Metabolic marker for cerebral arterial thrombosis recurrence risk analysis and application thereof

Abstract

The invention provides a metabolic marker combination for cerebral arterial thrombosis recurrence risk analysis and application of the metabolic marker combination. According to the present invention, by developing the reagent or the product capable of detecting the expression level of the target metabolite, the recurrence of cerebral arterial thrombosis can be effectively analyzed, the target metabolism marker is selected from one or more combinations of carnitine C10: 1, carnitine C8: 0, carnitine C6: 0, carnitine C10: 0 and carnitine C12: 0, and the novel objective evaluation tool can be provided for the secondary prevention of cerebral arterial thrombosis patients, the important clinical transformation value is realized.

Description

Technical Field

[0001] This invention relates to the field of diagnostic testing technology, specifically to a combination of metabolic biomarkers for analyzing the risk of recurrence of ischemic stroke and their application. Background Technology

[0002] Ischemic stroke is the second leading cause of death worldwide, characterized by high incidence, high disability rate, and high recurrence rate. Although secondary prevention strategies (such as antiplatelet therapy and risk factor control) have reduced some of the recurrence risk, patient prognosis remains unsatisfactory: the recurrence rate within 3 months of onset is 1.9%-6%, rising to 7.2%-8.0% within 1 year, and reaching as high as 41% at 5 years. Recurrent stroke is often accompanied by more severe neurological deficits and mortality, leading to increased consumption of medical resources and a greater socioeconomic burden. This situation highlights the urgent need for accurate recurrence risk assessment and optimized individualized interventions.

[0003] Currently, widely used clinical assessment tools mainly include the Essen Rating Scale (ESRS) and imaging indicators. Clinical scoring systems like the ESRS stratify risk by integrating variables such as age and past medical history, but their predictive power is limited (AUC values ​​are mostly below 0.7), and they do not cover emerging risk factors such as metabolic disorders. Imaging techniques such as vascular wall magnetic resonance imaging (VWMRI) can assess plaque enhancement and stenosis rates, but they rely on large equipment, are complex to operate, and are expensive, making them difficult to implement in primary healthcare institutions. Furthermore, while research based on blood biomarkers (such as Lp-PLA2 and S100β proteins) has made progress, the sensitivity and specificity of single biomarkers are insufficient, and multi-dimensional integrated analysis is lacking. These limitations make it difficult for existing methods to meet the needs of dynamic monitoring of recurrence risk in the era of precision medicine.

[0004] Liquid chromatography-mass spectrometry (LC-MS) has become a core tool for discovering metabolic biomarkers due to its high sensitivity and broad spectral coverage. Its application in stroke research is primarily focused on acute-phase diagnosis, such as differentiating stroke subtypes through metabolites like lysine and kynurenine. However, current research largely focuses on screening diagnostic biomarkers, while the metabolic profile characteristics for recurrence risk remain unclear, and there is a lack of prognostic models based on longitudinal follow-up data. Traditional models often neglect the dynamic interactions of metabolic pathways and the time-event data characteristics, leading to unstable predictive efficacy.

[0005] Therefore, it is necessary to provide a combination and application of plasma metabolic biomarkers for analyzing the risk of recurrence of ischemic stroke, to accurately quantify individualized recurrence risk, and to provide efficient decision support for optimizing clinical secondary prevention strategies. Summary of the Invention

[0006] To address the aforementioned issues, this study systematically integrates metabolite data and follow-up information from patients with ischemic stroke, identifies five key metabolic biomarkers, and constructs a risk analysis model. This invention provides the following technical solution:

[0007] The present invention provides a plasma metabolic biomarker for analyzing the risk of recurrence of ischemic stroke, wherein the plasma metabolic biomarker is selected from at least one of carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0.

[0008] As a simplified approach, the plasma metabolic marker is selected from at least one of carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0.

[0009] As a simplified approach, the plasma metabolic marker is selected from at least one of carnitine C10:1, carnitine C8:0, carnitine C6:0, and carnitine C12:0.

