CN112630344A

CN112630344A - Use of metabolic markers in cerebral infarction

Info

Publication number: CN112630344A
Application number: CN202011442620.2A
Authority: CN
Inventors: 张祥建; 张聪; 张培培
Original assignee: Second Hospital of Hebei Medical University
Current assignee: Second Hospital of Hebei Medical University
Priority date: 2020-12-08
Filing date: 2020-12-08
Publication date: 2021-04-09
Anticipated expiration: 2040-12-08
Also published as: CN112630344B

Abstract

The invention discloses the use of metabolic markers in cerebral infarction. The invention collects blood samples from cerebral infarction patients and healthy controls, and performs metabolomics analysis to find out the metabolites showing significant differences in the two groups, thereby Using this metabolite to predict early cerebral infarction, and then achieve early treatment of patients.

Description

Use of metabolic markers in cerebral infarction

Technical Field

The invention belongs to the field of biomedicine, and relates to application of a metabolic marker in cerebral infarction.

Background

Cerebral Infarction (CI) is ischemic stroke, which accounts for about 80% of stroke and is mainly a disease caused by the combined action of multiple risk factors such as blood vessels, heredity and life style (Das S, Kaul S, Jyothy A, et al.Association of APOE (E2, E3 and E4) gene variants and lipid in biochemical strings, its subtypes and heterologous strings in a South insulin pumping [ J ]. Neurosci Lett,2016,628: 136-. Studies show that the Incidence and fatality of Stroke in China are the first in the world, and account for 1/3(Wang W, Jiang B, Sun H, et al. Presence, Inc., and mortalities of Stroke in China: Results from a national position-Based Surveiy of 480687 additions [ J ]. Circulation,2017,135(8): 759) 771.). Although cerebral infarction patients regularly take anti-platelet aggregation, anticoagulation and plaque stabilization medicines and a series of cerebral apoplexy secondary prevention such as blood pressure, blood sugar and blood fat control, the recurrence rate of cerebral infarction is still high, the recurrence rate of the disease reaches 32% within 1 year (Wulong, Song dynasty week, Zhanyangming, etc.. Logistic regression model analysis [ J ] of ultrasound contrast evaluation carotid plaque new vessel grading prediction cerebral infarction recurrence, China medical ultrasound journal (electronic edition), 2018,15(01):43-47.), but the cerebral infarction recurrence rate is higher than that of the initial cerebral infarction, the prognosis is worse, and more serious neurological dysfunction is left (Lifeng, Chengming, well-known, etc.. MMP-2 gene polymorphism and correlation study of the initial and recurrence of atherosclerotic cerebral infarction [ J ] apoplexy and neurological disease journal, 2015,32(02): 104-). For the diseases with high morbidity, recurrence rate, mortality and disability rate, most of the current researches believe that diagnosis in early stage of cerebral infarction can effectively treat patients with cerebral infarction in time and reduce the mortality and disability rate of cerebral infarction.

Metabonomics (metabonomics) is the science of studying the type, amount and change law of endogenous metabolites, which are metabolites of biological systems after stimulation or perturbation. Under the condition of disease and the action of various drugs, the living organism will also cause the change of endogenous metabolites and metabolic networks at the whole body level. The metabonomics technology is used for inspecting and analyzing the change of the metabolites, and the metabonomics technology is greatly beneficial to the exploration of the essence of the disease and the elucidation of the action mechanism of the medicament.

In the invention, blood of a cerebral infarction patient and age-sex matched healthy control is collected, and qualitative and quantitative analysis is carried out on a serum metabolome by adopting a non-targeted combined targeted metabolome method. Potential metabolite markers are screened out through OPLS-DA supervised clustering analysis, difference multiple analysis and T test analysis, and further data analysis finds that the metabolite markers have good distinction on two groups, so that the method has good clinical application prospect.

Disclosure of Invention

Metabolomics is an emerging research area downstream of genomics, proteomics, and transcriptomics. There are 40,000 various metabolites in humans, the concentration of which can provide a snapshot of the current health status of an individual. The metabolome is a quantitative collection of low molecular weight compounds produced by metabolism, such as metabolic substrates and products, lipids, small peptides, vitamins and other protein cofactors. The metabolome is downstream of the transcriptome and proteome, so any changes from the normal state are amplified and are numerically easier to handle. Metabolomics can be an accurate, consistent, and quantitative tool for examining and describing cell growth, maintenance, and function.

