Association of Exhaled Breath Volatile Organic Compounds with Surgical Traumatic Stress during General Anaesthesia: An Exploratory Study

doi:10.21203/rs.3.rs-3982156/v1

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Association of Exhaled Breath Volatile Organic Compounds with Surgical Traumatic Stress during General Anaesthesia: An Exploratory Study

https://doi.org/10.21203/rs.3.rs-3982156/v1

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Surgical procedures can induce traumatic stress responses, which are associated with postoperative complications. Therefore, a perioperative monitor is needed to identify patients with a higher degree of stress responses. We explored the relationship between breath volatile organic compounds (VOCs), a non-invasive method widely explored in disease diagnosis, and surgical traumatic stress. Exhaled breath and blood samples were collected from 105 patients under general anaesthesia at three time points: prior to incision (Pre-op), 2h after incision (Intra-op), and prior to extubation (End-op). Differential VOCs between these time points were screened. Blood metabolomics analysis, traumatic stress-related biomarkers detection, and correlation analysis between VOCs and stress biomarkers were performed. We found that both the abundance of VOCs and blood metabolites changed significantly between these time points. Norepinephrine, epinephrine and cortisol all increased significantly in Intra-op and then reduced significantly in End-op. Correlation analysis showed both Comp_6 and Comp_23 were negatively correlated with norepinephrine in group comparison between Intra-op and End-op, the same trend as that in group comparison between Pre-op and Intra-op. We conclude that surgical traumatic stress resulting from surgical procedures may change the exhaled breath VOC profile in perioperative patients, providing preliminary evidence for VOC use in future monitoring.

Health sciences/Biomarkers

Health sciences/Health care

Health sciences/Medical research

Health sciences/Molecular medicine

exhaled breath

general anaesthesia

perioperative monitoring

surgical traumatic stress

volatile organic compounds

Higher invasiveness of surgery can induce a higher degree of stress responses which is associated with postoperative complications¹. Stress responses may comprise the release of catecholamines, cortisol, and blood glucose, as well as inflammatory mediators such as interleukins and tumour necrosis factor-α, and immune cell dysfunction ^2,3. These may lead to increased susceptibility to postoperative infection, delayed wound healing, multi-organ dysfunction, and increased risk of morbidity and mortality ⁴. Therefore, there is an urgent need to develop highly efficient methods to monitor these responses during the perioperative period and to help take steps to regulate surgical stress, inflammation, and immunity. To inhibit these complicated stress responses, mainly comprising of immunological, neuroendocrine responses and metabolic changes to surgery, and to evaluate their outcome, strenuous efforts have been made over the last several decades ⁵. Therefore, a variety of monitoring techniques have been developed to help manage these responses ⁶. However, advances tend to be controversial, and monitoring hemodynamic parameters, such as blood pressure and heart rate (HR), is still the first-line reference in clinical practice ⁷. It is accepted that both metabolism and hormone secretion take time to be detected by monitors, making it impossible to continuously monitor blood stress hormone levels and inflammatory or immunological status throughout the surgical procedure by invasive methods. Therefore, a non-invasive tool with high resolution is firmly needed.

Exhaled breath VOCs have recently been paid much attention as a promising diagnostic tool because of their easy accessibility as novel biomarkers ⁸. Studies focusing on cancers, respiratory infections, asthma, and cognitive dysfunctions have revealed the value of VOCs in disease diagnosis and monitoring ^8–11. However, it remains unclear whether it can be used to monitor traumatic stress responses associated with surgery.

In this exploratory study, we hypothesized that VOCs in the exhaled breath of patients undergoing abdominal surgery under general anaesthesia may be altered and thus reflect surgical traumatic stress. We aimed to explore the relationship between VOCs and surgical traumatic stress in this preliminary study. Thus, the primary endpoint is a change in the surgical traumatic stress-related exhaled breath VOCs characteristics during anaesthesia. Secondary endpoints included blood metabolomics, changes in surgical traumatic stress-related markers, and correlation between exhaled breath VOCs and surgical traumatic stress-related markers.

