Biomarkers for Early Prediction of Cardiovascular Disease (CVD) in Diabetic Patients and Their Uses

Abstract

The present application relates to a kit for predicting whether a subject has a cardiovascular disease, particularly a cardiovascular event, and to the use of a reagent for determining the level of a biomarker in a biological sample in the preparation of the kit. The biomarker of the present application can be used to predict the risk of cardiovascular disease, particularly cardiovascular events (such as acute myocardial infarction, acute heart failure, acute cerebral infarction, and sudden death) in a subject (such as a subject with diabetes).

Description

[0001] This divisional application is a divisional application of the original Chinese patent application with the application number CN202111315640.8, the application date of November 8, 2021, and the invention title of "Biomarkers for Early Prediction of Cardiovascular Disease (CVD) in Diabetic Patients and Their Uses". Technical Field

[0002] This application relates to the field of medicine. Specifically, this application relates to a kit for predicting whether a diabetic subject has a risk of developing cardiovascular disease, especially cardiovascular events, and the use of a reagent for determining the level of a biomarker in a biological sample in the preparation of the kit. Background Art

[0003] Diabetes is a major chronic disease that endangers human health. Diabetic patients have a higher and earlier risk of developing cardiovascular disease (CVD) compared to non-diabetic patients, and their risk of death also increases by 2 - 4 times. Currently, the exact molecular biological mechanism by which diabetes promotes the occurrence of cardiovascular disease is still unclear, making it difficult to effectively predict the risk of cardiovascular disease in the diabetic population at an early stage.

[0004] Early warning of the risk of cardiovascular and cerebrovascular diseases associated with diabetes can most effectively apply limited medical resources to high-risk populations of cardiovascular diseases and achieve precise treatment. Previous studies have shown that intracellular acylcarnitine of lipids is related to the risk of CVD. In prospective cohort studies and case-control studies, lipid profiles and plasma choline pathway metabolites are related to major cardiovascular events. However, there has been no report on related metabolites for early prediction of cardiovascular disease, especially cardiovascular events, in diabetes so far.

[0005] High-performance liquid chromatography tandem mass spectrometry detection technology combines liquid chromatography and mass spectrometry. While separating different polar components of a sample, it provides the relative molecular mass and structural information of metabolites, and is suitable for the analysis and determination of trace and ultra-trace substances in complex biological systems. It has the characteristics of high sensitivity, high separation efficiency, fast analysis speed, and is not affected by the volatility and stability of the sample.

[0006] Predicting the risk of cardiovascular disease, especially cardiovascular events, using factors other than traditional risk factors, improving the ability to early predict high-risk populations of cardiovascular diseases, and warning high-risk populations for early intervention are particularly important. Summary of the Invention

[0007] Through a large number of experiments and repeated explorations, the inventors of this application obtained biomarkers that can be used to characterize hyperglycemia-related cardiovascular diseases, especially cardiovascular events, through the analysis of 4 groups of different samples. Therefore, the biomarkers of this application are helpful for diabetic patients to predict the risk of cardiovascular disease, especially cardiovascular events.

[0008] Accordingly, in a first aspect, the present application provides the use of a reagent for determining the level of a biomarker in a biological sample in the preparation of a kit for predicting whether a subject with diabetes has a risk of cardiovascular disease, particularly a cardiovascular event; wherein the biomarker is selected from dihydroxyacetonephosphate, taurocyamine, lactosylceramide (d18:1 / 12:0), glycerophosphocholines PC (18:1 / 18:1), palmitoyl sphingomyelin, glycerophosphocholines PC (20:0 / 18:2), diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3), glycerophosphocholines PC (24:1 / 14:0), glucosylceramide (d18:1 / 18:0), diacylglycerol DAG (18:4 / 24:1 / 0:0), sphingomyelins SM (d18:1 / 24:1), 1-stearoylglycerophosphoinositol, stearoylcarnitine, or any combination thereof.

