Untargeted serum metabolomic profiling reveals altered choline-phospholipid and amino acid metabolism in patients receiving subcostal transversus abdominis plane block combined with general anesthesia for radical gastrectomy: A prospective comparative study

Abstract

Objective

This study aimed to investigate, using clinical serum metabolomics, the effects of general anesthesia combined with transversus abdominis plane block (TAPB) on systemic metabolism in patients undergoing radical gastrectomy compared with general anesthesia alone.

Methods

Fifty-six patients undergoing laparoscopic-assisted radical gastrectomy for gastric cancer were assigned to a general anesthesia–only group (control group, 30 patients) and a general anesthesia combined with TAPB group (treatment group, 26 patients). Clinical serum metabolomics analyses were performed to investigate alterations in systemic metabolism between the control and treatment groups.

Results

Kyoto Encyclopedia of Genes and Genomes analysis revealed that the top differential metabolic pathways between the treatment and control groups were choline metabolism in cancer, glycerophospholipid metabolism, adenosine triphosphate–binding cassette transporters, and protein digestion and absorption. In this study, altered choline metabolism in cancer and glycerophospholipid metabolism shared the same five metabolites, including 1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine, 1-stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine, phosphocholine, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (PC (16:0/16:0)), and glycerophosphocholine. The levels of these metabolites were decreased with TAPB. General anesthesia combined with TAPB reduced choline and glycerophospholipid metabolism and may serve as a potential indicator. In addition, DL-proline, lysine, DL-glutamic acid, and glutamic acid were shared with adenosine triphosphate–binding cassette transporters and protein digestion and absorption. Their decreased levels in TAPB contributed to the beneficial effects of anesthesia.

Conclusions

General anesthesia combined with TAPB treatment reduced sufentanil dose and pain scores (numeric rating scale and visual analog scale) on postoperative days 1 and 3 compared with the control during laparoscopic-assisted radical gastrectomy. Furthermore, majority of the top differential metabolites were associated with indicators of anesthesia management and may have contributed to the beneficial effects of combined anesthesia. These metabolites included prostaglandins and sucrose, which may have contributed to the effects of anesthesia, including TAPB.

Introduction

Gastric cancer is one of the most common malignant tumors in China and worldwide and is associated with a high mortality rate. ¹ Radical surgical resection remains the primary curative treatment for gastric cancer, ² in which tumor tissue is removed to the maximum extent, thereby effectively reducing local recurrence and improving long-term survival. Compared with open surgery, laparoscopic radical gastrectomy for gastric cancer offers advantages, including less invasion, pain, and lower postoperative complications. ³ However, even with minor invasive surgical trauma, surgical stress can cause adverse reactions, including stress response, immune dysfunction, and hemodynamic instability, which are not detrimental for the postoperative recovery and long-term prognosis in patients with gastric cancer.^4,5 Surgical pain is a critical factor that contributes to postoperative stress response and complications. Surgical stress can drive the mobilization of energy substrates, volume conservation, and hemostasis through activation of endocrine, metabolic, and endocrine–inflammatory pathways. ⁶ General anesthetic agents such as sevoflurane can also influence metabolism and metabolic connections.

Effective perioperative pain management can reduce postoperative stress and adverse events and accelerate postoperative recovery. Transversus abdominis plane block (TAPB) is an emerging regional anesthesia technique that involves the injection of a local anesthetic between the transversus and obliquus internus abdominis, targeting the corresponding thoracic and abdominal nerves from T7 to T11.^6,7 TAPB can effectively inhibit nerve transmission, alleviate pain and stress, and facilitate postoperative recovery. Previous studies have demonstrated that TAPB is a simple, safe, and effective multimodal analgesia strategy for abdominal surgery. Moreover, it reduces opioid consumption and stress reactions and facilitates postoperative recovery.^7,8 Studies have demonstrated that general anesthetic agents such as sevoflurane can also affect metabolism and metabolic connections. Sevoflurane alters central nervous system (CNS) metabolic activity, inhibits insulin secretion in rodents, and is associated with hyperglycemia in humans. ⁸ In addition, studies have demonstrated that TAPB combined with a multimodal analgesic strategy for general anesthesia reduces adverse effects and anesthetic drug use, enabling optimal pain management, and thereby enhancing overall surgical outcomes.^7,9 In patients undergoing radical gastrectomy for gastric cancer, the use of general anesthesia combined with TAPB can improve intraoperative and postoperative heart rate (HR), blood pressure, and hemodynamic changes compared with conventional general anesthesia.^7,9 In one study, this combined anesthesia reduced the levels of neuroendocrine mediators and cytokines during perioperative periods and blunted stress responses. Furthermore, it reportedly reduces the number of CD3⁺CD28⁺ and CD3⁺T cells in the peripheral blood and ameliorates immune function. ⁸ Chronic and unresolved inflammation is a key characteristic of cancer and is associated with malignant progression, surgical stress, pain, and anesthesia management.^10–12 Combined anesthesia is particularly crucial in vulnerable cancer patients with compromised health. ¹³

Metabolomics analysis is a rapidly developing tool for high-throughput metabolite characterization. It is used to elucidate metabolism in biological samples and identify biomarkers and mechanisms underlying various clinical phenomena, including general anesthesia.^14,15 Using metabolomics, a recent study has demonstrated that sevoflurane directly disrupts glucose metabolism, increasing glycolysis and lactate levels in aging marmoset brains. ¹⁶ A metabolomic study has also demonstrated that multiple metabolic abnormalities and related pathological status (e.g. oxidative stress) are linked to the emergence of agitation due to general anesthesia. ¹⁷ These results indicate that metabolites may affect anesthesia outcomes and complications. ¹⁸ Understanding the precise impact of anesthetics on systemic metabolism is crucial for perioperative patient management, as it may affect postoperative recovery, cognitive function, and infection risk. However, the exact regulation of human body metabolomics in TAPB combined with general anesthesia has not been fully defined. In the present study, we conducted a clinical metabolomics study to investigate the specific effects of general anesthesia combined with TAPB on systemic metabolism in patients undergoing radical gastrectomy, which may provide insights into the mechanisms associated with the use of this combined anesthesia approach and guide treatment decisions.

