A model building method for identifying abnormal metabolic changes in deep vein thrombosis

Abstract

The application discloses a model establishing method for identifying abnormal metabolic changes of deep vein thrombosis, and comprises the following steps: obtaining blood samples of deep vein thrombosis patients and healthy people without thrombosis, and obtaining plasma samples after pretreatment; extracting metabolite samples from the plasma samples; performing chromatographic separation on the metabolite samples, then performing mass spectrometric analysis to form mass spectrometric data, and then performing data analysis based on metabolomics; and obtaining metabolites through binary logistic regression, so as to establish an identification model.

Description

Technical Field

[0001] This invention relates to the field of deep vein thrombosis in biomedicine, and more particularly to a method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis. Background Technology

[0002] Venous thrombosis is the third leading cause of cardiovascular disease worldwide. Deep vein thrombosis (DVT) is the primary clinical manifestation of venous thrombosis, accounting for approximately two-thirds of all cases. The incidence of DVT is second only to coronary heart disease and hypertension, and its mortality rate is second only to cancer and myocardial infarction. Based on the location of the thrombus, DVT can be divided into calf muscle venous thrombosis (MCVT) and popliteal vein thrombosis (PVTE). DVT generally does not present with obvious clinical symptoms, especially MCVT, making it difficult for patients and doctors to detect and recognize. This can delay treatment, increase the risk of developing into pulmonary embolism, and thus pose a fatal threat.

[0003] Deep vein thrombosis (DVT) is triggered by platelet activation and aggregate formation. Platelets in the thrombus environment face metabolic challenges due to limited supply of nutrients and other essential substances. However, platelet activation metabolism is driven by energy-intensive processes, including rapid protein synthesis and cytoskeleton remodeling, suggesting that platelets must modulate their metabolic pathways to support thrombus formation. Resting platelets derive their energy primarily from glycolysis, and aerobic glycolysis promotes platelet activation, indicating that DVT may be closely related to metabolic activities such as glycolysis. Metabolomics is a powerful tool for analyzing biological samples and a unique tool for understanding the metabolic activities of diseases. Metabolomics has been used to identify disease-related alterations in energy metabolism and has become an important tool for elucidating disease pathophysiology and developing biomarkers.

[0004] Existing methods for identifying abnormal metabolic changes in deep vein thrombosis follow a sequential assessment process, including assessing clinical pre-screening probability, detecting D-dimer, and imaging tests.

[0005] Due to the heterogeneity of symptoms and signs, diagnosing deep vein thrombosis (DVT) based solely on clinical presentation is unreliable. Clinical prognostic probability is inherently subjective, making its results clearly unreliable. While D-dimer testing is highly sensitive, it lacks specificity and is affected by various factors such as cancer, pregnancy, and recent orthopedic surgery. Therefore, D-dimer alone is insufficient for diagnosing DVT. Combining assessment of clinical prognostic probability with D-dimer testing can improve the accuracy of DVT diagnosis. However, definitive diagnosis requires imaging examinations. Clearly, performing angiography on all patients is impractical due to its invasiveness, potential allergic reactions to contrast agents, and high cost. Clinically, less than 20% of suspected DVT patients receive a definitive diagnosis.

[0006] Therefore, existing technologies have limitations in identifying abnormal metabolic changes in deep vein thrombosis, including lack of specificity, potential allergic reactions to contrast agents, and high cost. Summary of the Invention

[0007] In view of the aforementioned deficiencies of the prior art, the technical problem to be solved by the present invention is that the existing technology has limitations in identifying abnormal metabolic changes in deep vein thrombosis (DVT), including lack of specificity, potential allergic reactions to contrast agents, and high cost. The present invention provides a model-building method for identifying abnormal metabolic changes in DVT, achieving accurate diagnosis of DVT (AUC = 0.7947), and further identifying two subtypes of DVT (AUC = 0.9143 for the MCVT diagnostic model and AUC = 0.8333 for the PVTE diagnostic model).

[0008] To achieve the above objectives, the present invention provides a method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis, comprising the following steps:

[0009] Blood samples were obtained from patients with deep vein thrombosis and healthy individuals without thrombosis, and plasma samples were obtained after preprocessing.

