Targeted metabolomics reveals serum biomarkers and metabolic alterations in cholesterol gallstone patients

Abstract

The molecular mechanisms underlying cholesterol gallstone (CG) formation remain incompletely elucidated, and effective diagnostic biomarkers are lacking. This study integrates high-resolution ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) with high-throughput targeted metabolomics to systematically reveal the serum metabolic profile of CG patients. The goal is to elucidate the pathological mechanisms of CG and identify potential high-value biomarkers. Serum samples were collected from 39 CG patients (who underwent laparoscopic cholecystectomy) and 32 healthy controls at Fudan University Affiliated Pudong Hospital between January 1, 2023, and March 1, 2024. A UHPLC-MS platform was utilized to precisely quantify 354 metabolites. Multivariate statistical analyses, pathway enrichment analysis, and receiver operating characteristic (ROC) curve evaluations were performed to assess the diagnostic efficacy of differentially expressed metabolites. A total of 100 significantly altered metabolites were identified, with notable enrichment in amino acid metabolism, including alanine, threonine, and deoxycholic acid (P < 0.01). The activity of ATP-binding cassette (ABC) transporter pathways was significantly downregulated (P < 0.001), suggesting a strong association between cholesterol homeostasis imbalance and gallstone formation. Notably, nucleotide metabolites such as S-adenosylmethionine exhibited high diagnostic potential, with an area under the ROC curve (AUC) ranging from 0.913 to 0.984. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis further highlighted amino acid metabolism, nucleotide metabolism, and carbon metabolism as central hubs of metabolic reprogramming in CG. This study systematically employed high-resolution UHPLC-MS-based targeted metabolomics to delineate the serum metabolic characteristics of CG. The findings reveal that dysregulation of amino acid and nucleotide metabolism is a key driver of disease progression. The identified metabolic biomarkers hold promise for assisting in CG diagnosis, while the discovery of ABC transporter and mucin synthesis-related pathways opens new avenues for targeted therapy. These results not only enhance the understanding of the molecular mechanisms of cholelithiasis but also provide a theoretical and technical foundation for precision medicine strategies.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-33996-3.

Introduction

Gallstone disease (GSD) is one of the most prevalent digestive disorders worldwide, with epidemiological data indicating an adult prevalence of 10%–33%, of which approximately 80% are cholesterol gallstones (CG)¹. The formation of CG is closely associated with cholesterol homeostasis imbalance, involving multiple interrelated factors such as excessive hepatic cholesterol secretion, impaired gallbladder motility, abnormal intestinal cholesterol absorption, and bile acid metabolism dysregulation^2,3. Despite advancements in understanding its pathophysiology, the dynamic regulatory network of serum metabolites in gallstone formation remains unclear. Furthermore, the lack of effective non-invasive diagnostic biomarkers poses challenges for early screening and precise intervention.

In recent years, metabolomics has provided novel insights into disease-specific metabolic characteristics. However, existing studies have primarily focused on bile composition analysis, such as bile acid profiling and lipid supersaturation indices. While bile directly participates in gallstone formation, its composition is subject to transient fluctuations influenced by diet, gut microbiota, and gallbladder contraction, making it difficult to comprehensively reflect systemic metabolic disturbances^4,5. In contrast, serum serves as a “dynamic mirror” of internal homeostasis, integrating systemic metabolic alterations and offering a more stable window into the molecular mechanisms of gallstone disease. Unfortunately, traditional untargeted metabolomics suffers from low resolution and limited metabolite coverage, making it difficult to precisely capture low-abundance metabolites such as nucleotide derivatives and key metabolic pathways. While targeted metabolomics provides high specificity, it lacks high-throughput capabilities, restricting its application in complex disease research^6–8.

Notably, cholesterol gallstone formation is not solely linked to lipid metabolism disorders. Recent studies suggest that dysregulation of amino acid metabolism—particularly proline and glutamate—may promote cholesterol crystallization by modulating mucin synthesis or calcium ion binding^9,10. Additionally, nucleotide metabolism disturbances may exacerbate gallbladder inflammation via oxidative stress pathways¹¹. However, these mechanisms have primarily been investigated in cellular or animal models, and systematic validation at the serum metabolomics level in humans remains lacking. Moreover, while the ATP-binding cassette (ABC) transporter family plays a pivotal role in cholesterol efflux, its metabolic regulatory network in gallstone disease remains poorly characterized.