Methods

Participants and samples collection

Fifty-six untreated patients who were initially diagnosed with gastric cancer and who underwent laparoscopic-assisted radical gastrectomy at the Affiliated Hospital of Putian University between October 2020 and October 2021 were enrolled in this study. The clinical trial number was not applicable. General information of the patients is presented in Table 1.

Table 1.

General information of the study participants.

	Control group (n = 30)	Treatment group (n = 26)	p-value
Age	64.87 ± 4.30	65.81 ± 7.64	0.657
Sex (M/F)	23/7	19/7	0.757
BMI	24.22 ± 2.49	23.07 ± 2.92	0.118
VAS	1.97 ± 0.18	1.92 ± 0.27	0.479

BMI: body mass index; VAS: visual analog scale; M: male; F: female.

The inclusion criteria were as follows: (a) patients who were scheduled to undergo radical gastrectomy for gastric cancer; (b) patients without contraindications to local anesthesia; (c) age range: 18–65 years; (d) weight >50 kg; and (e) American Society of Anesthesiologists grade I, II, or III. Grade I indicates no systemic diseases and only local pathological changes; Grade II indicates mild to moderate organ (heart, lung, kidney, and CNS) lesions, with good functional compensation; and Grade III indicates a condition with severe organ damage, but wherein organ function can be compensated.

The exclusion criteria were as follows: (a) emergency patients, those with preoperative infections, or those with local anesthetic allergies; (b) severe heart, brain, or lung diseases; (c) mental, immune, or endocrine disorders; (d) abnormal coagulation function; (e) long-term history of opioid or alcohol use; (f) history of chronic and severe pelvic or hip joint pain; and (g) history of radiotherapy and chemotherapy.

This study was approved by the Ethics Committee of the Affiliated Hospital of Putian University (Approval Number: PYFL2022055) and conducted in accordance with the Helsinki Declaration (World Medical Association, 1975, as revised in 2024). All patients were informed, and written consent forms were obtained for the study. Digested gastric cancer tissue (56 cases) was collected during the surgery, and all cases were diagnosed via pathological evaluation. Patient information was deidentified, and this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. ¹⁹

Management of general anesthesia

The patients were assigned to a general anesthesia–only group (control group, 30 patients) and a general anesthesia combined with TAPB group (treatment group, 26 patients). The same general anesthesia protocol was employed for both groups. Patients fasted overnight, were prohibited from drinking any fluids for 8 h before the surgery, and were not allowed to consume any sedatives or analgesics. Routine monitoring, including electrocardiography, blood pressure, and blood oxygen saturation, was established before anesthesia induction. All patients under standard general anesthesia were induced with midazolam (0.02 mg/kg, Jiangsu Enhua Pharmaceutical Co., Ltd, China), propofol (2.0–2.5 mg/kg, Fresenius Kabi Deiesland, Germany), sufentanil (0.3 μ g/kg, Yichang Hengrui Pharmaceutical Co., Ltd, China), and cisatracurium (0.15 mg/kg, Jiangsu Hengrui Pharmaceutical Co., Ltd, China). During the procedure, the minimum alveolar concentration (MAC) was maintained at 1.2%–1.5% by adjusting the inhalation concentrations of nitrous oxide and sevoflurane (Maruishi Pharmaceutical Co., Ltd, Japan). The pressure of carbon dioxide (PETCO₂) was maintained at 30–40 mmHg by adjusting the tidal volume and rate. When the hemodynamic values increased by 15% compared with baseline values before induction, appropriate doses of sufentanil (0–10 μg) were used for intervention; HR <40 beats/min (bradycardia) was intervened with 0.5 mg atropine, and mean arterial pressure (MAP) <60 mmHg (hypotension) was treated with 5 mg ephedrine. During surgery, body temperature was maintained between 36°C and 37°C.

Subcostal TAPB procedure

The ultrasound-guided TAPB was performed bilaterally according to group allocation. With the patient in the supine positions and arms abducted, a high-frequency linear probe (6–13 MHz, set to 10–12 MHz) was used as follows. After anesthesia induction and before skin incision, the probe was placed at the mid-axillary line between the costal margin and iliac crest to identify the fascial plane between the internal oblique and transversus abdominis muscles. Using an in-plane, lateral-to-medial approach, the needle tip was advanced into this plane, and 10 mL of 0.375% ropivacaine was injected at multiple points within the lateral abdominal wall. The probe was then relocated medially and cephalad to the subcostal area, where another 10 mL of 0.375% ropivacaine was injected using the same in-plane technique, ensuring continuous connectivity of the two injectate depots within the fascial plane. Each side received 20 mL (75 mg ropivacaine), making a total of 40 mL (150 mg) bilaterally. A single experienced regional anesthesiologist (with >5 years of practice in ultrasound-guided regional anesthesia) performed all blocks. Surgery commenced 15–20 min after block completion. As the blocks were performed under general anesthesia, conventional sensory testing (e.g. pinprick or cold) could not be performed; instead, adequate T6–T10 dermatomal coverage was indirectly confirmed via ultrasound-based visualization of fusiform hypoechoic local anesthetic spread within the transversus abdominis plane (TAP), combined with hemodynamic stability at skin incision.

