CN112669958B - Metabolites as biomarkers for disease diagnosis - Google Patents

Abstract

The invention discloses a metabolite as a biomarker for disease diagnosis, and particularly relates to the metabolite PC (O-16:0/18:2(9Z,12Z)) or PC (20:0/18:1(11Z)) as a biomarker for cerebral infarction diagnosis. The present invention discloses that the levels of said metabolites in cerebral infarction are significantly reduced compared to the levels in healthy controls, enabling effective discrimination between healthy controls and cerebral infarction populations.

Description

Metabolites as biomarkers for disease diagnosis

technical field

The invention belongs to the field of biomedicine and relates to metabolites as biomarkers for disease diagnosis.

Background technique

Cerebral infarction, also known as ischemic stroke, refers to a clinical syndrome in which blood circulation disorders in the brain caused by various reasons lead to ischemia and hypoxic necrosis of local brain tissue, resulting in corresponding neurological deficits. Stroke is the second leading cause of death in the world, approximately 5.5 million people died of stroke in 2016, and stroke is also the second most common cause of disability-adjusted life years globally (GBD 2016 Stroke Collaborators. Global, regional, and national burden of stroke, 1 990-2016: a systematic analysis for the Global Burden of DiseaseStudy 2016 [J]. Lancet Neurol, 2019, 1 8(5): 459-480.). Consistent with other developing countries, the incidence of stroke in my country has still increased in the past two decades, but it is different from the trend of decreasing incidence in developed countries (Wu S, Wu B, Liu M, et a1.Stroke). in China: advancements and challenges inepidemiology, prevention, and management [J]. Lancet Neurol, 2019, 1 8(4): 394-405.). Ischemic stroke accounts for 85% of all cerebrovascular diseases, transient or permanent ischemia and hypoxia of brain tissue caused by stenosis or occlusion of supplying arteries, and reperfusion therapy is estimated to damage an estimated 2 million neurons per minute of delay (. Saver JL. Timeis brain-quantified [J]. Stroke, 2006, 37(01): 263-266.). Because of its high morbidity, easy recurrence, and chronic sequelae after acute onset, it has the characteristics of disability and high mortality, and the annual treatment cost of stroke and brain cell protection is huge (Pandian JD, Gall SL, Kate MP, et a1. Prevention of stroke: a global perspective [J]. Lancet, 2018, 392(10154): 1269-1278.).

The detection of serum markers is convenient, the operation is simple, and the cost is not high, and its application in the diagnosis and treatment of some diseases has been recognized. Metabolomics is an emerging omics technology and an important part of "systems biology". The main research objects of metabolomics are small molecular compounds with mass less than 1000 in body fluids, cells and tissues of biological organisms. Analysis with modern instruments. It reveals changes in metabolic pathways in vivo by qualitatively or quantitatively studying changes in the type, quantity, and content of disturbed metabolites (endogenous metabolites) in the body. Metabolomics is at the end of transcription, gene and protein expression, and can directly and accurately reflect the current pathophysiological state of organisms. It is widely used in disease diagnosis, drug development, nutrition, toxicology, sports medicine and other fields, especially It is to provide a reliable theoretical basis and means for clinical disease diagnosis. The study of metabolites with significant differences in blood and the search for serum markers are of great significance for the early diagnosis of cerebral infarction.

SUMMARY OF THE INVENTION

In order to evaluate the correlation between metabolites and cerebral infarction, the present invention collects samples from healthy controls and cerebral infarction, comprehensively analyzes the metabolomics of the samples, and selects metabolites with significantly different contents in the two groups. The diagnostic efficacy of differential metabolites was further analyzed to discover biomarkers suitable for the diagnosis and treatment of cerebral infarction.

Specifically, the invention provides the following technical solutions:

The present invention provides an application of a reagent for detecting a biomarker in a sample in preparing a product for diagnosing cerebral infarction, the biomarker is selected from PC(O-16:0/18:2(9Z, 12Z)) or PC( 20:0/18:1(11Z)) either or both.

Further, the reagents include detection reagents for chromatography, spectroscopy, mass spectrometry, and chemical analysis.

As known to those skilled in the art, a mass spectrometer typically consists of three components: an ion source, a mass analyzer, and a detector. The ionizer converts a portion of the sample into ions. As described below, a wide variety of ionization techniques exist depending on the phase of the sample (solid, liquid, gas) and the efficiency of the various ionization mechanisms for the unknown species. Mass spectrometers also typically include an extraction system that removes ions from the sample before passing the ions through the mass analyzer and onto a detector. The difference in the mass-to-charge ratio (m/z) of the fragments allows the mass analyzer to classify ions by their mass-to-charge ratio. Finally, the detector measures the value of the amount of indicator, providing data for calculating the abundance of each ion present.