To address these research gaps, this study integrates high-resolution UHPLC-MS with high-throughput targeted metabolomics to systematically analyze the specific metabolic reprogramming of serum in CG. We aim to identify key metabolic pathways associated with disease progression and screen for highly sensitive non-invasive diagnostic biomarkers, providing a theoretical foundation and technical support for early screening and precision intervention in gallstone disease.

Materials and methods

Patient recruitment and data collection

This study was conducted at Fudan University Affiliated Pudong Hospital. The control group was matched based on age, sex, and BMI, while excluding factors that could affect cholesterol metabolism, such as diabetes, hepatobiliary diseases, and metabolic syndrome. A total of 39 patients with cholesterol gallstones and 32 healthy controls were included. Fasting venous blood samples were collected in the morning to minimize the influence of diet and circadian rhythms. This study adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Fudan University Affiliated Pudong Hospital. Written informed consent was obtained from all participants or their legal representatives. Patients who met the diagnostic criteria for gallstone disease were included in the study.

Sample collection and grouping

Plasma samples were obtained from patients within 24 h after gallstone diagnosis. The plasma was centrifuged at 4000 rpm for 10 min, and the upper serum layer was collected. The preliminary separated serum was further centrifuged at 12,000 rpm for 10 min at 4 °C, and the supernatant was collected and stored at −80 °C until further analysis. Plasma samples from the healthy control group were also collected for subsequent metabolomic analysis.

Instruments and reagents

The instruments used in this study included the Waters H-Class UPLC (Waters), a high-resolution 6500 + AB mass spectrometer (SCIEX, USA), a Heraeus Fresco17 centrifuge (Thermo Fisher Scientific), a BSA124S-CW analytical balance (Sartorius), a YM-080 S ultrasonic device (Shenzhen Fanggao Microelectronics Co., Ltd.), a JXFSTPRP-24 grinder (Shanghai Jingxin Technology Co., Ltd.), a CV600 refrigerated centrifugal concentrator (Beijing GM Technology Co., Ltd.), and a −80 °C freezer (Thermo Fisher, USA). Reagents included LC-MS-grade acetonitrile and methanol (CNW Technologies), LC-MS-grade ammonium acetate (SIGMA-ALDRICH), ammonia solution (Fisher Chemical), and ultrapure water (ddH2O) (Millipore).

Metabolomic analysis

Metabolite extraction

To correct batch effects, a 13 C-labeled internal standard (IS) was added during the extraction process to account for signal drift and quantification errors. Additionally, one quality control (QC) sample (a mixture of all serum samples in equal volume) was inserted every 10 experimental samples to monitor data stability. The liquid sample preprocessing steps were as follows: after thawing the samples on ice, they were vortex-mixed for 30 s to ensure uniformity. A 50 µL sample was added to a 2 mL EP tube, followed by 250 µL water and vortex mixing. Then, 1200 µL of extraction solvent (methanol: acetonitrile = 1:1) was added and vortex-mixed. The mixture was sonicated in an ice-water bath for 15 min, vortexed again, and left to stand at −40 °C for 2 h. It was then centrifuged at 12,000 rpm for 15 min at 4 °C, and 1200 µL of the supernatant was transferred to a new EP tube. The supernatant was concentrated to dryness via vacuum centrifugation (8 h). The dried samples were reconstituted in 120 µL acetonitrile: water (v: v = 6:4), vortexed to dissolve the residue, sonicated in an ice-water bath for 30 s, and centrifuged at 12,000 rpm for 15 min at 4 °C. A 70 µL aliquot of the supernatant was carefully transferred to an LC injection vial for analysis. A standard mixed solution was used as the QC sample for instrument calibration.

Instrumental analysis

Chromatographic separation of target compounds was performed using an ACQUITY UPLC H-Class (Waters) ultra-high-performance liquid chromatography system with a Waters Atlantis Premier BEH Z-HILIC column (1.7 μm, 2.1 mm × 150 mm). The mobile phase A consisted of ultrapure water and acetonitrile (8:2) with 10 mmol/L ammonium acetate, while mobile phase B was acetonitrile: ultrapure water = 9:1 with 10 mmol/L ammonium acetate. The pH of both phases was adjusted to 9 using ammonia. The sample tray was maintained at 8 °C, and the injection volume was 1 µL.Mass spectrometry analysis was performed using a SCIEX 6500 QTRAP + triple quadrupole mass spectrometer equipped with an IonDrive Turbo V ESI ion source in multiple reaction monitoring (MRM) mode. The ion source parameters were as follows: curtain gas = 35 psi, ion spray voltage = + 5000 V/−4500 V, temperature = 500 °C, ion source gas 1 = 50 psi, and ion source gas 2 = 50 psi. All mass spectrometry data acquisition and target compound quantification were performed using SCIEX Analyst WorkStation software (1.7.2) and Data Driven Flow (v-1.0.1).