Basic characteristics of study participants

The patient enrolment profile is shown in Fig. 1. Over the 212 patients enrolled in the paralleled EEG study, a total of 105 patients’ VOCs were collected for data analysis. As shown in Table 1, the average age of the study participants was 51.5 years old. 60 (57.1%) patients underwent open abdominal surgery, while 45 (42.9%) participants underwent laparoscopic surgery. 62 male (59.0%) and 43 (41.0%) female patients participated in our study. Other information including height, weight, body mass index, blood pressure, HR, RR, temperature, Spo_2, and ASA was also presented in Table 1 in detail.

Table 1

**The baseline characteristics of the patients.** a. 1 patient lacking data, and b. 14 patients lacking data due to incomplete medical records. The means and SDs reported in the table were calculated from the average values of individual patients in this study. SBP, systolic blood pressure; DBP, diastolic blood pressure; Spo₂, oxygen saturation measured by pulse oximetry; ASA, American Society of Anaesthesiologists.
Characteristics
Age(yr), mean (SD) Female, n (%) Height (cm), mean (SD) Weight (kg), mean (SD)		51.5 (8.9)
		43(41.0)
		162.78 (7.57)
		62.39 (10.54)
Body mass index (kg/m²), mean (SD)		23.47 (3.14)
Blood pressure (mmHg), mean (SD)	SBP	124.00 (15.25)
	DBP	80.67 (10.26)
Heart rate (beats/min) ^a, mean (SD) Respiratory rate (breaths/min) ^a, mean (SD) Temperature (℃), mean (SD) Spo₂ (%) ^b, mean (SD) ASA, n (%)		81.90 (13.87)
		19.34 (1.32)
		36.49 (0.27)
	Ⅰ Ⅱ Ⅲ	98.24 (1.43) 2(1.9) 87(82.9) 16(15.2)
Operation time (min), mean (SD)		238.79 (80.31)
Surgery type, n (%)	Open	60(57.1)
	Laparoscopy	45(42.9)

Surgical traumatic stress during general anaesthesia changes breath volatile organic compounds profile

We found good discriminatory abilities based on VOCs between time points. Specifically, R²X, R²Y, and Q² values were 0.3522, 0.8641, and 0.826, respectively, in the OPLS-DA model based on groups Pre-op and Intra-op, which showed good acceptability. However, R²X, R²Y, and Q² values were 0.3956, 0.251, and 0.095, respectively, in the model based on groups Intra-op and End-op. However, it did not influence the selection of changed organic compounds between groups. PCA, PLS-DA, and OPLS-DA analysis results were shown in Fig. 2(A-C for groups Pre-op and Intra-op; D-F for groups Intra-op and End-op). Figure 2G and 2H showed the permutation test for 100 times for above mentioned two models, respectively.

Venn diagram demonstrated that 25 of these VOCs changed both intraoperatively and postoperatively (Fig. 2I). Results showed that there were 135 VOCs with different m/z increased and 35 decreased in exhaled breath after 2h of operation when compared with the preoperative breath, respectively (Fig. 2J). However, 26 compounds increased and only 2 compounds decreased at the end of the operation, compared to intraoperative breath (Fig. 2K). Supplementary Figure S1 shows the trends of these 25 VOCs between group Pre-op, Intra-op, and End-op. Detailed information on all selected VOCs was provided in Supplementary Tables S1, and S2.

Surgical traumatic stress during general anaesthesia changes systemic metabolism

The detailed information on patients for metabolomics analysis was presented in Supplementary Table S3. From the multivariable analysis results, we can see an obvious metabolic change between different operative time points. Plasma metabolites cannot discriminate the different operative time points in the PCA model (Fig. 3A, D), an unsupervised model. However, this can be well achieved in PLS-DA (Fig. 3B, E) and OPLS-DA analysis (Fig. 3C, F), both of which are supervised models for data matrix analysis. These results were further validated by S plots, with intercepts less than 0 meaning a good model (Fig. 3G, H).

