CN112599239B - Metabolite marker and application thereof in cerebral infarction diagnosis - Google Patents
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Abstract
The invention discloses a metabolite marker and application thereof in cerebral infarction diagnosis, wherein the metabolite marker is PC (20:4(5Z,8Z,11Z,14Z)/18: 0). In embodiments of the invention, the metabolite markers are increased in levels in patients with cerebral infarction compared to healthy controls. Measurement of the marker in the sample may indicate that the subject has or is at risk of having a cerebral infarction.
Description
Technical Field
The invention belongs to the field of biomedicine, and relates to a metabolite marker and application thereof in cerebral infarction diagnosis.
Background
Cerebrovascular disease is one of ten causes of human death worldwide, and particularly seriously harms the health and life of middle-aged and elderly people. Stroke is the most clinical type of cerebrovascular disease, and the incidence rate of stroke in China is 250/10 ten thousands, which is the second place in the world. Cerebral infarction is also called ischemic stroke, and comprises three types of lacunar infarction, cerebral embolism and cerebral thrombosis. Cerebral infarction accounts for nearly 90% of cerebrovascular diseases, and is the third leading cause of death next to heart disease and cancer, with about 400 million stroke patients dying worldwide each year (Qiang Li, Mengdie Liu, Rui Fu, et al. alternation of circulating inert cells in tissues with an atherogenic clinical information [ J ]. American journel of metabolic resources, 2018,10(12): 4322.). Risk factors for cerebral infarction include diabetes, hypertension, hyperlipidemia and age, among others (Daniel Lindholm, Giovanna Sarno, David Erlink, et al. Combined association of key lipid factors on isocaloric lipids and sheeting in tissues with myocardial infarnation [ J ]. Heart (British Cardiac), 2019,105(15): hearnj-2018. alpha. 314590.), in addition genetic and environmental factors also influence the development of cerebral infarction (Navalli Devaraddi, G Jayalaks hmi, Narayan R Mutalik. CARASIL. a. genetic cause of stress in the apparatus [ J. Neurology, 11, 201234.). At present, the method finds out the reason causing the cerebrovascular disease and intervenes and treats the cerebrovascular disease as early as possible, and is an effective means for preventing the cerebrovascular disease.
Metabolomics is a newly developed subject following genomics and proteomics, and is an important component of system biology. The method is used for carrying out dynamic tracking analysis on body fluid secreted by cells and organisms to identify and analyze the physiological and pathological states of a researched object and the relationship between the physiological and pathological states and environmental factors, gene composition and the like, and is widely applied to the research fields of clinical diagnosis, drug development, toxicology, physiology, pathology and the like. The method searches for metabolites related to diseases by a metabonomics method, and currently, the method researches the occurrence mechanism of the diseases and realizes an important means for early diagnosis of the diseases.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a metabolite marker related to cerebral infarction, and whether a patient has cerebral infarction or is at risk of having cerebral infarction can be judged by detecting the level of the metabolite marker, so that a new means is provided for early diagnosis of cerebral infarction.
The term "metabolite" refers to both intermediates and end products of metabolism, (also sometimes referred to as small molecules or analytes having a molecular weight of less than 1500 daltons). Metabolites are classified as primary metabolites that are directly involved in normal growth, development and reproduction. Secondary metabolites do not directly participate in the latter processes, but may have important ecological functions (e.g. antibiotics, pigments). Exemplary biological functions of a metabolite include as an intermediate or end product in a biosynthetic pathway or as a cell signaling molecule.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect of the invention there is provided a metabolite marker associated with cerebral infarction, the metabolite marker comprising PC (20:4(5Z,8Z,11Z,14Z)/18: 0).
In a second aspect, the present invention provides the use of an agent for detecting the level of a metabolite marker according to the first aspect of the invention in a sample for the manufacture of a product for diagnosing cerebral infarction.
Further, the reagent detects the level of the metabolite in the sample by one or more of targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, and mass spectrometry.