Quality control and data processing

All raw data were processed using XCMS for noise reduction and baseline correction, followed by total ion current (TIC) normalization and log2 transformation in MetaboAnalyst 5.0 to ensure data comparability. The total ion chromatogram (TIC) in Figure S1 provides an overview of sample peak elution and response patterns, with the x-axis representing retention time and the y-axis representing response intensity. A mixed standard solution served as the QC sample, and one QC sample was inserted every 10 experimental samples. A higher correlation of QC samples (close to 1) indicates better methodological stability and data quality. Figure S2 shows a high correlation among QC samples, confirming excellent data quality. The overlap of QC sample TICs in Figure S3 further demonstrates the stability of instrument data acquisition. Figure S4 shows the internal standard RSD table in the QC sample. C_F(Final Concentration, µmol/L) refers to the final measured concentration of the sample, obtained by multiplying the instrument-derived C_C (Calculated Concentration, µmol/L) by the DF (Dilution Factor). The unit is µmol/L.C_M(Metabolite Concentration, nmol/L) represents the concentration of the target metabolite in the sample. It is calculated by multiplying the final measured concentration C_F by the CF (Concentration Factor) from sample preprocessing and the V_F (Final Volume, µL), then dividing by the original V_S (Sample Volume, mL). The unit is nmol/L.NA indicates that the target metabolite was not detected in the sample.The calculation formula is as follows: C_M[nmol/L] = C_F[µmol/L] * V_F[µL] * CF/V_S[mL].

Pathway enrichment analysis using KEGG

To elucidate the biological pathways associated with differentially expressed metabolites in cholesterol gallstone disease, we performed pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes database. The identities of significantly altered metabolites were mapped to KEGG compound identifiers and subsequently annotated to biological pathways^12,13.

Results

Patient cohort and baseline characteristics

A total of 71 serum samples were collected for this study, including 39 from the gallstone patient group and 32 from the healthy control group. The baseline characteristics of both groups are presented in Table 1. The average age of patients in the gallstone group was 51 years, with males accounting for 35.9%, whereas the control group had an average age of 51 years, with males comprising 40.7%. No significant differences were observed between the two groups in terms of age and sex. In the gallstone group, 69.2% of patients had elevated blood glucose levels, and 41% exhibited increased bilirubin levels, both of which were significantly higher than those in the control group.

Table 1.

Clinical baseline data of cholelithiasis group and healthy control group.

	Healthy control group (32 cases)		Cholelithiasis group (39 cases)
Clinical baseline data	Number	Proportion (%)	Number	Proportion (%)	P
Age
Average value	51		51		0.995
Standard Deviation	7.76		9.72
Gender
Male	13	40.7	14	35.9	0.891
Female	19	59.3	25	64.1
Blood sugar level
4.1–5.1.9mmol/L	31	96.9	12	30.8	0.0004
≥ 5.9mmol/L	1	3.1	26	69.2
Total bilirubin value
3–22µmol/L	32	100	23	59.0	0.0055
≥ 22µmol/L	0	0	16	41.0

Raw data preprocessing

The raw dataset included 71 experimental samples with a total of 354 detected metabolites. To ensure robust statistical analysis, data preprocessing was performed: metabolites with missing values exceeding 50% in either group or across all samples were excluded. Missing values were imputed using a random number between 0.1 and 0.5 times the minimum detected value. After preprocessing, 293 metabolites remained for further analysis. Detailed numerical results of these statistical analyses are provided in the Supplementary Material (Statistical Analysis Results.xlsx).

Multivariate statistical analysis

Principal component analysis (PCA) score plots (Fig. 1A) demonstrated distinct metabolic differences between the gallstone patient group and the control group. Figure 1B further highlighted significant variations in the types and quantities of metabolites between the two groups. Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots (Fig. 1C) indicated a clear separation trend between the two groups within a 95% confidence interval, further confirming metabolic differences. Permutation tests of the OPLS-DA model (Fig. 1D and E) revealed that the random models significantly outperformed the original model (p < 0.05), suggesting good predictive accuracy of the model.