121 circulatory metabolites increased while 89 decreased in group Intra-op, compared with group Pre-op (Supplementary Fig. S2). However, only 25 metabolites increased, and 68 metabolites decreased after the operation, compared to group Intra-op (Supplementary Fig. S2). Heatmaps were used to clearly show the level of the metabolites between these 3 groups (Supplementary Fig. S2). Overall analysis showed that metabolite change was mainly related to amino acid metabolism, lipid metabolism, and carbohydrate metabolism (Supplementary Fig. S2). Most importantly, pathway enrichment analysis demonstrated that administration of operation mainly regulated glucagon signalling pathway, galactose metabolism, central carbon metabolism, butanoate metabolism, glycolysis/gluconeogenesis, phenylalanine, tyrosine and tryptophan biosynthesis, purine metabolism, glycine, serine and threonine metabolism and ABC transporters (Supplementary Fig. S2). However, with the termination of operation and reduction of traumatic stress, biological processes like the tricarboxylic acid (TCA) cycle, branched-chain amino acid biosynthesis, central carbon metabolism in cancer, glucagon signalling pathway, pantothenate and CoA biosynthesis, butanoate metabolism, alanine, aspartate and glutamate metabolism and pyruvate metabolism were mainly regulated (Supplementary Fig. S2). Detailed information on all selected metabolites was provided in Supplementary Table S4, S5.

Surgical traumatic stress during general anaesthesia promotes catecholamines and cortisol secretion

To further validate the changes of VOCs in response to surgical traumatic stress, we measured blood traumatic stress markers including catecholamines and cortisol. We found that norepinephrine, epinephrine and cortisol all increased significantly in group Intra-op (mean difference, 128.60pg/mL, 420.50pmol/L, 112.80nmol/L; all P < 0.0001 vs. Pre-op) (Fig. 4A, B, C). However, compared with group Intra-op, plasma in group End-op displayed a significant reduction in levels of all these markers (mean difference, 79.05pg/mL, 212.80pmol/L, 50.09nmol/L; all P < 0.0001) (Fig. 4A, B, C), which suggested alleviation of traumatic stress.

Correlation and receiver operating characteristic analysis

We next performed a correlation analysis between VOCs and blood traumatic stress markers. It was shown that most of the 25 VOCs were positively correlated with norepinephrine, epinephrine, or cortisol, and only four compounds including Comp_140, Comp_6, Comp_23, and Comp_228, were negatively correlated with norepinephrine, epinephrine, or cortisol in group comparison between Pre-op and Intra-op (all r < 0, P < 0.05) (Fig. 5A). Only Comp_6 and Comp_23 was negatively correlated with norepinephrine in group comparison between Intra-op and End-op (r=-0.15, -0.14; P = 0.026, 0.049) (Fig. 5B), the same trend as that in group comparison between Pre-op and Intra-op (r=-0.21, -0.17; P = 0.003, 0.014). Therefore, we chose Comp_6 and Comp_23 as our candidate markers for monitoring surgical traumatic stress and performed the receiver operating characteristics curve (ROC) analysis. Our results showed that area under the receiver operating characteristics curve (AUC) of Comp_6 and Comp_23 in group comparisons between Pre-op and Intra-op, and between Intra-op and End-op, was 0.7360(95% confidence interval (CI), 0.6680–0.8040; P < 0.0001), 0.5990(95% CI, 0.5225–0.6756; P = 0.013), 0.7004(95% CI, 0.6302–0.7706; P < 0.0001) and 0.5832(95% CI; 0.5062–0.6603, P = 0.037), respectively (Fig. 5C, D, E, F). This suggested that Comp_6 and Comp_23 may be two promising breath biomarkers of surgical traumatic stress monitoring in perioperative practice. Every r value and corresponding P value for each 25 VOC with norepinephrine, epinephrine, and cortisol were listed in Supplementary information (Supplementary Table S6, S7 for pre-op vs. intra-op; Supplementary Table S8, S9 for intra-op vs. end-op).

To summarize, we demonstrated that the VOC profile in exhaled breath may change in response to surgical procedures and links to changes in norepinephrine in this exploratory study. We introduced the exhaled breath VOCs, which have been widely explored in different fields for disease diagnosis¹¹, and tested their possibility in monitoring surgical traumatic stress. Although VOC characteristics in the breath are changed in patients with acute kidney injury¹² and postoperative patients undergoing analgesic treatment¹³, whether and how the VOC profile in breath changes under surgical traumatic stress remains elusive. To fill the gap and provide preliminary evidence for future application in monitoring traumatic stress, we performed an untargeted analysis of exhaled VOCs based on UVP-TOF-MS.