[0010] Metabolite samples were extracted from plasma samples;

[0011] Metabolite samples are separated by chromatography, then mass spectrometry is performed to generate mass spectrometry data, and then data analysis is carried out based on metabolomics.

[0012] Metabolites are obtained through binary logistic regression, thereby establishing a recognition model.

[0013] Further, blood samples were obtained from patients with deep vein thrombosis and healthy individuals without thrombosis. After pretreatment, plasma samples were obtained. Specifically, blood samples from patients with deep vein thrombosis and healthy individuals without thrombosis were collected into K2-EDTA tubes, allowed to stand for 30 minutes, and then centrifuged at 3,000 rpm for 15 minutes at room temperature. The supernatant was then collected to obtain the plasma fraction, and the supernatant was evenly distributed into EP tubes and stored at -80°C until analysis.

[0014] Further, metabolite samples are extracted from the plasma samples, specifically as follows:

[0015] After thawing the plasma sample at 4°C, gently vortex to ensure thorough mixing;

[0016] Add 300 μL of methanol to an EP tube containing 100 μL of plasma, vortex for 1 minute, and let stand for 5 minutes to form a mixture;

[0017] The mixture was centrifuged at 13,000 rpm for 15 minutes at a controlled temperature of 4°C.

[0018] After centrifugation, 200 μL of the supernatant was added to the sample vial for testing.

[0019] Furthermore, 10 μL of each sample was extracted and aggregated into a quality control (QC) sample, with one QC sample added for every 10 plasma samples.

[0020] Furthermore, the metabolite samples were separated using ACQUITY. TM Premier CSH Phenylexyl Columns and Atlantis TM Chromatographic separation was performed using a Premier BEH Z-HILIC column.

[0021] Furthermore, ACQUITY TM In the Premier CSH phenylhexyl column analysis, the mobile phases were 5 mM ammonium acetate aqueous solution and acetonitrile, and chromatographic separation was performed under the set gradient elution conditions.

[0022] Furthermore, Atlantis TM The mobile phase used in the Premier BEH Z-HILIC column analysis was a 0.1% formic acid aqueous solution and a 0.1% formic acid acetonitrile solution, and chromatographic separation was performed under the set gradient elution conditions.

[0023] Furthermore, the column temperature was set to 40℃, the flow rate to 0.3 mL / min, and the injection volume to 2 μL.

[0024] Furthermore, the mass spectrometry analysis was performed using a full scan mode with a resolution of 70,000, a mass range of m / z 70–1050, and an AGC target value of 3e. 6 .

[0025] Technical effect

[0026] The present invention provides a method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis. The established model can achieve high accuracy, high sensitivity, and time-saving in judging the formation of deep vein thrombosis.

[0027] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention for establishing a model to identify abnormal metabolic changes in deep vein thrombosis.

[0029] Figure 2Box plots of differential metabolites of DVT, MCVT, and PVTE are provided in a preferred embodiment of the present invention for establishing a model to identify abnormal metabolic changes in deep vein thrombosis.

[0030] Figure 3 This is a schematic diagram of the metabolic pathways of differential metabolites in DVT, MCVT, and PVTE, representing a preferred embodiment of the present invention for establishing a model to identify abnormal metabolic changes in deep vein thrombosis.

[0031] Figure 4 This is a schematic diagram illustrating the specificity and sensitivity evaluation of a model in a preferred embodiment of the present invention for establishing a model to identify abnormal metabolic changes in deep vein thrombosis. Detailed Implementation

[0032] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0033] In the following description, specific details, such as particular internal procedures and techniques, are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will appreciate that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of the invention with unnecessary detail.

[0034] like Figure 1 As shown, this invention provides a method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis, comprising the following steps:

[0035] 1. Obtain blood samples from patients with deep vein thrombosis and healthy individuals without thrombosis, and obtain plasma samples after pretreatment. Specifically, collect blood samples from patients with deep vein thrombosis and healthy individuals without thrombosis into K2-EDTA tubes, let stand for 30 minutes, and then centrifuge at 3,000 rpm for 15 minutes at room temperature. Then collect the supernatant to obtain the plasma fraction, and then evenly distribute the supernatant into EP tubes and store at -80°C until analysis.