In order to evaluate the correlation between the metabolites and the cerebral infarction, metabolic markers suitable for the diagnosis and treatment of the cerebral infarction are found by collecting samples of healthy controls and the cerebral infarction, comprehensively analyzing metabonomics of the samples, screening metabolites whose contents show significant differences in the two groups, and further analyzing the diagnostic efficacy of the different metabolites.

Specifically, the invention provides the following technical scheme:

the invention provides application of a metabolic marker in preparing a product for early diagnosis of cerebral infarction, wherein the metabolic marker comprises Cer (d18:0/14: 0).

Further, the product comprises a reagent for detecting the level of a metabolic marker in a sample.

Further, the agent detects the level of a metabolic marker by one or more of: chromatography, spectroscopy, mass spectrometry, chemical analysis.

Further, the chromatography includes high performance liquid chromatography, thin layer chromatography, and gas chromatography.

The spectroscopy comprises nuclear magnetic resonance spectroscopy, refractive index spectroscopy, ultraviolet spectroscopy and near infrared spectroscopy.

The chemical analysis method comprises electrochemical analysis and radiochemical analysis.

Including tandem mass spectrometry, Matrix Assisted Laser Desorption Ionization (MALDI) time of flight (TOF) mass spectrometry, MALDI-TOF-TOF mass spectrometry, MALDI quadrupole-time of flight (Q-TOF) mass spectrometry, electrospray ionization (ESI) -TOF mass spectrometry, ESI-Q-TOF, ESI-TOF-ion trap mass spectrometry, ESI triple quadrupole mass spectrometry, ESI Fourier Transform Mass Spectrometry (FTMS), MALDI-FTMS, MALDI-ion trap-TOF, and ESI-ion trap TOF, to their most basic level, mass spectrometry involves ionizing a molecule and subsequently measuring the mass of the resulting ion. Since the molecules are ionized in a known manner, the molecular weight of the molecules can be accurately determined from the mass of the ions.

The tandem mass spectrometry involves first obtaining a mass spectrum of an ion of interest, then fragmenting the ion and obtaining a mass spectrum of the fragment. Tandem mass spectrometry thus provides molecular weight information and fragmentation spectra that can be used together with the molecular weight information to identify the exact sequence of a peptide or protein or small molecule (below 1500 daltons).

Preferably, the mass spectrometry is selected from liquid chromatography mass spectrometry combining the physical separation capabilities of Liquid Chromatography (LC) or High Performance Liquid Chromatography (HPLC) with the mass analysis capabilities of Mass Spectrometry (MS). HPLC offers advantages over LC with shorter analysis time and better resolution of the analyte. Thereby increasing the selectivity, accuracy and precision of the MS.

Preferably, if mass spectrometry is used, the amount of the metabolite is determined by reference to an internal metabolite standard (standard). The abundance of any molecular ion species (molecular ion species) generally carries information about concentration, but ion abundance is confounded by a number of features, including instrument response factors, ionization efficiency of the molecule, stability of the molecular ion species, and the presence of other ion-inhibiting molecules that may cause the analyte of interest. Thus, internal standards have been developed that can be used to generate appropriate calibration curves to convert ion abundance into a quantitative measure of the amount of metabolite. Non-limiting examples of internal standards include: a metabolite standard labeled with a stable isotope-labeled version of the metabolite to be quantified, with similar extraction recovery, ionization response, and similar chromatographic retention times; a compound analogue of the metabolite to be quantified which is similar to the compound to be quantified but differs slightly in the parent mass; or a chlorinated version of a metabolite in a undetermined amount, which typically has a similar chromatographic retention time.

At the beginning of sample preparation, internal standards are typically added to each sample, including the standards, at known concentrations, typically prior to sample preparation or solid phase extraction. It will be appreciated by those skilled in the art that the amount of internal standard needs to be above the limit of quantitation but low enough to avoid inhibiting ionization of the analyte. Based on the known concentration of the internal standard present in the sample, the measured value of the metabolite of interest can be quantified by interpolating the response ratio (response ratio) between the metabolite and the internal standard to a standard curve.