[0009] Specifically, the CAS number of dihydroxyacetone phosphate is CAS: 10030-20-3; the CAS number of taurocyamine is CAS: 543-18-0; the CAS number of lactosylceramide (d18:1 / 12:0) is CAS: 474943-80-1; the HMDB ID number of glycerophosphocholines PC (18:1 / 18:1) is HMDB: 0008103; the CAS number of palmitoyl sphingomyelin is CAS: 6254-89-3; the HMDB ID number of glycerophosphocholines PC (20:0 / 18:2) is HMDB: 0008270; the HMDB ID number of diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3) is HMDB0056266; the HMDB ID number of glycerophosphocholines PC (24:1 / 14:0) is HMDB: 0008788; the CAS number of glucosylceramide (d18:1 / 18:0) is CAS: 85305-87-9; the HMDB ID number of diacylglycerol DAG (18:4 / 24:1 / 0:0) is HMDB: 0007355; the CAS number of sphingomyelins SM (d18:1 / 24:1) is CAS: 94359-13-4; the CAS number of 1-stearoylglycerophosphoinositol is CAS: 327620-46-2; the CAS number of stearoylcarnitine is CAS: 25597-09-5.

[0010] On the other hand, the present application provides a method for predicting whether a subject with diabetes has a risk of cardiovascular disease, especially cardiovascular events, the method comprising:

[0011] (1) Detect the level of a biomarker in a biological sample from the subject, wherein the biomarker is selected from dihydroxyacetone phosphate, taurocyamine, lactosylceramide (d18:1 / 12:0), glycerophosphocholines PC (18:1 / 18:1), palmitoyl sphingomyelin, glycerophosphocholines PC (20:0 / 18:2), diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3), glycerophosphocholines PC (24:1 / 14:0), glucosylceramide (d18:1 / 18:0), diacylglycerol DAG (18:4 / 24:1 / 0:0), sphingomyelins SM (d18:1 / 24:1), 1-stearoylglycerophosphoinositol, stearoylcarnitine, or any combination thereof;

[0012] (2) Compare the level of the biomarker in the biological sample of the subject with the level of the corresponding biomarker in a control sample, wherein a change in the level of the biomarker can be used as a predictive indicator of the subject having a risk of cardiovascular disease, especially a cardiovascular event.

[0013] In certain embodiments, an increase in the level of a biomarker in the subject, as compared to the level of the biomarker in a control sample, can serve as an indicator for predicting that the subject has a risk of cardiovascular disease, particularly a cardiovascular event, and the biomarker is selected from dihydroxyacetone phosphate, lactosylceramide (d18:1 / 12:0), glycerophosphocholines PC (18:1 / 18:1), palmitoyl sphingomyelin, glycerophosphocholines PC (20:0 / 18:2), diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3), glycerophosphocholines PC (24:1 / 14:0), glucosylceramide (d18:1 / 18:0), diacylglycerol DAG (18:4 / 24:1 / 0:0), sphingomyelins SM (d18:1 / 24:1), 1-stearoylglycerophosphoinositol, stearoylcarnitine.

[0014] In certain embodiments, a decrease in the level of a biomarker in the subject, as compared to the level of the biomarker in a control sample, can serve as an indicator for predicting that the subject has a risk of having cardiovascular disease, and the biomarker is selected from taurocyamine.

[0015] In certain embodiments, the cardiovascular disease is selected from coronary heart disease, ischemic stroke, peripheral arterial disease or heart failure; the cardiovascular event is selected from acute myocardial infarction, acute heart failure, acute cerebral infarction and sudden death; and the diabetes is a related disease caused by hyperglycemia, such as type 1 diabetes and type 2 diabetes.

[0016] In certain embodiments, the cardiovascular event refers to the first occurrence of a lethal or non-lethal myocardial infarction, a lethal or non-lethal stroke, hospitalization for congestive heart failure or sudden death.