Collection of peripheral serum samples

Fasting elbow vein blood samples (5 mL) were collected from all 56 patients on the morning of postoperative day (POD) 1 using a dipotassium ethylenediaminetetraacetic acid (EDTA-K2) anticoagulant vacuum tube. The blood samples were placed in a centrifuge tube at room temperature for 1 h to promote coagulation and stratification and subsequently centrifuged at 3000 r/min for 10 min. The supernatant was transferred to a clean centrifuge tube, centrifuged at 12,000 r/min at 4°C for 10 min, and subsequently transferred to 1.5-mL centrifuge tubes (0.2 mL per tube). The collected serum samples were frozen in liquid nitrogen for 30 s and stored at −80°C for metabolomic analysis.

Metabolite profile analysis and data processing of liquid chromatography–tandem mass spectrometry (LC-MS/MS) analysis

The metabolomics analysis was performed by NovelBio (Shanghai, China). Ultra-high-performance liquid chromatography (UHPLC)-Q-Exactive Orbitrap mass spectrometry (MS) analysis was performed using an UHPLC (Vanquish UHPLC, Thermo) coupled to an Orbitrap (Q Exactive HF-X/Q Exactive HF) at Shanghai Applied Protein Technology Co., Ltd for hydrophilic interaction liquid chromatography (HILIC) separation. Samples were analyzed using a 2.1 mm × 100-mm ACQUIY UPLC BEH Amide 1.7 μm column (Waters, Ireland). In both electrospray ionization (ESI) positive (POS) and negative (NEG) modes, the mobile phase contained A (25 mM ammonium acetate and 25 mM ammonium hydroxide in water) and B (acetonitrile). The gradient started at 98% B for 1.5 min, linearly decreased to 2% over 10.5 min, subsequently held for 2 min, and finally increased to 98% over 0.1 min, with a 3-min re-equilibration period.

The ESI source conditions were set as follows: (a) Ion Source Gas1 (Gas1), 60; (b) Ion Source Gas2 (Gas2), 60; (c) curtain gas (CUR), 30; (d) source temperature, 600°C; and (e) IonSpray Voltage Floating (ISVF), ±5500 V. In MS-only acquisition, the instrument was set to acquire over the m/z range 80–1200 Da; resolution was set to 60,000; and accumulation time was set to 100 ms. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 70–1200 Da; resolution was set to 30,000; and accumulation time was set to 50 ms, excluding data within 4 s.

Data processing

The raw MS data were converted to MzXML format using ProteoWizard MSConvert and subsequently imported into the freely available XCMS software. For peak picking, the following parameters were used: (a) centWave m/z, 10 ppm; (b) peakwidth, c (10, 60); and (c) prefilter, c (10, 100). For peak grouping, bw of 5, mzwid of 0.025, and minfrac of 0.5 were used. The Collection of Algorithms for Metabolite Profile Annotation (CAMERA) was used to annotate isotopes and adducts. In the extracted ion features, only variables with >50% nonzero measurements in at least one group were retained. Compound identification of metabolites was performed by comparing m/z values (<10 ppm) and MS/MS spectra against an in-house database built using available authentic standards.

Statistical analyses

After sum normalization, the processed data were analyzed using the R package (‘ropls’), where they were subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). We used sevenfold cross-validation and response permutation testing to evaluate model robustness. We calculated the variable importance in projection (VIP) score for each variable in the OPLS-DA model to indicate its contribution to classification. Student's t-test was performed to determine the significance of differences between two groups of independent samples. VIP >1 and p-value <0.05 were used to screen significantly changed metabolites. Pearson's correlation analysis was performed to assess correlations between variables.

Results

Quality control (QC) of metabolomics

PCA was used to perform QC of the metabolic datasets used in this study. The QC samples clustered tightly under both POS and NEG-ion modes, as depicted in the PCA score plots (Figure 1(a) and (b)), indicating reliable and consistent QC in this study. In addition, discriminable trends were observed among samples from the control and treatment groups. Hotelling's T2 test uses multivariate modeling and defines 95% or 99% confidence intervals to identify outlier samples. Hotelling's T2 test showed that all samples in both POS and NEG-ion modes fell within the 99% confidence interval (Figure 1(c) and (d)). QC samples were further subjected to Pearson correlation analysis for QC, and all correlation coefficients (r) were ≥0.9, indicating strong indicators (Figure 1(e) and (f)). These QC tests supported the reliability and reproducibility of the metabolomics analysis in this study.

Figure 1.

Quality control (QC) of metabolomics. (a and b) Principal component analysis (PCA) score chart of overall samples under both (a) positive-ion mode and (b) negative-ion mode of metabolomics. Control represents the general anesthesia–only group, and treatment represents the general anesthesia combined with TAPB group. QC represents QC samples. (c and d) Hotelling's T2 plot of overall samples under both (c) positive-ion mode and (d) negative-ion mode. (e and f) Correlation diagram of QC samples under both (e) positive-ion mode and (f) negative-ion mode.\

TAPB: transversus abdominis plane block.

