAS), SSC, and in biochemical markers like AA sl , FR sl , Cp pl, and Pr pl (Table 1 ). These results confirmed that stress was successfully induced, in agreement with other studies that used the TSST 30 , 31 . While we had anticipated a significant increase in salivary cortisol (Cr sl ), no significant difference was eventually found, even though previous studies have shown that cortisol levels typically rise following induced stress 32 , 33 . This discrepancy could be attributed to the dynamics of cortisol production-saliva detection. Whereas α-amylase is released directly into oral fluid from salivary glands in response to the activation of the HPA axis, cortisol is first secreted from the adrenal glands into the bloodstream and only then it passively filters into saliva. This process results in a delay of up to 15–20 minutes before cortisol reaches its peak concentration in saliva in comparison with α-amylase 34 . Since saliva sample collection here was conducted immediately after stress induction, peak Cr sl concentration may not have been captured. Setting aside this limitation, our metabolomic analysis identified cortisol as a relevant blood biomarker of acute stress, with significant changes in its concentration distinguishing RS from SS (Table 3 ). Concerning sex differences, we observed significantly higher glucose and copeptin levels in men, in line with findings by Spanakis et al. 30 . This result supports the hypothesis that the HPA axis response to acute psychological stress varies by sex, according to previous studies 30 , 35 . This indicates that the risk of suffering from different diseases as a result of stress may vary by sex. Yet, further research is required to elucidate such sex-based disparities. In order to reduce the multiple dimensions of the psychological stress into its main components, a PCA method was performed. The top four out of seven components explain 84% of the variance. The first component correlates most strongly with psychometric tests, reflecting the variation in the quality of individuals' psychological state produced by the stressor. Whereas the second component was related to SNS activation (involving ΔAA sl and FR sl changes), the third and fourth components related to the HPA axis activation (Table 2 ). Probably because Cp pl and Pr pl are secreted by different sources (the posterior and anterior pituitary, respectively), they appear as separate components. These results highlight the close interaction between the SNS and HPA axis in eliciting stress response. By integrating these significant factors into the SRS scale 26 we could check its utility in quantifying the level of stress perceived by an individual 36 . Still, this has to be validated by additional studies. The predictive models built using machine learning techniques (decision trees, logistic regression, and random forest classifiers) exhibited a high level of robustness in determining the stress state of the subject (Fig. 2 ). Consistently, all models identified ΔAA sl and STAI-s as the main predictive biomarkers of acute psychological stress status. Such a result supports the importance of AA sl as a key biomarker in evaluating stressors that activate the SNS, in agreement with previous research reports 37 , 38 . Still, it is important to note that AA sl levels, like all other variables, may be influenced by a variety of factors such as exercise and medication 39 . In the case of the random forest model, FR sl was identified as an additional significant predictor of stress status. We are aware that, even though our models show high predictive accuracy, indicating their potential reliability for stress monitoring, the small sample size (n = 40) in this study limited the statistical power of our analyses, reducing the generalizability of our findings to a broader population. Nonetheless, our findings provide a sound basis for further studies. Regarding the metabolic signature of acute psychological stress explored here, our results are in line with previous research that has documented significant changes in the metabolomic profile in both animal models and humans subjected to different stressors 40 – 42 . In PCA plots of the metabolomic data, two clusters are clearly distinguished, thus indicating that RS and SS samples had remarkably differential metabolic compositions (Fig. 3 ). A total of 53 significantly differential metabolites (p 2) were identified from both ESI (+) and ESI (-) ion models. Of these, 9 were predominantly associated with RS, while 44 were predominantly associated with SS. These findings showed that acute psychological stress produces extensive changes across multiple metabolic pathways involved in the organism’s adaptive response. It is well established that prolonged stress-induced alterations can have detrimental effects on health. Consequently, chronic psychological stress is recognized as a serious risk factor for cardiovascular diseases and metabolic disorders 42 . Notably, one of our most striking findings concerns the significant changes in the lipid profile induced by acute mental stress, particularly the substantial increase in fatty acids, polyunsaturated fatty acids (PUFA), phosphocholines (PC), plasmalogens (PPC and PPE), and lysophosphatidylcholines (LPC) (Table 4 ). Recent studies indicate that these lipids and lipid-like molecules play critical roles in cell signaling pathways related to inflammation, immunity, and apoptosis 42 , 43 . The increases in PPC and PPE levels observed may be attributed to an increase in the brain’s demand for plasmalogens (PP) under acute stress conditions to keep an adequate neural function, endorse synaptic plasticity, and protect against stress-induced oxidative damage. Various researchers proposed that PP, particularly those containing omega-3 fatty acids such as LPC (20:5), PPE 16:0/22:6, PPE 18:0/22:6, and docosahexaenoic acid (DHA), as observed in our study, may reduce HPA axis activation in response to acute physiological stress, thereby protecting the brain from subsequent cellular damage 44 , 45 . When stress becomes chronic, this adaptive mechanism leads instead to a decline in PP levels, which is associated with several degenerative disorders and neurocognitive impairments 42 , 43 . In addition to the increased PP levels in SS, we also observed elevated levels of LPC. This finding is in line with previous studies suggesting that LPCs containing medium-chain saturated fatty acids may serve as potential biomarkers not only for stress but also for adiposity and inflammation 42 . LPCs are generated through the cleavage of phosphatidylcholine, a major phospholipid in the cell membrane, by phospholipase A 2 (PLA 2 ), producing free fatty acids, including arachidonic acid. The observed rise in LPC levels may reflect the body's complex response to the induced stress adaptation, involving the activation of PLA 2 by mitogen-activated protein (MAP) kinase-related kinase, a family of stress-activated protein kinases 46 , 47 . The function of LPCs depends on the length and degree of saturation of the fatty acid chain attached to the glycerol moiety 48 . For instance, elevated levels of LPC (18:0) and related PPs, PPC (18:0/20:4) and PPC (P18:0/22:6), have been associated with reduced inflammation, lower adiposity, and a decreased risk of cancer 42 , 48 . On the other hand, LPCs like 18:1 and 20:4 exert their biological roles by activating many downstream signaling pathways, including mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB). These pathways promote cell division, chemotaxis, oxidative stress, inflammatory cytokine release, and apoptosis, thereby accelerating the development of atherosclerosis 48 . Additionally, LPC (20:4) has been associated with stress index, and its free fatty acid, the arachidonic acid (20:4), has been suggested as a marker of depression and stress in humans 42 , 49 . Another predominant metabolite found under acute stress conditions was linoleic acid (18:2-n6), the most abundant PUFA in human nutrition. Linoleic acid (LA) is an essential n-6 PUFA and a precursor to arachidonic acid. While normal levels of LA are crucial for neurological and cognitive development and overall health, elevated levels of LA have been linked to inflammation and metabolic diseases 50 . Our data indicates that its metabolic pathway was among the most significantly affected. One such alteration involves the inhibition of the enzymes responsible for catalyzing LA epoxidation, leading to a reduction in its hypocholesterolemic effect 51 , 52 , followed by the consequent accumulation of arachidonic acid. Additionally, LA can undergo non-enzymatic oxidation to produce Oxlams, metabolites that have been shown to promote a strong pro-inflammatory response in rats 50 . An elevated level of cholesterol in SS, like the one observed here (Table 3 ), may lead to the generation of a variety of corticosteroids via steroidogenesis. Due to their lipophilic nature, corticosteroids cannot be pre-synthesized and stored in adrenal glands but have to be rapidly synthesized upon Adrenocorticotropic hormone (ACTH) stimulation, which is insteadregulated by the HPA axis 53 . Corticosteroids regulate multiple physiologic processes, including metabolism, development, homeostasis, cognition, and inflammation 53 . Corticosteroids like cortisol increase the bioavailability of glucose and the consequent release of energy to the brain 53 , as evidenced by the increased levels of carnitine, creatine, and glucogenic amino acids observed in this study, corroborating findings by Singh et al. 40 . Additionally, these amino acids could also serve as a substrate for the synthesis of protein required for the stress response process 54 . It has been established that each stressor has a neurochemical signature with distinct central and peripheral mechanisms 55 . Controversially, some studies have demonstrated that the two branches of the sympathoadrenal system (SAS) – the adrenal medulla and the sympathetic nerves– can be activated independently by different stressors 55 , 56 . Nonetheless, our study indicates that acute psychological stress induced by TSST-M activated both components of SAS. This stimulates the adrenal medulla system, elevating plasma Epi levels, and activates the sympathoneural system, increasing NE and dopamine plasma levels. Epi is known as the hormone preparing the body for a fight-or-flight response 57 . NE, which is the main sympathetic neurotransmitter in circulatory regulation, is also a central neurotransmitter thought to be involved in alertness, memory of distressing events, nociception, and anxiety 58 . Dopamine (DA) is a key neurotransmitter that regulates many processes in the CNS, including reward, motivation, and cognition. Importantly, DA can also be produced locally in several peripheral organs, where it has autocrine and paracrine effects influencing many organ functions 59 , 60 and is released in plasma in response to stress. This response is partly influenced by circulating cortisol levels in the body 61 , 62 . DA, moreover, regulates critical functions such as metabolic homeostasis, hormone release, sodium balance, blood pressure, renal activity, and gastrointestinal motility. It also modulates inflammatory and immunological processes 59 , 60 . Prolonged exposure to intense stressors inhibits the release of DA and disrupts the dopaminergic pathway, leading to psychological disorders such as depression and schizophrenia 63 , 64 . The elevated levels of cholesterol, steroid hormones, and adrenal catecholamines observed in this study could be explained accordingly by the increase in prolactin, known as the stress hormone , along with cortisol. There is substantial evidence supporting prolactin's multifaceted role in the adrenal response to stress 65 . More specifically, it has been shown to increase the secretion of ACTH, enhance the storage of cholesterol esters, and induce adrenal hypertrophy 65 – 67 . Under acute stress, prolactin secretion appears to play a crucial and complex role in maintaining metabolic and immune system homeostasis 67 – 69 . Therefore, while Pr may induce a protective proinflammatory state during acute stress, chronic exposure to prolactin can, by contrast, lead to habituation and potentially contribute to the development of cardiovascular pathologies 70 . Interestingly, we identified several metabolites that the literature suggests may have protective effects during acute stress. For instance, progesterone and pregnenolone (Table 3 ) are known to suppress HPA activity, thereby reducing stress levels 71 , 72 . Additionally, caprylic and capric acid have been identified as possessing anti-inflammatory properties, which counteract the inflammatory process often associated with stress 73 , 74 . Furthermore, 17β-oestradiol and oestrone have been shown to play a neuroprotective role against stress-related damage 75 , 76 . Collectively, these metabolites contribute to the body’s adaptive response aimed at restoring homeostasis and mitigating the adverse effects of stress. CONCLUSIONS This study employed the TSST-M to induce acute psychological stress, exploring the multifaceted effects of the stress response through the integration of psychometric assessments, biochemical analyses, and metabolomic profiling. Our findings underscore significant sex differences in the stress response, particularly in glucose and copeptin levels, emphasizing that stress impacts men and women differently. This highlights the need for sex-sensitive approaches in stress research, especially given their implications for disease risk assessment. Additionally, our study demonstrates the utility of the Stress Reference Scale (SRS) in stress quantification and the importance of machine learning predictive models in distinguishing stressed from relaxed states in individuals. Specifically, salivary α-amylase (AA sl ) and STAI-s were identified as prominent stress markers by our predictive models. An important strength of this study is the validation of a direct infusion MS method, which is minimally invasive, requiring only a finger prick and a drop of blood for metabolomics analysis 23 . Despite its limitations, our results further indicate that acute psychological stress significantly impacts bodily systems, triggering relevant metabolic alterations. These findings help to understand the intricate interplay between physiological and psychological domains in acute mental stress responses. Taken together, our results contribute to a deeper understanding of stress by integrating advanced analytical tools and methods for the diagnosis of acute psychological stress and understanding the mechanisms involved in this type of stress-related disorders. LIMITATIONS AND FUTURE DIRECTIONS Naturally, the study has its limitations. First, the relatively reduced sample size, integrated by healthy university students, limits the generalizability of our findings to a broader demographic. Yet, this does not counterbalance the fact that this study provides a foundational basis for future research involving a broader and more heterogeneous population. Second, we chose not to account for the use of contraceptives, which could potentially influence prolactin and other hormone levels variations. The strict focus on acute stress responses over a narrow timeframe renders the impact of cyclical hormonal fluctuations minimal. Similarly, since the study evaluates the variation (Δ) between pre- and post-stress induction, the prevalence of regular medication use, such as antiallergics or bronchodilators, has been deemed not relevant. In any event, our work still provides basic parameters for future research, thus potentially confirming the relation between biochemical, metabolic, and psychometric stress measures proposed here, extending the approach to larger and more heterogeneous populations. This would directly increase the generalizability of our findings and provide further validation for the diagnostic and measurement tools introduced here. Materials and methods Study Design To ascertain the effects of acute psychological stress on biochemical, psychological, and metabolomic variables, we performed a quasi-experimental pre-post study without a control group on a set of university volunteers. The study, designed and performed under the ES3P 19 , 23 , 26 , 27 framework, included two sessions: a 35-minute Relaxation (RS) protocol setting control conditions, followed by a 35-minute Stress-induction protocol (see Supplementary Fig. S1 for instructions and protocol steps) based on a modified form of the Trier Social Stress Test (TSST-M) previously described by Arza et al . 