Fig. 1.

Multivariate Statistical Analysis. (A) PCA score scatter plot for all samples. (B) 3D PCA score scatter plot for two groups of samples. (C) OPLS-DA score scatter plot for two groups of samples. (D, E) Permutation test results for the OPLS-DA model.

Metabolomic data analysis

Hierarchical clustering analysis (Fig. 2A) revealed a distinct separation in metabolite profiles between the two groups. Statistical significance was calculated for each metabolite, and those with significant differences (p < 0.05) were identified. Figure 2B presents the top ten metabolites with the most significant changes between the gallstone and control groups. Compared to the control group, the gallstone group exhibited elevated serum levels of 1-methyladenosine, 2’-deoxyguanosine 5’-monophosphate, 5’-deoxy-5’-methylthioadenosine, cytidine 5’-monophosphate-N-acetylneuraminic acid, hydroxyphenyl lactate, lactate, L-mannose, L-valine, N-acetylcarnosine, and S-adenosylmethionine. Conversely, the levels of 2-hydroxy-2-methylbutyric acid, adrenosterone, diphenylamine, L-citrulline, L-homoarginine, L-threonic acid, retinol, uracil, and xanthurenic acid were significantly decreased. These findings suggest that gallstone patients exhibit marked upregulation in nucleotide metabolism and certain amino acid metabolic pathways.

Fig. 2.

Metabolic Data Analysis. (A) Hierarchical clustering heatmap of control group vs. gallstone group. (B) Boxplot of control group vs. gallstone group.

Pathway enrichment analysis of differential metabolites

We highlight the most significant upand downregulated metabolites (Fig. 3A, FigureS5). Key upregulated metabolites in the gallstone group included ribulose 5-phosphate, mannitol, and inosine, whereas oleamide, retinoic acid, and adenosine monophosphate were significantly reduced. Spearman correlation analysis was employed to classify the sources of differential metabolites, with results visualized via a chord diagram (Fig. 3B). KEGG pathway enrichment analysis identified major metabolic alterations in gallstone patients, primarily involving nucleotide metabolism, purine metabolism, amino acid metabolism, and carbon metabolism (Fig. 3C). Further abundance analysis of differential metabolites revealed significant upregulation of metabolites in amino acid and nucleotide metabolism pathways, while metabolites related to membrane transport pathways were relatively reduced (Fig. 3D). Additionally, topological analysis indicated that the arginine and proline metabolism pathway, as well as alanine, aspartate, and glutamate metabolism, might play crucial roles in the metabolic abnormalities associated with gallstone disease (Fig. 3E).

Fig. 3.

Differential Metabolite Pathway Enrichment Analysis. (A) Stick plot analysis of differential metabolites. (B) Chord diagram analysis of differential metabolites. (C) KEGG enrichment analysis of differential metabolites. (D) Differential abundance score plot of control group vs. gallstone group. (E) Pathway analysis plot of differential metabolites.

Prognostic analysis of differential metabolites

To assess the diagnostic potential of differential metabolites, receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. ROC curve analysis demonstrated that several metabolites had high diagnostic value (AUC > 0.9), including 1-methyladenosine (AUC = 0.8998), 2-hydroxy-2-methylbutyric acid (AUC = 0.8966), cytidine 5’-monophosphate-N-acetylneuraminic acid (AUC = 0.9135), diphenylamine (AUC = 0.9247), L-mannose (AUC = 0.9167), retinol (AUC = 0.8998), S-adenosylmethionine (AUC = 0.9840), uracil (AUC = 0.9223), and xanthurenic acid (AUC = 0.9639) (Fig. 4). These metabolites may serve as potential biomarkers for gallstone disease, facilitating early screening and risk prediction.

Fig. 4.

ROC curves of differential metabolites in the gallstone group.

Discussion

Current research on cholelithiasis primarily focuses on lipid metabolism and alterations in bile composition^14,15. This study is the first to systematically analyze serum metabolic differences between gallstone patients and healthy individuals using ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). Compared to bile, which is directly secreted into the digestive tract, serum provides a more comprehensive reflection of systemic metabolic changes in gallstone patients. Our targeted metabolomics analysis revealed a significant dysregulation in amino acid metabolism and nucleotide metabolism in CG patients. These specific metabolic alterations lead us to hypothesize about their potential mechanistic roles in gallstone pathogenesis.