[0036] 2. Extracting metabolite samples from plasma samples; specifically including the following steps:

[0037] After thawing the plasma sample at 4°C, gently vortex to ensure thorough mixing;

[0038] Add 300 μL of methanol to an EP tube containing 100 μL of plasma, vortex for 1 minute, and let stand for 5 minutes to form a mixture;

[0039] The mixture was centrifuged at 13,000 rpm for 15 minutes at a controlled temperature of 4°C.

[0040] After centrifugation, 200 μL of the supernatant was added to the sample vial for testing.

[0041] 10 μL of each sample was extracted and collected into a QC sample, with one QC sample added for every 10 plasma samples.

[0042] 3. Metabolite samples were separated by chromatography, followed by mass spectrometry analysis to generate mass spectrometry data, which was then analyzed based on metabolomics. The metabolite samples were separated using ACQUITY. TM Premier CSH Phenylexyl Columns and Atlantis TM Chromatographic separation was performed using a Premier BEH Z-HILIC column. TM In the Premier CSH phenylhexyl column analysis, the mobile phases were 5 mM ammonium acetate aqueous solution and acetonitrile, and chromatographic separation was performed under set gradient elution conditions. Atlantis TM The mobile phase used in the Premier BEH Z-HILIC column analysis was a 0.1% formic acid aqueous solution and a 0.1% formic acid acetonitrile solution, with chromatographic separation performed under pre-defined gradient elution conditions. The column temperature was set at 40℃, the flow rate at 0.3 mL / min, and the injection volume at 2 μL. Mass spectrometry analysis was performed in full scan mode with a resolution of 70,000, a mass range of m / z 70–1050, and an AGC target value of 3e. 6 .

[0043] 4. Obtain metabolites through binary logistic regression, thereby establishing a recognition model.

[0044] This invention extracts plasma metabolites from 23 deep vein thrombosis (DVT) patients and 18 control subjects, and performs UHPLC-MS / MS analysis. The subjects can be further divided into two subgroups: a medulloblastic thrombosis (MCVT) group (14 MCVT patients and 10 control subjects) and a vegetative-transmitted thrombosis (PVTE) group (6 MCVT patients and 5 control subjects). Metabolite identification is performed using UHPLC-MS / MS analysis, and differentially expressed metabolites within the DVT group and subgroups are determined using univariate analysis. Enrichment analysis of the differentially expressed metabolites yields differential metabolic pathway results. To screen for candidate biomarkers, a diagnostic model is established using binary logistic regression on the differentially expressed metabolites, and ROC analysis is used to evaluate the diagnostic efficacy of the model. Details are as follows:

[0045] 1. Sample collection

[0046] All participants in this study were from Luodian Hospital in Shanghai and were in the acute phase of cerebral infarction, including 23 patients with deep vein thrombosis (DVT) and 18 control participants without thrombosis (Ctrl). The inclusion criteria were: age over 18 years; meeting the diagnostic criteria for acute stroke established at the Fourth National Conference on Cerebrovascular Diseases of the Chinese Medical Association; meeting the diagnostic criteria for lower extremity deep vein thrombosis in the "Guidelines for the Diagnosis and Treatment of Deep Vein Thrombosis" published by the Vascular Surgery Branch of the Chinese Medical Association; and obtaining informed consent from the clinical medical ethics committee. DVT was further divided into MCVT and PVTE groups. Subjects with non-thromboembolic diseases, including malignant tumor embolism, amniotic fluid embolism, fat embolism, systemic infection, myeloproliferative disorders, systemic lupus erythematosus, recent surgery or trauma, fixation, pregnancy, postpartum period, use of hormones, central venous catheters, atrial fibrillation, polycythemia, hereditary protein C or protein S deficiency, abnormal fibrinogenemia, activated protein C resistance, hyperhomocysteinemia, antithrombin III deficiency, antiphospholipid antibody syndrome, and liver and kidney dysfunction, are not included in this study.

[0047] Blood samples were collected into K2-EDTA tubes and allowed to stand for 30 minutes, then centrifuged at 3,000 rpm for 15 minutes at room temperature. The supernatant was then collected to obtain the plasma fraction. The supernatant was then evenly distributed into EP tubes and stored at -80°C until analysis.

