Biomarker for identifying superior mesenteric artery dissection related intestinal dysfunction

Abstract

The invention discloses a biomarker for identifying superior mesenteric artery dissection related intestinal dysfunction. The biomarker is lysophospholipid. By means of the biomarker, a patient with intestinal dysfunction caused by SISMAD can be recognized in an early stage, operation intervention (mainly stent implantation) can be conducted in time, and malignant consequences such as intestinal necrosis, large-area intestinal resection, short bowel syndrome and infection death are avoided.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis technology, and in particular to a biomarker for identifying intestinal dysfunction associated with superior mesenteric artery dissection. Background Art

[0002] Spontaneous isolated superior mesenteric artery dissection (SISMAD) refers to a dissection of the superior mesenteric artery, excluding secondary causes such as aortic dissection, trauma, or iatrogenic factors. As a rare arterial disease that predominates in middle-aged and elderly men, autopsy data suggest a population incidence of approximately 0.06%. With the widespread use of enhanced CT in recent years, the detection rate of SISMAD has significantly increased, with over 4,000 cases reported worldwide. Clinically, SISMAD patients range from asymptomatic patients discovered incidentally on imaging studies to symptomatic patients with prominent manifestations of intestinal dysfunction such as abdominal pain, nausea, vomiting, and gastrointestinal bleeding.

[0003] The pathogenesis of SISMAD remains unclear, but it is currently believed to be related to the unique anatomical curvature of the superior mesenteric artery. The anterior wall of the superior mesenteric artery, 1-3 cm proximal to the superior mesenteric artery, experiences increased shear stress, leading to endothelial damage and dissection. Furthermore, studies have linked SISMAD to hypertension, smoking, and genetics. Retrospective clinical studies have found that up to 42.3% of SISMAD patients have hypertension, confirming that hypertension is an independent risk factor for SISMAD. Hypertension primarily increases intimal shear stress, promotes local inflammatory responses in the arterial wall, and induces circumferential stretching of endothelial cells, reducing radial endothelial cell overlap, thinning the intima, and reducing stress intensity, making it more susceptible to intimal tears and dissection. Smoking is also a risk factor for SISMAD. In a study of 513 patients with SISMAD, up to 45% were current or former smokers, significantly higher than the 23.7% reported in the general population. Smoking contributes to SISMAD by causing intimal dysfunction and exacerbating the progression of atherosclerosis. Mutations at specific chromosomal sites may be associated with SISMAD. Mutations at chromosome 5q13-14 have been found in some SISMAD patients. Whole-exome sequencing of a SISMAD patient revealed mutations in the SNIP1 and COL1A2 genes in all family members. These two gene mutations may contribute to SISMAD. Because the vast majority of SISMAD patients do not undergo pathological examination, and there have been no reports of clear familial clustering, the genetic hypothesis requires further verification.

[0004] In recent years, the treatment of SISMAD remains controversial. While SISMAD is primarily treated conservatively, approximately 14% to 20% of patients undergo surgical or endovascular therapy due to inadequate response to medical therapy or intestinal dysfunction. Surgical indications for SISMAD include intestinal ischemia and necrosis, progression of dissection with enlarged false lumen, pre-rupture or rupture of a dissecting aneurysm, symptomatic failure with conservative treatment, thrombosis within the true lumen, and dissections unsuitable for or failing endovascular therapy. The goal of surgical treatment is to restore the superior mesenteric artery blood supply and resect the ischemic and necrotic intestinal segment. Major surgical approaches include thrombectomy, simple small bowel resection, superior mesenteric artery endarterectomy, aorta-superior mesenteric artery bypass, iliac artery-superior mesenteric artery bypass, and right gastroepiploic artery-superior mesenteric artery bypass. However, due to the high surgical trauma, operational difficulty, and high complication rates, open surgery has gradually been replaced by endovascular therapy. The first successful percutaneous endoluminal stent implantation for SISMAD was reported in 2000. Since then, with advances in stent materials and the rapid development and widespread adoption of endovascular technology, an increasing number of SISMAD patients have been treated with endovascular therapy, achieving satisfactory results due to its advantages of minimal invasiveness, high success rate, few complications, and rapid recovery. Endovascular treatment options primarily include single-layer bare-metal stent implantation, double-layer bare-metal stent implantation, dense mesh stent implantation, catheter-directed thrombolysis, covered stent implantation, and stent-assisted coil embolization. The primary goal of endovascular therapy is to rapidly restore blood flow to the superior mesenteric artery lumen, prevent dissection thrombosis or rupture, promote dissection repair, and thus avert intestinal ischemia and necrosis. However, the appropriate timing for endovascular intervention in SISMAD remains controversial. Clinically, early intervention for SISMAD is primarily based on imaging findings and symptoms of intestinal dysfunction. However, both superior mesenteric artery dissection and intestinal ischemia can cause abdominal pain, making it difficult to accurately determine whether a patient is currently suitable for endovascular surgery. When symptoms of intestinal dysfunction such as intestinal wall thickening, gas accumulation, and insufficient mucosal reinforcement appear, patients have often missed the best time for endoluminal treatment. If conservative treatment is abandoned too early and switched to endoluminal treatment, patients may be at risk of stent restenosis and stent-covered branch vessel stenosis. With the early and mid-term research results of endoluminal treatment, symptom-oriented, conservative treatment-based treatment strategies are facing challenges. Therefore, there is an urgent need to develop new indications to determine the timing of endoluminal or surgical treatment for SISMAD, so that clinicians can choose a reasonable individualized treatment method based on the patient's specific condition, reducing complications while improving patient prognosis.