Previous studies have established that mucins play a crucial role in the formation of cholesterol gallstones¹⁶. Healthy individuals have lower gallbladder mucin levels, whereas elevated phospholipid and unsaturated free fatty acid levels can promote mucin synthesis and secretion via the prostaglandin pathway¹⁷. Excessive mucin secretion not only increases the risk of cholelithiasis but is also associated with conditions such as cholecystitis and cholangiocarcinoma¹⁸. Mucins are high-molecular-weight glycoproteins secreted by goblet cells, primarily composed of serine, threonine, proline, and glutamate. These amino acids interact hydrophobically with bile lipids, forming aggregation cores for cholesterol crystal deposition, ultimately leading to cholesterol gallstone formation^19–21. Furthermore, studies have indicated that mRNA expression levels of mucins such as MUC1, MUC3, MUC5B, and MUC6 are significantly elevated in gallstone patients, while MUC2 and MUC4 exhibit distinct expression patterns²². Consistent with these findings, our study demonstrated that serum levels of serine, threonine, methionine, and proline were significantly increased in gallstone patients. Amino acids such as threonine, serine, and proline are fundamental building blocks for mucin glycoproteins²³. While we did not directly measure mucin secretion, the elevated serum levels of these precursors suggest a potential increase in their utilization for mucin synthesis. This is consistent with the well-established theory that mucin hypersecretion provides a scaffold for cholesterol crystal nucleation^9,24,25.

Additionally, low-molecular-weight acidic proteins play a vital role in biomineralization by regulating calcium ion binding and deposition through acidic amino acid residues²⁶. These residues have been implicated in ectopic biomineralization processes, such as cardiovascular calcification and urolithiasis^27,28. Similarly, our study found that serum levels of glutamate and aspartate were significantly elevated in gallstone patients, suggesting that these acidic amino acids may contribute to cholesterol crystal formation.

The ATP-binding cassette (ABC) transporter family is widely expressed across multiple physiological systems and is responsible for transmembrane transport of lipids, steroids, vitamins, and peptides^29,30. Among them, ABCG5 and ABCG8 play central roles in cholesterol reabsorption and excretion³¹. These heterodimeric transporters, localized to the brush-border membrane of intestinal epithelial cells and the canalicular membrane of hepatocytes, facilitate cholesterol efflux into bile, where it forms bile salt micelles under high-cholesterol conditions, reducing cholesterol deposition risk^32,33. Notably, the G19D mutation in ABCG8 has been linked to increased biliary cholesterol levels, while the R50C mutation in ABCG5 enhances cholesterol transport activity and decreases intestinal absorption^34,35. Furthermore, ABCB4-knockout mice exhibit severe hepatic lipid accumulation, whereas gene therapy with human ABCB4 mRNA significantly reduces liver lipid deposition^36,37. We observed significantly lower serum levels of ABC transporter-associated metabolites in gallstone patients compared to healthy controls. Metabolomic analysis identified elevated levels of glutamate, glycine, and cystine, consistent with enhanced glutathione (GSH) biosynthesis and indicative of chronic oxidative stress³⁸. This oxidative stress activates inflammatory pathways, which in turn suppress the expression of ABCB11 and ABCB4. Functional impairment of these transporters directly promotes cholesterol supersaturation in bile^39,40. Concurrently, reduced glutamine levels reflect increased consumption by activated immune cells, which exacerbates inflammatory responses and further represses ABCB11 and ABCB4 expression⁴¹. Additionally, lower biotin levels impair acetyl-CoA carboxylase activity, resulting in reduced fatty acid synthesis and compromised cholesterol esterification. This dysregulation leads to the accumulation of free cholesterol within hepatocytes⁴². Although our study could not identify the exact transporter isoforms involved, this finding may suggest a systemic impairment in cholesterol and lipid transport. This is particularly significant given the central role of transporters such as ABCG5/G8 in biliary cholesterol excretion³². Functional impairment of these transporters may lead to pre-lithogenic cholesterol supersaturation and further promote cholesterol crystal formation.