Further, the chromatography comprises gas chromatography, capillary electrophoresis, liquid chromatography, high-performance liquid chromatography, and ultra-high performance liquid chromatography.
The spectroscopy comprises ultraviolet-visible spectroscopy, infrared spectroscopy, near-infrared spectroscopy and nuclear magnetic resonance spectroscopy.
Mass spectrometry includes, for example, 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. At its most basic level, mass spectrometry involves ionizing molecules and subsequently measuring the mass of the resulting ions. 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.
Liquid chromatography mass spectrometry combines 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. This therefore increases the selectivity, accuracy and accuracy of the MS.
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).
Further, the reagent detects the level of the metabolite in the sample by chromatography-mass spectrometry.
In the present invention, the sample is a biological sample. Samples of biological origin (i.e. biological samples) typically comprise a plurality of metabolites. Preferred experimental samples to be used in the method of the invention are samples from body fluids, preferably from blood, plasma, serum, lymph, sweat, saliva, tears, semen, vaginal fluid, faeces, urine or cerebrospinal fluid, or from cells, tissues or organs, for example by biopsy. This also includes samples comprising subcellular compartments or organelles (e.g., mitochondria, golgi networks, or peroxisomes). In addition, biological samples also include gaseous samples, such as volatiles from organisms. Biological samples are subjects as specified elsewhere herein. Techniques for obtaining the different types of biological samples described above are well known in the art. For example, a blood sample is obtained by blood collection and a urine sample is obtained by urine collection.
Further, the sample is selected from blood, serum, plasma.
Further, the above-mentioned sample is pretreated before being used for the detection of the present invention. The pretreatment may include treatments required to release or isolate compounds, or to remove unwanted materials or waste. Suitable techniques include centrifugation, extraction, fractionation, purification and/or enrichment of compounds. In addition, other pre-treatments are performed to provide the compound in a form or concentration suitable for analysis of the compound. For example, if gas chromatography coupled with mass spectrometry is used in the methods of the invention, it will be necessary to derivatize the compounds prior to said gas chromatography. Suitable and necessary pretreatments depend on the means for carrying out the process of the invention and are well known to the person skilled in the art. The pre-treated sample as described before is also encompassed by the term "sample" as used in the present invention.
Further, when the level of the metabolite marker in the subject is up-regulated, the subject has or is at risk of having a cerebral infarction.
In case a reference result is obtained from a subject or population known not to suffer from a cerebral infarction, the disease or predisposition may be diagnosed based on the difference between the test result obtained from the test sample and the above reference result, i.e. based on the difference in qualitative or quantitative composition with respect to the at least one metabolite. The difference may be an increase in the absolute or relative amount of a metabolite (sometimes referred to as metabolite upregulation) or a decrease or no detectable amount of the amount of a metabolite (sometimes referred to as metabolite downregulation). Preferably, the difference in relative or absolute amounts is significant, i.e. outside the reference value interval of 45 to 55 percentiles, 40 to 60 percentiles, 30 to 70 percentiles, 20 to 80 percentiles, 10 to 9 percentiles, 5 to 95 percentiles. Preferred values for changes in relative amounts (i.e., "fold" changes) or types of changes (i.e., "up" or "down" adjustments resulting in higher or lower relative amounts and/or absolute amounts). The relative and/or absolute amount of a given metabolite will increase if it is "up-regulated" in a subject and decrease if it is "down-regulated". Furthermore, a "fold" change indicates the degree of increase or decrease, e.g., a 2-fold increase means that the amount is twice the amount of metabolite as compared to a reference.
Thus, in a preferred embodiment a reference from a subject or group known to suffer from a cerebral infarction, or a subject or group known to have a predisposition therefor, is included. Most preferably, the same or similar result (i.e. a similar relative or absolute amount of the at least one metabolite) of the test sample and the reference is in this case indicative for a cerebral infarction or a predisposition therefor. In another preferred embodiment of the invention, the reference is from a subject known not to have a cerebral infarction or a subject known not to have a predisposition therefor, or the reference is a calculable reference.