[0005] Metabolomics is a technology that uses mass spectrometry, chromatography, high-resolution nuclear magnetic resonance, mass spectrometry-chromatography, liquid chromatography-mass spectrometry, and other methods to detect small molecule metabolites in high throughput. Metabolomics includes two methods: targeted and non-targeted. Targeted research studies certain pre-defined metabolites, while non-targeted research does not pre-determine metabolites. Instead, it focuses on all metabolites as a whole and discovers metabolites that differ between the two groups of subjects. Metabolites play an important role in the occurrence and development of various diseases. Because metabolomics can detect hundreds of small molecule metabolites simultaneously, it is used in various fields, including cancer, hypertension, arterial dissection, and other diseases. Metabolomics studies have revealed that differential metabolites between patients with acute aortic dissection and those with coronary artery disease are histidine, glycine, serine, citrate, ornithine, hydroxyproline, proline, and sarcosine; differential metabolites between patients with chronic aortic dissection and those with acute aortic dissection are glutamate and phenylalanine; and differential metabolites between patients with coronary artery disease and those with chronic aortic dissection are citrate, aminobutyric acid, glutamate, and cysteine. Therefore, amino acid profiling is expected to become a novel, non-invasive diagnostic method for aortic dissection, enabling early diagnosis and potentially saving lives. Metabolomics-based approaches can identify signature metabolites for early diagnosis, targeted treatment, and improved prognosis of aortic dissection. SISMAD is a type of aortic dissection. Metabolomics analysis of metabolites in SISMAD patients identifies and screens potential biomarkers, which will play a crucial role in assessing intestinal dysfunction and determining the timing of early endovascular treatment in SISMAD patients. Summary of the Invention

[0006] The purpose of the present invention is to provide a biomarker for identifying intestinal dysfunction related to superior mesenteric artery dissection, providing a new idea for selecting the timing of intraluminal treatment of SISMAD.

[0007] The technical solution adopted by the present invention to solve its technical problem is: A biomarker for identifying intestinal dysfunction associated with superior mesenteric artery dissection, wherein the biomarker is lysophospholipid.

[0008] The lysophospholipids include lysophosphatidylcholine (LySoPC) and lysophosphatidylethanolamine (LySoPE).

[0009] Application of lysophospholipids as biomarkers for identifying intestinal dysfunction associated with superior mesenteric artery dissection.

[0010] The beneficial effect of the present invention is that the present invention can identify patients with intestinal dysfunction caused by SISMAD at an early stage through the biomarker, and can provide timely surgical intervention (mainly stent implantation) to avoid malignant consequences such as intestinal necrosis, large-area intestinal resection, short bowel syndrome, infection and death. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Figure 1 shows the results of differentially expressed metabolites screening between patients without and with intestinal dysfunction. A. The contributions of PC1 and PC2 were 30.38% and 9.81%, respectively. B. OPLS-DA showed good differentiation between SISMAD and the healthy control group, with PC1 contributing 14.47% and PC2 contributing 23.05%. C. OPLS-DA was validated using a permutation test (the x-axis represents the correlation coefficient between the original and permuted y variables), and the y-axis represents the R² and Q² values. D. Volcano plot of different metabolites. Each dot in the volcano plot represents a metabolite. Blue dots represent differentially downregulated and upregulated metabolites, red dots represent differentially upregulated metabolites, and gray dots represent metabolites detected but not significantly different. E. Matchstick plot of the top 20 differentially upregulated and downregulated metabolites. The color of the dots represents upregulation and downregulation, with blue representing downregulation and red representing upregulation. The length of the bars represents the log² (fold-change) magnitude, and the size of the dots represents the VIP value.