[0017] In certain embodiments, the biomarker is selected from any one or more combinations of the following groups (1) to (7):

[0018] (1) Glycerophosphocholines PC(18:1 / 18:1), Glycerophosphocholines PC(20:0 / 18:2), and Glycerophosphocholines PC(24:1 / 14:0);

[0019] (2) Palmitoyl sphingomyelin and sphingomyelins SM(d18:1 / 24:1);

[0020] (3) Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3) and Diacylglycerol DAG(18:4 / 24:1 / 0:0);

[0021] (4) Lactosylceramide(d18:1 / 12:0) and Glucosylceramide(d18:1 / 18:0);

[0022] (5) Dihydroxyacetone phosphate;

[0023] (6) Taurocyamine;

[0024] (7) 1-stearoylglycerophosphoinositol and Stearoylcarnitine.

[0025] In certain embodiments, the biological sample is selected from whole blood, serum, plasma, or any combination thereof obtained from a subject.

[0026] In certain embodiments, the subject is a mammal, such as a human.

[0027] In certain embodiments, the reagent (e.g., the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh reagent, twelfth reagent, and / or thirteenth reagent or reagent combination) determines the level of a biomarker in the biological sample by methods such as chromatographic and / or mass spectrometric determination, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), and turbidimetry.

[0028] In certain embodiments, the reagent determines the level of a biomarker in the biological sample by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography coupled with mass spectrometry.

[0029] In certain embodiments, the kit further includes reagents and / or consumables for chromatography (e.g., liquid chromatography).

[0030] In certain embodiments, the reagents and / or consumables for chromatography are selected from chromatographic columns, aqueous acetonitrile solutions, ammonium acetate, ammonium formate, formic acid, or any combination thereof.

[0031] In another aspect, the present application provides a kit for predicting whether a subject with diabetes has a risk of cardiovascular disease, particularly cardiovascular events. The kit contains reagents for determining the level of biomarkers in a biological sample of the subject. The biomarkers are selected from dihydroxyacetone phosphate, taurocyamine, lactosylceramide (d18:1 / 12:0), glycerophosphocholines PC (18:1 / 18:1), palmitoyl sphingomyelin, glycerophosphocholines PC (20:0 / 18:2), diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3), glycerophosphocholines PC (24:1 / 14:0), glucosylceramide (d18:1 / 18:0), diacylglycerol DAG (18:4 / 24:1 / 0:0), sphingomyelins SM (d18:1 / 24:1), 1-stearoylglycerophosphoinositol, stearoylcarnitine, or any combination thereof.

[0032] In certain embodiments, the cardiovascular disease is selected from coronary heart disease, ischemic stroke, peripheral arterial disease, or heart failure; the cardiovascular event is selected from acute myocardial infarction, acute heart failure, acute cerebral infarction, and sudden death; and the diabetes is a related disease caused by hyperglycemia, such as type 1 diabetes and type 2 diabetes.

[0033] In certain embodiments, the cardiovascular event refers to the first occurrence of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, hospitalization due to congestive heart failure, or sudden death.

[0034] In certain embodiments, the biomarker is selected from any one or more combinations of the following groups (1) to (7):

[0035] (1) Glycerophosphocholines PC(18:1 / 18:1), Glycerophosphocholines PC(20:0 / 18:2), and Glycerophosphocholines PC(24:1 / 14:0);

[0036] (2) Palmitoyl sphingomyelin and sphingomyelins SM(d18:1 / 24:1);

[0037] (3) Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3) and Diacylglycerol DAG(18:4 / 24:1 / 0:0);

[0038] (4) Lactosylceramide(d18:1 / 12:0) and Glucosylceramide(d18:1 / 18:0);

[0039] (5) Dihydroxyacetone phosphate;

[0040] (6) Taurocyamine;

[0041] (7) 1-stearoylglycerophosphoinositol and Stearoylcarnitine.

[0042] In certain embodiments, the biological sample is selected from whole blood, serum, plasma, or any combination thereof obtained from a subject.

[0043] In certain embodiments, the subject is a mammal, such as a human.

[0044] In certain embodiments, the reagent (e.g., the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, tenth, eleventh, twelfth reagent, and / or the thirteenth reagent or reagent combination) determines the level of a biomarker in the biological sample by the following methods: determination by chromatography and / or mass spectrometry, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), and turbidimetry.