In a third aspect, the present invention provides a kit for diagnosing cerebral infarction, the kit comprising reagents for detecting the level of the metabolite markers according to the first aspect of the present invention in a sample.
Further, the agent detects the level of the metabolite in the sample by one or more of targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry.
Further, the kit further comprises reagents for processing the sample.
Further, the kit also includes instructions for using the kit to assess whether the subject has had a cerebral infarction or is at risk of having an atherosclerotic, nong' an infarction.
The most reliable results are possible when processing blood samples in a laboratory environment. For example, a blood sample may be taken from a subject in a doctor's office and then sent to a hospital or commercial medical laboratory for further testing. However, in many cases, it may be desirable to provide immediate results at the clinician's office or to allow the subject to perform the test at home. In some cases, the need for testing that is portable, prepackaged, disposable, ready to use by the subject without assistance or guidance, etc., is more important than a high degree of accuracy. In many cases, especially in the presence of a physician's follow-up, it may be sufficient to perform a preliminary test, even a test with reduced sensitivity and/or specificity. Thus, assays provided in kit form can involve the detection and measurement of relatively small amounts of metabolites to reduce the complexity and cost of the assay.
Any form of blood assay capable of detecting blood metabolites described herein may be used. Typically, the assay will quantify blood metabolites to an extent, such as whether their concentration or amount is above or below a predetermined threshold. Such kits may take the form of test strips, dipsticks, cartridges, chip-based or bead-based arrays, multi-well plates, or a series of containers, and the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of a selected blood metabolite. The subject's blood may be dispensed directly into the assay or indirectly into the assay from a stored or previously obtained sample. The presence or absence of a metabolite above or below a predetermined threshold may be indicated, for example, by chromogenic, fluorogenic, electrochemiluminescent or other output (e.g., as in an Enzyme Immunoassay (EIA), such as an enzyme-linked immunoassay (ELISA)).
In the present invention, the kit may comprise a solid substrate such as a chip, slide, array or the like, having reagents capable of detecting and/or quantifying one or more blood metabolites immobilized at predetermined locations on the substrate. As an illustrative example, the chip may be provided with reagents immobilized at discrete predetermined locations for detecting and quantifying the concentration of a metabolite marker, any number thereof, or any combination thereof, in a blood sample.
In a fourth aspect, the present invention provides the use of a metabolite marker of the first aspect of the invention in the construction of a computational model for predicting cerebral infarction.
In the present invention, there are a number of methods for assessing selected metabolites to assess whether a subject is suffering from or susceptible to atherosclerotic cerebral infarction. The measured values of the metabolites may be mathematically combined and the combined values may be correlated to potential status issues. Metabolite values may be combined by any suitable mathematical method. Mathematical methods for correlating metabolites or combinations with disease occurrence may employ methods such as, but not limited to, Discriminant Analysis (DA) (i.e., linear, quadratic, regularized DA), nuclear methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, enhancement/bagging methods), generalized linear models (i.e., logarithmic regression), principal component-based methods (i.e., SIMCA), generalized additive models, fuzzy logic-based methods, neural network-and genetic algorithm-based methods. For SVM models, the linear coefficients of each feature in the SVM classifier can be used to select the most important feature. Those features having the largest absolute values may be selected and the SVM model may be recalculated using only the selected features and the training set as desired.
When comparing test results of two different populations (e.g., one diseased and the other not), perfect separation between the two groups is rarely observed. Indeed, the distributions of test results will overlap, and therefore, when an intercept point or a standard value is selected and applied that distinguishes two populations, the disease will in some cases be correctly classified as positive (true positive score), but some cases of disease will be classified as negative (false negative score). On the other hand, some cases without disease will be correctly classified as negative (true negative score) while some cases without disease will be classified as positive (false positive score).