[0048] 2. Extraction of plasma metabolites

[0049] After thawing plasma samples at 4°C, they were gently vortexed to ensure homogeneity. Furthermore, to assess the overall stability of the experiment, 10 μL of each sample was drawn and pooled into a QC sample. 300 μL of methanol was added to an EP tube containing 100 μL of plasma, vortexed for 1 minute, and allowed to stand for 5 minutes. The mixture was then centrifuged at 13,000 rpm for 15 minutes at a controlled temperature of 4°C. After centrifugation, 200 μL of the supernatant was added to a sample vial for analysis. During sample injection, one QC sample was added for every 10 plasma samples. The metabolite extract was the supernatant after plasma processing.

[0050] 3. UHPLC-MS / MS analysis

[0051] The embodiments of this invention employ a Vanquish UHPLC system for separation. ACQUITY was used respectively. TM PremierCSH phenylhexyl column (1.7 μm, 2.1*100 mm, Waters, Milford, MA, USA) and Atlantis TMChromatographic separation was performed using a PremierBEH Z-HILIC column (2.5 μm, 2.1*100 mm, Waters, Milford, MA, USA). The mobile phases used in the phenylhexyl column analysis were 5 mM ammonium acetate aqueous solution (A) and acetonitrile (B), with gradient elution conditions set as shown in Table 1.

[0052] Table 1. Elution gradient of mobile phase for phenylhexyl chromatography column

[0053]

[0054] The mobile phase used in the Z HILIC column analysis was 0.1% formic acid aqueous solution (A) and 0.1% formic acid acetonitrile solution (B). The gradient elution conditions are set as shown in Table 2.

[0055] Table 2. Elution gradient of Z HILIC column mobile phase

[0056]

[0057] Throughout the analysis, the column temperature was set at 40℃, the flow rate at 0.3 mL / min, and the injection volume at 2 μL. SPSS version 26.0 software (IBM, Armonk, NY, USA) was used to determine the sample injection sequence to minimize its impact.

[0058] Mass spectrometry analysis was performed using a Q-Exactive Plus Orbitrap MS mass spectrometer equipped with a heated electrospray ionization (HESI) source. Full scan mode was used with a resolution of 70,000 m / z and a mass range of 70–1050 m / z. The AGC target value was 3e. 6 Parallel reaction monitoring (PRM) data for quality control samples were obtained at a resolution of 17,500 and an AGC target value of 2e. 5 The results were obtained under the following conditions. Mass spectrometry detection was performed using positive and negative ion modes. The ion source parameters were set as follows: positive ion injection voltage was 3.8 kV, negative ion injection voltage was 3.2 kV; sheath gas and auxiliary gas were nitrogen (purity ≥99.99%), with flow rates of 30 arb and 10 arb, respectively; capillary temperature was 320 °C; and auxiliary gas heater temperature was 300 °C.

[0059] 4. Metabolomics data analysis

[0060] Mass spectrometry data were processed using Thermo Scientific Xcalibur Workstation version 4.0 (Thermo Fisher Scientific, Rockford, IL, USA) and Compound Discover version 3.3 software (Thermo Fisher Scientific, Rockford, IL, USA). Raw data were imported into Compound Discoverer (software for metabolite identification) for peak alignment, retention time correction, peak area extraction, and metabolite structure identification. Differential metabolites were screened using p-values ​​obtained from univariate statistical analysis (independent samples t-test), with p < 0.05 used as a threshold to determine if statistically significant differences existed. MetaboAnalyst version 5.0 (http: / / www.metaboanalyst.ca) was used to analyze the metabolic pathways of DVT, MCVT, and PVTE. In clinical practice, using a large number of metabolites for diagnosis is often impractical. A simplified and reliable metabolite model was established through binary logistic regression. In SPSS software, identified differentially expressed metabolites were included in the collinearity diagnosis. Results showed that the VIF values ​​of all independent variables were <3, indicating the absence of multicollinearity among the independent variables. The independent variable selection method for binary logistic regression employed a forward stepwise (LR) method based on maximum likelihood estimation, with the second-order scored dependent variables being (0 = Ctrl, 1 = DVT), (0 = Ctrl, 1 = MCVT), and (0 = Ctrl, 1 = PVTE). To evaluate the diagnostic accuracy of the model, additional ROC analysis was performed; an AUC value exceeding 0.75 was considered excellent for diagnostic performance.