19 yielding acute psychological SS. Participants and Ethical Declaration Volunteers aged 20 to 30 (both sexes) were recruited from the University of Zaragoza. Exclusion criteria included: (1) signs of depression or a history of other mental disorders; (2) regular use of psychotropic substances; and (3) pregnancy or breastfeeding at the time of the study (see Supplementary Table S1 for participant details). All participants were informed about the study procedures and provided written informed consent. This documentation is securely archived at the Psychiatric Unit, HCULB, in compliance with the EU’s General Data Protection Regulation. The whole study was conducted in accordance with the World Medical Association (WMA) Declaration of Helsinki (2013) and was approved by the Clinical Research Ethics Committee of Aragon (CEICA; protocol number PI14/0044). Stress Induction and Relaxation Protocols The sessions were carried out on different days, but at the same hour, around 10:00 AM, to avoid variations in the circadian rhythm 77 . The relaxation session (RS) comprised a baseline (B RS ) and relax stage (R RS ), whereas the stress session (SS) comprised a baseline stage (B SS ) and five distinct stages to induce acute psychological stress 19 . For the relaxation session, the subjects were seated in a comfortable position in a dimly lit room and were exposed to audio recording and guided relaxation to induce autogenic relaxation following Schultz’s method 78 . The stress sessions followed a TSST-M, which is a robust, reliable, and well-documented protocol widely used in stress research 30 , 31 , 77 , 79 – 82 , with slight modifications, as described in 19 . The stress session consisted of storytelling (STS), memory test (MTS), stress anticipation (SAS), video display (VDS), and arithmetic task (ATS). (Supplementary Fig. S1 ). At the end of each session, RS and SS, the participants were required to complete psychometric questionnaires. Saliva samples were collected at the end of the baseline stages (B RS and B SS ) and repeated after RS and SS, whereas blood and plasma samples were only collected after RS and SS. Stress Evaluation and Measurement: Psychometric Evaluation Before administering psychometric questionnaires, participants were asked to indicate their perception of their stress levels (Perceived Stress) on a scale of 0–100 arbitrary units (Supplementary Table 1). The professionals of the zarademp group from the Psychiatric Service (HCU-LB) and the Department of Medicine and Psychiatry (University of Zaragoza) selected the tests, verified the corresponding Spanish versions, administered the tests to the subjects, and subsequently interpreted the results. This team also applied a test designed by themselves on behalf of the ES3 Project 27 , ‘the Symptomatic Stress Scale” (SSC). The SSC scale is a Likert-type Scale that consists of 20 questions that evaluate the subjective effect of the stressor on the subject from somatic and psycho-cognitive points of view. This scale was validated by Garzón-Rey 83 and applied in a recent study by Garcia Pages et al . 36 The validated psychometric tests used were the Spanish versions of the Perceived Stress Scale (PSS) 84 , Visual Analogue Scale (VAS), and State-Trait Anxiety Inventory tests (STAI) 85 . PSS is widely used to assess stress levels in young people and adults 86 . It evaluates the degree to which an individual perceives life as unpredictable, uncontrollable, or overloading. VAS measures subjective stress on a numeric scale ranging from 0 to 100 87 . This test highlights the differences in stress levels between groups and determines the connection between the VAS stress assessment and the evaluation of various related concepts 88 , 89 . Finally, two STAI questionnaires were used: one to measure the trait or general tendency to increase anxiety in stressful situations (STAI-t), and another to evaluate the state of the subject in a specific situation (STAI-s) 90 . Measurement of Biochemical Variables Biological samples for analysis were collected by professionals from HCU-LB and stored in sterile, airtight compartments at an adequate temperature until analysis. Biochemical markers determined were glucose (Glu sr ) from serum samples; prolactin (Pr pl ), copeptin (Cp pl ), and osmolality (Osm pl ) from plasma samples; and salivary cortisol (Cr sl ), salivary flow rate (FR sl ), and α-amylase (AA sl ) in saliva samples. All samples were processed using the same tests to avoid inter-test variability, thereby achieving intra-test variation coefficients < 5% in all cases. Salivette tubes were used to collect saliva, following the manufacturer’s recommendations (Sarstedt AG & Co., Nümbrecht, Germany). Subsequently, samples were immediately preserved on ice and later kept frozen at − 20°C until processing, according to the protocol previously described by Garcia Pages et al. 36 . Concentrations of Cr sl and AA sl were measured in the endocrinology and radioimmune analysis service of Neurosciences Institute at the Universitat Autònoma de Barcelona (UAB) using fully validated immunoassay and kinetics enzyme assay kits from Salimetrics/USA, respectively 36 . The changes in salivary cortisol (ΔCr sl ), α-amylase (ΔAA sl ), and flow rate (ΔFR sl ) in response to the applied stress stimulus were calculated. The extracted blood was partitioned into two tubes: one with EDTA anticoagulant and the other with a clot accelerator and gel serum separator. Both samples were preserved on ice and later centrifuged at 3000 rpm for 10 min. Plasma and serum were kept frozen at − 20°C until processing at the Biomedical Diagnostics Centre at the Hospital Clinic of Barcelona. Quantification of Glu sr , Pr pl , Cp pl, and Osm pl was performed using molecular absorption and immunoassay spectrometry techniques. Stress Reference Scale The stress reference scale (SRS) was proposed by Garzon-Rey et al . 26 as a reference standard for measuring acute emotional stress. Significant biochemical and psychometric parameters were used to compute the scale using a multivariate approach as described previously. To assign weights to the different variables, their mean scores were first normalized by rescaling to a 0-100 range of arbitrary units using the following Eq. (2): $$\:\begin{array}{c}y=\frac{100*\left(x-Min+\sigma\:*0.5\right)}{\left(Max-Min+\sigma\:\right)}\#\left(2\right)\end{array}$$ where the variable ( x ) with a standard deviation ( σ) , minimum ( Min ), and maximum (Max) values are transformed into a variable ( y) ranging from 0 to100. Afterwards, the principal components analysis (PCA) was performed to assign the corresponding weights to each variable. Only features with eigenvalues greater than 0.8, which explained 84% of the total variance, were selected to build the scale. Statistical Analyses Statistical analyses were performed using IBM® SPSS® Statistics 25.0 and RStudio for Microsoft Windows, along with its corresponding packages available on CRAN or Bioconductor repositories. The states of the volunteers at the end of each session, RS and SS, were considered to be the lower and higher ranges of the stress state. The variations in psychometric, biochemical, and SRS variables between RS and SS were analyzed using the Wilcoxon signed-rank test, a non-parametric statistical test, because the data were not normally distributed after testing for normality using the Lilliefors test. Correlations were computed using Spearman’s rank correlation for non-parametric distributions. For all analyses, the significance level was set at α = 5%. Variables were passed on to create predictive models. Categorical variables were encoded as factors. The grouping RS or SS was considered as the response variable for the models, and the other variables were considered as predictors of the state of the group. The study employed the Recursive PARTitioning ( rpart) algorithm based on CART (classification and regression tree) to build decision tree models ( https://cran.r-project.org/web/packages/rpart/rpart.pdf ). The adabag package 91 was used to build a bagging predicting model, and the Random-Forest algorithm software package ( https://cran.r-project.org/web/packages/randomForest/index.html ) to obtain the variable relative importance rankings of variables. We used 70% of the original data as a training set and the remaining as a testing set to assess the model afterwards. The Gini Index was used to split nodes, and pruning was performed to avoid overfitting the model. A multivariate logistic regression model was constructed and compared with the decision tree, bagging, and random forest models. Metabolomic Sample Processing and Data Analysis A semi-quantitative direct-infusion mass spectrometry (DI-MS) untargeted metabolomic study was conducted to characterize biochemical responses to acute psychological stress and as a biomarker development tool. This innovative technique, involving direct injection into the ionization source of the mass spectrometer without prior chromatographic separation with an electrospray ionization (ESI) source, already presents proven advantages and robust results 23 , 92 , 93 . Blood samples were collected by pricking participants’ fingers. Approximately 0.5 mL of total blood was collected into an empty and sterilized Eppendorf™ tube. No anticoagulants were used. Samples were immediately protected from light and stored at -80°C until analysis. Sample preparation was carried out as previously described 23 . For positive mode MS detection, immediately before analysis, each sample was diluted 1:1000 with a protonating agent solution of LC-MS grade methanol with 0.1% formic acid (Fluka) at 99% purity. For negative mode detection, a dilution of 1:1000 of the sample was made with MS-grade dichloromethane (Fluka): methanol (ratio 1:1). Samples to be analyzed were pumped directly into the mass spectrometer. Measurements were taken in both positive and negative modes using a hybrid triple quadrupole/linear ion trap mass spectrometer 4000 QTRAP LC/MS/MS System (AB Sciex) with electrospray ionization (ESI) source interface for high-sensitivity, full-scan MS, MS/MS, and MS 3 spectra with high selectivity from true triple quadrupole precursor ion (PI) and neutral loss (NL) scans. Data acquisition and pre-processing were carried out using Analyst® software version 1.5.2 (Build 5704) (Sciex) as previously described 23 . A scan range of 50 − 1,200 m/z was used. The mass accuracy and resolution were 5 ppm and 20,000 ppm, respectively. The instrument settings were as follows: ion spray voltage, 5,000 V; curtain gas, 20 AU; GS1 and GS2, 50 and 30 psi, respectively; probe temperature, 550°C; and run time, 10.0 min. For MS/MS analysis, the collision-induced dissociation (CID) mode was used and was set to 30–50% normalized collision energy (CE) for selected mass-to-charge ratio (m/z) peaks. Data normalization, statistical and functional analyses, and compound identification were performed following the protocol previously described by Lorenzo et al . 23 . Enrichment and pathway topology analyses were performed using the corresponding modules of MetaboAnalyst 5.0 94 and categorized with the KEGG pathway Homo sapiens database 95 , 96 . Pathway enrichment analysis allowed for the identification of those pathways significantly affected by the stressor, and thus, to better understand the impact of acute psychological stress on an individual’s metabolism. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Abbreviations ΔAA sl (difference in α-amylase concentrations between the second and first samples), AA sl (Salivary α-amylase), ACTH (Adrenocorticotropic hormone), B RS (Baseline relaxation session), B SS (Baseline stress session), CNS (Central Nervous System), Cp pl (Plasma copeptin), ΔCr sl (difference in salivary cortisol between the second and first samples),Cr sl (Salivary cortisol), DHA (docosahexaenoic acid), DIMS (Direct Infusion Mass Spectrometry), DOC (11-deoxycorticosterone), Epi (epinephrine), ΔFR sl (difference in salivary flow rate between the second and first samples),FR sl (Salivary flow rate), ESI (Electrospray Ionisation), Glu sr (Serum glucose), HPA (Hypothalamic-Pituitary-Adrenal), KEGG (Kyoto Encyclopaedia of Genes and Genomes), LA (Linoleic acid), LC-MS (Liquid Chromatography - Mass Spectrometry), LPC (Lyso-phosphatidylcholine), MAPK (mitogen-activated protein kinase), NAG (N-acetyl glutamine), NE (norepinephrine), NF-κB (nuclear factor kappa B), Osm pl (Osmolarity from plasma samples), PC (Phosphocholines), PSNS (Parasympathetic Nervous System), PPC (Choline-plasmalogens), PPE (Ethanolamine-plasmalogens), Pr pl (Plasma prolactin), PSS (Perceived Stress Scale), PUFA (polyunsaturated fatty acids), SNS (Sympathetic Nervous System), SS (Stress session), SSC (Symptomatic stress scale), STAI-s/t (State-Trait Anxiety Inventory state and trait tests, respectively), RS (Relaxation session), TSST-M (Modified form of the Trier Social Stress Test), VAS (Visual Analogue Scale) Declarations Acknowledgements G.G. gratefully acknowledges Roche Institute Foundation for their support in funding her Master’s in Bioinformatics, Computational Biology, and Personalized Medicine provided by Universitat Politècnica de València (UPV). The skills and knowledge gained directly contributed to our research. Special thanks to the Proteomics Core Research Facility of the Aragon Health Sciences Institute (IACS-CIBA) for their technical assistance. Additional Information section Authorship contribution statement This work is part of a multidisciplinary project formed with the objective of studying different aspects of the genesis of stress and its adverse effects on health. G.A.F. and G.G. performed the formal analysis of the psychometric and biochemical data, wrote the computer code and algorithms for the machine learning statistical analysis, and conducted the literature review. They also drafted the original manuscript and prepared the tables. G.A.F. generated Figures 1 and 4, analysed sex differences, performed the enrichment and pathway topology analyses of the metabolomic data, and contributed to formatting the manuscript according to the journal’s stylesheet. G.G. and E.M.R. wrote the final sections (Discussion and Conclusion), thoroughly reviewed the manuscript, and assembled the final version by incorporating minor corrections, rephrasing, and restructuring content to improve conciseness, clarity, fluency, and readability. G.G. also generated Figure 2, was responsible for conceptualisation, validation, and visualisation, and oversaw and completed the entire submission process. M.L.T., C.D.L.C., J.A., R.B., and M.B. designed the study. M.L.T. performed the formal analysis for the metabolomic study and generated Figure 3 and Tables 3–4. C.D.L.C. conducted stress and relaxation sessions, administered psychometric tests, coordinated the fieldwork, and compiled its results. E.M.R. contributed to the cognitive component of the manuscript, assisted with the overall review process, and revised English grammar, terminology, and phrasing throughout the final version. J.L. managed the database registry and contributed to the design of the metabolomics study. R.B. (Principal Investigator) and J.A. (Co-investigator) supervised project development and managed project funding. M.B. collected and prepared the biological samples for analysis, supervised and coordinated the study, contributed resources, and collaborated on the literature review. All authors reviewed and approved the final version of the manuscript. Competing Interests The authors have no conflicts of interest to declare. FUNDING This work was partially funded by the Ministry of Science and Innovation, Spain (TED2021- 131106B-I00), the European Social Fund (EU), and the Aragon Government, Spain through the BSICoS group, Spain (T39 23R). References Chrousos, G. P. Stress and disorders of the stress system. Nat. Rev. Endocrinol. 