A recent breath analysis via secondary electrospray ionization mass spectrometry in combination with conventional polysomnography found that a switch to wake reduces fatty acid oxidation, while slow-wave sleep increases it, and the rapid eye movement sleep transition leads to higher production of TCA cycle intermediates¹⁴. Gas chromatography-mass spectrometry (GC-MS) linked with headspace solid phase microextraction (HS-SPME), seen as the golden standard of VOC analysis, is most often used for microbial VOC detection¹⁵. Despite the advantage of GC-MS in compound qualitative analysis, the complicated preconcentration and long separation process limit its application in real-time clinical monitoring. By contrast, we implemented UVP-TOF-MS to analyse the breath sample. Time-of-flight analyser can accomplish faster detection with higher sensitivity and get simpler detected spectrum¹⁶. Furthermore, this machine is seldom affected by the humidity of gas sample¹⁷, which facilitates its online monitoring and detection in anesthetized patients. A recent work investigated its usefulness in rapid and online detection of foodborne bacteria via microbial VOCs fingerprint spectra detection and got a propelling conclusion¹⁸, which further supports the high detection value of this new tool.

The m/z of Comp_6 and Comp_23 was detected as 42.9177 and 61.0104, respectively. Their negative correlation with norepinephrine suggests that they may originate from pathways that can be inhibited by norepinephrine. However, non-consistent correlation was found between VOCs and epinephrine, nor cortisol, which may result from different production patterns of stress-related hormones. In addition, what they really are still needs further investigation due to the existence of isomers and the lack of a compound identification library attached to this mass spectrometry. Studies have shown that breath VOCs mainly comprise hydrocarbons, alcohols, ketones, aldehydes, esters¹⁹, and volatile products from peroxidation of unsaturated fatty acids by oxidative stress exposure²⁰. This provides some clues to our speculation. Three time points were selected for gas collection because we determined to find a general change at first. We focused on these three time points and selected surgical traumatic stress markers also by referring to reports from other researchers²¹.

The intensity of surgical stress, depending on the severity and duration of tissue injury, can be reflected by the increased activation of the sympathetic nervous system (SNS) and secretion of pituitary hormones such as epinephrine, norepinephrine, and cortisol. Therefore, we measured their plasma levels at 3 different time points to assess surgical trauma levels and to provide verification for VOCs, in line with another pilot study ²². Our results here were consistent with other studies^23–25, which reflected the existence of surgical trauma and provided the foundation to produce VOCs. Catecholamines, namely norepinephrine, epinephrine, and dopamine, have a critical role in the physiological regulation of multiple systems including metabolic systems like glucose metabolism²⁶, which can further change circulating metabolites. The hypothalamic–pituitary–adrenal axis is also an important part of the neuroendocrine system and is involved in controlling responses to stress. Products of this axis such as cortisol also have an important role in regulating systemic metabolism and are involved in many metabolic diseases ²⁷. All these facts support the upstream regulatory effects of stress-related hormones on VOCs production.

To further determine the influences of surgical procedure on systemic metabolism, and exhaled VOCs, we performed untargeted metabolomics analysis on plasma from 10 randomly selected representative participants. Our results further validated our VOCs results. We demonstrated biological processes regulation of operation and inferred that VOCs detected in exhaled breath may come from these metabolic processes. Our study clearly showed the upstream circulating metabolism changes and provided evidence for an association between VOCs and circumstances like operation that change metabolism in patients, although it is needed to explore the one-to-one relationship between VOCs and circulatory metabolites.

Together with others^{10,11,28–30}, our present exploratory work further expands the application of the new concept of VOCs in medicine and tests the possibility in perioperative monitoring. We also provide new insights into the thorny issue of perioperative traumatic stress monitoring, a necessary measure to prevent multiple complications in perioperative patients. Specifically, our results implicate VOCs with m/z 42.9177 and 61.0104 might be used in monitoring surgical traumatic stress in the future. For example, anaesthesiologists might decide when and how many anti-stress drugs should be administered to patients under surgery to prevent stress-related complications like postoperative cognitive dysfunction, which is challenging in the present aging world³¹. However, extra work is still needed before real translation to solve problems including the relationship between time, perioperative drugs, prior pathologies, and breath VOCs.