In a typical mass spectrometry procedure, the first step involves the ionization of the sample. In one embodiment, the ionization includes electron ionization (E1), which includes bombarding the sample with electrons. In another embodiment, the ionization includes chemical ionization (CI), according to which ions are generated by collision of the analyte with ions of a reactive gas present in the ion source (examples of suitable reactive gases include methane, ammonia and Isobutane). In another embodiment, the ionization comprises atmospheric pressure chemical ionization (APCI). In another embodiment, the ionization comprises atmospheric pressure photon ionization (APPI).

When the ionization is electron ionization, this usually results in a mass ion with the same mass (M) as the parent molecule, but with a charge (M ⁺ or M- ⁾ . When the ionization is chemical ionization, this usually results in a mass ion with the mass of the parent molecule and the chemical species used to ionize the molecule, well known examples include [M+H] ⁺ , [MH] ⁻ , [M+NH4] ⁺ and [ M+Na] ⁺ . Such molecular ions are also referred to in this specification as "pseudomolecular ions".

In another embodiment, the ionization comprises electrospray ionization (ESI), wherein a liquid containing the analyte of interest is dispersed into a fine aerosol by electrospray. In another embodiment, the ionization includes matrix-assisted laser desorption/ionization (MALDI), which generally involves a three-step process, as follows: (1) The sample is mixed in a suitable matrix material and applied to a surface, usually a metal (2) irradiating the sample, typically with a pulsed laser, triggering ablation and desorption of the sample and matrix material; (3) ionizing analyte molecules by protonation or deprotonation in a thermal plume of ablation gas The ions are accelerated into the mass spectrometer used to analyze them. These ionization techniques are well known to those skilled in the art. Ionization, especially electron ionization, can cause the molecules of some samples to fragment into charged fragments.

After ionization, the ions generated in the first step are separated according to the mass-to-charge ratio (m/z) in the mass analyzer. This is usually done by one or more of the following mass-to-charge ratio separation techniques: by quadrupole electric fields used in quadrupole mass spectrometers, by ion trap quadrupole electric fields used in ion trap mass spectrometers, by longitudinal ions used in time-of-flight mass spectrometers Propagation time, and electric and/or magnetic field deflections traditionally used by electric and magnetic sector mass spectrometers. The last technique involves accelerating ions and subjecting them to an electric or magnetic field that deflects the ions. Ions with the same mass-to-charge ratio will experience the same amount of deflection.

After separation, the ions are detected. Typically, the detector records the induced charge or current that is generated when ions pass or strike a surface. In a scanning instrument, the signal generated in the detector during the scan and the position of the instrument in the scan will produce a mass spectrum, a record of the ions as a function of m/z.

Further, the mass spectrometry is chromatography-mass spectrometry.

As an alternative embodiment, the chromatographic technique is gas chromatography, and the combined technique is called gas chromatography-mass spectrometry (GC/MS, GCMS or GC-MS). In this technique, gas chromatography is used to separate the different compounds, as known to those skilled in the art. The separated compound stream is sent to a mass spectrometer for ionization, mass analysis, and detection as described above.

As another preferred embodiment, the chromatography technique is liquid chromatography, and the combined technique is called liquid chromatography-mass spectrometry (LC/MS, LCMS or LC-MS). As known to those skilled in the art, this technique uses liquid mobile phase chromatography to separate compounds. Typically, the liquid phase is a mixture of water and an organic solvent. The separated compound stream is then fed to a mass spectrometer for ionization, mass analysis and detection as described above.

As a preferred embodiment, the mass spectrometry is tandem mass spectrometry. The tandem mass spectrometry is selected from ion trap mass spectrometry, quadrupole time-of-flight mass spectrometry, triple quadrupole mass spectrometry, quadrupole ion trap mass spectrometry, ion mobility-quadrupole ion trap-time-of-flight mass spectrometry, Quadrupole-Orbitrap Mass Spectrometry, Ion Mobility Spectrometer-Quadruple Ion Trap Mass Spectrometry, Quadrupole-Orbitrap Mass Spectrometry, Triple Quadrupole-Orbitrap Mass Spectrometry, Quadrupole Ion Trap-Orbitrap Mass Spectrometry Mass spectrometry, time of flight or ion trap-Fourier transform mass spectrometry.

"Sample" and "sample" are used interchangeably herein, and as used herein refer to a composition obtained or derived from a subject (eg, an individual of interest) that contains Cells and/or other molecular entities to be characterized and/or identified by physiological characteristics. For example, the phrase "disease sample" or variants thereof refers to any sample obtained from a subject of interest that is expected or known to contain the cellular and/or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (eg, tumor tissue samples), primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph, synovial fluid, follicles follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture medium , tissue extracts such as homogenized tissue, tumor tissue, cell extracts, and combinations thereof.

Further, the sample is selected from blood, serum, and plasma.

After isolation and analysis by appropriate mass spectrometry, the metabolites identified in the subject's sample can be used to detect cerebral infarction in the subject. Typically, this step involves comparing the level of the biomarker in a sample of the subject to a reference value, wherein the level of the biomarker in the sample compared to the reference value is indicative of the level of the biomarker in the subject cerebral infarction.