[0010] As a simplified approach, the plasma metabolic markers include at least one of carnitine C10:1, carnitine C8:0, and carnitine C12:0.

[0011] As a simplified approach, the plasma metabolic markers include at least one of carnitine C10:1 and carnitine C8:0.

[0012] The present invention also provides the application of the metabolic biomarkers described above for analyzing the risk of recurrence of ischemic stroke in the preparation of kits for analyzing the risk of recurrence of ischemic stroke.

[0013] The present invention provides a kit comprising standards for metabolic biomarkers for analyzing the risk of recurrence of ischemic stroke, specifically including at least one standard selected from carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0.

[0014] The test kit may further include a solvent and an internal standard. Preferably, the solvent is methanol or a 50% aqueous solution of acetonitrile, and the internal standard is L-phenylalanine.

[0015] This invention also provides a method for screening metabolic biomarkers for ischemic stroke recurrence risk analysis, comprising the following steps: collecting samples from the ischemic stroke recurrence group and the ischemic stroke non-recurrence group respectively; using LC-MS to detect the samples from the ischemic stroke recurrence group and the ischemic stroke non-recurrence group, and obtaining candidate differential metabolites through discriminant analysis; performing receiver operating characteristic curve analysis on the differential metabolites and their combinations to determine metabolic biomarkers for ischemic stroke recurrence risk analysis and assessment.

[0016] The present invention also provides an LC-MS detection method, comprising the following steps:

[0017] (1) Sample processing

[0018] Blood samples were treated with methanol internal standard extract of L-phenylalanine and 50% acetonitrile aqueous solution to prepare test samples;

[0019] (2) Using the standard sample of the metabolic biomarker to be tested as an external standard, LC-MS detection was performed to analyze the content of the biomarker to be tested in the sample;

[0020] The metabolic biomarker to be tested is selected from at least one compound selected from carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0 and carnitine C12:0.

[0021] (3) Store and output the detection results.

[0022] In some embodiments, the conditions for LC-MS detection are:

[0023] Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm*100mm;

[0024] Mobile phase: Phase A is an aqueous solution containing 0.04% acetic acid, and Phase B is an acetonitrile solution containing 0.04% acetic acid; flow rate: 0.4 mL / min.

[0025] The elution gradient program is as follows:

[0026] At 0 min, the volume ratio of phase A to phase B was 95:5;

[0027] At 11.0 min, the volume ratio of phase A to phase B was 10:90;

[0028] At 12.0 min, the volume ratio of phase A to phase B was 10:90;

[0029] At 12.1 min, the volume ratio of phase A to phase B was 95:5;

[0030] At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0031] The beneficial effects of this invention are:

[0032] This invention compares the differences in plasma metabolomes between individuals with recurrent ischemic stroke and those without, and identifies five metabolic biomarkers for analyzing the risk of recurrent ischemic stroke. The area under the ROC curve (AUC) of a single metabolic biomarker is greater than 0.8, ranging from 0.89 to 0.931. The performance of combinations of multiple metabolic biomarkers is significantly better than that of a single metabolic biomarker, with AUC values ​​ranging from 0.915 to 0.934.

[0033] When using the five plasma metabolic markers of this invention for detection and diagnosis, the cost of assessing the risk of recurrence of ischemic stroke can be reduced, while the screening of high-risk groups for recurrence of ischemic stroke can be improved. Diagnosis can be achieved by blood testing alone, without the need for additional tissue samples. It can effectively replace or reduce the need for existing diagnostic modalities or methods such as imaging examinations and immunological tests, thereby improving the diagnostic rate and convenience, and has the advantages of clinical accessibility and cost-effectiveness.

[0034] The metabolites selected in this invention cover lipid metabolism, energy metabolism, and mitochondrial function, providing new directions for mechanism research and intervention target development. Attached Figure Description

[0035] Figure 1 This is an OPLS-DA statistical chart of the metabolites according to Example 1 of the present invention;

[0036] Figure 2 Differential metabolite volcano plots in the relapse and non-relapse groups;

[0037] Figure 3 Box plots of metabolite content obtained from screening in the relapse and non-relapse groups;

[0038] Figure 4 The ROC curve of the metabolite provided in Example 1;

[0039] Figure 5 The image shows the Kaplan-Meier survival analysis curve of the metabolite provided in Example 1. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can more clearly understand the present invention.