Identification of the different metabolites between groups

This study employed a locally built in-house database (Shanghai Applied Protein Technology APTBIO, Shanghai, China) to identify standard products. By matching retention time, molecular weight (within <10 ppm), secondary fragmentation spectra, collision energy, and other metabolite information against the local database, the structural and biological properties of the metabolites in the sample were evaluated. The identification was manually verified through secondary verification. Metabolite identification fulfilled the level 2 criteria, as defined by the Metabolomics Standards Initiative (MSI) of the Metabolomics Society. ²⁰ In total, 1874 metabolites were identified in both POS- (1224) and NEG- (650) ion modes. All metabolites identified in this study, using both POS- and NEG-ion modes, were classified and counted according to their chemical taxonomy. The proportion of each metabolite type is depicted in Figure 2(a). The collected dataset was processed for subsequent multivariate and univariate analyses. As presented in Figure 1(a) and (b), the metabolomes of the control and treatment groups in the POS-ion model clustered separately in the PCA. The model interpretation rates (R²X(cum)) for the POS- and NEG-ion models were 0.529 and 0.519, respectively. The POS-ion model had more identified metabolites and a higher R²X (cum); therefore, we used only the POS-ion model for subsequent analyses. Subsequently, we performed multivariate analyses using supervised OPLS-DA (Figure 2(a) and (c)), which indicated the explanatory power and influence of each metabolite on classification by VIP scores (>1.0). The OPLS-DA score chart revealed a clear separation between the control and treatment groups (Figure 2(b)). A permutation test was used to assess model performance and prevent overfitting. The 200-cycle permutation test confirmed the OPLS-DA model's strong reliability (R2: 0.0, 0.5432) and predictive ability (Q2: 0.0, −0.2615) (Figure 2(c)). We combined fold change (FC) analysis, t-test, and volcano plot to perform univariate analysis. All metabolites detected in POS-ion mode (including unidentified metabolites) were processed in the combined analysis (FC > 1.5, FC < 0.67, and p-value < 0.05) and visualized using volcano plots (Figure 2(d)). The detailed differential metabolites identified via a comparison of the two groups according to screening criteria (OPLS-DA: VIP score > 1 and t-test: p < 0.05) are shown in Figure 3(a). Hierarchical clustering analysis of differential metabolites combined the relative expression level, FC, p-value, VIP, and metabolite classification information of multiple elements to filter the metabolites (Figure 3(b)). The list of the top 40 altered metabolites is presented in Table 2 and Supplementary Table 1 for POS-ion mode and Table 3 and Supplementary Table 2 for NEG-ion mode. A complete list of all tested features has been provided in Supplementary Table 3.

Figure 2.

Identification of the different metabolites between the two groups. (a) The identified metabolites were classified and counted according to chemical taxonomy and their proportions were calculated. (b) Orthogonal partial least squares discrimination analysis (OPLS-DA) score plot of the serum metabolome in the control and treatment groups. (c) Permutation plot of the serum metabolome in the control and treatment groups. (d) Volcano plot of the serum metabolome in the control and treatment groups.

Figure 3.

Clustering analysis of differential metabolites. (a) Bar graph of the metabolites with differential fold change between the control and treatment groups. (b) Hierarchical clustering chart of the top differential metabolites between the control and treatment groups. Red represents up-regulation, and blue represents down-regulation.

Table 2.

Top 20 differential metabolites in the positive-ion mode.

Name	VIP	Fold change	p-value	Superclass	Class	Subclass
Hc toxin	8.76316	1381.37	1.64E−19	Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs
Tentoxin	10.623	986.535	1.28E−18	Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs
Prostaglandin F2α methyl ester	5.82798	911.113	4.01E−24	Lipids and lipid-like molecules	Fatty acyls	Eicosanoids
Octaethylene glycol	6.7053	883.336	1.68E−19	Organic oxygen compounds	Organooxygen compounds	Ethers
Beclomethasone	4.64902	844.235	6.08E−16	Lipids and lipid-like molecules	Steroids and steroid derivatives	Hydroxysteroids
Astemizole	8.16568	472.702	6.93E−25	Organic acids and derivatives	Astemizole	Astemizole
Prostaglandin F2α isopropyl ester	6.62145	353.127	1.38E−21	Lipids and lipid-like molecules	Fatty acyls	Eicosanoids
Isoprostaglandin-F2α	2.0833	325.655	8E−08	Lipids and lipid-like molecules	Fatty acyls	Eicosanoids
Arg-Ile-Lys	5.78627	311.861	6.66E−17	Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs
1-pentyl-1h-indole-3-carboxamide	22.1812	276.868	8.96E−18	Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs
Crustecdysone	5.92454	159.006	3.59E−26	Lipids and lipid-like molecules	Steroids and steroid derivatives	Bile acids, alcohols, and derivatives
Tributylphosphine oxide	3.55756	144.678	3.41E−06	Organophosphorus compounds	Organic phosphines	Organophosphine oxides
Hexaethylene glycol	15.3452	121.793	3.01E−19	Organic oxygen compounds	Organooxygen compounds	Ethers
Artemisinin	2.43176	85.9869	2E−13	Lipids and lipid-like molecules	Prenol lipids	Terpene lactones
Flutamide	2.48533	58.5114	4.13E−21	Benzenoids	Benzene and substituted derivatives	Trifluoromethylbenzenes
Diaveridine	2.15457	49.3802	3.73E−20	Benzenoids	Benzene and substituted derivatives	Methoxybenzenes
Pentaethylene glycol	10.5687	39.7297	7.66E−20	Organic oxygen compounds	Organooxygen compounds	Ethers
Pyridaben	2.43689	37.9288	2.79E−11	Benzenoids	Benzene and substituted derivatives	Phenylpropanes
Edpetiline	1.32582	36.1846	2.08E−21	Lipids and lipid-like molecules	Steroids and steroid derivatives	Steroidal glycosides
Cimicifugoside h 2	1.38944	31.8612	4.4E−24	Lipids and lipid-like molecules	Steroids and steroid derivatives	Cycloartanols and derivatives

VIP: variable importance in projection.

Table 3.

Top 20 differential metabolites in the negative-ion model.