[0012] Figure 2 shows differential metabolite analysis. A. Heatmap of differential metabolite cluster analysis. B. Correlation analysis of differential metabolites. Red indicates positive correlation, blue indicates negative correlation. The size of the dot indicates the strength of the correlation. C. Chordal plot of different metabolites. The color of the dot represents the different metabolites, and the size of the dot represents the log2 (fold change) value. The thickness of the line between the dots indicates the degree of correlation between the two metabolites (red indicates positive correlation; blue indicates negative correlation).

[0013] Figure 3 The following are differential metabolites between patients without intestinal dysfunction and those with intestinal dysfunction. A. Main classification information of the KEGG pathway. The horizontal axis represents the percentage of annotated metabolites in a specific pathway compared to all annotated metabolites. The vertical axis represents the functional pathway of the second-level KEGG classification, and the rightmost axis represents the first-level pathway to which the pathway belongs. B. Bubble chart of KEGG enrichment. The horizontal axis is x / y (number of differential metabolites in the corresponding metabolic pathway / total number of metabolites identified in the pathway). The larger the value, the higher the enrichment of differential metabolites in the pathway. The color of the dot represents the p-value of the hypergeometric test. The smaller the value, the higher the reliability of the test and the greater the statistical significance. The size of the dot represents the number of different metabolites in the corresponding pathway. The larger the dot, the more differential metabolites in the pathway. CE. GSEA analysis of differential metabolites.

[0014] Figure 4The following are receiver operating characteristic (ROC) curves for different lysophosphatidylethanolamines. The area under the ROC curve is called the Area Under the Curve (AUC). The AUC is used to assess the sensitivity and specificity of a biomarker in predicting the occurrence of an event. The sensitivity and specificity of each metabolite are determined by the optimal threshold of the ROC curve. When the AUC value is 0.5, the biomarker is completely ineffective in predicting the occurrence of an event and has no predictive value. When the AUC value is >0.5, the closer the AUC value is to 1, the higher the predictive accuracy. DETAILED DESCRIPTION

[0015] The technical solution of the present invention is further described in detail below through specific embodiments.

[0016] In the present invention, unless otherwise specified, the raw materials and equipment used can be purchased from the market or are commonly used in the art. The methods in the following examples, unless otherwise specified, are all conventional methods in the art.

[0017] Example 1: 1. Research subjects: Thirty patients diagnosed with SISMAD were screened from the First Affiliated Hospital of Zhejiang University School of Medicine (Ethics Approval No. IT20240766B-R1). Twenty-four patients were assigned to the symptomatic group (SY) and six to the asymptomatic group (ASY). Symptoms were defined as abdominal pain, bloating, constipation, and other symptoms associated with intestinal dysfunction.

[0018] 2. Sample processing: 10 ml of peripheral venous blood was collected from the subjects and placed into dedicated EDTA anticoagulant tubes. The tubes were centrifuged at 3000 rpm for 10 min at room temperature (Thermo ST 16R, USA). After centrifugation, the upper layer of pale yellow plasma was collected and aliquoted into 1 ml cryovials, with each tube containing ≥ 200 ml of plasma. The tubes were then rapidly frozen in liquid nitrogen for 30 seconds, transferred to the biobank, and stored in a -80°C freezer to avoid repeated freeze-thaw cycles until sequencing.

[0019] 3. Sequencing process: This project uses liquid chromatography-mass spectrometry (LC-MS) technology for untargeted metabolomics research. The experimental process primarily involves: First, biological samples (such as blood, urine, and cell extracts) are pretreated to remove proteins and precipitated impurities to improve analytical accuracy. Common pretreatment methods include organic solvent precipitation, ultrafiltration, organic solvent combined with high-speed centrifugation, and solid-phase extraction. The pretreated sample is then injected into a liquid chromatography system, where the metabolites are separated using the chromatographic columns. Different separation modes (such as reversed-phase, normal-phase, and ion exchange) can be used to separate metabolites with different properties. During chromatographic separation, the separated metabolites are detected by a mass spectrometer. The mass spectrometer measures the mass-to-charge ratio (m / z) and ion intensity of the metabolites, providing structural information and relative abundance. Processing and analysis of the mass spectrometry data allows for the identification and characterization of different metabolites, as well as qualitative and quantitative analysis. Common data analysis methods include mass spectral database comparison, metabolic pathway analysis, and statistical analysis.