[0045] In certain embodiments, the reagent determines the level of a biomarker in the biological sample by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography coupled with mass spectrometry.

[0046] In certain embodiments, the kit further includes reagents and / or consumables for chromatography (e.g., liquid chromatography).

[0047] In certain embodiments, the reagents and / or consumables for chromatography are selected from chromatographic columns, aqueous acetonitrile solutions, ammonium acetate, ammonium formate, formic acid, or any combination thereof.

[0048] Definition of Terms

[0049] In the present invention, unless otherwise specified, scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. Moreover, the operating steps such as molecular genetics, nucleic acid chemistry, chemistry, molecular biology, biochemistry, cell culture, microbiology, cell biology, genomics, and recombinant DNA used herein are all conventional steps widely used in the corresponding fields. Meanwhile, for a better understanding of the present invention, the definitions and explanations of related terms are provided below.

[0050] As used herein, the term "Biomarker" refers to a biochemical index that can mark the changes of a system, organ, tissue, cell, and subcellular structure or them, and has very wide applications. Biomarkers can be used for predicting disease risks, determining disease stages, or evaluating the safety and effectiveness of new drugs or new therapies in the target population.

[0051] As used herein, the term "metabolite" or "metabolic product" refers to a substance produced in a chemical or physical process in the human body. It includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage, or consumption of a biomolecule. Such molecules include, but are not limited to: acids and related compounds; mono-, di-, and tricarboxylic acids (saturated, unsaturated aliphatic, aryl, alkaryl); aldehydo acids, keto acids; lactone forms; gibberellins; abscisic acid; alcohols, polyols, derivatives, and related compounds; ethanol, benzyl alcohol, methanol; propylene glycol, glycerol, phytol; inositol, furfuryl alcohol, menthol; aldehydes, ketones, quinones, derivatives, and related compounds; acetaldehyde, butyraldehyde, benzaldehyde, acrolein, furfural, glyoxal; acetone, butanone; anthraquinone; carbohydrates; monosaccharides, disaccharides, trisaccharides; alkaloids, amines, and other bases; pyridine (including nicotinic acid, nicotinamide); pyrimidine (including cytosine, thymine); purine (including guanine, adenine, xanthine / hypoxanthine, kinetin); pyrrole; quinoline (including isoquinoline); morphinan, tropane, cinchonans, nucleotides, oligonucleotides, derivatives, and related compounds; guanosine, cytosine, adenosine, thymidine, inosine; amino acids, oligopeptides, derivatives, and related compounds; esters; phenols and related compounds; heterocyclic compounds and derivatives; pyrrole, tetrapyrrole; flavonoids; indole; lipids (including fatty acids and triglycerides), derivatives, and related compounds; carotenoids, phytoene, and sterols, isoprenoids, including terpenes; and any modified forms of the above molecules. In some embodiments, the metabolite is a product of the metabolism of an endogenous substance. In some embodiments, the metabolite is a product of the metabolism of an exogenous substance. In some embodiments, the metabolite is a product of the metabolism of an endogenous substance and an exogenous substance.

[0052] Certain metabolites are represented by CAS numbers. Certain metabolites are represented by the ID numbers of the HMDB (Human Metabolome Database).

[0053] As described above, a "CAS number" refers to a CAS number assigned to each substance by the Chemical Abstracts Service (CAS), a division of the American Chemical Society. In the field of biochemistry, a CAS number is a synonym for the unique identifier of a substance, i.e., each CAS number corresponds to a unique substance. Similarly, an HMDB ID number refers to the unique ID number corresponding to each substance in the Human Metabolome Database.

[0054] As used herein, the term "complication" refers to the occurrence of another disease or symptom in the same patient during the development of a disease, and the latter is the complication of the former.