Name	VIP	Fold change	p-value	SuperClass	Class	SubClass
Ethanesulfonic acid	10.29959	314.3759	6.84E−22	Organoheterocyclic compounds	Diazinanes	Piperazines
Sucrose	6.211486	59.37724	3.96E−27	Organic oxygen compounds	Organooxygen compounds	Carbohydrates and carbohydrate conjugates
7-keto-8-aminopelargonic acid	2.495395	10.43517	4.84E−18	Lipids and lipid-like molecules	Fatty Acyls	Fatty acids and conjugates
9-phenanthrol	1.278525	10.10557	1.54E−11	Benzenoids	Phenanthrenes and derivatives	Phenanthrols
N5-(1-iminoethyl)-l-ornithine	1.340899	7.480896	8.33E−17	Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs
Capric acid	2.331662	5.078949	0.022232	Lipids and lipid-like molecules	Fatty acyls	Fatty acids and conjugates
4 alpha-mannobiose	1.050208	4.715869	1.85E−11	Organic oxygen compounds	Organooxygen compounds	Carbohydrates and carbohydrate conjugates
Modafinil acid	1.416816	3.253608	0.003607	Benzenoids	Benzene and substituted derivatives	Diphenylmethanes
Benzenesulfonic acid	21.05481	2.982989	0.0004	Benzenoids	Benzene and substituted derivatives	Benzenesulfonic acids and derivatives
5a,6-anhydrotetracycline	1.255618	2.951752	0.000622
Cyclohexylsulfamate	3.926734	2.51356	0.007371
Beta-hydroxybutyrate	8.306077	2.39467	0.004363	Organic acids and derivatives	Hydroxy acids and derivatives	Beta hydroxy acids and derivatives
Methylphosphonothioic acid o-ethyl ester	3.493149	2.024578	0.009279
3-Methyl-2-oxopentanoate	2.870569	1.859996	0.007047	Organic acids and derivatives	Keto acids and derivatives	Short-chain keto acids and derivatives
Acetoacetic acid	2.197946	1.776945	0.004166	Organic acids and derivatives	Keto acids and derivatives	Short-chain keto acids and derivatives
L-Gulonic gamma-lactone	5.060829	1.773803	1.66E−06	Organoheterocyclic compounds	Lactones	Gamma butyrolactones
2-keto-3-deoxyoctonic acid	5.155151	1.771573	2.51E−07	Organic oxygen compounds	Organooxygen compounds	Carbohydrates and carbohydrate conjugates
Glutaric acid	2.413714	1.768632	0.003171	Organic acids and derivatives	Carboxylic acids and derivatives	Dicarboxylic acids and derivatives
sn-glycero-3-phosphocholine	1.302075	1.675469	0.005538	Organoheterocyclic compounds	Thiophenes	2,5-disubstituted thiophenes
Valeric acid	2.325446	1.54935	0.017806	Lipids and lipid-like molecules	Fatty acyls	Fatty acids and conjugates

VIP: variable importance in projection.

Enrichment analysis of differential metabolic pathways between groups

To explore alterations in potential metabolic pathways between groups, the differential metabolites were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analysis using the Differential abundance score (DAS) method. ²¹ Enriched KEGG analysis of the metabolites indicated that the most altered metabolic pathway was the choline metabolism in cancer, glycerophospholipid metabolism, adenosine triphosphate–binding cassette (ABC) transporters, and protein digestion and absorption (Figure 4(a)). The DAS obtained from the KEGG analysis indicated that the most altered pathways were choline metabolism in cancer, glycerophospholipid metabolism, ABC transporters and protein digestion and absorption (Figure 4(b)). All differential metabolic pathways were classified at pathway hierarchy level 2. The list of metabolites involved in these pathways are listed in Table 4. Among these metabolites, the choline metabolism in cancer and glycerophospholipid metabolism shared the same five metabolites: (a) 1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine; (b) 1-stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC); (c) phosphocholine; (d) PC 16:0/16:0); and (e) glycerophosphocholine (Figure 4(a)). Furthermore, sucrose was most differential among them and DL-proline, Lysine, DL-glutamic acid, and glutamic acid were shared with ABC transporters and protein digestion and absorption (Figure 4(d)).

Figure 4.

Enrichment analysis of differential metabolic pathways between the two groups. (a) Enrichment bubble chart of the top differential KEGG pathways between the control and treatment groups. (b) Differential abundance score plot of the KEGG analysis for the top altered pathways, classified at pathway hierarchy level 2. (c) The metabolites shared choline metabolism in cancer and glycerophospholipid metabolism. (d) The metabolites in ABC transporters, protein digestion, absorption, and choline metabolism.

FC: fold change; ABC: adenosine triphosphate–binding cassette; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Table 4.

Top differential pathways in study.

ABC transporters	Fold change	p-value	Protein digestion and absorption	Fold change	p-value
Sucrose	59.3772	3.96E−27	DL-proline	0.8240	0.01257169
Melibiose	5.1327	5.6E−17	Lysine	0.4201	0.00464616
Phosphoric acid	1.4054	0.013045	DL-glutamic acid	0.3339	4.5467E−06
DL-proline	0.8240	0.012572	Glutamic acid	0.2980	2.27E−06
Xylitol	0.4497	0.000933	Phenol	0.5993	0.04121367
Lysine	0.4201	0.004646
DL-glutamic acid	0.3339	4.55E−06
Glutamic acid	0.2980	2.27E−06
D-sorbitol	0.2396	0.00364
Choline metabolism in cancer	Fold change	p-value	Glycerophospholipid metabolism	Fold change	p-value
1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine	0.5179	2.12E−07	1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine	0.5179	2.12E−07
1-stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC)	0.9029	0.03576	1-stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC)	0.9029	0.03575985
Phosphocholine	0.7929	0.033478	Phosphocholine	0.7929	0.03347772
PC (16:0/16:0)	0.6700	8.37E−06	PC (16:0/16:0)	0.6700	8.3722E−06
Glycerophosphocholine	0.6684	1.07E−05	Glycerophosphocholine	0.6684	1.073E−05

ABC: adenosine triphosphate–binding cassette; PC: 1,2-dipalmitoyl-sn-glycero-3-phosphocholine.