Finally, we performed prognostic analysis of differential metabolites associated with cholelithiasis. The results demonstrated that L-glycine (a lysine metabolism product), xanthurenic acid (a tryptophan metabolism product), and nucleotide metabolites—including 1-methyladenosine, cytidine 5’-monophosphate-N-acetylneuraminic acid, S-adenosylmethionine, and uracil—all exhibited area under the curve (AUC) values exceeding 0.9, indicating their potential significance in gallstone formation. Previous studies have reported that compared to healthy individuals, patients with gallbladder carcinoma exhibit significantly elevated serum levels of nucleotides such as uridine, adenosine, and cytidine, which may induce pathological changes in gallbladder epithelial cells and ultimately promote cholesterol crystal deposition⁴³. We therefore hypothesize that the observed disorder in nucleotide metabolism, marked by altered levels of S-adenosylmethionine and uracil, may be associated with enhanced gallbladder epithelial cell turnover and inflammation, thereby fostering a local microenvironment conducive to stone formation.

While this study provides novel insights into the serum metabolic profile of CG patients, we acknowledge its limitations. First, although the sample size was adequate to identify significant metabolic differences, future validation requires multicentre studies with larger cohorts. This was an exploratory investigation designed to generate hypotheses and identify potential biomarker candidates. Consequently, the generalizability of our findings may be limited. The high AUC values observed for individual metabolites, while promising, must be interpreted cautiously due to the risk of overfitting inherent in small datasets. To robustly confirm the diagnostic utility and clinical applicability of the identified metabolites, validation in large-scale, independent, multicentre cohorts is essential. Future studies incorporating prespecified power calculations will be crucial for translating these findings into clinical practice.

In conclusion, this study utilized UHPLC-MS technology to elucidate significant metabolic alterations in the serum of gallstone patients, with a particular focus on the roles of amino acid metabolism, ABC transporter pathways, and nucleotide metabolism in gallstone pathogenesis. These findings not only enhance our understanding of the molecular mechanisms underlying cholelithiasis but also provide new perspectives for future clinical diagnosis and therapeutic strategies.

Conclusion

This study employed high-resolution ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) for high-throughput targeted metabolomics analysis, significantly enhancing the sensitivity, specificity, and coverage of metabolite detection. Compared to conventional metabolomics approaches, this technology enables the detection of low-abundance metabolites across a broader dynamic range while providing more precise quantitative data, thereby revealing significant alterations in specific serum metabolites and the key metabolic pathways involved in cholelithiasis.

Our findings indicate that amino acid metabolism, ABC transporter pathways, and nucleotide metabolism play crucial roles in the development and progression of cholelithiasis. KEGG enrichment analysis further confirmed a strong association between cholelithiasis and pathways related to amino acid metabolism, carbon metabolism, and nucleotide metabolism, offering novel insights into the molecular pathophysiology of this disease. Additionally, prognostic analysis of differential metabolites demonstrated that specific metabolites, such as S-adenosylmethionine, exhibited exceptional diagnostic performance (AUC > 0.9), highlighting their potential clinical utility as biomarkers.

In summary, this study leveraged the high resolution and high-throughput capabilities of UHPLC-MS to comprehensively characterize the serum metabolic profile of cholelithiasis patients. These findings not only provide new scientific evidence and technological support for early diagnosis and personalized treatment of cholelithiasis but also lay the groundwork for further exploration of its pathogenesis, with significant implications for both research and clinical applications.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Wenzhi Jin conceptualized and designed the study. Zhijie Zhou collected and processed the serum samples from patients. Wenzhi Jin performed the metabolomics analysis. Wenzhi Jin, Ganggang Wang and Xin Zhang conducted data analysis, graphical presentation, and manuscript writing. Xiaoliang Wang, Yulong Yang reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by the Talent Training Program of Pudong Hospital affiliated with Fudan University (Project no. LJ202101), Fudan University Affiliated Pudong Hospital Discipline Construction Project (Grant No. Yjzdxk2025-02)，Fudan Zhangjiang Clinical Medicine Innovation Fund Project (Grant No. KP7202105), Outstanding Leaders Training Program of Pudong Health Committee of Shanghai (Grant No. PWR12022-04), the Scientific Research Foundation provided by Pudong Hospital affiliated with Fudan University (Project nos. Zdxk2020-01, Zdzk2020-09, and YJYJRC202104), and the Pudong New Area Clinical Characteristic Discipline Project (Grant No. PWYts2021-11).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All procedures involving human participants were conducted in accordance with the ethical standards of the Ethics Committee of Shanghai Pudong Hospital and the 1964 Declaration of Helsinki and its later amendments. Informed written consent was obtained from all participants. Ethical approval number: QWJWLX-01.