Preferably, the internal metabolite standard is a stable isotope labeled standard. As is known, stable in the context of isotopes means that the isotopes are nonradioactive, i.e. they do not decay spontaneously. Metabolite standards labeled with stable isotopes are stable isotope-labeled versions of metabolites of undetermined amounts and are well known in the art. Typically, the isotopes used are stable isotopes of carbon, nitrogen or hydrogen, such as¹²C and¹³C、¹⁴n and¹⁵n and²h (deuterium).

In the present invention, the term "sample" refers to a biological sample, such as, for example, cells, tissue (from any organ) or fluid (including serum, plasma, whole blood), which has been isolated or obtained from an individual or from a cell culture component of a cell culture including cells of a subject. Any tissue or liquid sample obtained from a patient and/or subject comprising cells may be used for the assessment of the amount of one or more metabolites according to the methods of the invention. Preferred samples for assessing the amount of a metabolite according to the method of the invention are blood, serum and/or plasma.

The invention provides a product for diagnosing cerebral infarction, which comprises a reagent for detecting the level of a metabolic marker Cer (d18:0/14:0) in a sample.

Further, the reagent detects metabolite levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

Further, the product also includes reagents for processing the sample.

Further, the product comprises a kit and a chip.

As an alternative embodiment, the components of the kit may be packaged in one or more containers, such as one or more vials. In addition to the metabolite standards, the kit preferably further comprises a preservative or buffer for storage. In addition, the kit may contain instructions for use.

As an alternative embodiment, the chip has a reagent capable of detecting and/or quantifying one or more metabolites immobilized at predetermined locations on the substrate. As an illustrative example, a chip may be provided with reagents immobilized at discrete predetermined positions for detecting and quantifying the amount or concentration of Cer (d18:0/14:0) in a sample; as described above, an increased level of the metabolite is found in a sample of a subject suffering from a cerebral infarction. The chip may be configured such that a detectable output (e.g. a colour change) is provided only when the amount or concentration of the metabolite exceeds a threshold value selected or differentiated between a concentration of the metabolite indicative of a healthy subject and an amount or concentration of the metabolite indicative of suffering from or being susceptible to cerebral infarction. Thus, the presence of a detectable output (such as a color change) immediately indicates that the sample contains a significantly increased level of the metabolite, indicating that the subject has or is predisposed to a cerebral infarction.

The invention provides application of Cer (d18:0/14:0) in constructing a calculation model for predicting cerebral infarction, wherein the calculation model takes the level of Cer (d18:0/14:0) as an input variable.

The invention provides application of Cer (d18:0/14:0) in preparation of a medicament for treating cerebral infarction.

Further, the medicament includes an inhibitor of Cer (d18:0/14:0) that reduces the level of Cer (d18:0/14: 0).

The invention has the advantages and beneficial effects that:

the invention discovers a metabolite marker related to cerebral infarction for the first time, wherein the metabolite marker is Cer (d18:0/14:0), the marker is used as a detection variable to distinguish the cerebral infarction from a healthy control group and has higher accuracy (AUC >0.9), and based on the discovery, whether a subject suffers from the cerebral infarction and the risk of suffering from the cerebral infarction can be judged by detecting the level of the metabolite marker, so that the early diagnosis of the cerebral infarction is realized, the intervention treatment is carried out at the early stage of the cerebral infarction, and the living quality of the patient is improved.

Drawings

FIG. 1 is a total ion flow diagram for each set of chromatograms, wherein diagram A is a total ion flow diagram for each set of reverse chromatograms positive ions, diagram B is a total ion flow diagram for each set of reverse chromatograms negative ions, and diagram C is a total ion flow diagram for each set of hydrophilic chromatograms positive mode; the upper part of fig. A, B, C is a total ion flow graph of cerebral infarction and the lower part is a total ion flow graph of healthy control.

FIG. 2 is a statistical analysis diagram of OPLS-DA, wherein diagram A is a statistical analysis diagram of reverse chromatographic positive ions; FIG. B is a diagram of a negative ion statistical analysis of the reverse chromatogram; panel C is a hydrophilic chromatographic positive ion statistical analysis.

FIG. 3 is a graph of the levels of Cer (d18:0/14:0) in different groups.

FIG. 4 is a graph of the diagnostic performance with Cer (d18:0/14:0) as the test variable.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental methods in the examples, in which specific conditions are not specified, are generally carried out under conventional conditions.