[0055] As used herein, the term "cardiovascular event" refers to the first occurrence of a fatal or non-fatal myocardial infarction, a fatal or non-fatal stroke, hospitalization for heart failure, or sudden cardiac death. Cardiovascular events generally refer to the serious consequences of diseases caused by cardiovascular diseases, including sudden cardiac death, acute myocardial infarction, acute heart failure, acute cerebral infarction, etc., and are the main causes of disability or death in patients. Acute myocardial infarction refers to an acute condition in which the coronary artery is suddenly blocked, the heart muscle becomes necrotic due to lack of blood supply, the heart function is damaged, and life may be endangered. Acute heart failure refers to a clinical syndrome in which the myocardial contractility is reduced and the heart load is increased due to the acute onset or exacerbation of left ventricular dysfunction, resulting in a sudden drop in acute cardiac output, an increase in pulmonary circulation pressure, an increase in peripheral vascular resistance, causing pulmonary congestion and acute pulmonary congestion, pulmonary edema, and may be accompanied by tissue and organ hypoperfusion and cardiogenic shock, with left heart failure being the most common. Acute heart failure can be acutely exacerbated on the basis of pre-existing chronic heart failure or can occur suddenly. Acute heart failure often endangers life and must be rescued emergently. Acute cerebral infarction refers to the necrosis of brain tissue caused by a sudden interruption of cerebral blood supply, mainly due to atherosclerosis and thrombosis of the arteries supplying blood to the brain, resulting in stenosis or occlusion of the lumen, leading to focal acute cerebral hypoperfusion and onset.

[0056] As used herein, the term "change in level" refers to a value that has changed relative to a control level or a normal level. The normal level is based on historical normal control samples or normal control samples tested in the same experiment. The specific "normal" value will depend on, for example, the type of analysis (e.g., ELISA, enzyme activity, immunohistochemistry, PCR, spectroscopy), the sample to be tested (e.g., cell type and culture conditions, test sample), and other considerations known to those skilled in the art. Control samples can be used to define the threshold between normal and abnormal.

[0057] As used herein, the term "control level" refers to the level of a biomarker in a sample from a subject or group of subjects known to have a specific condition or not have a specific condition.

[0058] As used herein, the term "control sample" refers to any clinically relevant comparative sample, including, for example, samples from healthy subjects without cardiovascular disease, or samples from an earlier time point, e.g., prior to treatment, or from a subject at an earlier stage of treatment or disease. A control sample can be a purified sample, e.g., a protein, nucleic acid, and / or lipid extracted using a kit. Such a control sample can be diluted, e.g., serially diluted, to permit quantitative determination of an analyte in a test sample. A control sample can include samples from one or more subjects. A control sample can be a sample obtained from a subject to be evaluated at an earlier time point, e.g., prior to onset, at an earlier stage of the disease, or prior to administration of a treatment or a portion of a treatment for cardiomyopathy or other condition (especially with an agent known to induce cardiomyopathy, e.g., treatment with a type 2 diabetes drug, a chemotherapeutic agent). A control sample can also be a sample from an animal model, or a sample from a tissue or cell line obtained from an animal model of cardiovascular disease. The activity or expression level of a biomarker in a control sample consisting of a set of measurements can be determined, e.g., according to any suitable statistical metric, such as, for example, a measure of central tendency (including mean, median, or modal value).

[0059] In one embodiment, the control is a standardized control, e.g., a control predetermined using the mean expression level of one or more markers from a population without cardiovascular disease. In additional embodiments of the invention, the control level of a marker is the level of the marker in a normal sample from a subject.

[0060] As used herein, the term "biological sample" is a body fluid or tissue in which a target biomarker may be present. In certain embodiments, the sample is blood, vomit, saliva, lymph, cyst fluid, urine, fluid collected by bronchoalveolar lavage, fluid collected by peritoneal lavage, or a gynecological fluid. In one embodiment, the subject sample is a blood sample or a component thereof (e.g., serum). The sample can be a tissue sample from a subject, e.g., a heart tissue sample from a subject. In certain embodiments, the tissue is selected from bone, connective tissue, cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, and skin. Cell samples or samples from laboratory animals can be used in many of the same experimental methods provided herein for human biological or subject samples.