5 , 374–381 (2009). Chrousos, G. P. & Gold, P. W. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. 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Estructura factorial del Cuestionario de Ansiedad Estado-Riesgo (STAI) para pacientes diagnosticados con depresión. Salud mental . 38 , 293–298 (2015). Cohen, S., Kamarck, T. & Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 24 , 385–396 (1983). McCormack, H. M., de Horne, D. J., Sheather, S. & L. & Clinical applications of visual analogue scales: a critical review. Psychol. Med. 18 , 1007–1019 (1988). Lesage, F. X. & Berjot, S. Validity of occupational stress assessment using a visual analogue scale. Occup. Med. (Chic Ill) . 61 , 434–436 (2011). Lesage, F. X., Berjot, S. & Deschamps, F. Clinical stress assessment using a visual analogue scale. Occup. Med. (Chic Ill) . 62 , 600–605 (2012). Spielberger, C. D. State-Trait Anxiety Inventory. in The Corsini Encyclopedia of Psychology 1–1 (Wiley, (2010). Alfaro, E., Gáamez, M. & García, N. adabag: An R Package for Classification with Boosting and Bagging. J. Stat. Softw. 54 , 1–35 (2013). González-Domínguez, R. & Sayago, A. Fernández-Recamales, Á. Direct infusion mass spectrometry for metabolomic phenotyping of diseases. Bioanalysis 9 , 131–148 (2017). Haijes, H. A. et al. Direct Infusion Based Metabolomics Identifies Metabolic Disease in Patients’ Dried Blood Spots and Plasma. Metabolites 9, (2019). Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49 , W388–W396 (2021). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: Biological Systems Database As A Model Of The Real World. Nucleic Acids Res. 53 , D672–D677 (2025). Kanehisa, M. & Goto, S. K. E. G. G. Kyoto Encyclopedia Of Genes And Genomes. Nucleic Acids Res. 28 , 27–30 (2000). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6503620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":467527196,"identity":"80044b56-e1db-465a-b8cc-abb9cced3fa2","order_by":0,"name":"Gifty Animwaa Frempong","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Gifty","middleName":"Animwaa","lastName":"Frempong","suffix":""},{"id":467527201,"identity":"858209fe-e5c1-4387-8fa4-42bb631ac666","order_by":1,"name":"Guillermina Goni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYLCChAoGBjYStZwhWQtjGymq5f0XH3vwcN7hPD7+w88+MFTUEdZieONZukHitsPFbAzHjGcwnDlMhJYZZ8wkgFoS2xgbjIEuPECsljlALczsnxkY/xHhMHn+HqCWBqAWNh6gLQ3MhLUYSLClSSQcS09s4+EpZkg4RoRf5PsPH5P8UWOdOL//+GaGDzVEOMzgRgISLwGHKjRbDhCjbBSMglEwCkY0AAD45Td/+DQobwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Zaragoza","correspondingAuthor":true,"prefix":"","firstName":"Guillermina","middleName":"","lastName":"Goni","suffix":""},{"id":467527205,"identity":"d075e722-995b-425e-aa47-7b1a741e44d2","order_by":2,"name":"Mónica Lorenzo-Tejedor","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"","lastName":"Lorenzo-Tejedor","suffix":""},{"id":467527208,"identity":"5f4af493-29a7-4e9e-abde-8f91af29e791","order_by":3,"name":"Concepción de la Cámara","email":"","orcid":"","institution":"Aragon Institute of Health Research (IIS Aragon)","correspondingAuthor":false,"prefix":"","firstName":"Concepción","middleName":"de la","lastName":"Cámara","suffix":""},{"id":467527211,"identity":"a43af034-fe71-45ee-8cc1-5af6de5bec10","order_by":4,"name":"Jesús Lázaro","email":"","orcid":"","institution":"Aragon Institute of Health Research (IIS Aragon)","correspondingAuthor":false,"prefix":"","firstName":"Jesús","middleName":"","lastName":"Lázaro","suffix":""},{"id":467527215,"identity":"a4751045-f694-447f-8843-3d190b6d45c0","order_by":5,"name":"Eugenia Mangialavori Rasia","email":"","orcid":"","institution":"National Scientific and Technical Research Council","correspondingAuthor":false,"prefix":"","firstName":"Eugenia","middleName":"Mangialavori","lastName":"Rasia","suffix":""},{"id":467527219,"identity":"e56fe0a1-4861-46a6-86ec-f6b5d0968cee","order_by":6,"name":"Jordi Aguiló","email":"","orcid":"","institution":"Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina","correspondingAuthor":false,"prefix":"","firstName":"Jordi","middleName":"","lastName":"Aguiló","suffix":""},{"id":467527220,"identity":"53bfb26d-ec86-4556-af12-629d9c080328","order_by":7,"name":"Raquel Bailon","email":"","orcid":"","institution":"Aragon Institute of Health Research (IIS Aragon)","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Bailon","suffix":""},{"id":467527221,"identity":"20dcd9c7-84c0-4506-82d3-15498d73b5cd","order_by":8,"name":"María Luisa Bernal","email":"","orcid":"","institution":"University of Zaragoza","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Luisa","lastName":"Bernal","suffix":""}],"badges":[],"createdAt":"2025-04-22 11:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6503620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6503620/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-29572-4","type":"published","date":"2026-01-10T15:58:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84366716,"identity":"faaf3799-4a4e-425b-8b7b-7ef1903a118f","added_by":"auto","created_at":"2025-06-11 06:10:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259092,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman rank correlation coefficient matrix heatmap of biochemical and physiological variables (STAIs = STAI-s, STAIt = STAI-t, dAA= ΔAA\u003csub\u003esl\u003c/sub\u003e, dCr = ΔCr\u003csub\u003esl,\u003c/sub\u003e dFR = ΔFR\u003csub\u003esl\u003c/sub\u003e, Pr = Pr\u003csub\u003epl\u003c/sub\u003e, Cp = Cp\u003csub\u003epl\u003c/sub\u003e, Glu = Glu\u003csub\u003esr\u003c/sub\u003e and Osm = Osm\u003csub\u003epl\u003c/sub\u003e) generated using \u003cem\u003eggcorplot\u003c/em\u003e in RStudio for windows. The bar on the left side of the map indicates the colour legend of the Spearman correlation coefficients.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/78eeef9ee62eecf1a0fa11d5.png"},{"id":84365567,"identity":"8d2f09e0-b0b9-4edb-86b2-1270e0514253","added_by":"auto","created_at":"2025-06-11 06:02:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138587,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree model obtained for stress prediction; dAA=ΔAAsl, dFR=ΔFRsl, dCr=ΔCr\u003csub\u003esl\u003c/sub\u003e, CopOsm=Copetin/Osmolarity, STRAI.s= STAI-s, STRAI.t= STAI-t\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/e05f58210eff95f98e6859f4.png"},{"id":84367135,"identity":"bef85d8e-9352-4837-ae1c-877217690584","added_by":"auto","created_at":"2025-06-11 06:18:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343030,"visible":true,"origin":"","legend":"\u003cp\u003eScore plot of principal component analysis (PCA) on metabolomic data acquired in ESI (+) (A) and in ESI (-) (B) modes. Each dot represents a blood sample. Samples obtained after relax state (R) are in blue, and the ones obtained after stress induction (S) are in red.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/6b7c921869bc2f8ac8757af0.jpeg"},{"id":84367134,"identity":"520aa29b-43a1-4f16-a6a1-80912a2ad245","added_by":"auto","created_at":"2025-06-11 06:18:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174987,"visible":true,"origin":"","legend":"\u003cp\u003ePathway analysis of altered metabolic pathways after inducing psychological stress. Dots represent the altered pathways. The Y-axis represents the log-transformed p-value adjusted for multiple comparisons, while the X-axis represents the pathway impact. The color denotes importance, ranging from white (not significant) to red (most significant). The dot size reflects the impact score. The figure was generated using MetaboAnalyst 5.0.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/4b23a5895eae4b22fe2f544e.png"},{"id":100070303,"identity":"2618757f-01c5-49f8-a4c4-c18299e3e635","added_by":"auto","created_at":"2026-01-12 16:17:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2666225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/332ad0ac-e5ec-4f18-b4ad-1b18bcd866a2.pdf"},{"id":84366720,"identity":"82f75a17-2eb9-489f-a7e0-655f23370abb","added_by":"auto","created_at":"2025-06-11 06:10:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":449586,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileFrempongGonietal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6503620/v1/334a84941368fa1f2f5ffb8c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"New Insights into Stress Metabolomics. Looking for new Diagnostic Biomarkers. ","fulltext":[{"header":"INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStress\u003c/h2\u003e \u003cp\u003ePhysiological systems in the body are inherently programmed following rigorous fine-tuning of regulated variables. These variables ​​must be kept within an acceptable dynamic range, known as \u003cem\u003ehomeostatic state\u003c/em\u003e, which is essential for life and well-being \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, this optimal balance is constantly challenged by intrinsic and extrinsic adverse forces or \u003cem\u003estressors\u003c/em\u003e. While some stressors, like unexpected events, urgent tasks, traumatic events, and adverse social, economic, and environmental circumstances, often produce psychological effects \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e others, such as injuries, noise, or exposure to extreme temperatures, could have physical consequences \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStressors, when perceived as a threat, lead to a maladaptive stress response or disharmony called \u003cem\u003edistress\u003c/em\u003e (popularly referred to as bare \u0026lsquo;stress\u0026rsquo;). Stress triggers a complex interplay of physiological and behavioral responses aimed at reestablishing homeostasis, hence improving survival chances \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This process involves an intricate network engaging the Central Nervous System (CNS) and peripheral organs, leading to the activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis and Sympathetic Nervous System (SNS), followed by the inhibition of the Parasympathetic Nervous System (PSNS) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. If this response is not adequate enough to preserve the balance required, an inflammatory response is triggered in an attempt to restore the system to its homeostatic state \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These biochemical and physiological changes can be consequently used to determine and monitor stress. However, because each individual responds differently according to inherent personality traits along with a myriad of genetic, environmental, and developmental parameters, inter-subject variability is another factor making stress diagnosis and monitoring even more challenging \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStress is generally classified into three main types: acute, chronic, and negative.