There are limitations to this study. First, we only collected breath and blood at three time points for analysis, given the fact that surgical traumatic stress may vary all through the operation. Next, only a correlation analysis between 25 commonly changed VOCs and traumatic stress-related markers at three time points was performed. Finally, we failed to identify the VOCs in this study due to technological reasons. Future analysis is strongly recommended based on equipment like GC-MS and public or commercial libraries like The National Institute of Standards and Technology (NIST).

Participants

This observational exploratory study was conducted during elective abdominal surgery under general anaesthesia between July 2022 and June 2023, according to the Ethical Principles for Medical Research Involving Human Subjects outlined in the Declaration of Helsinki and amended by the World Medical Association. The Ethics Committee of West China Hospital of Sichuan University (Chengdu, China) approved our study on June 2, 2022. The study was registered at the China Clinical Trials Registry (ChiCTR2200062188) (https://www.chictr.org.cn/index.html) on July 28th, 2022 (Principal investigator: Yuyi Zhao), before the start of the trial and any patient enrolment undertaken. All participants gave written informed consent the day before surgery. This manuscript adheres to the applicable STROBE guidelines.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) Elective surgery patients aged 18–65; (2) American Society of Anaesthesiologists (ASA) classification I-III; (3) Elective abdominal surgery (gastrointestinal tract, pancreas, gallbladder, liver, etc.) under general anaesthesia; (4) Voluntarily join the research and sign the informed consent; (5) The estimated operation time is more than 2h. The exclusion criteria were as follows: (1) History of mental or nervous system diseases such as autism, attention deficit hyperactivity disorder, epilepsy, cerebral infarction, etc; (2) History of craniocerebral surgery; (3) History of alcohol, and opioid or other psychotropic substance abuse; (4) Long-term use of sedative drugs; (5) Language communication impairment and intellectual disability; (6) Allergy to the anaesthetics used; (7) Family history of malignant hyperthermia.

Conduct of the study

This is an observational study and was conducted without interfering with the clinical decision-making of anaesthetists. Basic information such as age, female number, ASA, body mass index, oxygen saturation measured by pulse oximetry (Spo₂), and surgery type was recorded.

After admission to the operating room, all patients were monitored with non-invasive blood pressure (NIBP, including systolic blood pressure, diastolic blood pressure, and mean arterial pressure), Spo₂, electrocardiogram, HR, and end-tidal pressure of carbon dioxide (P_ETco₂). Radial artery cannula was placed under local anaesthesia while the patients were awake and invasive arterial blood pressure was continuously monitored. The SEDLine (Masimo Corp, Irvine, CA, USA) EEG sensors were properly secured to the forehead of the patients. All patients were given 2mg of midazolam intravenously and observed for 2 minutes. Then a tube of propofol (3µg/ml) was infused with a Target Control Infusion (TCI) bump (body weight was set as their ideal weight). The patient's level of sedation was assessed using the Modified Observer's Assessment of Alertness/Sedation Scale (MOAA/S). When the MOAA/S score reached 1 for the first time, it was assessed 2 more times after that point, and the patients were completely unconscious when MOAA/S = 1 on 3 consecutive occasions. The investigator then administered cis-atracurium 0.2mg/kg and sufentanil 0.4µg/kg intravenously. When the train-of-four (TOF) cnt was ≤ 2, tracheal intubation was performed. During anaesthetic maintenance, the inhalation concentration of desflurane was adjusted to target the Patient Sedation Index (PSI) in a range of 25–50. Oxygen (O₂) concentration of 40–60%, fresh gas flow of 2-3L/min, respiratory rate (RR) of 10–18 times per minute, and tidal volume of 6–10 ml/kg were set to maintain P_ETco₂ at 35-45mmHg and Spo₂ 97–100%.