As an alternative embodiment, a decrease in the level of the biomarker in the sample compared to the reference value is indicative of cerebral infarction in the subject. In one embodiment, an increase in the level of a metabolite in the sample compared to the reference value is indicative of cerebral infarction in the subject. The difference from the reference value may be an increase as defined and exemplified below, or a decrease as defined and exemplified below.

Typically, an increase or decrease in the level of a biomarker in a sample compared to a reference value is measured as the % mean difference. In this specification, the term "% mean difference" refers to the % difference in the total ion count of each mass ion in a subject with cerebral infarction compared to the total ion count in a reference subject (ie, control) .

Where the measured value includes an increase in the total ion count of ions of appropriate mass in subjects with cerebral infarction, the % mean difference compared to the reference value is measured as (mean disease/mean control) x 100%. Where the measurement includes a reduction in total ion counts for ions of appropriate mass in subjects with disease, the % mean difference compared to the reference value is measured as (mean control/mean disease) x 100%. Therefore, the % mean difference always exceeds 100%, except in the case where the total ion count for ions of appropriate mass in subjects with cerebral infarction is exactly the same as the reference value.

In embodiments wherein an increase in the level of the biomarker in the sample compared to the reference value is indicative of cerebral infarction in the subject, the % mean difference in the level of the one or more biomarkers in the sample compared to the reference value is not Special restrictions. In one embodiment, the % mean difference is at least 100%, such as at least 101%, such as at least 102%, such as at least 103%, such as at least 104%, such as at least 105%, such as at least 106%, such as at least 107% , such as at least 108%, such as at least 109%, such as at least 110%, such as at least 112%, such as at least 114%, such as at least 116%, such as at least 118%, such as at least 120%, such as at least 130%, such as at least 140% , such as at least 150%, such as at least 160%, such as at least 170%, such as at least 180%, such as at least 190%, such as at least 200%, such as at least 250%, such as at least 300%, such as at least 350%, such as at least 400% , such as at least 450%, such as at least 500%, such as at least 550%, such as at least 600%, such as at least 650%, such as at least 700%, such as at least 750%, such as at least 800%, such as at least 850%, such as at least 900% , such as at least 950%, such as at least 1000%, such as at least 1100%, such as at least 1200%, such as at least 1300%, such as at least 1400%, such as at least 1500%, such as at least 1600%, such as at least 1700%, such as at least 1800% , such as at least 1900%, such as at least 2000%, such as at least 2500%, such as at least 3000%, such as at least 3500%, such as at least 4000%, such as at least 4500%, such as at least 5000%, such as at least 5500%, such as at least 6000% , such as at least 6500%, such as at least 7000%, such as at least 7500%, such as at least 8000%, such as at least 8500%, such as at least 9000%, such as at least 9500%, such as at least 10,000%, such as at least 11,000%, such as at least 12,000% , such as at least 13,000%, such as at least 14,000%, such as at least 15,000%, such as at least 16,000%, such as at least 17,000%, such as at least 18,000%, such as at least 19,000%, such as at least 20,000%, such as at least 25,000%, such as at least 30,000% , such as at least 35,000%, such as at least 40,000%, such as at least 45,000%, such as at least 50,000%, such as at least 55,000%, such as at least 60,000%, such as at least 65,000%, such as at least 70,000%, such as at least 75,000%, such as at least 80,000% , such as at least 85,000%, such as at least 90,000%, such as at least 95,000%, such as at least 100,000%.

In embodiments where an increase in the level of the biomarker in the sample compared to a reference value is indicative of cerebral infarction in the subject, the % mean difference is typically 101% to 15,000%, such as 105% to 12,000%, such as 110% to 10,000%, such as 110% to 9000%, such as 120% to 8000%, such as 130% to 7000%, such as 140% to 6000%, such as 150% to 5000%, such as 160% to 4000%, such as 170% to 3000 %, such as 180% to 2500%, such as 190% to 2250%, such as 200% to 2000%, such as 250% to 1900%, such as 300% to 1800%, such as 350% to 1700%, such as 400% to 1600% , eg 450% to 1550%, eg 500% to 1500%.