Top differential metabolites associated with anesthesia management

Based on the above analysis, the top 40 metabolites (Tables 2 and 3) were selected from the most altered VIP groups and further analyzed to explore their roles in and associations with anesthesia management. Correlation analysis can help measure metabolic closeness (metabolic proximity) between metabolites that may jointly participate in a biological process, namely, functional correlation. The top 20 metabolites were used for correlation analysis; there were mainly positive correlations between the metabolites, with correlation coefficients r >0.8 and p <0.05 (Figure 5(a)). To reveal the co-regulatory relationships between various metabolites more intuitively, the correlation matrix (Figure 5(a)) was transformed into a network graph (Figure 5(b)). The network graphs indicated that the top metabolites were mainly associated with choline and glycerol metabolism. To explore the association between the top differential metabolites and anesthesia management, we selected anesthesia management indicators during the perioperative period between the control and treatment groups (Table 5). These indicators demonstrated that treatment decreased sufentanil dosage and postoperative pain scores (numeric rating scale (NRS) and visual analog scale (VAS) scores) on POD1 and POD3. Level of the blood inflammation indicator, interleukin 6 (IL-6), also decreased (Table 5). Subsequently, we performed Pearson correlation analysis to evaluate the associated between the metabolites and anesthesia management using these ameliorated indicators. Majority of the top differential metabolites were associated with anesthesia management indicators (Table 6).

Figure 5.

Correlation between differential metabolites. (a) The matrix heatmap of correlation coefficients between the top differential metabolites. (b) The matrix network diagram of the top differential metabolites.

Table 5.

The ameliorated indicators of anesthesia management between the two groups.

		Control (n = 30)	Treatment (n = 26)	p-value
	Sufentanil dosage (µg)	35.03 ± 7.99	27.88 ± 3.51	0.0001
Preoperative	IL-6	4.29 ± 1.98	2.82 ± 3.56	0.0575
	CRP	7.83 ± 8.42	4.92 ± 8.37	0.2024
	PCT	0.06 ± 0.07	0.08 ± 0.14	0.3672
POD1	NRS	2.53 ± 0.51	2.00 ± 0.28	<0.0001
	VAS	2.40 ± 0.50	1.96 ± 0.20	0.0001
	IL-6	103.72 ± 68.18	63.84 ± 44.46	0.0137
	CRP	58.76 ± 30.55	41.96 ± 31.71	0.0488
	PCT	0.64 ± 0.52	0.48 ± 0.44	0.2014
POD3	NRS	1.73 ± 0.47	1.38 ± 0.50	0.0204
	VAS	1.73 ± 0.45	1.38 ± 0.50	0.0079

POD: postoperative day; PCT: procalcitonin test; NRS: numeric rating scale; VAS: visual analog scale; CRP: C-reactive protein; IL-6: interleukin 6.

Table 6.

Pearson correlation analysis of metabolites and anesthesia management.

Metabolites	Sufentanil dosage (µg)	POD1 NRS	POD1 VAS	POD1 IL-6	POD3 NRS	POD3 VAS
Hc toxin	−0.450a	−0.526a	−0.471a	−0.306b	−0.268b	−0.371a
Prostaglandin. methyl ester	−0.496a	−0.537a	−0.474a	−0.272b	−0.24	−0.369a
Octaethylene glycol	−0.440a	−0.516a	−0.465a	−0.281b	−0.309b	−0.380a
Astemizole	−0.519a	−0.510a	−0.450a	−0.293b	−0.235	−0.380a
Prostaglandin. isopropyl ester	−0.469a	−0.496a	−0.443a	−0.291b	−0.241	−0.328b
Crustecdysone	−0.521a	−0.538a	−0.478a	−0.263b	−0.258	−0.383a
Hexaethylene glycol	−0.522a	−0.527a	−0.474a	−0.213	−0.249	−0.393a
Flutamide	−0.502a	−0.514a	−0.445a	−0.228	−0.186	−0.321b
Diaveridine	−0.513a	−0.510a	−0.466a	−0.252	−0.265b	−0.381a
Pentaethylene glycol	−0.493a	−0.546a	−0.499a	−0.266b	−0.148	−0.283b
Edpetiline	−0.520a	−0.528a	−0.454a	−0.251	−0.203	−0.351a
Cimicifugoside h 2	−0.529a	−0.533a	−0.465a	−0.255	−0.222	−0.369a
Khellin	−0.567a	−0.543a	−0.466a	−0.215	−0.179	−0.319b
Agmatine	−0.489a	−0.522a	−0.460a	−0.272b	−0.235	−0.306b
2-Piperidinecarboxylic acid	−0.524a	−0.548a	−0.469a	−0.206	−0.228	−0.340b
Trp-Ile-Lys	−0.526a	−0.521a	−0.465a	−0.254	−0.227	−0.342a
Irisflorentin	0.461a	−0.526a	−0.454a	−0.276b	−0.257	−0.340b
Myclobutanil	0.987a	0.577a	0.580a	0.404a	0.288b	0.314b
Cortisone	0.444a	0.580a	0.592a	0.411a	0.296b	0.313b
Bupirimate	0.450a	0.587a	0.583a	0.421a	0.308b	0.331b
Ethanesulfonic acid	−0.505a	−0.531a	−0.463a	−0.234	−0.225	−0.355a
Sucrose	−0.524a	−0.555a	−0.485a	−0.256	−0.239	−0.329b
7-Keto-8-aminopelargonic acid	−0.429a	−0.532a	−0.454a	−0.268b	−0.362a	−0.385a
9-Phenanthrol	−0.481a	−0.455a	−0.387a	−0.183	−0.209	−0.288b
N5-(1-iminoethyl)-l-ornithine	−0.428a	−0.503a	−0.430a	−0.297b	−0.346a	−0.382a
4.alpha0-mannobiose	−0.482a	−0.433a	−0.372a	−0.287b	−0.292b	−0.331b
L-gulonic gamma-lactone	−0.273b	−0.232	−0.361a	−0.198	−0.219	−0.227
2-Keto-3-deoxyoctonic acid	−0.416a	−0.305b	−0.409a	−0.234	−0.248	−0.196
Sn-glycero-3-phosphocholine	0.494a	0.347a	0.285b	−0.017	0.13	0.077
Linolenic acid	0.561a	0.280b	0.325b	0.307b	0.329b	0.293b
Glutamic acid	0.435a	0.382a	0.273b	0.336b	0.222	0.271b
Mollicellin	0.379a	0.475a	0.542a	0.357a	0.314b	0.382a
Propofol	0.400a	0.462a	0.554a	0.351a	0.275b	0.316b
Val-met	0.384a	0.458a	0.552a	0.347a	0.265b	0.311b
Bufexamac	0.267 b	0.485a	0.579a	0.391a	0.278b	0.287b
aminoethanone	0.409a	0.482a	0.600a	0.317b	0.360a	0.341b
Propofol beta-d-glucuronide	0.412a	0.494a	0.522a	0.424a	0.241	0.345a
Valganciclovir	0.412a	0.509a	0.544a	0.427a	0.247	0.343a
Kendomycin	0.358a	0.367a	0.302b	0.013	0.212	0.1
docosahexaenoic acid	0.429a	0.251	0.161	0.118	0.168	0.206