Example screening of metabolites associated with cerebral infarction and potency determination

1. Sample collection

Blood samples were collected from 21 patients with cerebral infarction and 18 healthy controls.

Inclusion criteria for cerebral infarct groups:

1) the subject has signed an informed consent

2) Meets the acute cerebral infarction diagnosis standard of Chinese acute ischemic stroke diagnosis and treatment guidelines (2014 edition).

3) Age 18-65 years old.

4)BMI 18.5-23.9kg/m²。

5) Blood routine: red blood cell count, MCHC, hemoglobin, white blood cell count, lymphocyte count, neutrophil count, monocyte count are in the normal range.

6) TG, TC, HDL-C, LDL-C, blood glucose, and glycated hemoglobin were in the normal range.

Exclusion criteria:

1) the combination of other diseases: nervous system diseases (past cerebral infarction, cerebral hemorrhage, multiple sclerosis, etc.); various chronic digestive system diseases, acute digestive system diseases within 3 months; circulatory disorders (coronary heart disease, heart failure, atrial fibrillation); respiratory diseases (chronic obstructive pulmonary disease, chronic bronchitis, asthma); metabolic diseases (obesity, hyperlipidemia, diabetes, metabolic syndrome, osteoporosis); urinary system diseases (chronic kidney disease, renal failure, kidney stones); hematological disorders (anemia); others (gout, depression, psychiatric disorders, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).

2) The history of blood transfusion, operation and trauma of digestive system diseases.

3) Patients with abnormal electrocardiograms.

4) The following drugs were taken within 3 months: antibiotics, laxatives, clonazepam, sex hormone drugs, oral contraceptives, mesalamine, TNF-alpha inhibitors, immunosuppressants, antidepressants, PPIs, rupatadine, opioids, calcium agents, vitamin D, metformin, folic acid, beta-sympathetic inhalants, traditional Chinese medicines.

5) The probiotic preparation is administered within 3 months.

6) Antiplatelet and statins are applied before the disease.

7) Patients undergoing intravenous thrombolysis and endovascular intervention.

8) Pregnant or lactating women.

9) During this study, the patient had enrolled or planned to enroll in another clinical drug or device/interventional study.

Healthy control groups were included as standards:

1) the subject has signed an informed consent.

2) Age 18-65 years old.

3)BMI 18.5-23.9kg/m²。

4) Blood routine: red blood cell count, MCHC, hemoglobin, white blood cell count, lymphocyte count, neutrophil count, monocyte count are in the normal range.

5) TG, TC, HDL-C, LDL-C, blood glucose, and glycated hemoglobin were in the normal range.

Exclusion criteria:

1) there are other diseases: nervous system diseases (cerebral infarction, cerebral hemorrhage, multiple sclerosis, etc.); various chronic digestive system diseases, acute digestive system diseases within 3 months; circulatory disorders (coronary heart disease, heart failure, atrial fibrillation); respiratory diseases (chronic obstructive pulmonary disease, chronic bronchitis, asthma); metabolic diseases (obesity, hyperlipidemia, diabetes, metabolic syndrome, osteoporosis); urinary system diseases (chronic kidney disease, renal failure, kidney stones); hematological disorders (anemia); others (gout, depression, psychiatric disorders, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).

3) The electrocardiogram is abnormal.

4) The following drugs were taken within 3 months: antibiotics, laxatives, clonazepam, sex hormones, oral contraceptives, mesalamine, TNF-alpha inhibitors, immunosuppressants, antidepressants, PPIs, rupatadine, opioids, calcium agents, vitamin D, metformin, folic acid, beta-sympathetic inhalants, traditional Chinese medicines, antiplatelet drugs, and statins.

5) The probiotic preparation is administered within 3 months.

6) Pregnant or lactating women.

7) During this study, the subject has enrolled or is scheduled to enroll in another clinical drug or device/interventional study.

2. Non-targeted metabolomics detection

2.1 serum sample preparation

2.1.1 reverse phase chromatography method for processing serum samples

1) The plasma/serum samples were thawed on ice at 4 ℃ for 30-60 min.

2) Mu.l serum was taken to a labeled 1.5ml centrifuge tube and 300. mu.l methanol and 1ml methyl tert-butyl ether were added.

3) The protein was precipitated by shaking thoroughly for 15 s. Centrifuging at 12000rpm and 4 deg.C for 10min, collecting upper layer solution 100 μ l, placing in 200 μ l liner tube, and testing.