In embodiments wherein a decrease in the biomarker level in the sample compared to the reference value is indicative of cerebral infarction in the subject, the % mean difference in the biomarker level in the sample compared to the reference value is not particularly limited. In one embodiment, the % mean difference is at least 100%, such as at least 101%, such as at least 102%, such as at least 103%, such as at least 104%, such as at least 105%, such as at least 106%, such as at least 107% , such as at least 108%, such as at least 109%, such as at least 110%, such as at least 112%, such as at least 114%, such as at least 116%, such as at least 118%, such as at least 120%, such as at least 130%, such as at least 140% , such as at least 150%, such as at least 160%, such as at least 170%, such as at least 180%, such as at least 190%, such as at least 200%, such as at least 250%, such as at least 300%, such as at least 350%, such as at least 400% , such as at least 450%, such as at least 500%, such as at least 550%, such as at least 600%, such as at least 650%, such as at least 700%, such as at least 750%, such as at least 800%, such as at least 850%, such as at least 900% , such as at least 950%, such as at least 1000%, such as at least 1100%, such as at least 1200%, such as at least 1300%, such as at least 1400%, such as at least 1500%, such as at least 1600%, such as at least 1700%, such as at least 1800% , such as at least 1900%, such as at least 2000%, such as at least 2500%, such as at least 3000%, such as at least 3500%, such as at least 4000%, such as at least 4500%, such as at least 5000%, such as at least 5500%, such as at least 6000% , such as at least 6500%, such as at least 7000%, such as at least 7500%, such as at least 8000%, such as at least 8500%, such as at least 9000%, such as at least 9500%, such as at least 10,000%, such as at least 11,000%, such as at least 12,000% , such as at least 13,000%, such as at least 14,000%, such as at least 15,000%, such as at least 16,000%, such as at least 17,000%, such as at least 18,000%, such as at least 19,000%, such as at least 20,000%, such as at least 25,000%, such as at least 30,000% , such as at least 35,000%, such as at least 40,000%, such as at least 45,000%, such as at least 50,000%, such as at least 55,000%, such as at least 60,000%, such as at least 65,000%, such as at least 70,000%, such as at least 75,000%, such as at least 80,000% , such as at least 85,000%, such as at least 90,000%, such as at least 95,000%, such as at least 100,000%.

In embodiments where a decrease in the level of the biomarker in the sample compared to a reference value is indicative of cerebral infarction in the subject, the % mean difference is typically 101% to 15,000%, such as 105% to 12,000%, such as 110% to 10,000%, such as 110% to 9000%, such as 120% to 8000%, such as 130% to 7000%, such as 140% to 6000%, such as 150% to 5000%, such as 160% to 4000%, such as 170% to 3000 %, such as 180% to 2500%, such as 190% to 2250%, such as 200% to 2000%, such as 250% to 1900%, such as 300% to 1800%, such as 350% to 1700%, such as 400% to 1600% , eg 450% to 1550%, eg 500% to 1500%.

Preferably the biomarkers are statistically significant (ie p-value less than 0.05 and/or q-value less than 0.10, eg using Welch's T-test or Wilcoxon's rank-sum Test) determined) levels differed.

The present invention provides a kit for diagnosing cerebral infarction, the kit comprises PC(O-16:0/18:2(9Z,12Z)) and/or PC(20:0/18:1) in a detection sample (11Z)); and instructions for using the kit to assess whether a subject has or is susceptible to cerebral infarction.

Further, the reagent detects the content and/or concentration of PC(0-16:0/18:2(9Z,12Z)) and/or PC(20:0/18:1(11Z)).

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

The most reliable results are likely to be obtained when samples are processed in a laboratory setting. For example, a sample can be obtained from a subject in a doctor's office and sent to a hospital or commercial medical laboratory for further testing. However, in many cases it may be desirable to provide immediate results in a clinician's office or allow subjects to be tested at home. In some cases, the need for a test that is portable, prepackaged, disposable, usable by a subject without assistance or guidance, etc., is more important than a high degree of accuracy. In many cases, especially with physician follow-up, an initial test, or even a test with reduced sensitivity and/or specificity, may be sufficient. Thus, assays provided in kit form may involve the detection and measurement of relatively small amounts of metabolites to reduce assay complexity and cost.

Any form of sample assay described herein that is capable of detecting sample metabolites can be used. Typically, the assay will quantify metabolites in a sample to a certain 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, cassettes, cartridges, chip- or bead-based arrays, multiwell plates or series of containers, and the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of selected sample metabolites. The subject's sample can be allocated directly to the assay, or indirectly from a stored or previously obtained sample. The presence or absence of metabolites above or below a predetermined threshold can be detected, for example, by chromogenic, fluorescent, electrochemiluminescent or other output (eg as in an enzyme immunoassay (EIA) such as an enzyme-linked immunoassay (ELISA) ) to display.

In one embodiment, a kit may comprise a solid substrate such as a chip, slide, array, etc., with reagents capable of detecting and/or quantifying one or more sample metabolites immobilized at predetermined locations on the substrate . As an illustrative example, a chip may be provided with reagents immobilized at discrete predetermined locations for use in detecting and quantifying the presence and/or concentration and/or amount of a biomarker in a sample. As described above, reduced levels of the biomarkers were found in samples from subjects with cerebral infarction. The chip can be configured such that a detectable output (eg, a color change) is provided only when the concentration of one or more of these metabolites exceeds a threshold that is selected or that distinguishes a biomarker indicative of a control subject The concentration and/or amount of the biomarker is the concentration and/or amount of the biomarker indicative of a patient suffering from or susceptible to cerebral infarction. Thus, the presence of a detectable output, such as a color change, immediately indicates that the sample contains significantly reduced levels of biomarkers, indicating that the subject has or is susceptible to cerebral infarction.