[0061] Beneficial Effects of the Invention

[0062] Compared with the prior art, the biomarker of the present application can be used as a biomarker for characterizing hyperglycemia-related CVD. Further, through these biomarkers, it is possible to predict whether a diabetic patient has cardiovascular disease, especially the risk of cardiovascular events. Therefore, the biomarker of the present application can be used to predict the risk of cardiovascular disease, especially cardiovascular events (such as acute myocardial infarction, acute heart failure, acute cerebral infarction, and sudden death) in a subject (e.g., a subject with diabetes). The biomarker can effectively serve as a biomarker for predicting the risk of cardiovascular disease in diabetic patients, and has the advantages of being non-invasive, accurate, early, and highly precise.

[0063] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings and examples. However, those skilled in the art will understand that the following drawings and examples are only used to illustrate the present invention and do not limit the scope of the present invention. According to the following detailed description of the drawings and preferred embodiments, various objects and advantageous aspects of the present invention will become apparent to those skilled in the art. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Shows the results of principal component analysis of plasma metabolites in different populations. Among them, "NDM (non CVD)" represents samples of diabetes without CVD; "NDM (with CVD)" represents samples of diabetes with CVD; "NGT (non CVD)" represents samples of normal blood glucose without CVD; "NGT (with CVD)" represents samples of normal blood glucose with CVD. CVD refers to cardiovascular events that have occurred, such as myocardial infarction, cerebral infarction, or hospitalization due to heart failure.

[0065] Figure 2 Shows the results of OPLS-DA analysis of plasma metabolic differential products between the population with diabetes and CVD and the population with normal blood glucose and without CVD in the positive ion detection mode.

[0066] Figure 3 Shows the results of OPLS-DA analysis of plasma metabolic differential products between the population with diabetes and CVD and the population with normal blood glucose and without CVD in the negative ion detection mode.

[0067] Figure 4 Shows the results of OPLS-DA analysis of plasma metabolic differential products between the population with diabetes without CVD and the population with normal blood glucose and without CVD in the positive ion detection mode.

[0068] Figure 5 Shows the results of OPLS-DA analysis of plasma metabolic differential products between the population with diabetes without CVD and the population with normal blood glucose and without CVD in the negative ion detection mode.

[0069] Figure 6The OPLS-DA analysis results of plasma metabolic differential products between the normal blood glucose combined with CVD group and the normal blood glucose without CVD group in the positive ion detection mode are shown.

[0070] Figure 7 The OPLS-DA analysis results of plasma metabolic differential products between the normal blood glucose combined with CVD group and the normal blood glucose without CVD group in the negative ion detection mode are shown.

[0071] Figure 8 A in [reference] shows the comparative graph of the relative contents of the biomarker dihydroxyacetone phosphate in different groups. Among them, "NGT-CVD" in the abscissa represents the sample with normal blood glucose and no CVD, "NDM-CVD" represents the sample with diabetes and no CVD, and "NDM+CVD" represents the sample with diabetes and having CVD; Figure 8 B in [reference] shows the ROC analysis results.

[0072] Figure 9 A in [reference] shows the comparative graph of the relative contents of the biomarker taurocyamine in different groups. Among them, "NGT-CVD" in the abscissa represents the sample with normal blood glucose and no CVD, "NDM-CVD" represents the sample with diabetes and no CVD, and "NDM+CVD" represents the sample with diabetes and having CVD; Figure 9 B in [reference] shows the ROC analysis results.

[0073] Figure 10 A in [reference] shows the comparative graph of the relative contents of the biomarker lactosylceramide (d18:1 / 12:0) in different groups. Among them, "NGT-CVD" in the abscissa represents the sample with normal blood glucose and no CVD, "NDM-CVD" represents the sample with diabetes and no CVD, and "NDM+CVD" represents the sample with diabetes and having CVD; Figure 10 B in [reference] shows the ROC analysis results.