[0041] The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the specific embodiments of the invention without inventive effort are within the protection scope of the invention.

[0042] In the embodiments of the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the embodiments of the present invention, unless specifically specified, the technical means used are conventional means well known to those skilled in the art.

[0043] Key experimental reagents are shown in Table 1 below:

[0044] Table 1 Experimental Reagents

[0045] compound CAS number brand methanol 67-56-1 Merck Acetonitrile 75-05-8 Merck Acetic acid 64-19-7 Aladdin L-Phenylanine 63-91-2 isoreag

[0046] Key instrument information is shown in Table 2 below:

[0047] Table 2 Information on Experimental Instruments

[0048] name model brand HPLC-TOF-MS TripleTOF6600 SCIEX LC-MS / MS QTRAP6500+ SCIEX centrifuge 5424R Eppendorf Centrifugal concentrator CentriVap LABCONCO vortex mixer VORTEX-5 Kyllin-Be11

[0049] Example 1

[0050] This embodiment provides a method for screening plasma metabolic biomarkers for analyzing the risk of recurrence of ischemic stroke, comprising the following steps:

[0051] S1, Sample collection

[0052] With patient consent, this study collected peripheral venous blood plasma samples from 443 patients with non-recurrent ischemic stroke and 169 patients with recurrent ischemic stroke at the Clinical Medical Research Center. The ischemic stroke patients were from the follow-up ischemic stroke population. All samples were from patients with no history of other malignant tumors, other major systemic diseases, or a history of long-term medication use for chronic diseases.

[0053] Blood samples were collected in the early morning on an empty stomach. All plasma samples were centrifuged and stored at -80°C. Samples were thawed before each study for subsequent analysis.

[0054] S2, broadly targeted plasma metabolomics analysis

[0055] (1) Sample pretreatment

[0056] Remove the samples collected in step S1 from the -80℃ freezer and thaw them on ice until no ice remains (all subsequent operations must be performed on ice). After thawing, vortex for 10 seconds to mix, and add 50 μL of the sample to the corresponding numbered centrifuge tube. Add 300 μL of pure methanol internal standard extraction buffer (containing 100 ppm L-phenylalanine internal standard). Vortex for 5 minutes, let stand for 24 hours, and then centrifuge at 12000 rpm and 4℃ for 10 minutes. Collect 270 μL of the supernatant and concentrate for 24 hours. Add 100 μL of the reconstitution solution (composed of acetonitrile and water in a 1:1 volume ratio) for LC-MS / MS analysis. Take 20 μL of each sample and mix them to form a quality control sample (QC), which is collected every 15 samples.

[0057] (2) Detection of metabolites in samples

[0058] The liquid chromatography conditions were determined as follows: column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm*100mm; column temperature: 40℃; injection volume: 2μL.

[0059] Mobile phases: Phase A was an aqueous solution containing 0.04% acetic acid, and Phase B was an acetonitrile solution containing 0.04% acetic acid. The elution gradient program was as follows: 0 min, volume ratio of Phase A to Phase B 95:5; 11.0 min, volume ratio of Phase A to Phase B 10:90; 12.0 min, volume ratio of Phase A to Phase B 10:90; 12.1 min, volume ratio of Phase A to Phase B 95:5; 14.0 min, volume ratio of Phase A to Phase B 95:5. Flow rate: 0.4 mL / min.

[0060] The mass spectrometry conditions were determined as follows: electrospray ionization (ESI) temperature 500℃, mass spectrometry voltage 5500V (positive) or -4500V (negative), ion source gas I (GS I) 55psi, gas II (GS II) 60psi, curtain gas (CUR) 25psi, and collision-activated dissociation (CAD) parameter set to high.