The present invention provides an application of biomarkers selected from PC(0-16:0/18:2(9Z) in constructing a computational model for predicting cerebral infarction or a system or device embedded with the computational model. ,12Z)) or PC (20:0/18:1(11Z)) or both.

The computational model employs algorithms developed and obtained by applying statistical methods. For example, suitable statistical methods are discriminant analysis (DA) (ie linear, quadratic, regular DA), Kernel methods (ie SVM), nonparametric methods (ie k-nearest neighbor classifier), PLS (partial least squares) , tree-based methods (i.e. logistic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (i.e. logarithmic regression), principal component-based methods (i.e. SIMCA), generalized superposition models, fuzzy-based Logical methods, methods based on neural networks and genetic algorithms. The skilled artisan will have no problem choosing suitable statistical methods for evaluating the marker combinations of the invention and thereby obtaining suitable mathematical algorithms. In one embodiment, the statistical method used to obtain the mathematical algorithm used in the assessment of cerebral infarction is selected from the group consisting of DA (ie linear, quadratic, ruled discriminant analysis), Kernel methods (ie SVM), nonparametric methods (ie k-nearest Neighbor Classifier), PLS (Partial Least Squares), tree-based methods (i.e. logistic regression, CART, random forest methods, boosting methods), or generalized linear models (i.e. logarithmic regression).

The area under the receiver operating curve (=AUC) is an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of a diagnostic method is best described by its receiver operating characteristic (ROC). The ROC plot is a line plot derived from all sensitivity/specificity pairs that continuously change the decision threshold over the entire data range observed.

The clinical performance of a laboratory test depends on its diagnostic accuracy, or ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy measures the ability to correctly discriminate between two different conditions of the subject under investigation. Such conditions are eg health and disease or disease progression versus no disease progression. In each case, the ROC line plot depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity for the entire range of decision thresholds. On the y-axis is the sensitivity, or true positive score [defined as (number of true positive test results)/(number of true positives + number of false negative test results)]. This is also known as a positivity for the presence of a disease or condition. It is calculated from the affected subgroup only. On the x-axis is the false positive score, or 1-specificity [defined as (number of false positive results)/(number of true negatives + number of false positive results)]. It is an indicator of specificity and is calculated entirely from the unaffected subgroup. Because the true and false positive scores are calculated completely separately by using test results from two different subgroups, the ROC line graph does not depend on the prevalence of disease in the sample. Each point on the ROC line graph represents a sensitivity/1-specificity pair corresponding to a specific decision threshold. A test with perfect discrimination (two outcome distributions do not overlap) has an ROC line plot through the upper left corner, where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity) ). The theoretical line plot for a test that is indistinguishable (the two groups have the same distribution of results) is a 45° diagonal line from the lower left to the upper right. Most line graphs fall between these two extremes. (If the ROC line graph falls completely below the 45° diagonal, then this is easily corrected by reversing the criteria for "positive" from "greater than" to "less than" or vice versa.) Qualitatively, the closer the line graph is to the upper left corner, The overall accuracy of the test is higher.

A convenient goal of quantifying the diagnostic accuracy of a laboratory test is to express its performance by a single numerical value. The most common global measure is the area under the ROC curve (AUC). Conventionally, this area is always ≥ 0.5 (if this is not the case, the decision rule can be reversed to make it so). The value range is between 1.0 (test values that perfectly separate the two groups) and 0.5 (no significant distributional difference between the test values of the two groups). The area depends not only on specific parts of the line graph such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how the ROC line graph is close to the perfect one (area = 1.0).

The overall assay sensitivity will depend on the specificity required to perform the methods disclosed herein. In some preferred settings, a specificity of 75% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk of cerebral infarction is based on a specificity of 80%, 85%, or also preferably 90% or 95%.

The present invention provides a computational model of cerebral infarction or a system or device embedded with the computational model. The level of 0/18:1(11Z)) is used as the input variable, and the discriminant value is output.

The discriminant value is calculated based on the concentration and/or amount of the biomarker in the sample and the discrimination with the concentration and/or amount of the biomarker stored in a computational model as explanatory variables.

The present invention provides the application of biomarkers in the preparation of medicines for treating cerebral infarction, the biomarkers are selected from PC(0-16:0/18:2(9Z, 12Z)) or PC(20:0/18 :1(11Z)) either or both.

Further, the medicament comprises an agent that increases the level of PC(0-16:0/18:2(9Z,12Z)) or PC(20:0/18:1(11Z)).

Advantages and beneficial effects of the present invention:

The present invention first discovered biomarkers related to cerebral infarction: PC(O-16:0/18:2(9Z,12Z)), PC(20:0/18:1(11Z)), by detecting biomarkers Compared with healthy controls, it can judge whether subjects have cerebral infarction and the risk of cerebral infarction, in order to achieve early diagnosis of cerebral infarction, so as to intervene in the early stage of cerebral infarction and improve the life and survival of patients quality.