[0061] The model established in the embodiments of this invention was applied to differentially metabolites associated with deep vein thrombosis (DVT), and eight differentially metabolites associated with DVT were identified, including L-aspartate, adenine, hypoxanthine, sedoheptulose 7-phosphate, D-glyceraldehyde 3-phosphate, D-fructose 6-phosphate, D-erythrose 4-phosphate, and 10-pentadecanoic acid. Three differentially metabolites associated with central venous thrombosis (MCVT) were also identified, including L-aspartate, adenine, and hypoxanthine. Six differentially metabolites associated with vegetative thrombosis (PVTE) were identified, including hypoxanthine, sedoheptulose 7-phosphate, D-ribulose 5-phosphate, D-glyceraldehyde 3-phosphate, D-fructose 6-phosphate, and D-erythrose 4-phosphate (Table 3). Hypoxanthine is a differentially metabolite common to DVT, MCVT, and PVTE. Box plots of the differentially metabolites are shown in Table 3. Figure 2 .

[0062] Table 3. Differential metabolites of DVT, MCVT and PVTE.

[0063]

[0064]

[0065] Differential metabolites from deep vein thrombosis (DVT) are involved in 13 metabolic pathways, primarily the pentose phosphate pathway, fructose and mannose metabolism, glycolysis / gluconeogenesis, and purine metabolism. Differential metabolites from massive venous thrombosis (MCVT) involve 7 metabolic pathways, primarily purine metabolism, beta-alanine metabolism, and the metabolism of alanine, aspartate, and glutamate. Differential metabolites from polymorphonuclear pulmonary thrombosis (PVTE) are involved in 8 metabolic pathways, primarily the pentose phosphate pathway, fructose and mannose metabolism, glycolysis / gluconeogenesis, and purine metabolism. These differential metabolites are enriched in pathways including energy metabolism, purine metabolism, and amino acid metabolism. Figure 3 (See Table 4). Further analysis revealed that these differential metabolites were mainly enriched in amino acid metabolism and glucose metabolism in DVT, in amino acid metabolism in MCVT, and in glucose metabolism in PVTE.

[0066] Table 4. Metabolic pathways of differentially metabolites in DVT, MCVT and PVTE.

[0067]

[0068]

[0069] A diagnostic model was derived by integrating differential metabolite analysis and binary logistic regression analysis. In SPSS software, identified differential metabolites were included in the collinearity diagnosis. The results showed that the VIF values ​​of all independent variables were <3, indicating that there was no multicollinearity among the independent variables. The independent variable selection method for binary logistic regression adopted the forward stepwise (LR) method based on maximum likelihood estimation, with secondary scoring dependent variables of (0=Ctrl,1=DVT), (0=Ctrl,1=MCVT), and (0=Ctrl,1=PVTE). The results showed that L-aspartate effectively identified patients with deep vein thrombosis, adenine and hypoxanthine effectively identified patients with MCVT, and 5-phosphate-D-ribulose effectively identified patients with PVTE.

[0070] The final result of the deep vein thrombosis diagnostic model is P(DVT=1)=1 / (1+ez), z=3.474087-0.00000024679*L-aspartate. The final result of the MCVT diagnostic model is P(MCVT=1)=1 / (1+ez), z=7.835773-0.000005*adenine-0.0000000017483*hypoxanthine. The final result of the PVTE diagnostic model is P(PVTE=1)=1 / (1+ez), z=2.156471-0.00000010273*5-phosphate-D-ribulose.

[0071] Further evaluation of diagnostic performance was conducted using ROC curve analysis. Figure 4 The simplified diagnostic metabolites for deep vein thrombosis (DVT) effectively distinguished between 18 patients with Ctrls (chronic thrombosis) and 23 patients with deep vein thrombosis (DVT), with an AUC of 0.7947 (95% confidence interval (CI): 0.6492–0.9402). Specificity and sensitivity were 0.7222 and 0.8261, respectively. The simplified diagnostic metabolites for MCVT effectively distinguished between 10 patients with Ctrls (chronic thrombosis) and 14 patients with MCVT, with an AUC of 0.9143 (95% CI: 0.8046–1.0000). Specificity and sensitivity were 0.8000 and 0.8571, respectively. The simplified diagnostic metabolites for PVTE effectively distinguished between 5 patients with Ctrls (chronic thrombosis) and 6 patients with PVTE, with an AUC of 0.8333 (95% CI: 0.5304–1.0000). Specificity and sensitivity were 0.8000 and 1.000, respectively. The results showed that the diagnostic model could accurately identify deep vein thrombosis and had diagnostic characteristics that could distinguish between MCVT and PVTE.