[0061] In the triple quadrupole (Qtrap), each ion pair is detected by MRM mode scanning based on optimized declustering potential (DP) and collision energy (CE).

[0062] Samples were analyzed under defined liquid chromatography and mass spectrometry conditions: 20% of samples were randomly selected from both the recurrent ischemic stroke group and the non-recurrent ischemic stroke group. A metabolomics method combining enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) with multiple reaction monitoring (MRM) was used to construct a plasma metabolite database for ischemic stroke, integrating a local standard database. The collected plasma samples were analyzed using liquid chromatography-mass spectrometry coupled with the constructed ischemic stroke plasma metabolite database to obtain raw mass spectrometry data for each plasma sample.

[0063] (3) Preprocessing and integration of peak area in the spectrum

[0064] Based on a database of plasma-specific metabolites from ischemic stroke, mass spectrometry was used for qualitative and quantitative analysis of metabolites in samples. Liquid chromatography (LC) can separate metabolites of different molecular weights. A triple quadrupole multiple reaction monitoring (MRM) mode was used to screen for characteristic ions of each substance, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The sample mass spectrometry file was opened using MultiQuant software. The raw mass spectrometry data was preprocessed and corrected according to the mass-to-charge ratio and retention time. Peak integration and correction were performed. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Peaks with an S / N > 5 and a retention time shift not exceeding 0.2 min were retained. The relative content information of metabolites was obtained by calculating the peak area based on the mass spectrometry peak intensity. Finally, all integrated peak area data were exported and saved for further statistical analysis.

[0065] (4) Experimental quality control

[0066] By overlaying and analyzing the total ion chromatograms of mass spectrometry analysis of different QC samples, the repeatability of metabolite extraction and detection, i.e., technical repeatability, can be determined. The high stability of the instrument provides crucial assurance for data repeatability and reliability. The CV value, or Coefficient of Variation, is the ratio of the standard deviation to the mean of the original data, reflecting the degree of data dispersion. Using the Empirical Cumulative Distribution Function (ECDF), the frequency of CV values ​​for substances with values ​​less than the reference value can be analyzed. A higher proportion of substances with lower CV values ​​in the QC samples indicates more stable experimental data: a proportion of substances with CV values ​​less than 0.5 exceeding 85% indicates relatively stable experimental data; a proportion of substances with CV values ​​less than 0.3 exceeding 75% indicates very stable experimental data. Simultaneously monitoring the change in the CV value of the L-phenylalanine internal standard during detection, a change of less than 20% in the internal standard CV value indicates good instrument stability during the detection process.

[0067] (5) Data processing and analysis

[0068] All peak area integral data from the sample tests were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis. Figure 1 By establishing an orthogonal partial least squares discriminant analysis (OPLS-DA) model, we identified metabolites (VIP>1.0) that contribute significantly to the difference between patients with non-recurrent ischemic stroke and those with recurrent ischemic stroke. Figure 2 The size of the midpoint is marked as the VIP value, and the horizontal axis represents the fold change (FC) of metabolite expression levels between groups. Metabolites with VIP > 1.0 and FC > 1.5 or < 0.667 are selected. A T-test is then performed, with a P-value < 0.05 as the statistical significance criterion. Finally, metabolites with VIP > 1.0, P-value < 0.05, and FC > 1.5 or < 0.667 are identified as potential metabolic biomarkers for analyzing the risk of recurrent ischemic stroke.

[0069] The plasma metabolic biomarkers screened for potential ischemic stroke recurrence risk analysis described above were used to infer their molecular weight and molecular formula based on retention time, primary and secondary mass spectrometry, and compared with spectral information in a metabolite spectral database for qualitative identification. Finally, the structures of the metabolic biomarkers were verified by purchasing standards and comparing their molecular weight, chromatographic retention time, and corresponding multi-stage MS fragmentation spectra.

[0070] In this embodiment, five differentially expressed metabolites were screened using a binary logistic regression forward stepwise method to assess the risk of recurrence of ischemic stroke: carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0. Specific information on these metabolites is shown in Tables 3 and 4 below.