Exhaled breath and peripheral arterial blood samples were collected at three time points: Preoperative (Pre-op group, before incision), Intraoperative (Intra-op group, 2h after incision), and End of operation (End-op group, before extubation) for analysis. Tedlar™ PVF sampling bags (500ml, Dalian Delin gas packaging Co., Ltd., Dalian, Liaoning, China) washed with high-purity nitrogen and vacuumed before usage were utilized for breath collection. Sampling bags were connected to the exhalation end of the breathing circuit to collect exhaled breath samples at the plateau period until 500mL of breath was acquired. The operating room was controlled at a temperature of 20–25°C and a humidity of 45–50%. The collected breath was immediately stored at -80°C for further analysis within 48h.

Measurements and data handling

Untargeted ultraviolet photoionization time-of-flight mass spectrometry analysis

Untargeted analysis based on ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF-MS 2000 PLUS, Aliben Science and Technology, Chengdu, Sichuan, China) was performed according to product manuals. The mass spectrometer was warmed up before each sample analysis. The breath sample was introduced into the ionization region through a PEEK capillary of 0.50m (inner diameter: 2.1mm) long from the sampling bags directly. The instrument included a vacuum ultraviolet ion source and a time-of-flight mass analyser. The pressure in the ion source, gas flow, and electrical current were set to 7.7kPa, 70ml/min and 0.99mA, respectively. The temperature of both the PEEK capillary and the ion source was set to 100℃ to minimize the condensation and surface adsorption of exhaled VOCs. All mass spectra detected were accumulated for 20s. Mass spectrum peaks with a mass to charge ratio (m/z) range of 4 ~ 404 were detected by UVP-TOF-MS for each sample. A 50-ppb customized benzene standard gas was tested daily to correct for the signal fluctuation of UVP-TOF-MS as reported¹⁸. We also checked the analyser regularly to guarantee sensitivity and stability. Given that signals for m/z < 30 and m/z > 350 were too weak, we detected 296 VOC ions data in a m/z range of 30 ~ 350, which were recognized as feature ions in the following analysis.

Original spectrum pre-processing included peak identification, peak integration through Monte Carlo for peak area calculation, and alignment. After that, all the peaks were normalized to the peak of the greatest intensity. MetaboAnalyst5.0 (https://www.metaboanalyst.ca/) was utilized for statistical analysis, VOCs screening, and data visualization. Multivariate statistical analysis including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were performed to determine the discriminatory ability of exhaled VOCs profile between different operation time points. A permutation test was performed. R² and Q² were used to evaluate the model. VOCs with a fold change (FC) > 1.2 or < 0.83, as well as an adjusted P < 0.05, were screened as potential biomarkers of monitoring surgical traumatic stress. We acquired adjusted P values herein to reduce the false discovery rate (FDR) that would increase in multiple hypothesis testing.

Untargeted metabolomics analysis

To validate the responses of VOCs to surgical procedures and provide clues to the origin of these compounds, we next performed an untargeted metabolomics analysis based on 10 representative patients. Briefly, plasma samples stored at -80°C (including sample preparation QC) were placed in a 4°C refrigerator until no ice cubes existed in the samples. After metabolites extraction, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis was performed with Waters UPLC I-Class Plus(Waters Corp., Milford, Massachusetts, USA)tandom Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA) for metabolites separation and detection. Chromatographic separation was applied on a Waters ACQUITY UPLC BEH C18 column (1.7µm, 2.1mm × 100mm, Waters Corp., Milford, Massachusetts, USA) at 45°C. The flow rate was set to 0.35Ml/min while the injection volume was 5µL. Q Exactive (Thermo Fisher Scientific, Waltham, Massachusetts, USA) was utilized to perform primary and secondary spectrometry data acquisition. The full scan range was between 70 and 1050 m/z with a resolution of 70000. Compound discoverer 3.3 (Thermo Fisher Scientific, Waltham, Massachusetts, USA) software(https://mycompounddiscoverer.com/), bmdb (BGI metabolome database), mzcloud database and chemspider online database were used for metabolite ion peak extraction and metabolite identification. After data preprocessing and quality control, multivariate statistical analysis including PCA, PLS-DA, and OPLS-DA, screening of different metabolites between groups, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Metabolites with variable importance in projection (VIP) > 1, as well as adjusted P < 0.05 of the Mann-Whitney test were regarded as the metabolic biomarkers.