2.1.2 hydrophilic chromatography serum sample treatment method:

1) the plasma/serum samples were thawed on ice at 4 ℃ for 30-60 min.

2) Mu.l serum was taken to a labeled 1.5ml centrifuge tube and 150. mu.l acetonitrile was added.

2.2 chromatographic conditions

Chromatographic separation serum samples were analyzed by reverse phase chromatography and hydrophilic chromatography using U3000 flash liquid chromatography from Thermo Scientific.

2.2.1 reverse phase chromatographic separation conditions

Chromatography column waters UPLC HSS T3(1.8 μm 2.1mm 100 mm);

mobile phases a (acetonitrile/water 4:6, 0.1% formic acid, 10mM ammonium acetate) and B (acetonitrile/isopropanol 9:1, 0.1% formic acid, 10mM ammonium acetate);

elution procedure: see table 1;

flow rate: 0.3 ml/min;

the sample injection amount is 1.0 mu L;

column temperature: at 50 ℃.

TABLE 1C 18 reverse phase chromatography determination of elution procedure

2.2.1 conditions for hydrophilic chromatographic separation

Chromatography column waters UPLC BEH Amide (1.7 μm 2.1mm 100 mm);

mobile phases a (acetonitrile, 0.1% formic acid, 10mM ammonium acetate) and B (water, 0.1% formic acid, 10mM ammonium acetate);

elution procedure: see table 2;

flow rate: 0.3 ml/min;

sample introduction amount: 1.0 μ L;

column temperature: at 40 ℃.

TABLE 2 HILIC determination of polar Small molecule elution procedure

2.3 Mass Spectrometry conditions

Mass spectrometry uses a quadrupole rod orbited ion trap mass spectrometer equipped with a thermoelectric spray ion source. The voltages of the positive and negative ion sources were 3.7kV and 3.5kV, respectively. The capillary heating temperature was 320 ℃. The warp air pressure was 30psi and the assist air pressure was 10 psi. The evaporation temperature was 300 ℃ with volume heating. The tilted gas and the auxiliary gas are both nitrogen. The collision gas is nitrogen and the pressure is 1.5 mTorr. The first-order full scan parameters are: resolution 70000, automatic gain control target of 1 × 10⁶Maximum isolation time 50ms, mass to charge ratio scan range 50-1500. The liquid system is controlled by Xcaliibur 2.2SP1.48 software, and both data acquisition and targeted metabolite quantitative processing are operated by the software.

3. Targeted metabonomic detection

3.1 serum sample processing method

1) Plasma samples were thawed by standing at 4 ℃ for 30 min.

2) A50. mu.l plasma sample was taken into a 1.5ml centrifuge tube, 150. mu.l methanol (containing indoleacetic acid-D2500 ppb, indolepropionic acid-D250 ppb) was added, and vortexed for 30 min.

3) Centrifuging at 12000rpm for 5min, collecting supernatant 100 μ l, placing in 200 μ l liner tube, and testing.

3.2 chromatographic conditions

The chromatographic separation adopts a Waters ACQUITY UPLC I-CLASS ultrahigh pressure liquid chromatographic system, and the chromatographic separation conditions are as follows:

chromatography column Waters UPLC BEH C8(1.7 μm 2.1mm 100 mm);

mobile phase A (water, 0.5Mm NH)₄F) And B (methanol);

elution gradient: see table 3;

flow rate: 0.3 ml/min;

sample introduction amount: 1.0 μ L;

column temperature: at 45 ℃.

TABLE 3 elution procedure

3.3 Mass Spectrometry conditions

The mass spectrometer is a Waters XEVO TQ-XS type tandem quadrupole mass spectrometer. The voltage of the positive ion source is 3kv, and the voltage of the taper hole is 20V. The desolvation temperature is 550 ℃, and the source temperature is 150 ℃. The desolventizing air flow rate is 1000L/Hr, and the taper hole air flow rate is 7L/h.

3.4 Targeted Metabolic group data treatment

The peak area calculation of the targeted metabolome data adopts masslynx quantitative software, and the retention time allows the error to be 15 s. And the concentration calculation adopts a single-point isotope internal standard method to obtain a quantitative result.