[0071] Table 3. Five plasma metabolic biomarkers used for risk analysis of recurrent ischemic stroke.

[0072]

[0073] Table 4. Metabolic differences between the ischemic stroke recurrence group and the ischemic stroke non-recurrence group.

[0074] Chinese name Difference multiple VIP Pvalue Carnitine C10:1 2.59 1.07 1.28E-22 Carnitine C8:0 2.76 1.14 1.14E-22 Carnitine C6:0 2.23 1.06 8.85E-24 Carnitine C10:0 2.77 1.13 5.68E-24 Carnitine C12:0 2.29 1.07 9.34E-26

[0075] From the above table and Figure 3 It can be seen that, compared with patients with non-recurrent ischemic stroke, the levels of carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0 metabolites were synchronously increased in the group of patients with recurrent ischemic stroke.

[0076] Receiver operating characteristic (ROC) curves were further used to analyze the performance of metabolites on the risk of recurrent ischemic stroke. The AUC values ​​for single and multiple metabolites used in combination for prediction are shown in Tables 5 and 6.

[0077] Table 5. AUC values ​​of single metabolites for the diagnosis of recurrent ischemic stroke.

[0078]

[0079]

[0080] Table 6. AUC values ​​for the combined use of any metabolite in the risk analysis of recurrence-free ischemic stroke.

[0081] Number of unions AUC Any two >0.8 Any three >0.9 Any four >0.9

[0082] The statistical results in the table above show that: Carnitine C8:0 and Carnitine C10:0, as individual differential metabolites, are highly effective in analyzing the risk of recurrence of ischemic stroke, with area under the ROC curve (AUC) greater than 0.9, indicating clinical significance. When these five differential metabolites are used to assess the risk of recurrence of ischemic stroke, the AUC further improves, and the combined AUC of all five for predicting the risk of recurrence of ischemic stroke reaches 0.934. (See [reference needed]). Figure 4 The following are some examples of preferred combinations of metabolic biomarkers and their model statistical results:

[0083] A diagnostic model for the risk of recurrence of ischemic stroke was constructed using carnitine C10:0 and carnitine C12:0. The combined AUC value of these two metabolic markers for diagnosing the risk of recurrence of ischemic stroke reached 0.927.

[0084] A diagnostic model for the risk of recurrent ischemic stroke was constructed using carnitine C10:1, carnitine C8:0, and carnitine C12:0. The combined AUC value of these three metabolic markers for diagnosing the risk of recurrent ischemic stroke reached 0.932.

[0085] A diagnostic model for the risk of recurrent ischemic stroke was constructed using carnitine C10:1, carnitine C8:0, carnitine C6:0, and carnitine C12:0. The AUC value of the model for diagnosing the risk of recurrent ischemic stroke using these four metabolic biomarkers combined reached 0.932.

[0086] Additionally, see Figure 5 Survival analysis was performed based on the recurrence time of ischemic stroke patients. Kaplan-Meier survival curves were plotted between different sample groups based on the differentially expressed metabolites, demonstrating that metabolic biomarkers can effectively distinguish high-risk individuals for ischemic stroke.

[0087] Example 2

[0088] This embodiment provides a diagnostic kit for diagnosing or monitoring the risk of recurrence of ischemic stroke, comprising:

[0089] (1) Standards of metabolic markers: carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0, individually packaged or mixed in packaging.

[0090] (2) Solvent:

[0091] Pure methanol and a 50% acetonitrile aqueous solution were used for sample extraction.

[0092] A 50% aqueous solution of acetonitrile can be used as a solvent to dissolve standards.

[0093] (3) Internal standard: L-phenylalanine.

[0094] Example 3

[0095] The screening method using the detection kit for ischemic stroke recurrence risk analysis in Example 2 includes the following steps:

[0096] S1. Collect plasma samples and preprocess them according to Example 1 to obtain the test solution.

[0097] S2, the test solution was analyzed by LC-MS to obtain information on the changes in the content of carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0, and carnitine C12:0.