[0020] 4. Bioinformatics Analysis First, the raw data from the mass spectrometer was converted to mzXML format using ProteoWizard. XCMS software was then used for peak extraction, alignment, and retention time correction. Finally, the total peak area extracted within the sample was corrected, and peaks with a missing rate greater than 50% in each group of samples were filtered out. The corrected and filtered results were used to obtain metabolite identification information by searching the Novo local database. Subsequently, multivariate statistical analysis of the metabolites was performed, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), which revealed differences in metabolic patterns among different groups; hierarchical clustering (HCA) and metabolite correlation analysis, which revealed relationships between samples and between metabolites; and functional analysis such as metabolic pathways to explain the biological significance of metabolites.

[0021] Figure 1 Metabolic profile test results of patients without intestinal dysfunction (ASY) and with intestinal dysfunction (SY): Figure 1 A. Figure 1 B. Figure 1 C shows that there are significant differences in metabolic profiles between the ASY and SY groups, and the model is well constructed, and the data are suitable for further differential analysis; Figure 1 D is a volcano plot, illustrating the number of differential metabolites up-regulated and down-regulated in the negative ion mode; Figure 1 E illustrates the specific differential metabolites that were up-regulated and down-regulated.

[0022] Figure 2 Differential metabolite analysis. Figure 2A The distribution of differential metabolites in different samples is displayed through heat maps; Figure 2B 、 Figure 2CThe correlation between differential metabolites was illustrated, among which multiple lysophospholipids (including lysophosphatidylcholine, lysophosphatidylethanolamine, etc.) showed significant correlation.

[0023] Figure 3 Differential metabolites between the group without intestinal dysfunction and patients with intestinal dysfunction. Figure 3 A. KEGG pathway classification information. The horizontal axis represents the percentage of annotated metabolites in a specific pathway relative to all annotated metabolites. The vertical axis represents the functional pathway of the KEGG second-level classification, with the first-level pathway to which the pathway belongs being the rightmost axis. Figure 3 B. Bubble plot of KEGG enrichment. The horizontal axis is x / y (number of differential metabolites in the corresponding metabolic pathway / total number of metabolites identified in the pathway). Larger values ​​indicate greater enrichment of differential metabolites in the pathway. The color of the dots indicates the p-value of the hypergeometric test. Smaller values ​​indicate higher test reliability and greater statistical significance. The size of the dots indicates the number of differential metabolites in the corresponding pathway. Larger dots indicate more differential metabolites in the pathway. Figure 3 CE and GSEA analysis of differential metabolites. Figure 3 A shows that the metabolic pathways of SISMAD patients with intestinal dysfunction (SY group) were changed compared with those in the ASY group, especially lipid metabolism; Figure 3 B shows that the SY group showed significant changes in lysophospholipid metabolism compared with the ASY group, suggesting the potential diagnostic value of lysophospholipids in SISMAD patients with intestinal dysfunction and prompting further analysis; Figure 3 CE demonstrated the important role of metabolic pathways (including the entire metabolic environment, tryptophan and amino acid metabolism) in SISMAD patients with intestinal dysfunction.

[0024] Figure 4 ROC curves of different lysophospholipids. Figure 4 The results showed that different lysophospholipids could significantly differentiate the presence of intestinal dysfunction in SISMAD. The lowest AUC value for LysoPE was 0.729, while the highest AUC value for LysoPE reached 0.986, close to 1, demonstrating significant diagnostic value. The AUC value for LysoPC was 0.826, demonstrating reasonable diagnostic accuracy.

[0025] Taking the cut-off values ​​(calculated by MetaboAnalyst 6.0) of LysoPE(0:0 / 14:0), LysoPE(20:3(11Z,14Z,17Z)), and LysoPE(18:3(6Z,9Z,12Z)) (respectively representing a specific form of LysoPE, defined by its fatty acid chain structure) as examples, LysoPE(0:0 / 14:0): if lower than 21405174.39, SISMAD intestinal dysfunction may exist; LysoPE(20:3(11Z,14Z,17Z)): if lower than 923371.58, SISMAD intestinal dysfunction may exist; LysoPE(18:3(6Z,9Z,12Z)): if lower than 10152836.15, SISMAD intestinal dysfunction may exist.

[0026] The embodiment described above is only a preferred solution of the present invention and does not limit the present invention in any form. Other variations and modifications are possible without exceeding the technical solution described in the claims.

Claims

1. A biomarker for identifying intestinal dysfunction associated with superior mesenteric artery dissection, characterized in that: The biomarker is lysophospholipid.

2. The biomarker according to claim 1, characterized in that The lysophospholipids include lysophosphatidylcholine and lysophospholipid ethanolamine.

3. Application of lysophospholipids as biomarkers for identifying intestinal dysfunction associated with superior mesenteric artery dissection.

4. The use according to claim 3, characterized in that The lysophospholipids include lysophosphatidylcholine and lysophospholipid ethanolamine.

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