\u003c/p\u003e \u003cp\u003eAcute stress triggers a time-limited set of cognitive-behavioral and physiological changes as an immediate response to a stressor \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Neuropsychologically, acute stress concomitantly enhances alertness and vigilance. Physiologically, intermediate metabolism is adjusted to increase nutrient levels; increased respiratory and heart rates augment oxygen and cardiac output, supporting cardiovascular tone \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The resulting nutrient-enriched blood is redistributed to organs directly involved in stress response orchestration (brain, heart, and skeletal muscles). This comes at the expense of a critical but temporary reduction of blood supply to energy-consuming vegetative functions like digestion, renal and intestinal excretion, reproduction, growth, and immunity \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronic stress involves a constant stress stimulus. This can consequently lead to a stage where the body can no longer achieve homeostatic balance, and the individual can no longer deal with the stressors \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNegative stress (distress), in turn, \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e yields detrimental effects on several psychological and physiological functions, such as altered cognitive and affective capacities, mental processing, and sleep-arousal cycle disorders, along with simultaneous inhibition of vegetative functions, such as feeding and reproduction. It can also affect gastrointestinal and cardiovascular function, growth, metabolism, reproduction, and immune competence. Individual performance, behavior, and personality development can be equally affected \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNonetheless, stress reactivity depends on: (i) the type of stressor, for different stressors activate different metabolic pathways; (ii) the intensity and duration of the stressor, such that the higher the degree of stress, the lower specificity of the adaptive response, and (iii) inter-subject variability, considering the manner in which each individual perceives stressors \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Stress and Distress\u003c/h2\u003e \u003cp\u003eGiven its influence on human decision-making, psychological stress (negative stress) represents a major public health concern \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. According to the World Health Organization (WHO) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, the prevalence of social and medical problems associated with mental stress is globally increasing, also in children, seriously affecting their mental health and well-being. There are many factors contributing to global stress increase. The COVID-19 pandemic, for instance, became a universal stressor centrally involved in a global mental health crisis, since it implied enduring unprecedented short and long-term stressful situations that undermined the mental health of millions \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In any event, and especially when chronic, mental stress exacerbates our susceptibility to several diseases, eventually becoming a common cause of morbidity and mortality \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Consequently, mental stress has a visible impact on the Health System, resulting in elevated healthcare costs, invalidity, or productivity loss. In view of this, finding objective and precise diagnostic methods is nowadays a pressing question to be resolved \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStress Diagnosis\u003c/h3\u003e\n\u003cp\u003eTo date, stress diagnosis and estimation remain complex and clouded, carrying considerable chances of uncertainty. Current standard diagnostic methods build on validated psychometric questionnaires, tracking stress-induced changes in cognitive and behavioral abilities \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Although they are considered highly reliable methods, the interpretation of the questions by the patients and/or the results by the specialist is still highly subjective, thus leading to various biases that can compromise the diagnosis itself \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this sense, and despite many efforts, an objective and reliable method for stress diagnosis has not yet been developed. While different biomarkers have been proposed for acute psychological stress determination in the literature, important disparities in the results still exist \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the distinctive feature of stress response is the activation of SNS and, most importantly, the HPA axis, \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, the most promising biomarkers point to metabolites released as a result.\u003c/p\u003e \u003cp\u003eGiven the multidimensional nature of stress, we submit that determining one or only a few reliable biomarkers for diagnosis is unlikely to be a feasible goal. Reported inconsistencies in the literature may probably be the result of oversimplifying the overall process \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo solve this, we propose an omics analysis aiming to identify a significant set of empirically relevant biomarkers, which would result in a more effective approach. In this proposal, metabolomics is presented as the most appropriate strategy \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It involves systematic identification and quantification of the metabolite profile that characterizes the phenotype of an organism in a specific situation. Moreover, metabolomics allows the simultaneous determination of the altered set of metabolites in response to stress, providing a global vision of the metabolic changes arising as a result. Metabolites are the intermediate or end-products of cellular regulatory pathways, and their levels can be regarded as the ultimate response of biological systems to genetic and environmental changes \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study, integrated into a multidisciplinary project (\u003cem\u003eES3-P\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e) aimed at assessing acute psychological stress, we develop a proposal where the main goal is to determine the metabolomic fingerprint of acute psychological stress in a cohort of volunteeringuniversity students. This would directly contribute to the discovery of new stress biomarkers and help to unveil the molecular basis of adverse outcomes. As a secondary goal, we will analyze the potential utility of diverse biomarkers proposed in the literature and determine how gender differences operate in stress response.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eForty-one healthy young participants were enrolled. One participant opted out, resulting in a final sample size of 40. The group is constituted by young male and female participants in similar proportions (mean age of 22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 years), and a normal Body Mass Index (BMI of 22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 kg/m\u003csup\u003e2\u003c/sup\u003e) according to guidelines established by the WHO \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe perceived stress levels measured before administering psychometric tests (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) showed an average of 49.4 units on a scale from 0 to 100, indicating no to mild stress.\u003c/p\u003e \u003cp\u003eBased on habits, the majority of the subjects were non-smokers (85%), occasional consumers of alcoholic beverages (82.5%), and engaged in extracurricular activities (62.5%), mainly practiced sports regularly, learned foreign languages, or engaged in other types of artistic activities. Approximately half of the participants (45%) reported regular coffee consumption. In terms of their social background, most participants lived in urban areas (77.5%), were single (72.5%), and lived with their families (72.5%). With regard to health status, the vast majority of participants did not suffer from chronic diseases (95%) or take medications (75%). However, a small percentage (5%) had chronic diseases such as allergies, migraines, or intestinal reflux, and only 25% were on prescribed medications (mainly contraceptives, antihistamines, and antiasthmatics), which did not hinder the measurement sessions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStress Evaluation and Measurement\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePsychometric tests\u003c/h2\u003e \u003cp\u003eScores for STAI-s, VAS, and SSC showed statistically significant increases between RS and SS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), thus confirming that the participants had become stressed after applying the TSST-M test. The PSS and STAI-t tests did not show significant variation between the states. This reflects coherence in the evaluation since these questionnaires indicate one\u0026rsquo;s predisposition (trait) to respond to stressful situations, but do not evaluate the subject\u0026rsquo;s current state.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiochemical variables\u003c/h3\u003e\n\u003cp\u003eStatistically significant increases in the biochemical stress markers ΔAA\u003csub\u003esl\u003c/sub\u003e, ΔFR\u003csub\u003esl\u003c/sub\u003e, Cp\u003csub\u003epl\u003c/sub\u003e, and Pr\u003csub\u003epl\u003c/sub\u003e were observed between sessions. In contrast, the levels of ΔCr\u003csub\u003esl\u003c/sub\u003e and Glu\u003csub\u003esr\u003c/sub\u003e did not change significantly after the stressor was applied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSex-based disparities were observed in Cp\u003csub\u003epl\u003c/sub\u003e and Glu\u003csub\u003esr\u003c/sub\u003e, with comparatively lower levels in females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is worth mentioning that all variables were within the clinically accepted normal range.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-subject median and median absolute deviation (MAD) of stress markers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelax session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStress session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelax session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStress session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelax session\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStress session\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychometric variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSS (0\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAI-s (0\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAI-t (0\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC (0\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS (0-100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCp\u003csub\u003epl\u003c/sub\u003e (pmol/L)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsm\u003csub\u003epl\u003c/sub\u003e (mOsm/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e304.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e299.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e304.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e306.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePr\u003csub\u003epl\u003c/sub\u003e (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔCr\u003csub\u003esl\u003c/sub\u003e (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔAA\u003csub\u003esl\u003c/sub\u003e (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003csub\u003esr\u003c/sub\u003e (ng/ml)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔFR\u003csub\u003esl\u003c/sub\u003e (ml/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe variations in psychometric variables and biochemical variables between RS and SS were analysed using Wilcoxon Signed-Rank Test at a significance level of α\u0026thinsp;=\u0026thinsp;5%. Marked features show significant differences between sessions; *p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003csup\u003ea\u003c/sup\u003e: statistically significant differences between sexes (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eCorrelations among the studied variables\u003c/h3\u003e\n\u003cp\u003eOur findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated a significant positive correlation (r) between VAS and ΔAA\u003csub\u003esl\u003c/sub\u003e (r\u0026thinsp;=\u0026thinsp;0.351, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a significant negative correlation (r) between VAS and ΔFR\u003csub\u003esl\u003c/sub\u003e (r = -0.277, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, a positive association was observed among all psychometric variables, whereas a much less significant association (r) for VAS and PSS (r\u0026thinsp;=\u0026thinsp;0.198, p\u0026thinsp;=\u0026thinsp;0.078). The correlation (r) between ΔFR\u003csub\u003esl\u003c/sub\u003e and ΔAA\u003csub\u003esl\u003c/sub\u003e was negative (r = -0.387, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast, no association (r) was observed between ΔAA\u003csub\u003esl\u003c/sub\u003e and ΔCr\u003csub\u003esl\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStress Reference Scale (SRS)\u003c/h2\u003e \u003cp\u003eTo build the SRS, psychometric and biochemical variables that were statistically significant in differentiating RS and SS states were included. The results of the PCA with n\u0026thinsp;=\u0026thinsp;80 (40 RS and 40 SS) and seven dimensions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The first four components exhibited eigenvalues greater than 0.7 and explained 84% of the total variance. The loading vectors (correlation coefficient scores) of each component allowed for the interpretation of the type of information collected by each component (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, the first component mainly collected information corresponding to the psychometric tests, while the second component was positively associated with ΔFR\u003csub\u003esl\u003c/sub\u003e and negatively with ΔAA\u003csub\u003esl\u003c/sub\u003e. The third component had the highest scores for Cp\u003csub\u003epl,\u003c/sub\u003e and the fourth had a strong positive correlation with Pr\u003csub\u003epl\u003c/sub\u003e. Together, these components provide information on the different aspects (factors) involved in responses to acute psychological stress. The proposed SRS is expressed as Eq.\u0026nbsp;(1):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}SRS=\\left(0.15*{STAI}_{s}+0.14*VAS+0.14*SSC+0.12*{AA}_{sl}+0.11*{FR}_{sl}+0.19*Cp+0.15*Pr\\right)\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOur findings indicated that SRS scores were significantly higher in SS than in RS (p\u0026thinsp;=\u0026thinsp;1.299e-05). In addition, no significant sex-based variation was observed in SRS scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal Components Analysis (PCA) summary with eigenvalues, explained variances, and weights of the proposed SRS reference scale.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ePCA Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeight (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePr\u003csub\u003epl\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2466550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00162197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57448912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.776963442\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4094267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.74777448\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22566106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.143047078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAI-s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8509134\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38870408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10066183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005090755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8341677\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30798238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04963621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.004881756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8367070\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01633558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19598637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.094137137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFR\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3964135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71681296\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20191654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.086553101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCp\u003csub\u003epl\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1713332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09291078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79956896\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.518754316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigen value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5349358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3278334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1120487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9096437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.213368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.969049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.886410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.994910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCum. variance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.21337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.18242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.06883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.06374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance expl. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCum. variance: Cumulative variance; Variance expl.: Percentage of variance explained, proportional to the total variance explained by the four components. *variables with the highest weights in each component.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMachine Learning: Decision Tree and Statistical Models\u003c/h2\u003e \u003cp\u003eModels created to predict whether an individual is stressed or relaxed provided similar results, indicating their robustness. Decision tree, bagging decision tree, and logistic regression models revealed that the most important variables for the prediction of acute psychological stress were ΔAA\u003csub\u003esl\u003c/sub\u003e and STAI-s, whereas the random forest models indicated ΔFR\u003csub\u003esl\u003c/sub\u003e as an additional predictor of acute stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The predictive accuracy of the decision tree model was 65.21%, while the random forest and logistic regression models had accuracies of 73.91% and an area under the receiver operating curves (ROC) of 0.84 and 0.85, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic Analyses\u003c/h2\u003e \u003cp\u003eRaw DIMS profiles showed approximately 1500 signals for each mode (ESI (+) and ESI (-)). After data curation, the features that remained were passed on for subsequent statistical analysis.\u003c/p\u003e \u003cp\u003ePCA plots revealed a clear separation between blood metabolites for RS and SS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) for both ESI (+) and ESI (-), suggesting a clear influence of acute psychological stress on the blood metabolome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe loading diagram for both modes showed that the number of potential biomarkers in SS was significantly larger than that in RS (Supplementary Fig. S2). PLS-DA models built with ESI (+) and ESI (-) data provided good clustering of the samples and displayed a clear classification of each state. For ESI (+) mode, the model provided good explained variance (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and predictive variance (Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) parameters with values of 0.8 and 0.259, respectively. Differential metabolites, with a Variable Importance in Projection (VIP) score\u0026thinsp;\u0026gt;\u0026thinsp;2 \u003csup\u003e29\u003c/sup\u003e, and variation coefficients (CV%) below 20%, to avoid subjectivity in the selection process, both for RS and SS in ESI (+), are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Most of the signals obtained in ESI (+) mode showed significantly altered blood levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of various amino acids and related metabolites (serine, indole, alanine, phenylalanine, valine, histidine, N-acetyl glutamine), altered sterols and steroid hormone biosynthesis (hydrocortisone, aldosterone, corticosterone, 11-deoxycorticosterone (DOC), progesterone, pregnenolone, cholesterol, 17α-hydroxypregnenolone, 11-deoxycortisol, 17-deoxycortisol, 17β-oestradiol, oestrone), and catecholamine neurotransmitters (dopamine, norepinephrine, epinephrine) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The remaining significantly altered metabolites in SS corresponded largely to fatty acids and cellular membrane components (isobutyrate, choline, glycerophosphocholine, lyso-phosphatidylcholine (LPC)), sucrose sugar, and changes in muscle-related metabolites (creatine and carnitine). Nonetheless, the most predominant metabolites in RS included tyrosine, tryptophan, and its derivatives (the neurotransmitter serotonin, the neurotoxin quinolinic acid, and the hormone melatonin), derivatives of nitrogenous bases of nucleic acids (hypoxanthine and 2,4-dihydroxypyrimidine), and derivate of B3 vitamin N-methylnicotinamide (NMN) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of blood samples in ESI (-) mode showed a comparable R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.84, but a comparatively lower Q\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.04. The significant signals obtained in this mode were identified as fatty acids and phospholipids (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that stress leads to a substantial alteration of the lipid profile.\u003c/p\u003e \u003cp\u003eSubsequent pathway analysis revealed many metabolic pathways that were significantly altered by acute mental stress. These included steroid hormone biosynthesis (p\u0026thinsp;=\u0026thinsp;1.09E-07), glycerophospholipid metabolism (p\u0026thinsp;=\u0026thinsp;4.03E-04), linoleic acid metabolism (p\u0026thinsp;=\u0026thinsp;3.27E-03), aminoacyl-tRNA biosynthesis (p\u0026thinsp;=\u0026thinsp;1.09E-02), and tyrosine metabolism (p\u0026thinsp;=\u0026thinsp;4.14E-02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificantly differential metabolites determined using positive ion mode (ESI (+)) after relax session (RS) and after stress induction (SS).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominant metabolites in SS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003em /z\u003c/em\u003e [M\u0026thinsp;+\u0026thinsp;H] \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔm (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrocortisone\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363.4653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAldosterone\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e28\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361.4485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosterone\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347.2245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOC\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331.2253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone (P4)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315.2314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnenolone (P5)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317.2498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e27\u003c/sub\u003eH\u003csub\u003e46\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387.3598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17-OHP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333.2403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11-deoxycortisol\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347.2257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17-deoxycortisol\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347.2257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17β-oestradiol\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273.1878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOestrone (E1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271.1706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSucrose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342.29648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.0514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndole\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.0670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.09318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.0858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDopamine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154.0857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsobutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.0971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.63\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorepinephrine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.0826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpinephrine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184.0959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.1628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.1463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eN\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eN\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155.1545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarnitine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e15\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161.1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAG\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188.1811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPCh\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eNO\u003csub\u003e6\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257.2212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPC (18:1)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e26\u003c/sub\u003eH\u003csub\u003e52\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521.6673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPC (18:0)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e26\u003c/sub\u003eH\u003csub\u003e54\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e523.6832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredominant metabolites in RS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFormula\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003em / z\u003c/b\u003e \u003cb\u003e[M\u0026thinsp;+\u0026thinsp;H]\u003c/b\u003e \u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eΔm (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCV (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eVIP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Tryptophan\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205.0967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.10\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerotonin\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelatonin\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e13\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233.1270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eN\u003csub\u003e1\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.1885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.15\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.1115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.27\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuinolinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eNO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.1189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2, 4- dihydroxypyrimidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e6\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.35\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-Methylnicotinamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.1512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.90\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMS/MS: Tandem Mass Spectrometry data, and elucidation of fragmentation patterns for each m/z, which confirms unequivocal structural and chemical characterization in all the cases. \u003cem\u003ep\u003c/em\u003e-value was calculated by T-test analysis for each of the m/z / intensity relations, considering significant values of p\u0026thinsp;\u0026le;\u0026thinsp;0.05. \u003cem\u003eΔm\u003c/em\u003e: mass error expressed in ppm. CV: coefficient of variation was considered values\u0026thinsp;\u0026lt;\u0026thinsp;20% to obtain a method with good reproducibility. VIP: variable importance in projection was set up at a minimum value of 2 to ensure selection of predominant m/z in each group. DOC: 11-deoxycorticosterone; 17-OHP: 17α-hydroxypregnenolone; NAG: N-acetyl glutamine; GPCh: glycerophosphocholine; LPC: lysophosphatidylcholine.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e: previously published in a preliminary report by Lorenzo-Tejedor \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificantly differential metabolites identified after stress induction using negative mode (ESI (-))\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredominant metabolites in SS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS/MS product ions \u003cem\u003em/z\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔm (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaprylic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.10 (\u0026minus;\u0026thinsp;H+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e3.7\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.10 (\u0026minus;\u0026thinsp;H+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e3.3\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinoleic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279.20 (\u0026minus;\u0026thinsp;H+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.3\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e22\u003c/sub\u003eH\u003csub\u003e32\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327.20 (\u0026minus;\u0026thinsp;H+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e5.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPC (20:5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e28\u003c/sub\u003eH\u003csub\u003e48\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359.26, 184.07, 104.10, 86.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e3.1\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPE (16:0/22:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e43\u003c/sub\u003eH\u003csub\u003e74\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e746.50 (\u0026minus;\u0026thinsp;H+), 327.23, 196.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.6\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPE (18:1/20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e43\u003c/sub\u003eH\u003csub\u003e76\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e748.50 (\u0026minus;\u0026thinsp;H+), 303.30, 196.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e5.4\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPE (18:0/20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e43\u003c/sub\u003eH\u003csub\u003e78\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750.50 (\u0026minus;\u0026thinsp;H+), 303.20, 196.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.2\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPE (18:0/22:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e45\u003c/sub\u003eH\u003csub\u003e78\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e774.50 (\u0026minus;\u0026thinsp;H+), 327.20, 196.