[0098] S3. Based on the changes in the levels of the above-mentioned metabolic markers, determine whether the patient may be at high risk of recurrence of ischemic stroke.

[0099] Doctors can make a diagnosis based on the test results, such as the rate of change of the marker with the highest content change, or the number of markers with a rate of change higher than a certain threshold.

[0100] As an example, this invention provides the following calculation formula and judgment method when detecting 5 metabolic biomarkers:

[0101] Score = -0.1510 + 0.0970 × Carnitine C10:1 + 0.1016 × Carnitine C8:0 + 0.0949 × Carnitine C6:0 + 0.1010 × Carnitine C10:0 + 0.0953 × Carnitine C12:0

[0102] In the above formula, "carnitine C10:1" means the relative abundance of carnitine C10:1 as detected by mass spectrometry.

[0103] The critical value of the model is 0.508. The relative abundance of the corresponding compounds of the biomarkers in the serum is input into the model: when the score is ≤0.508, the probability of being diagnosed with stroke recurrence risk is high, and when the score is >0.508, the probability of being diagnosed with stroke recurrence risk is low.

[0104] It should be noted that the above embodiments are only for further elaboration and explanation of the technical solution of the present invention, and are not intended to further limit the technical solution of the present invention. The method of the present invention is only a preferred embodiment and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A plasma metabolite marker for ischemic stroke recurrence risk analysis, characterized by, The plasma metabolic marker is at least one selected from carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0 and carnitine C12:

0.

2. The plasma metabolite marker for ischemic stroke recurrence risk analysis according to claim 1, characterized by, The plasma metabolic marker is at least one selected from carnitine C10:1, carnitine C8:0, carnitine C6:0, and carnitine C12:

0.

3. The plasma metabolic marker for ischemic stroke recurrence risk analysis according to claim 1, characterized in that, The plasma metabolic marker is at least one selected from carnitine C10:1, carnitine C8:0, and carnitine C12:

0.

4. The plasma metabolic marker for ischemic stroke recurrence risk analysis according to claim 1, characterized in that, The plasma metabolic marker is at least one selected from carnitine C10:1 and carnitine C8:

0.

5. Use of the metabolic marker for ischemic stroke recurrence risk analysis according to any one of claims 1 to 4 in the preparation of a kit for analyzing the risk of ischemic stroke recurrence.

6. A kit for ischemic stroke recurrence risk analysis, characterized by, The kit comprises a standard selected from at least one of carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0 and carnitine C12:

0.

7. The kit for analyzing the risk of recurrence of ischemic stroke according to claim 6, characterized in that, The kit further comprises a solvent and an internal standard.

8. The kit for use in the analysis of the risk of recurrence of ischemic stroke according to claim 7, characterized in that, The solvent is methanol and 50% acetonitrile aqueous solution, and the internal standard is L-phenylalanine.

9. A method for screening a metabolic marker for risk analysis of recurrence of ischemic stroke according to any one of claims 1 to 4, characterized by, The kit comprises the following steps: Collecting samples from the ischemic stroke recurrence group and the ischemic stroke non-recurrence group, respectively; Using LC-MS to detect the samples from the ischemic stroke recurrence group and the ischemic stroke non-recurrence group, and obtaining candidate differential metabolites through discriminant analysis; Performing receiver operating characteristic curve analysis on the differential metabolites and their combinations to determine the metabolic marker for ischemic stroke recurrence risk analysis.

10. An LC-MS detection method, characterized in that: (1) Sample processing, Processing the blood sample with the methanol internal standard extract of L-phenylalanine and 50% acetonitrile aqueous solution to prepare the sample to be tested; (2) Using the standard sample of the metabolic marker to be tested as an external standard, performing LC-MS detection to analyze the content of the metabolic marker to be tested in the sample to be tested; The metabolic marker to be tested is at least one compound selected from carnitine C10:1, carnitine C8:0, carnitine C6:0, carnitine C10:0 and carnitine C12:0; (3) Storing and outputting the detection results.

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