[0072] This example demonstrates that thrombosis initially affects amino acid metabolism, particularly aspartate metabolism, leading to impaired protein synthesis and subsequently developing into more common and milder MCVT. If intervention is delayed, the thrombus worsens, potentially affecting glycolysis, the pentose phosphate pathway, and other metabolic pathways, resulting in altered energy metabolism and the development of PVTE.

[0073] In this embodiment of the invention, ultra-high performance liquid chromatography-mass spectrometry / mass spectrometry (UHPLC-MS / MS) metabolomics is used to identify differential metabolites associated with deep vein thrombosis and its subtypes, in order to overcome the limitations of imaging diagnosis, including high cost, low efficiency and potential allergic reactions.

[0074] Ultra-high performance liquid chromatography-mass spectrometry / mass spectrometry metabolomics analysis was used to identify differentially expressed metabolites of deep vein thrombosis (DVT), medium-terminal venous thrombosis (MCVT), and polymorphic venous thrombosis (PVTE) in plasma samples, aiming to develop diagnostic models that can differentiate these conditions. The identified differentially expressed metabolites improved the accuracy of deep vein thrombosis diagnosis. Metabolic pathway analysis showed that energy metabolism, purine metabolism, and amino acid metabolism are crucial in the development of deep vein thrombosis.

[0075] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis, characterized in that, Includes the following steps: Blood samples were obtained from patients with deep vein thrombosis and healthy individuals without thrombosis, and plasma samples were obtained after preprocessing. Metabolite samples were extracted from the plasma sample; The metabolite samples were separated by chromatography, then mass spectrometry was performed to generate mass spectrometry data, and then data analysis was performed based on metabolomics. Metabolites are obtained through binary logistic regression, thereby establishing a recognition model.

2. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 1, characterized in that, Blood samples were obtained from patients with deep vein thrombosis and healthy individuals without thrombosis. Plasma samples were obtained after pretreatment. Specifically, blood samples from patients with deep vein thrombosis and healthy individuals without thrombosis were collected into K2-EDTA tubes, allowed to stand for 30 minutes, and then centrifuged at 3,000 rpm for 15 minutes at room temperature. The supernatant was then collected to obtain the plasma fraction, and the supernatant was evenly distributed into EP tubes and stored at -80°C until analysis.

3. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 2, characterized in that, The specific steps for extracting metabolite samples from the plasma sample are as follows: After thawing the plasma sample at 4°C, gently vortex to ensure thorough mixing; Add 300 μL of methanol to an EP tube containing 100 μL of plasma, vortex for 1 minute, and let stand for 5 minutes to form a mixture; The mixture was centrifuged at 13,000 rpm for 15 minutes at a controlled temperature of 4°C. After centrifugation, 200 μL of the supernatant was added to the sample vial for testing.

4. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 3, characterized in that, 10 μL of each sample was extracted and pooled into a quality control (QC) sample, with one QC sample added for every 10 plasma samples.

5. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 1, characterized in that, The metabolite samples were separated using ACQUITY. TM Premier CSH Phenylexyl Columns and Atlantis TM Chromatographic separation was performed using a Premier BEH Z-HILIC column.

6. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 5, characterized in that, ACQUITY TM In the Premier CSH phenylhexyl column analysis, the mobile phases were 5 mM ammonium acetate aqueous solution and acetonitrile, and chromatographic separation was performed under the set gradient elution conditions.

7. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 5, characterized in that, Atlantis TM The mobile phase used in the Premier BEH Z-HILIC column analysis was a 0.1% formic acid aqueous solution and a 0.1% formic acid acetonitrile solution, and chromatographic separation was performed under the set gradient elution conditions.

8. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 5, characterized in that, The column temperature was set to 40℃, the flow rate to 0.3 mL / min, and the injection volume to 2 μL.

9. The method for establishing a model to identify abnormal metabolic changes in deep vein thrombosis as described in claim 1, characterized in that, Mass spectrometry analysis was performed using a full scan mode with a resolution of 70,000 and a mass range of m / z 70–1050. The AGC target value was 3e. 6 .

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