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e1.9\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC (16:0/20:5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e44\u003c/sub\u003eH\u003csub\u003e78\u003c/sub\u003eNO\u003csub\u003e8\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313.20, 359.30, 184.10, 104.10, 86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPC (16:0/22:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e46\u003c/sub\u003eH\u003csub\u003e80\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387.20, 184.0, 104.10, 86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.2\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPC (18:1/22:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e48\u003c/sub\u003eH\u003csub\u003e82\u003c/sub\u003eNO\u003csub\u003e7\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385.20, 184.0, 104.10, 86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e6.3\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC (18:1/20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e46\u003c/sub\u003eH\u003csub\u003e82\u003c/sub\u003eNO\u003csub\u003e8\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339.20, 361.0, 184.0, 104.10,86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2.6\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC (18:0/22:6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e48\u003c/sub\u003eH\u003csub\u003e84\u003c/sub\u003eNO\u003csub\u003e8\u003c/sub\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e341.0, 38.0, 184.0, 104.10, 86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e3.0\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMS/MS (Tandem Mass Spectrometry) data, and elucidation of fragmentation patterns for each m/z which confirms unequivocal structural and chemical characterization in all the cases; \u003cem\u003ep\u003c/em\u003e-value was calculated by T-test analysis for each of the m/z / intensity relations and considering significant values of p\u0026thinsp;\u0026le;\u0026thinsp;0.05; \u003cem\u003eΔm\u003c/em\u003e is the mass error expressed in ppm; CV: coefficient of variation were considered values\u0026thinsp;\u0026lt;\u0026thinsp;20% to obtain a method with good reproducibility; VIP: variable importance in projection was set up at a minimum value of 2 to ensure selection of predominant m/z in each group. DHA: docosahexaenoic acid; LPC: lyso-phosphatidylcholine; PPE: ethanolamine-plasmalogen; PC: phosphocholine; PPC: choline-plasmalogen.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, a modified form of the Trier Social Stress Test (TSST-M) was used to induce acute stress in university students. We found significant differences between RS and SS in psychometric tests (STAI-s, VAS), SSC, and in biochemical markers like AA\u003csub\u003esl\u003c/sub\u003e, FR\u003csub\u003esl\u003c/sub\u003e, Cp\u003csub\u003epl,\u003c/sub\u003e and Pr\u003csub\u003epl\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results confirmed that stress was successfully induced, in agreement with other studies that used the TSST \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. While we had anticipated a significant increase in salivary cortisol (Cr\u003csub\u003esl\u003c/sub\u003e), no significant difference was eventually found, even though previous studies have shown that cortisol levels typically rise following induced stress\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This discrepancy could be attributed to the dynamics of cortisol production-saliva detection. Whereas α-amylase is released directly into oral fluid from salivary glands in response to the activation of the HPA axis, cortisol is first secreted from the adrenal glands into the bloodstream and only then it passively filters into saliva. This process results in a delay of up to 15\u0026ndash;20 minutes before cortisol reaches its peak concentration in saliva in comparison with α-amylase \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Since saliva sample collection here was conducted immediately after stress induction, peak Cr\u003csub\u003esl\u003c/sub\u003e concentration may not have been captured. Setting aside this limitation, our metabolomic analysis identified cortisol as a relevant blood biomarker of acute stress, with significant changes in its concentration distinguishing RS from SS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning sex differences, we observed significantly higher glucose and copeptin levels in men, in line with findings by Spanakis et \u003cem\u003eal.\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This result supports the hypothesis that the HPA axis response to acute psychological stress varies by sex, according to previous studies \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This indicates that the risk of suffering from different diseases as a result of stress may vary by sex. Yet, further research is required to elucidate such sex-based disparities.\u003c/p\u003e \u003cp\u003eIn order to reduce the multiple dimensions of the psychological stress into its main components, a PCA method was performed. The top four out of seven components explain 84% of the variance. The first component correlates most strongly with psychometric tests, reflecting the variation in the quality of individuals' psychological state produced by the stressor. Whereas the second component was related to SNS activation (involving ΔAA\u003csub\u003esl\u003c/sub\u003e and FR\u003csub\u003esl\u003c/sub\u003e changes), the third and fourth components related to the HPA axis activation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Probably because Cp\u003csub\u003epl\u003c/sub\u003e and Pr\u003csub\u003epl\u003c/sub\u003e are secreted by different sources (the posterior and anterior pituitary, respectively), they appear as separate components. These results highlight the close interaction between the SNS and HPA axis in eliciting stress response. By integrating these significant factors into the SRS scale \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e we could check its utility in quantifying the level of stress perceived by an individual \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Still, this has to be validated by additional studies.\u003c/p\u003e \u003cp\u003eThe predictive models built using machine learning techniques (decision trees, logistic regression, and random forest classifiers) exhibited a high level of robustness in determining the stress state of the subject (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Consistently, all models identified ΔAA\u003csub\u003esl\u003c/sub\u003e and STAI-s as the main predictive biomarkers of acute psychological stress status. Such a result supports the importance of AA\u003csub\u003esl\u003c/sub\u003e as a key biomarker in evaluating stressors that activate the SNS, in agreement with previous research reports \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStill, it is important to note that AA\u003csub\u003esl\u003c/sub\u003e levels, like all other variables, may be influenced by a variety of factors such as exercise and medication \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In the case of the random forest model, FR\u003csub\u003esl\u003c/sub\u003e was identified as an additional significant predictor of stress status.\u003c/p\u003e \u003cp\u003eWe are aware that, even though our models show high predictive accuracy, indicating their potential reliability for stress monitoring, the small sample size (n\u0026thinsp;=\u0026thinsp;40) in this study limited the statistical power of our analyses, reducing the generalizability of our findings to a broader population. Nonetheless, our findings provide a sound basis for further studies.\u003c/p\u003e \u003cp\u003eRegarding the metabolic signature of acute psychological stress explored here, our results are in line with previous research that has documented significant changes in the metabolomic profile in both animal models and humans subjected to different stressors \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In PCA plots of the metabolomic data, two clusters are clearly distinguished, thus indicating that RS and SS samples had remarkably differential metabolic compositions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A total of 53 significantly differential metabolites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;2) were identified from both ESI (+) and ESI (-) ion models. Of these, 9 were predominantly associated with RS, while 44 were predominantly associated with SS. These findings showed that acute psychological stress produces extensive changes across multiple metabolic pathways involved in the organism\u0026rsquo;s adaptive response. It is well established that prolonged stress-induced alterations can have detrimental effects on health. Consequently, chronic psychological stress is recognized as a serious risk factor for cardiovascular diseases and metabolic disorders \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, one of our most striking findings concerns the significant changes in the lipid profile induced by acute mental stress, particularly the substantial increase in fatty acids, polyunsaturated fatty acids (PUFA), phosphocholines (PC), plasmalogens (PPC and PPE), and lysophosphatidylcholines (LPC) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Recent studies indicate that these lipids and lipid-like molecules play critical roles in cell signaling pathways related to inflammation, immunity, and apoptosis \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe increases in PPC and PPE levels observed may be attributed to an increase in the brain\u0026rsquo;s demand for plasmalogens (PP) under acute stress conditions to keep an adequate neural function, endorse synaptic plasticity, and protect against stress-induced oxidative damage. Various researchers proposed that PP, particularly those containing omega-3 fatty acids such as LPC (20:5), PPE 16:0/22:6, PPE 18:0/22:6, and docosahexaenoic acid (DHA), as observed in our study, may reduce HPA axis activation in response to acute physiological stress, thereby protecting the brain from subsequent cellular damage \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. When stress becomes chronic, this adaptive mechanism leads instead to a decline in PP levels, which is associated with several degenerative disorders and neurocognitive impairments \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the increased PP levels in SS, we also observed elevated levels of LPC. This finding is in line with previous studies suggesting that LPCs containing medium-chain saturated fatty acids may serve as potential biomarkers not only for stress but also for adiposity and inflammation \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. LPCs are generated through the cleavage of phosphatidylcholine, a major phospholipid in the cell membrane, by phospholipase A\u003csub\u003e2\u003c/sub\u003e (PLA\u003csub\u003e2\u003c/sub\u003e), producing free fatty acids, including arachidonic acid. The observed rise in LPC levels may reflect the body's complex response to the induced stress adaptation, involving the activation of PLA\u003csub\u003e2\u003c/sub\u003e by mitogen-activated protein (MAP) kinase-related kinase, a family of stress-activated protein kinases \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe function of LPCs depends on the length and degree of saturation of the fatty acid chain attached to the glycerol moiety \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. For instance, elevated levels of LPC (18:0) and related PPs, PPC (18:0/20:4) and PPC (P18:0/22:6), have been associated with reduced inflammation, lower adiposity, and a decreased risk of cancer \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. On the other hand, LPCs like 18:1 and 20:4 exert their biological roles by activating many downstream signaling pathways, including mitogen-activated protein kinase (MAPK) and nuclear factor kappa B (NF-κB). These pathways promote cell division, chemotaxis, oxidative stress, inflammatory cytokine release, and apoptosis, thereby accelerating the development of atherosclerosis \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Additionally, LPC (20:4) has been associated with stress index, and its free fatty acid, the arachidonic acid (20:4), has been suggested as a marker of depression and stress in humans \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother predominant metabolite found under acute stress conditions was linoleic acid (18:2-n6), the most abundant PUFA in human nutrition. Linoleic acid (LA) is an essential n-6 PUFA and a precursor to arachidonic acid. While normal levels of LA are crucial for neurological and cognitive development and overall health, elevated levels of LA have been linked to inflammation and metabolic diseases \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Our data indicates that its metabolic pathway was among the most significantly affected. One such alteration involves the inhibition of the enzymes responsible for catalyzing LA epoxidation, leading to a reduction in its hypocholesterolemic effect \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, followed by the consequent accumulation of arachidonic acid. Additionally, LA can undergo non-enzymatic oxidation to produce Oxlams, metabolites that have been shown to promote a strong pro-inflammatory response in rats \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn elevated level of cholesterol in SS, like the one observed here (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), may lead to the generation of a variety of corticosteroids via steroidogenesis. Due to their lipophilic nature, corticosteroids cannot be pre-synthesized and stored in adrenal glands but have to be rapidly synthesized upon Adrenocorticotropic hormone (ACTH) stimulation, which is insteadregulated by the HPA axis \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Corticosteroids regulate multiple physiologic processes, including metabolism, development, homeostasis, cognition, and inflammation \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Corticosteroids like cortisol increase the bioavailability of glucose and the consequent release of energy to the brain \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, as evidenced by the increased levels of carnitine, creatine, and glucogenic amino acids observed in this study, corroborating findings by Singh et al. \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Additionally, these amino acids could also serve as a substrate for the synthesis of protein required for the stress response process \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt has been established that each stressor has a neurochemical signature with distinct central and peripheral mechanisms \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Controversially, some studies have demonstrated that the two branches of the sympathoadrenal system (SAS) \u0026ndash; the adrenal medulla and the sympathetic nerves\u0026ndash; can be activated independently by different stressors \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Nonetheless, our study indicates that acute psychological stress induced by TSST-M activated both components of SAS. This stimulates the adrenal medulla system, elevating plasma Epi levels, and activates the sympathoneural system, increasing NE and dopamine plasma levels.