Description of drawings

Figure 1 is the total ion flow diagram of each group of chromatography, wherein, Figure A is the total ion flow diagram of each group of positive ions in reverse chromatography, Figure B is the total ion flow diagram of each group of negative ions in reverse chromatography, and Figure C is the positive mode of hydrophilic chromatography The total ion current map of each group; the upper part in Figures A, B, and C is the total ion current map of cerebral infarction, and the lower part is the total ion current map of the healthy control. Figure 2 is the OPLS-DA statistical analysis diagram, wherein Figure A is a reverse chromatography positive ion statistical analysis diagram; Figure B is a reverse chromatography negative ion statistical analysis diagram; Figure C is a hydrophilic chromatography positive ion statistical analysis diagram.

Figure 3 is a graph of the diagnostic efficacy of biomarkers.

Detailed ways

The present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are only used to illustrate the present invention and not to limit the scope of the present invention. The experimental methods that do not specify specific conditions in the examples are usually in accordance with conventional conditions.

Example Screening and efficacy judgment of cerebral infarction-related metabolites

1. Sample collection

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

Inclusion criteria for cerebral infarction group:

1) Subjects have signed informed consent

2) Meet the diagnostic criteria for acute cerebral infarction in "China Guidelines for Diagnosis and Treatment of Acute Ischemic Stroke (2014 Edition)".

3) Age 18-65 years old.

4) BMI 18.5-23.9 kg/m ² .

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

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

Exclusion criteria:

1) Combined with other diseases: nervous system diseases (previous cerebral infarction, cerebral hemorrhage, multiple sclerosis, etc.); various chronic digestive system diseases, acute digestive system diseases within 3 months; circulatory system diseases (coronary heart disease, heart failure, etc.) , 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, mental illness, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).

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

3) Patients with abnormal ECG.

4) Taking the following drugs within 3 months: antibiotics, laxatives, clonazepam, sex hormone drugs, oral contraceptives, mesalazine, TNF-α inhibitors, immunosuppressants, antidepressants, PPIs, rupata Drugs, opioids, calcium, vitamin D, metformin, folic acid, beta-sympathetic inhalers, traditional Chinese medicine.

5) Take probiotics within 3 months.

6) Antiplatelet and statins should be used before the onset of the disease.

7) Patients undergoing intravenous thrombolysis and endovascular interventional therapy.

8) Pregnant or lactating women.

9) During this study, the patient has been enrolled or planned to be enrolled in another clinical drug or device/intervention study.

Inclusion criteria for healthy control group:

1) The subjects have signed the informed consent.

2) Age 18-65 years old.

3) BMI 18.5-23.9 kg/m ² .

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

5) TG, TC, HDL-C, LDL-C, blood sugar and glycosylated 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 system diseases (coronary heart disease, heart failure, atrial fibrillation); respiratory disease (chronic obstructive pulmonary disease, chronic bronchitis, asthma); metabolic disease (obesity, hyperlipidemia, diabetes, metabolic syndrome, osteoporosis); urinary system disease (chronic kidney disease) disease, kidney failure, kidney stones); blood disorders (anemia); other (gout, depression, mental illness, chronic fatigue syndrome, fibromyalgia, food allergies, tumors).

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

3) Abnormal ECG.

4) Taking the following drugs within 3 months: antibiotics, laxatives, clonazepam, sex hormone drugs, oral contraceptives, mesalazine, TNF-α inhibitors, immunosuppressants, antidepressants, PPIs, rupata Tablets, opioids, calcium, vitamin D, metformin, folic acid, beta-sympathetic inhalers, traditional Chinese medicine, antiplatelet drugs and statins.

5) Take probiotics within 3 months.

6) Pregnant or lactating women.

7) During this study, the subject has been enrolled or planned to be enrolled in another clinical drug or device/intervention study.

2. Non-targeted metabolomics detection

2.1 Serum sample preparation

2.1.1 Serum sample processing method for reversed-phase chromatography

1) Plasma/serum samples were thawed on ice at 4°C for 30-60min.

2) Take 40 μl of serum into a labeled 1.5 ml centrifuge tube, add 300 μl of methanol and 1 ml of methyl tert-butyl ether.

3) Fully shake for 15s to perform protein precipitation. 12000rpm, 4°C, centrifuge for 10min, take 100μl of the upper layer solution and put it in a 200μl lined tube for testing.

2.1.2 Serum sample processing method for hydrophilic chromatography analysis:

1) Plasma/serum samples were thawed on ice at 4°C for 30-60min.

2) Take 50 μl of serum into a labeled 1.5 ml centrifuge tube, and add 150 μl of acetonitrile.

3) Fully shake for 15s to perform protein precipitation. 12000rpm, 4°C, centrifuge for 10min, take 100μl of the upper layer solution and put it in a 200μl lined tube for testing.