Catecholamines and cortisol detection

The blood samples collected with EDTA tubes were kept at 4℃ immediately before transportation to the central laboratory. They were centrifuged at a speed of 1000r/min for 10 minutes, after which the supernatant was stored at -80°C for final testing. We analysed plasma norepinephrine, epinephrine, and cortisol levels using ELISA kits (KA1877, KA1877, KA6518, Abnova, Taipei, Taiwan, China) according to the manufacturer’s instructions. Details on intra- and inter-assay variability, lower limits of detection and linearity-of-dilution assessment for ELISAs were provided in Supplementary information (Antibody Validation Quality Control Report).

Primary and secondary endpoints

The primary endpoint is a change in the surgical traumatic stress-related exhaled breath VOCs characteristics during anaesthesia. Secondary endpoints included blood metabolomics, changes in surgical traumatic stress-related markers, and correlation between exhaled breath VOCs and surgical traumatic stress-related markers.

Sample size calculation

This exploratory study was performed to explore the VOCs' responses to surgical traumatic stress paralleled to another clinical trial aiming to investigate EEG responses to noxious stimulation. Therefore, we only enrolled some of the participants included in the EEG study as an innovative exploratory study according to similar past studies, aiming to generate the hypothesis of VOCs use in perioperative monitoring, and sample size calculation for this study was not vigorously performed³².

Statistical analyses

Our statistical analysis plan was approved by the authors before analyses began, and our submission is based on testing a hypothesis prior to the main study results being known. Statistical analyses were performed with GraphPad Prism 8.0.1 (GraphPad, La Jolla, CA, USA) and the Shapiro-Wilk Normality test was applied to determine data distribution. Data were presented as the mean (SD) (normally distributed) or the median (25-75th percentile) (non-normally distributed). Continuous variables were analysed using repeated measures ANOVA. Two-sided P < 0.05 was regarded as statistically significant. We used the “Wu Kong” platform (https://www.omicsolution.com/wkomics/main/) to identify correlations between VOCs and traumatic stress-related biomarkers. The receiver operating characteristics curve (ROC) analysis was performed, and the area under the receiver operating characteristics curve (AUC) was calculated to evaluate the performance of VOCs. Missing data was excluded from the analysis. The primary outcome was defined and established a priori at initiation of the study design, and subgroup or sensitivity analyses were not performed.

Acknowledgements

Not applicable.

Author contributions

Patient recruitment, data collection, and analysis: QW, YZhao, SL, XL, HW; manuscript drafting: QW; study design, supervision, and funding acquisition: YZuo; All authors helped to revise critically for important intellectual content, approved the final version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Data availability statement

The datasets used and/or analysed during the current study, including metabolomics raw data, are available from the corresponding author upon reasonable request.

Additional Information

Competing Interests Statement

A Chinese patent based on this study is under review. The authors declare no competing interests.

Funding

This work was supported by the “1▪ 3 ▪ 5 Project for Disciplines of Excellence” of West China Hospital, Sichuan University, China.

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No competing interests reported.

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Association of Exhaled Breath Volatile Organic Compounds with Surgical Traumatic Stress during General Anaesthesia: An Exploratory Study

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Abstract

Figures

Introduction

Results

Basic characteristics of study participants

Surgical traumatic stress during general anaesthesia changes breath volatile organic compounds profile

Surgical traumatic stress during general anaesthesia changes systemic metabolism

Surgical traumatic stress during general anaesthesia promotes catecholamines and cortisol secretion

Correlation and receiver operating characteristic analysis

Discussion

Methods

Participants

Inclusion and exclusion criteria

Conduct of the study

Measurements and data handling

Untargeted ultraviolet photoionization time-of-flight mass spectrometry analysis

Untargeted metabolomics analysis

Catecholamines and cortisol detection

Primary and secondary endpoints

Sample size calculation

Statistical analyses

Declarations

References

Additional Declarations

Supplementary Files

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