\u003c/p\u003e \u003cp\u003eEpi is known as the hormone preparing the body for a fight-or-flight response \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. NE, which is the main sympathetic neurotransmitter in circulatory regulation, is also a central neurotransmitter thought to be involved in alertness, memory of distressing events, nociception, and anxiety \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Dopamine (DA) is a key neurotransmitter that regulates many processes in the CNS, including reward, motivation, and cognition. Importantly, DA can also be produced locally in several peripheral organs, where it has autocrine and paracrine effects influencing many organ functions \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and is released in plasma in response to stress. This response is partly influenced by circulating cortisol levels in the body \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. DA, moreover, regulates critical functions such as metabolic homeostasis, hormone release, sodium balance, blood pressure, renal activity, and gastrointestinal motility. It also modulates inflammatory and immunological processes \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Prolonged exposure to intense stressors inhibits the release of DA and disrupts the dopaminergic pathway, leading to psychological disorders such as depression and schizophrenia \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe elevated levels of cholesterol, steroid hormones, and adrenal catecholamines observed in this study could be explained accordingly by the increase in prolactin, known as \u003cem\u003ethe stress hormone\u003c/em\u003e, along with cortisol.\u003c/p\u003e \u003cp\u003eThere is substantial evidence supporting prolactin's multifaceted role in the adrenal response to stress \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. More specifically, it has been shown to increase the secretion of ACTH, enhance the storage of cholesterol esters, and induce adrenal hypertrophy \u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Under acute stress, prolactin secretion appears to play a crucial and complex role in maintaining metabolic and immune system homeostasis \u003csup\u003e\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Therefore, while Pr may induce a protective proinflammatory state during acute stress, chronic exposure to prolactin can, by contrast, lead to habituation and potentially contribute to the development of cardiovascular pathologies \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, we identified several metabolites that the literature suggests may have protective effects during acute stress. For instance, progesterone and pregnenolone (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are known to suppress HPA activity, thereby reducing stress levels \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Additionally, caprylic and capric acid have been identified as possessing anti-inflammatory properties, which counteract the inflammatory process often associated with stress \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Furthermore, 17β-oestradiol and oestrone have been shown to play a neuroprotective role against stress-related damage \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Collectively, these metabolites contribute to the body\u0026rsquo;s adaptive response aimed at restoring homeostasis and mitigating the adverse effects of stress.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study employed the TSST-M to induce acute psychological stress, exploring the multifaceted effects of the stress response through the integration of psychometric assessments, biochemical analyses, and metabolomic profiling. Our findings underscore significant sex differences in the stress response, particularly in glucose and copeptin levels, emphasizing that stress impacts men and women differently. This highlights the need for sex-sensitive approaches in stress research, especially given their implications for disease risk assessment.\u003c/p\u003e \u003cp\u003eAdditionally, our study demonstrates the utility of the Stress Reference Scale (SRS) in stress quantification and the importance of machine learning predictive models in distinguishing stressed from relaxed states in individuals. Specifically, salivary α-amylase (AA\u003csub\u003esl\u003c/sub\u003e) and STAI-s were identified as prominent stress markers by our predictive models.\u003c/p\u003e \u003cp\u003eAn important strength of this study is the validation of a direct infusion MS method, which is minimally invasive, requiring only a finger prick and a drop of blood for metabolomics analysis \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Despite its limitations, our results further indicate that acute psychological stress significantly impacts bodily systems, triggering relevant metabolic alterations.\u003c/p\u003e \u003cp\u003eThese findings help to understand the intricate interplay between physiological and psychological domains in acute mental stress responses. Taken together, our results contribute to a deeper understanding of stress by integrating advanced analytical tools and methods for the diagnosis of acute psychological stress and understanding the mechanisms involved in this type of stress-related disorders.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS AND FUTURE DIRECTIONS\u003c/h2\u003e \u003cp\u003eNaturally, the study has its limitations. First, the relatively reduced sample size, integrated by healthy university students, limits the generalizability of our findings to a broader demographic. Yet, this does not counterbalance the fact that this study provides a foundational basis for future research involving a broader and more heterogeneous population.\u003c/p\u003e \u003cp\u003eSecond, we chose not to account for the use of contraceptives, which could potentially influence prolactin and other hormone levels variations. The strict focus on acute stress responses over a narrow timeframe renders the impact of cyclical hormonal fluctuations minimal. Similarly, since the study evaluates the variation (Δ) between pre- and post-stress induction, the prevalence of regular medication use, such as antiallergics or bronchodilators, has been deemed not relevant.\u003c/p\u003e \u003cp\u003eIn any event, our work still provides basic parameters for future research, thus potentially confirming the relation between biochemical, metabolic, and psychometric stress measures proposed here, extending the approach to larger and more heterogeneous populations. This would directly increase the generalizability of our findings and provide further validation for the diagnostic and measurement tools introduced here.\u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eTo ascertain the effects of acute psychological stress on biochemical, psychological, and metabolomic variables, we performed a quasi-experimental pre-post study without a control group on a set of university volunteers. The study, designed and performed under the ES3P \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e framework, included two sessions: a 35-minute Relaxation (RS) protocol setting control conditions, followed by a 35-minute Stress-induction protocol (see Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for instructions and protocol steps) based on a modified form of the Trier Social Stress Test (TSST-M) previously described by Arza \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e yielding acute psychological SS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Ethical Declaration\u003c/h2\u003e \u003cp\u003eVolunteers aged 20 to 30 (both sexes) were recruited from the University of Zaragoza. Exclusion criteria included: (1) signs of depression or a history of other mental disorders; (2) regular use of psychotropic substances; and (3) pregnancy or breastfeeding at the time of the study (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for participant details). All participants were informed about the study procedures and provided written informed consent. This documentation is securely archived at the Psychiatric Unit, HCULB, in compliance with the EU\u0026rsquo;s General Data Protection Regulation. The whole study was conducted in accordance with the World Medical Association (WMA) Declaration of Helsinki (2013) and was approved by the Clinical Research Ethics Committee of Aragon (CEICA; protocol number PI14/0044).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStress Induction and Relaxation Protocols\u003c/h2\u003e \u003cp\u003eThe sessions were carried out on different days, but at the same hour, around 10:00 AM, to avoid variations in the circadian rhythm \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The relaxation session (RS) comprised a baseline (B\u003csub\u003eRS\u003c/sub\u003e) and relax stage (R\u003csub\u003eRS\u003c/sub\u003e), whereas the stress session (SS) comprised a baseline stage (B\u003csub\u003eSS\u003c/sub\u003e) and five distinct stages to induce acute psychological stress \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. For the relaxation session, the subjects were seated in a comfortable position in a dimly lit room and were exposed to audio recording and guided relaxation to induce autogenic relaxation following Schultz\u0026rsquo;s method \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. The stress sessions followed a TSST-M, which is a robust, reliable, and well-documented protocol widely used in stress research \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan additionalcitationids=\"CR80 CR81\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, with slight modifications, as described in \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The stress session consisted of storytelling (STS), memory test (MTS), stress anticipation (SAS), video display (VDS), and arithmetic task (ATS). (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the end of each session, RS and SS, the participants were required to complete psychometric questionnaires. Saliva samples were collected at the end of the baseline stages (B\u003csub\u003eRS\u003c/sub\u003e and B\u003csub\u003eSS\u003c/sub\u003e) and repeated after RS and SS, whereas blood and plasma samples were only collected after RS and SS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStress Evaluation and Measurement: Psychometric Evaluation\u003c/h2\u003e \u003cp\u003eBefore administering psychometric questionnaires, participants were asked to indicate their perception of their stress levels (Perceived Stress) on a scale of 0\u0026ndash;100 arbitrary units (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe professionals of the zarademp group from the Psychiatric Service (HCU-LB) and the Department of Medicine and Psychiatry (University of Zaragoza) selected the tests, verified the corresponding Spanish versions, administered the tests to the subjects, and subsequently interpreted the results. This team also applied a test designed by themselves on behalf of the ES3 Project \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, \u0026lsquo;the Symptomatic Stress Scale\u0026rdquo; (SSC). The SSC scale is a Likert-type Scale that consists of 20 questions that evaluate the subjective effect of the stressor on the subject from somatic and psycho-cognitive points of view. This scale was validated by Garz\u0026oacute;n-Rey \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e and applied in a recent study by Garcia Pages \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe validated psychometric tests used were the Spanish versions of the Perceived Stress Scale (PSS) \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, Visual Analogue Scale (VAS), and State-Trait Anxiety Inventory tests (STAI) \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. PSS is widely used to assess stress levels in young people and adults \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. It evaluates the degree to which an individual perceives life as unpredictable, uncontrollable, or overloading. VAS measures subjective stress on a numeric scale ranging from 0 to 100 \u003csup\u003e87\u003c/sup\u003e. This test highlights the differences in stress levels between groups and determines the connection between the VAS stress assessment and the evaluation of various related concepts \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Finally, two STAI questionnaires were used: one to measure the trait or general tendency to increase anxiety in stressful situations (STAI-t), and another to evaluate the state of the subject in a specific situation (STAI-s) \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of Biochemical Variables\u003c/h2\u003e \u003cp\u003eBiological samples for analysis were collected by professionals from HCU-LB and stored in sterile, airtight compartments at an adequate temperature until analysis. Biochemical markers determined were glucose (Glu\u003csub\u003esr\u003c/sub\u003e) from serum samples; prolactin (Pr\u003csub\u003epl\u003c/sub\u003e), copeptin (Cp\u003csub\u003epl\u003c/sub\u003e), and osmolality (Osm\u003csub\u003epl\u003c/sub\u003e) from plasma samples; and salivary cortisol (Cr\u003csub\u003esl\u003c/sub\u003e), salivary flow rate (FR\u003csub\u003esl\u003c/sub\u003e), and α-amylase (AA\u003csub\u003esl\u003c/sub\u003e) in saliva samples. All samples were processed using the same tests to avoid inter-test variability, thereby achieving intra-test variation coefficients\u0026thinsp;\u0026lt;\u0026thinsp;5% in all cases.\u003c/p\u003e \u003cp\u003eSalivette tubes were used to collect saliva, following the manufacturer\u0026rsquo;s recommendations (Sarstedt AG \u0026amp; Co., N\u0026uuml;mbrecht, Germany). Subsequently, samples were immediately preserved on ice and later kept frozen at \u0026minus;\u0026thinsp;20\u0026deg;C until processing, according to the protocol previously described by Garcia Pages et al. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Concentrations of Cr\u003csub\u003esl\u003c/sub\u003e and AA\u003csub\u003esl\u003c/sub\u003e were measured in the endocrinology and radioimmune analysis service of Neurosciences Institute at the Universitat Aut\u0026ograve;noma de Barcelona (UAB) using fully validated immunoassay and kinetics enzyme assay kits from Salimetrics/USA, respectively \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The changes in salivary cortisol (ΔCr\u003csub\u003esl\u003c/sub\u003e), α-amylase (ΔAA\u003csub\u003esl\u003c/sub\u003e), and flow rate (ΔFR\u003csub\u003esl\u003c/sub\u003e) in response to the applied stress stimulus were calculated.\u003c/p\u003e \u003cp\u003eThe extracted blood was partitioned into two tubes: one with EDTA anticoagulant and the other with a clot accelerator and gel serum separator. Both samples were preserved on ice and later centrifuged at 3000 rpm for 10 min. Plasma and serum were kept frozen at \u0026minus;\u0026thinsp;20\u0026deg;C until processing at the Biomedical Diagnostics Centre at the Hospital Clinic of Barcelona. Quantification of Glu\u003csub\u003esr\u003c/sub\u003e, Pr\u003csub\u003epl\u003c/sub\u003e, Cp\u003csub\u003epl,\u003c/sub\u003e and Osm\u003csub\u003epl\u003c/sub\u003e was performed using molecular absorption and immunoassay spectrometry techniques.