2.2 Chromatographic conditions

Chromatographic separation Serum samples were analyzed using a Thermo Scientific U3000 Fast Liquid Chromatography using reversed-phase and hydrophilic chromatography.

2.2.1 Reversed-phase chromatography separation conditions

Chromatographic column: waters UPLC HSS T3 (1.8μm 2.1mm*100mm);

Mobile phase: A (acetonitrile/water 4:6, 0.1% formic acid, 10 mM ammonium acetate) and B (acetonitrile/isopropanol 9:1, 0.1% formic acid, 10 mM ammonium acetate);

Elution procedure: see Table 1;

Flow rate: 0.3ml/min;

The injection volume is 1.0 μL;

Column temperature: 50°C.

Table 1 C18 Reversed Phase Chromatography Determination Elution Procedure

2.2.1 Hydrophilic chromatography separation conditions

Chromatographic column: waters UPLC BEH Amide (1.7μm 2.1mm*100mm);

Mobile phase: A (acetonitrile, 0.1% formic acid, 10 mM ammonium acetate) and B (water, 0.1% formic acid, 10 mM ammonium acetate);

Elution procedure: see Table 2;

Flow rate: 0.3ml/min;

Injection volume: 1.0 μL;

Column temperature: 40°C.

Table 2 HILIC determination of polar small molecule elution procedure

2.3 Mass spectrometry conditions

Mass spectrometry was performed using a quadrupole orbital ion trap mass spectrometer equipped with a thermoelectrospray ion source. The positive and negative ion source voltages were 3.7kv and 3.5kV, respectively. The capillary heating temperature is 320°C. Air pressure 30psi, auxiliary air pressure 10psi. Volume heating evaporation temperature 300 ℃. Both the air and auxiliary gas are nitrogen. The collision gas was nitrogen at a pressure of 1.5 mTorr. The first-level full scan parameters are: resolution 70000, automatic gain control target of 1×10 ⁶ , maximum isolation time 50ms, and mass-to-charge ratio scan range of 50-1500. The LC/MS system was controlled by Xcalibur 2.2SP1.48 software, which operated both data acquisition and targeted metabolite quantification.

3. Targeted metabolomics detection

3.1 Serum sample processing method

1) Plasma samples were thawed at 4°C for 30 minutes.

2) Take 50 μl of plasma sample into a 1.5 ml centrifuge tube, add 150 μl of methanol (containing indoleacetic acid-D2 500ppb, indolepropionic acid-D2 50ppb), and vortex for 30 minutes.

3) Centrifuge at 12000 rpm for 5 min, take 100 μl of the supernatant, and place it in a 200 μl lined tube for testing.

3.2 Chromatographic conditions

Chromatographic separation adopts Waters ACQUITY UPLC I-CLASS ultra-high pressure liquid chromatography system, and the chromatographic separation conditions are as follows:

Chromatographic column: Waters UPLC BEH C8 (1.7 μm 2.1mm*100mm);

Mobile phase: A (water, _0.5Mm NH4F) and B (methanol);

Elution gradient: see Table 3;

Flow rate: 0.3ml/min;

Injection volume: 1.0 μL;

Column temperature: 45°C.

Table 3 Elution procedure

3.3 Mass spectrometry conditions

The mass spectrometer was an XEVO TQ-XS tandem quadrupole mass spectrometer from Waters. Positive ion ion source voltage is 3kv, cone voltage is 20V. Desolvation temperature 550°C, source temperature 150°C. The desolvation gas flow rate was 1000L/Hr, and the cone gas flow rate was 7L/h.

3.4 Targeted metabolome data processing

The peak area of the target metabolome data was calculated using masslynx quantitative software, and the error of retention time was 15s. The concentration was calculated using the single-point isotope internal standard method to obtain quantitative results.

4. Data processing

4.1 Data quality control

To evaluate the stability and repeatability of the system during sample collection, quality control samples were used. Quality control samples are obtained after all samples are pipetted and mixed in a fixed volume. Allegation samples were prepared in the same way as other samples. To obtain reliable and reproducible metabolites, three factors need to be considered: 1) retention time, 2) signal intensity, and 3) mass accuracy. In this experiment, 5 blank samples were used to equilibrate the chromatographic column first, and then 3 quality control samples were used to equilibrate the column conditions. Then every 6-8 samples were inserted a quality control sample to monitor the stability and repeatability of the whole liquid-mass system. At the same time, the coefficient of variation values of the metabolic features extracted from the quality control samples were calculated, and the metabolic features with a coefficient of variation exceeding 15% were deleted.

4.2 PCA analysis

All collected data, regardless of the separation mode or positive and negative ion mode, were processed by ProgenesisQI software, including the steps of importing raw data, peak alignment, peak extraction, normalization, and finally forming retention time, mass-charge A table of ratios and peak intensities. The peak extraction times of reversed-phase chromatography and hydrophilic chromatography were 1 to 16 and 1 to 12 min, respectively. Various additive ions such as hydrogenation and sodium addition are deconvolved into each ion feature. Metabolite identification was performed with first-order molecular weight matching using the Human Metabolome Database and the Lipid Database.