Tools such as ROC curve analysis can be used to assess the performance of such tests, or to test the accuracy with which disease groups are distinguished from control groups. The ROC curve is a graphical representation of sensitivity and specificity spectra generated using sensitivity as the y-axis, 1-specificity as the x-axis, and various cut-offs. In the ROC curve, the true positive rate (sensitivity) is plotted as a function of FP rate (100-specificity) for different cut-off points. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The ROC curve for the test with perfect discriminatory power (no overlap in the two distributions) passes through the upper left corner (sensitivity 100%, specificity 100%). Thus, qualitatively, the closer the graph is to the upper left corner, the higher the overall accuracy of the test. The area under the ROC curve (AUC) reflects the accuracy of the test and is shown in the lower left corner of the graph.
In a specific embodiment of the invention, PC (20:4(5Z,8Z,11Z,14Z)/18:0) has high accuracy, area under the curve >0.8, specificity >0.9, and can achieve effective differentiation between healthy control and cerebral infarction patient groups.
The invention has the advantages and beneficial effects that:
the invention discovers a metabolite marker-PC (20:4(5Z,8Z,11Z,14Z)/18:0) related to cerebral infarction for the first time, and can effectively distinguish healthy controls from patients with cerebral infarction by detecting the level of the metabolite marker so as to realize early diagnosis of cerebral infarction, thereby carrying out intervention treatment at the early stage of cerebral infarction and improving the quality of life of patients.
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 top panel of fig. A, B, C is the gross ion flowsheet for cerebral infarction and the bottom panel is the gross ion flowsheet for healthy controls.
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 chromatography positive ion statistical analysis.
FIG. 3 is a graph of the levels of PC (20:4(5Z,8Z,11Z,14Z)/18:0) in different groups.
FIG. 4 is a graph of diagnostic performance with PC (20:4(5Z,8Z,11Z,14Z)/18:0) 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 infarction 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 sugar, and glycated hemoglobin are 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, purgatives, 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.
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 the study, the patient has enrolled or is scheduled 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).
2) The history of blood transfusion, operation and trauma of digestive system diseases.
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 the study, the subject has enrolled or is scheduled to enroll in another clinical drug or device/interventional study.
2. Non-targeted metabolomic 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.
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.
2.2 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
Column water 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 deg.C.
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 mu 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 cocking pressure is 30psi, and the auxiliary pressure is 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-level 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 (A)Alcohol);
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. In order 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 was further performed on the two groups of samples using 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, SPSS is used for drawing a receiver operating characteristic curve (ROC), two accurate confidence spaces are calculated, and the diagnostic efficacy of the different 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
The results of differential analysis showed that the level of PC (20:4(5Z,8Z,11Z,14Z)/18:0) was significantly increased in the cerebral infarction group compared to the atherosclerosis group (fig. 3).
The diagnostic efficacy was judged using the amount of PC (20:4(5Z,8Z,11Z,14Z)/18:0) as the detection variable, and the results showed that the area under the curve was 0.804, the cutoff value was 0.671, the sensitivity was 0.667, and the specificity was 0.944 (FIG. 4), which was highly sensitive and specific.
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 (5)
1. Use of an agent for detecting the level of the metabolite marker PC (20:4(5Z,8Z,11Z,14Z)/18:0) in a sample for the manufacture of a product for diagnosing cerebral infarction, wherein the sample is selected from the group consisting of blood, serum, and plasma.
2. The use of claim 1, wherein the agent detects the level of the metabolite in the sample by one or more of targeted or non-targeted nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry.
3. Use according to claim 2, wherein the reagent detects the level of a metabolite in the sample by chromatography-mass spectrometry.
4. The use of any one of claims 1-3, wherein the subject has or is at risk of having a cerebral infarction when the level of the metabolite marker in the subject is down-regulated.
5. Use according to claim 1, wherein the product further comprises reagents for processing the sample.
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