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eStress Reference Scale\u003c/h2\u003e \u003cp\u003eThe stress reference scale \u003cem\u003e(SRS)\u003c/em\u003e was proposed by Garzon-Rey \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e as a reference standard for measuring acute emotional stress. Significant biochemical and psychometric parameters were used to compute the scale using a multivariate approach as described previously. To assign weights to the different variables, their mean scores were first normalized by rescaling to a 0-100 range of arbitrary units using the following Eq.\u0026nbsp;(2):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}y=\\frac{100*\\left(x-Min+\\sigma\\:*0.5\\right)}{\\left(Max-Min+\\sigma\\:\\right)}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the variable (\u003cem\u003ex\u003c/em\u003e) with a standard deviation (\u003cem\u003eσ)\u003c/em\u003e, minimum (\u003cem\u003eMin\u003c/em\u003e), and maximum \u003cem\u003e(Max)\u003c/em\u003e values are transformed into a variable (\u003cem\u003ey)\u003c/em\u003e ranging from 0 to100. Afterwards, the principal components analysis (PCA) was performed to assign the corresponding weights to each variable. Only features with eigenvalues greater than 0.8, which explained 84% of the total variance, were selected to build the scale.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM\u0026reg; SPSS\u0026reg; Statistics 25.0 and RStudio for Microsoft Windows, along with its corresponding packages available on CRAN or Bioconductor repositories.\u003c/p\u003e \u003cp\u003eThe states of the volunteers at the end of each session, RS and SS, were considered to be the lower and higher ranges of the stress state. The variations in psychometric, biochemical, and SRS variables between RS and SS were analyzed using the Wilcoxon signed-rank test, a non-parametric statistical test, because the data were not normally distributed after testing for normality using the Lilliefors test. Correlations were computed using Spearman\u0026rsquo;s rank correlation for non-parametric distributions. For all analyses, the significance level was set at α\u0026thinsp;=\u0026thinsp;5%.\u003c/p\u003e \u003cp\u003eVariables were passed on to create predictive models. Categorical variables were encoded as factors. The grouping RS or SS was considered as the response variable for the models, and the other variables were considered as predictors of the state of the group. The study employed the Recursive PARTitioning (\u003cem\u003erpart)\u003c/em\u003e algorithm based on \u003cem\u003eCART\u003c/em\u003e (classification and regression tree) to build decision tree models (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/rpart/rpart.pdf\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/rpart/rpart.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The \u003cem\u003eadabag\u003c/em\u003e package \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e was used to build a bagging predicting model, and the \u003cem\u003eRandom-Forest\u003c/em\u003e algorithm software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/randomForest/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/randomForest/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain the variable relative importance rankings of variables. We used 70% of the original data as a training set and the remaining as a testing set to assess the model afterwards. The Gini Index was used to split nodes, and pruning was performed to avoid overfitting the model. A multivariate logistic regression model was constructed and compared with the decision tree, bagging, and random forest models.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMetabolomic Sample Processing and Data Analysis\u003c/h2\u003e \u003cp\u003eA semi-quantitative direct-infusion mass spectrometry (DI-MS) untargeted metabolomic study was conducted to characterize biochemical responses to acute psychological stress and as a biomarker development tool. This innovative technique, involving direct injection into the ionization source of the mass spectrometer without prior chromatographic separation with an electrospray ionization (ESI) source, already presents proven advantages and robust results \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood samples were collected by pricking participants\u0026rsquo; fingers. Approximately 0.5 mL of total blood was collected into an empty and sterilized Eppendorf\u0026trade; tube. No anticoagulants were used. Samples were immediately protected from light and stored at -80\u0026deg;C until analysis. Sample preparation was carried out as previously described \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor positive mode MS detection, immediately before analysis, each sample was diluted 1:1000 with a protonating agent solution of LC-MS grade methanol with 0.1% formic acid (Fluka) at 99% purity. For negative mode detection, a dilution of 1:1000 of the sample was made with MS-grade dichloromethane (Fluka): methanol (ratio 1:1). Samples to be analyzed were pumped directly into the mass spectrometer.\u003c/p\u003e \u003cp\u003eMeasurements were taken in both positive and negative modes using a hybrid triple quadrupole/linear ion trap mass spectrometer 4000 QTRAP LC/MS/MS System (AB Sciex) with electrospray ionization (ESI) source interface for high-sensitivity, full-scan MS, MS/MS, and MS\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e spectra with high selectivity from true triple quadrupole precursor ion (PI) and neutral loss (NL) scans. Data acquisition and pre-processing were carried out using Analyst\u0026reg; software version 1.5.2 (Build 5704) (Sciex) as previously described \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. A scan range of 50\u0026thinsp;\u0026minus;\u0026thinsp;1,200 m/z was used. The mass accuracy and resolution were 5 ppm and 20,000 ppm, respectively. The instrument settings were as follows: ion spray voltage, 5,000 V; curtain gas, 20 AU; GS1 and GS2, 50 and 30 psi, respectively; probe temperature, 550\u0026deg;C; and run time, 10.0 min. For MS/MS analysis, the collision-induced dissociation (CID) mode was used and was set to 30\u0026ndash;50% normalized collision energy (CE) for selected mass-to-charge ratio (m/z) peaks.\u003c/p\u003e \u003cp\u003eData normalization, statistical and functional analyses, and compound identification were performed following the protocol previously described by Lorenzo \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEnrichment and pathway topology analyses were performed using the corresponding modules of MetaboAnalyst 5.0 \u003csup\u003e94\u003c/sup\u003e and categorized with the KEGG pathway \u003cem\u003eHomo sapiens\u003c/em\u003e database \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. Pathway enrichment analysis allowed for the identification of those pathways significantly affected by the stressor, and thus, to better understand the impact of acute psychological stress on an individual\u0026rsquo;s metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eΔAA\u003csub\u003esl\u003c/sub\u003e (difference in α-amylase concentrations between the second and first samples), AA\u003csub\u003esl\u003c/sub\u003e (Salivary\u0026nbsp;α-amylase), ACTH (Adrenocorticotropic hormone), B\u003csub\u003eRS\u0026nbsp;\u003c/sub\u003e(Baseline relaxation session), B\u003csub\u003eSS\u0026nbsp;\u003c/sub\u003e(Baseline stress session),\u0026nbsp;CNS (Central Nervous System), Cp\u003csub\u003epl\u003c/sub\u003e (Plasma copeptin), ΔCr\u003csub\u003esl\u003c/sub\u003e (difference in salivary cortisol between the second and first samples),Cr\u003csub\u003esl\u003c/sub\u003e (Salivary cortisol), DHA (docosahexaenoic acid), DIMS (Direct Infusion Mass Spectrometry), DOC (11-deoxycorticosterone), Epi (epinephrine),\u0026nbsp;ΔFR\u003csub\u003esl\u003c/sub\u003e (difference in salivary flow rate between the second and first samples),FR\u003csub\u003esl\u003c/sub\u003e (Salivary flow rate), ESI (Electrospray Ionisation), Glu\u003csub\u003esr\u003c/sub\u003e (Serum glucose), HPA (Hypothalamic-Pituitary-Adrenal), KEGG (Kyoto Encyclopaedia of Genes and Genomes), LA (Linoleic acid), LC-MS (Liquid Chromatography - Mass Spectrometry), LPC\u0026nbsp;(Lyso-phosphatidylcholine),\u0026nbsp;MAPK (mitogen-activated protein kinase),\u0026nbsp;NAG (N-acetyl glutamine),\u0026nbsp;NE (norepinephrine), NF-κB (nuclear factor kappa B),\u0026nbsp;Osm\u003csub\u003epl\u003c/sub\u003e (Osmolarity from plasma samples), PC (Phosphocholines), PSNS (Parasympathetic Nervous System), PPC\u0026nbsp;(Choline-plasmalogens),\u0026nbsp;PPE\u0026nbsp;(Ethanolamine-plasmalogens),\u0026nbsp;Pr\u003csub\u003epl\u003c/sub\u003e (Plasma prolactin), PSS (Perceived Stress Scale), PUFA (polyunsaturated fatty acids), SNS (Sympathetic Nervous System), SS (Stress session), SSC (Symptomatic stress scale), STAI-s/t (State-Trait Anxiety Inventory state and trait tests, respectively), RS (Relaxation session), TSST-M (Modified form of the Trier Social Stress Test), VAS (Visual Analogue Scale)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.G. gratefully acknowledges Roche Institute Foundation for their support in funding her Master\u0026rsquo;s in Bioinformatics, Computational Biology, and Personalized Medicine provided by Universitat Polit\u0026egrave;cnica de Val\u0026egrave;ncia (UPV). The skills and knowledge gained directly contributed to our research. Special thanks to the Proteomics Core Research Facility of the Aragon Health Sciences Institute (IACS-CIBA) for their technical assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdditional Information section\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthorship contribution statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of a multidisciplinary project formed with the objective of studying different aspects of the genesis of stress and its adverse effects on health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eG.A.F. and G.G. performed the formal analysis of the psychometric and biochemical data, wrote the computer code and algorithms for the machine learning statistical analysis, and conducted the literature review. They also drafted the original manuscript and prepared the tables. G.A.F. generated Figures 1 and 4, analysed sex differences, performed the enrichment and pathway topology analyses of the metabolomic data, and contributed to formatting the manuscript according to the journal\u0026rsquo;s stylesheet.\u003c/p\u003e\n\u003cp\u003eG.G. and E.M.R. wrote the final sections (Discussion and Conclusion), thoroughly reviewed the manuscript, and assembled the final version by incorporating minor corrections, rephrasing, and restructuring content to improve conciseness, clarity, fluency, and readability. G.G. also generated Figure 2, was responsible for conceptualisation, validation, and visualisation, and oversaw and completed the entire submission process. M.L.T., C.D.L.C., J.A., R.B., and M.B. designed the study.\u003c/p\u003e\n\u003cp\u003eM.L.T. performed the formal analysis for the metabolomic study and generated Figure 3 and Tables 3\u0026ndash;4. C.D.L.C. conducted stress and relaxation sessions, administered psychometric tests, coordinated the fieldwork, and compiled its results. E.M.R. contributed to the cognitive component of the manuscript, assisted with the overall review process, and revised English grammar, terminology, and phrasing throughout the final version. J.L. managed the database registry and contributed to the design of the metabolomics study. R.B. (Principal Investigator) and J.A. (Co-investigator) supervised project development and managed project funding. M.B. collected and prepared the biological samples for analysis, supervised and coordinated the study, contributed resources, and collaborated on the literature review. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFUNDING\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially funded by the Ministry of Science and Innovation, Spain (TED2021-\u003c/p\u003e\n\u003cp\u003e131106B-I00), the European Social Fund (EU), and the Aragon Government, Spain\u003c/p\u003e\n\u003cp\u003ethrough the BSICoS group, Spain (T39 23R).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChrousos, G. P. Stress and disorders of the stress system. \u003cem\u003eNat. Rev. Endocrinol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 374\u0026ndash;381 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChrousos, G. P. \u0026amp; Gold, P. W. 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KEGG: Biological Systems Database As A Model Of The Real World. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D672\u0026ndash;D677 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. \u0026amp; Goto, S. K. E. G. G. Kyoto Encyclopedia Of Genes And Genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental Stress Reactivity, Metabolic Responses, Biomarkers, Metabolomics, Trier Social Stress Test, Direct Infusion Mass Spectrometry (DI-MS), Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-6503620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6503620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStress is associated with the onset of various neurological disorders such as depression, PTSD, and anxiety. Despite the extensive research performed, metabolic changes triggered in response to acute psychological stress remain unclear. This study evaluates acute stress biomarkers and its adverse effects in university students through psychometric, biochemical, and metabolomic analyses, implementing Machine Learning on statistical models. In the study, forty participants were subject to relaxation and stress induction using autogenic training and a modified Trier Social Stress Test (TSST-M). Psychometric questionnaires confirmed the achievement of these states, while saliva and blood were sampled for biochemical analyses. Additionally, blood samples were applied to untargeted metabolomic approaches.\u003c/p\u003e \u003cp\u003eThe results reveal that although most biomarkers showed changes under stress state, the machine learning predictive model successfully identified salivary α-amylase and State-Trait Anxiety Inventory-state (STAI-s) as prominent stress markers. Additionally, several metabolic pathways, including steroid hormone biosynthesis, glycerophospholipid metabolism, linoleic acid metabolism, tyrosine metabolism, and aminoacyl-tRNA biosynthesis, were affected. Alterations of this sort, we conclude, allow us to gain further understanding into the adverse effects systematically associated with stress. In this way, our findings highlight the significant impact of acute mental stress on multiple metabolic pathways directly implicated in stress-related disorders.\u003c/p\u003e","manuscriptTitle":"New Insights into Stress Metabolomics. Looking for new Diagnostic Biomarkers. 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