4.3 OPLS-DA analysis

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

The VIP (Variable Importance in the Projection) value of the OPLS-DA model (threshold value>1) was used in combination with the p value of the t-test (p<0.05) to find differentially expressed metabolites. The qualitative method for differential metabolites is: searching the online database (HMDB) (comparing mass-to-charge ratio m/z or exact molecular mass mass of mass spectra, with an error limit of 0.01 Da).

4.4 ROC analysis

According to the levels of metabolites, SPSS was used to draw receiver operating characteristic (ROC) curves, and binomial exact confidence spaces were calculated to analyze the diagnostic efficacy of differential metabolites.

5. Results

Figure 1 shows the total ion currents of positive and negative ions in reversed-phase chromatography and positive in hydrophilic chromatography in the cerebral infarction group and the healthy control group.

The quality control results show that the quality control samples are relatively clustered together, the system repeatability is good, and the collected data can be further studied.

The results of reverse chromatographic positive ions, reverse chromatographic negative ions, and hydrophilic chromatographic positive ions are shown in Table 4 and Figure 2, respectively.

Table 4 OPLS-DA analysis model parameters

The results of bioinformatics analysis showed that compared with the healthy group, PC(O-16:0/18:2(9Z,12Z)) (healthy control vs cerebral infarction group: 62578932.74 vs 38669067.2), PC(20:0/18 :1(11Z)) (healthy control vs cerebral infarction group: 4859829.494 vs 2729506.257) was significantly lower.

ROC curves were used to analyze PC(O-16:0/18:2(9Z,12Z)), PC(20:0/18:1(11Z)), and PC(O-16:0/18:2( The diagnostic efficacy of 9Z, 12Z)) and PC (20:0/18:1(11Z)), the ROC curve and the corresponding area under the curve are shown in Figure 3 and Table 5, respectively, and the cutoff values are 0.674, 0.678, 0.830, indicating that PC(O-16:0/18:2(9Z,12Z)), PC(20:0/18:1(11Z)) alone or in combination as markers for detecting cerebral infarction have a higher diagnosis Efficacy, especially the combination of the two, has high accuracy, sensitivity and specificity.

Table 5 Area under the curve of biomarkers

a. Under the nonparametric assumptions

b. Null hypothesis: real area = 0.5

The description of the above embodiment is only for understanding the method and the core idea of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims (9)

1. Use of a reagent for detecting a biomarker in a sample selected from the group consisting of blood, serum, plasma, in the manufacture of a product for diagnosing cerebral infarction, wherein the biomarker is selected from the group consisting of PC (O-16:0/18:2(9Z,12Z)) and PC (20:0/18:1(11Z)), wherein the level of PC (O-16:0/18:2(9Z,12Z)) or PC (20:0/18:1(11Z)) is reduced in a patient with cerebral infarction as compared to a healthy control.

2. The use of claim 1, wherein the reagent comprises a reagent for chromatography, spectroscopy, mass spectrometry, or chemical analysis detection.

3. Use according to claim 2, wherein the mass spectrometry is chromatography-mass spectrometry.

4. Use according to claim 3, wherein said mass spectrometry is tandem mass spectrometry.

5. The use of claim 4, wherein said tandem mass spectrometry is selected from the group consisting of ion trap mass spectrometry, quadrupole time-of-flight mass spectrometry, triple quadrupole mass spectrometry, quadrupole ion trap mass spectrometry, ion mobility-quadrupole ion trap-time-of-flight mass spectrometry, quadrupole-orbitrap mass spectrometry, ion mobility spectrometer-quadrupole ion trap mass spectrometry, quadrupole-orbitrap mass spectrometry, triple quadrupole-orbitrap mass spectrometry, quadrupole ion trap-orbitrap mass spectrometry, time-of-flight or ion trap-Fourier transform mass spectrometry.

6. The use of claim 1, wherein the product comprises instructions for assessing whether a subject is suffering from or susceptible to a cerebral infarction.

7. Use according to claim 1, wherein the product further comprises reagents for processing the sample.

8. Use of a biomarker selected from the group consisting of PC (O-16:0/18:2(9Z,12Z)) and PC (20:0/18:1(11Z)) in the construction of a computational model for diagnosing cerebral infarction, or a system or device in which the computational model is embedded, wherein the computational model outputs a discrimination value, determining whether a subject has or is at risk of having a cerebral infarction, using the levels of the biomarkers PC (O-16:0/18:2(9Z,12Z)) and PC (20:0/18:1(11Z)) as input variables.

9. A computational model for diagnosing cerebral infarction or a system or device embedded with the computational model is characterized in that the computational model takes the levels of biomarkers PC (O-16:0/18:2(9Z,12Z)) and PC (20:0/18:1(11Z)) as input variables, outputs discrimination values and judges whether a subject has cerebral infarction or is at risk of cerebral infarction.

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