4. Data processing

4.1 data quality control

To evaluate the stability and reproducibility of the system during sample collection, quality control samples were used. The quality control sample is obtained by transferring all samples into a fixed volume and uniformly mixing. The pretreatment method of the finger-controlled sample is the same as that of other samples. To obtain a reliable and reproducible metabolite, three factors need to be considered: 1) retention time, 2) signal strength, 3) mass accuracy. In the experiment, 5 blank sample balance chromatographic columns are adopted firstly, and then 3 quality control sample balance chromatographic columns are adopted. Then every 6-8 samples insert 1 quality control sample for monitoring the whole liquid quality system stability and repeatability. And simultaneously calculating the coefficient of variation value of the metabolic features extracted from the quality control samples, and deleting the metabolic features of which the coefficient of variation exceeds 15%.

4.2 PCA analysis

All collected data, no matter what separation mode or positive and negative ion mode, are processed by Progenetics QI software, and the steps include importing original data, aligning peaks, extracting peaks, normalizing, and finally forming a table of retention time, mass-to-charge ratio and peak intensity. The time for extracting peaks by the reversed phase chromatography and the hydrophilic chromatography is 1 to 16 and 1 to 12min in sequence. Various additive ions such as hydrogen and sodium are deconvoluted into each ion signature. Metabolite identification primary molecular weight matching was performed using the human metabolome database and the lipid database.

4.3 OPLS-DA analysis

In order to obtain metabolite information showing significant differences between the cerebral infarction group and the healthy control group, statistical analysis is further performed on the two groups of samples by adopting a supervised multidimensional statistical method, namely partial least squares discriminant analysis (OPLS-DA).

Differentially expressed metabolites were searched for using the VIP (variable immunity in the project) value (threshold >1) of the OPLS-DA model in combination with the p-value of t-test (p < 0.05). The qualitative method of differential metabolites was: search the online database (HMDB) (compare mass to charge ratio m/z of mass spectra or exact molecular mass, error limit 0.01 Da).

4.4 ROC analysis

According to the levels of the metabolites, a receiver operating characteristic curve (ROC) is drawn, two accurate confidence spaces are calculated, and the diagnostic efficacy of the differential metabolites is analyzed.

5. Results

The total ion flow chart of reverse phase chromatography positive ions and negative ions and hydrophilic chromatography positive ions of each group of the cerebral infarction group and the healthy control group is shown in figure 1.

The quality control result shows that the quality control samples are relatively gathered together, the system has good repeatability, and the acquired data can be further researched.

The results of the reverse chromatography positive ion, the reverse chromatography negative ion, and the hydrophilic chromatography positive ion are shown in table 4 and fig. 2, respectively.

TABLE 4 OPLS-DA analytical model parameters

Bioinformatic analysis results showed that Cer (d18:0/14:0) was significantly increased in the cerebral infarcted group compared to the healthy control group (fig. 3).

The diagnosis efficiency was judged by using the content of Cer (d18:0/14:0) as a detection variable, and the results showed that the area under the curve was 0.934, the cutoff value was 7088751.490, the sensitivity was 0.905, and the specificity was 0.889 (FIG. 4), which had high sensitivity and specificity.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims

1. Use of a metabolic marker for the manufacture of a product for the early diagnosis of cerebral infarction, wherein the metabolic marker comprises Cer (d18:0/14: 0).

2. The use of claim 1, wherein the product comprises a reagent for detecting the level of a metabolic marker in a sample.

3. The use of claim 2, wherein the agent detects metabolic marker levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

4. Use according to claim 3, wherein the chromatography comprises high performance liquid chromatography, thin layer chromatography, gas chromatography; the spectroscopy comprises nuclear magnetic resonance spectroscopy, refractive index spectroscopy, ultraviolet spectroscopy and near infrared spectroscopy; the chemical analysis method comprises electrochemical analysis and radiochemical analysis.

5. The use according to any one of claims 2 to 4, wherein the sample is selected from blood, plasma or serum.

6. A product for diagnosing cerebral infarction, characterized in that it comprises a reagent for detecting the level of the metabolic marker Cer (d18:0/14:0) in a sample.

7. The product of claim 6, wherein the agent detects metabolic marker levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

8. The product of claim 6, further comprising a reagent for processing the sample.

9. The product according to any one of claims 6 to 8, wherein the product comprises a kit, a chip.

Use of Cer (d18:0/14:0) in the construction of a computational model for predicting cerebral infarction.