[0074] Figure 11 A in [reference] shows the comparative graph of the relative contents of the biomarker glycerophosphocholines PC (18:1 / 18:1) in different groups. Among them, "NGT-CVD" in the abscissa represents the sample with normal blood glucose and no CVD, "NDM-CVD" represents the sample with diabetes and no CVD, and "NDM+CVD" represents the sample with diabetes and having CVD; Figure 11 B in [reference] shows the ROC analysis results.

[0075] Figure 12 A in it shows a comparative graph of the relative content of the biomarker palmitoyl sphingomyelin in different groups. Among them, "NGT-CVD" in the abscissa represents samples with normal blood glucose and no CVD, "NDM-CVD" represents samples with diabetes and no CVD, and "NDM+CVD" represents samples with diabetes and having CVD; Figure 12 B in it shows the ROC analysis results.

[0076] Figure 13 A in it shows a comparative graph of the relative content of the biomarker glycerophosphocholine (20:0 / 18:2) (Glycerophosphocholines PC(20:0 / 18:2)) in different groups. Among them, "NGT-CVD" in the abscissa represents samples with normal blood glucose and no CVD, "NDM-CVD" represents samples with diabetes and no CVD, and "NDM+CVD" represents samples with diabetes and having CVD; Figure 13 B in it shows the ROC analysis results.

[0077] Figure 14 A in it shows a comparative graph of the relative content of the biomarker diacylglycerol (22:1n9 / 0:0 / 22:6n3) (Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3)) in different groups. Among them, "NGT-CVD" in the abscissa represents samples with normal blood glucose and no CVD, "NDM-CVD" represents samples with diabetes and no CVD, and "NDM+CVD" represents samples with diabetes and having CVD; Figure 14 B in it shows the ROC analysis results.

[0078] Figure 15 A in it shows a comparative graph of the relative content of the biomarker glycerophosphocholine (24:1 / 14:0) (Glycerophosphocholines PC(24:1 / 14:0)) in different groups. Among them, "NGT-CVD" in the abscissa represents samples with normal blood glucose and no CVD, "NDM-CVD" represents samples with diabetes and no CVD, and "NDM+CVD" represents samples with diabetes and having CVD; Figure 15 B in it shows the ROC analysis results.

[0079] Figure 16A in it shows the comparative graph of the relative contents of the biomarker glucosylceramide (d18:1 / 18:0) in different groups. Among them, "NGT-CVD" in the abscissa represents the samples with normal blood glucose and no CVD, "NDM-CVD" represents the samples with diabetes and no CVD, and "NDM+CVD" represents the samples with diabetes and having CVD; Figure 16 B in it shows the ROC analysis results.

[0080] Figure 17 A in it shows the comparative graph of the relative contents of the biomarker diacylglycerol (18:4 / 24:1 / 0:0) in different groups. Among them, "NGT-CVD" in the abscissa represents the samples with normal blood glucose and no CVD, "NDM-CVD" represents the samples with diabetes and no CVD, and "NDM+CVD" represents the samples with diabetes and having CVD. Figure 17 B in it shows the ROC analysis results.

[0081] Figure 18 A in it shows the comparative graph of the relative contents of the biomarker sphingomyelins SM (d18:1 / 24:1) in different groups. Among them, "NGT-CVD" in the abscissa represents the samples with normal blood glucose and no CVD, "NDM-CVD" represents the samples with diabetes and no CVD, and "NDM+CVD" represents the samples with diabetes and having CVD; Figure 18 B in it shows the ROC analysis results.

[0082] Figure 19 A in it shows the comparative graph of the relative contents of the biomarker 1-stearoylglycerophosphoinositol in different groups. Among them, "NGT-CVD" in the abscissa represents the samples with normal blood glucose and no CVD, "NDM-CVD" represents the samples with diabetes and no CVD, and "NDM+CVD" represents the samples with diabetes and having CVD; Figure 19 B in it shows the ROC analysis results.