^ap < 0.01; ^bp < 0.05.

POD: postoperative day; NRS: numeric rating scale; VAS: visual analog scale; CRP: C-reactive protein; IL-6: interleukin 6.

Discussion

Several studies have demonstrated that TAPB combined with general anesthesia in abdominal surgery reduces surgical stress responses, facilitates a smoother anesthesia course, and improves patient outcomes compared with general anesthesia alone. These benefits were linked to key metabolic pathways, and this study investigated systemic metabolism in patients undergoing radical gastrectomy who received general anesthesia combined with TAPB (treatment group) or general anesthesia alone (control group). Our results showed distinct metabolomic profiles between the treatment and control groups, and we identified 1874 metabolites in both POS- (1224) and NEG- (650) ion modes. KEGG analysis identified the top differential metabolic pathways between the treatment and control groups as choline metabolism in cancer, glycerophospholipid metabolism, ABC transporters, and protein digestion and absorption pathways. Furthermore, majority of the top differential metabolites were associated with indicators of anesthesia management and might have contributed to the benefits of combined anesthesia.

Based on the DASs obtained from the KEGG analysis, the most altered pathways were choline metabolism in cancer, glycerophospholipid metabolism, ABC transporters, and protein digestion and absorption. In this study, altered choline metabolism in cancer and glycerophospholipid metabolism shared the same five metabolites: (a) 1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine; (b) SOPC; (c) phosphocholine; (d) PC (16:0/16:0); and (e) glycerophosphocholine (Figure 4(a)), which were decreased in TAPB treatment. Choline metabolism plays a critical role in maintaining cell membrane integrity, methylation, and neurotransmitter synthesis. ²² Glycerophosphocholine is vital for maintaining cell membrane stability and contributes to cell signaling and lipid transport.^23,24 Glycerophosphocholine is derived from phosphatidylcholine and is involved in the cytidine 5′-diphosphocholine–choline pathway. ²³ The study showed that concentrations of lysophosphocholine (a soluble form released from phosphatidylcholine) and glycerophosphocholine were higher in the serum of adult patients with emergence agitation (EA) under general anesthesia than in no-EA patients.

Anesthetic agents may affect neurotransmission by disrupting lipid rafts and the surrounding ion channels, such as Nav channels, to induce anesthesia. ²⁵ General anesthetic agents are highly lipid-soluble, can easily cross the blood–brain barrier, and bind to multiple targets such as membrane lipids. Anesthetic agents alter lipid membrane structure, increasing phospholipase D2 (PLD2) activity, phosphatidylinositol (4,5)-bisphosphate (PIP2) production, and membrane ion permeability. PIP2 helps regulate receptor trafficking and modulates Nav channel function.^26,27 Recent studies have shown significant aberrant lipid metabolism after prolonged anesthetic (sevoflurane) exposure.^17,28,29 This study showed that choline and glycerophospholipid metabolism were the key altered pathways between the treatment and control groups, supporting the idea that general anesthesia can disrupt these pathways, which might be involved in anesthesia-related complications. General anesthesia combined with TAPB reduced choline and glycerophospholipid metabolism and might be an indicator of benefit.

Two notable metabolic pathways were ABC transporters and protein digestion and absorption. This study showed that DL-proline, lysine, DL-glutamic acid, and glutamic acid were associated with ABC transporters and protein digestion and absorption, and their levels decreased with TAPB treatment. ABC transporters facilitate the movement of various substrates across cellular membranes, including those within the CNS. Transportable substrates include ions, amino acids, peptides, sugars, and other hydrophilic compounds. ²⁸ In the human body, glutamic acid (L-glutamic acid) easily converts to glutamate under physiological conditions. DL-glutamic acid is not a distinct compound but a racemic mixture, containing L-glutamic acid and D-glutamic acid in equal proportions (50:50 ratio). In this study, TAPB reduced glutamic acid levels, increased anesthesia depth, and reduced the anesthetic dose.

We further investigated the association between the most altered metabolites and indicators of anesthesia management. Table 4 indicates that general anesthesia combined with TAPB can reduce the inflammatory marker IL-6, sufentanil dose, and pain scale scores (NRS and VAS scores) on POD1 and POD3 (Table 4). The most altered metabolites were lipids and lipid-like molecules (Tables 2 and 3). We performed correlation analyses to evaluate the association between these metabolites and indicators of anesthesia management, including sufentanil dose and pain scores (Table 4). Analysis revealed that most metabolites were associated with sufentanil dose and pain scale scores (NRS and VAS scores) on POD1 and POD3 (Table 4). Additionally, altered metabolites were mainly associated with lipid, amino acid, and purine metabolism. Glycerophosphocholine metabolism is part of lipid metabolism, whereas proline, DL-glutamic acid, and glutamic acid are involved in amino acid metabolism. The prominently altered metabolites included increased prostaglandin (PG) F2α methyl ester and PG F2α isopropyl ester, both of which were analogs of PGF2α. PGs, a group of key lipid mediators, activate G-protein coupled receptors (GPCRs) and are involved in physiological systems and pathophysiological processes. In the CNS, PGs play a neuroprotective or neurotoxic role, depending on the specific receptor. ²⁹ Consistently, the top altered metabolites in our data revealed that cortisone was notably decreased, indicating reduced stress in the treatment group. Another metabolite, sucrose, was increased in TAPB-treated samples. Interestingly, sucrose is effective for reducing procedural pain in newborn infants. ³⁰ Some experiments have indicated that sucrose treatment increases pain threshold, possibly via an increase in endogenous opioids. ³¹ This result may support the use of sucrose for pain management or anesthesia. TAPB delivers a large dose of ropivacaine into a vascular fascial plane, and its metabolites may exist postoperatively. However, there were no ropivacaine-related metabolites among the top differential metabolites. A recent study investigated the metabolomics of another regional analgesic technique, the erector spinae plane (ESP) block, for breast cancer surgery and reported that the altered metabolites included primary bile acids, steroid hormone biosynthesis, and glutathione metabolism. ESP block also attenuated increases in serum vascular endothelial growth factor (VEGF), cortisol, and C-reactive protein (CRP) levels. Notably, it dampened cortisol and 21-deoxycortisol release more effectively than general anesthesia alone, indicating a biochemical correlation with stress modulation and immune responses. ³²

Although our findings provide novel insights into the metabolomic signature associated with postoperative pain trajectories, certain study limitations must be considered when interpreting these results. First, the study's sample size was modest, which may have limited the statistical power of our analyses to detect subtle metabolomic differences, particularly within subgroup analyses. Consequently, studies on larger, independent cohorts are needed to confirm our findings. Second, this investigation was conducted at a single tertiary care center with a specific patient population and perioperative protocol. Multicenter studies are needed to assess the external validity of our candidate biomarkers across diverse clinical settings. Third, our analysis captured metabolomic data at only a single postoperative time point (24 h). Serial sampling, including preoperative baseline, immediate postoperative, and later recovery phases, is required for establishing temporal trajectories and causal relationships between metabolomic alterations and pain outcomes. Fourth, metabolite identification was performed at the putative level using tandem MS and spectral library matching. Stringent filtering criteria need to be applied, with confirmation of definitive structures via authentic chemical standards for all features. Fifth, despite our efforts to adjust for key confounders, residual confounding such as that owing to sufentanil cannot be excluded. We did not incorporate cumulative preoperative opioid exposure, intraoperative sevoflurane MAC-hours, or total surgical duration as covariates. The absence of these variables may have introduced bias and limited our ability to isolate the specific metabolic effects attributable to pain trajectory. Finally, we did not assess correlations between identified metabolites and clinically meaningful long-term outcomes, such as persistent postsurgical pain, functional recovery, quality of life, and opioid consumption beyond the immediate postoperative period. The present findings should be considered mechanistic/hypothesis-generating finding; they do not establish a biomarker for clinical application.

Conclusion

Our data indicate that general anesthesia combined with TAPB (treatment group) may reduce sufentanil dose and pain scale scores (NRS and VAS scores) on POD1 and POD3 compared with general anesthesia (control group) during laparoscopic-assisted radical gastrectomy. Serum metabolic analysis performed using KEGG revealed that the top differential metabolic pathways between the treatment and control groups were choline metabolism in cancer, glycerophospholipid metabolism, ABC transporters, and protein digestion and absorption. In this study, altered choline metabolism in cancer and glycerophospholipid metabolism shared the same five metabolites: (a) 1-stearoyl-2-arachidonyl-sn-glycero-3-phosphocholine; (b) SOPC; (c) phosphocholine; (d) PC (16:0/16:0); and (e) glycerophosphocholine, all of which were decreased with TAPB treatment. General anesthesia combined with TAPB reduced choline and glycerophospholipid metabolism and might serve as an indicator of its beneficial effects. This study also indicated that DL-proline, lysine, DL-glutamic acid, and glutamic acid were shared with ABC transporters and protein digestion and absorption, which was decreased with TAPB treatment. Furthermore, majority of the top differential metabolites were associated with indicators of anesthesia management and were potential contributors to the benefit of combined anesthesia.