[0083] Figure 20 A in it shows the comparative graph of the relative contents of the biomarker stearoylcarnitine in different groups. Among them, "NGT-CVD" in the abscissa represents the samples with normal blood glucose and no CVD, "NDM-CVD" represents the samples with diabetes and no CVD, and "NDM+CVD" represents the samples with diabetes and having CVD.Figure 20 In [reference] B shows the results of the ROC analysis.

[0084] Figure 21 The ROC analysis results of the biomarker palmitoyl sphingomyelin in different samples are shown. Detailed implementation manners

[0085] The present invention will now be described with reference to the following examples which are intended to illustrate, but not limit, the present invention.

[0086] Unless otherwise specified, the experiments and methods described in the examples are carried out substantially according to the conventional methods well-known in the art and described in various reference documents. For example, for the conventional techniques such as immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics, and recombinant DNA used in the present invention, reference can be made to Sambrook, Fritsch, and Maniatis, "Molecular Cloning: A Laboratory Manual", 2nd Edition (1989); "Current Protocols in Molecular Biology" (edited by F.M. Ausubel et al., (1987)); the "Methods in Enzymology" series (Academic Press): "PCR 2: A Practical Approach" (edited by M.J. MacPherson, B.D. Hames, and G.R. Taylor (1995)), and "Animal Cell Culture" (edited by R.I. Freshney (1987)).

[0087] In addition, for those not specifying specific conditions in the examples, they are carried out according to the conventional conditions or the conditions recommended by the manufacturer. For those reagents or instruments without indicating the manufacturer, they are all conventional products that can be obtained commercially. Those skilled in the art know that the examples describe the present invention by way of illustration and are not intended to limit the scope claimed by the present invention. All the published cases and other reference materials mentioned herein are incorporated herein by reference in their entirety.

[0088] Example 1. Experimental Samples and Supplies

[0089] Experimental samples: 240 cases of plasma samples from 4 groups of clinical patients (fasting plasma) were selected, including: samples of normal blood glucose without CVD (NGT non CVD), samples of normal blood glucose with CVD (NGT with CVD), samples of diabetes without CVD (NDM non CVD), and samples of diabetes with CVD (NDM with CVD).

[0090] Experimental reagents and consumables: Chromatographic grade methanol, acetonitrile solvent (Merck), chromatographic grade formic acid (Thermo Fisher Scientific), 1.5 ml and 2.0 ml EP tubes.

[0091] Instrumentation: Ultra-high performance liquid chromatography (Waters Acquity TM UPLC system), high-resolution time-of-flight mass spectrometer Waters SYNAPT G2 HDMS (Waters Corp., Manchester, UK), cryogenic centrifuge (Thermo Fisher), micropipette (Eppendorf), ultra-low temperature freezer (Thermo Fisher).

[0092] Example 2. Extraction and Analysis of Metabolites

[0093] I. Experimental methods

[0094] 1. Preparation of sample solutions

[0095] Before the experiment, the samples were taken out of the -80 °C ultra-low temperature freezer and thawed at room temperature. 800 μL of methanol-acetonitrile (v / v = 4:1) was added to 200 μL of plasma sample, and centrifuged at 4 °C, 12,000 g for 30 min. The supernatant was taken for detection and analysis by liquid chromatography-mass spectrometry.

[0096] 2. Detection conditions of liquid chromatography-mass spectrometry

[0097] Ultra-high performance liquid chromatography detection conditions: The instrument used was Waters AcquityTM Ultra Performance LCSystem (Waters Corporation, Milford, MA, USA); chromatographic column: Waters Acquity BEH C18 column (100 mm × 2.1 mm, 1.7 μm); mobile phase: Phase A: 0.1% formic acid aqueous solution + 2 mM ammonium formate; Phase B: 0.1% formic acid acetonitrile solution + 2 mM ammonium formate; elution gradient program: 0 - 2 minutes, 1.0% to 45% B; 2 - 10 minutes, 45% to 70% B; 10 - 13 minutes, 70% B to 99%; 13 - 22 minutes, 99% B; 22 - 24 minutes, elution equilibrium with 1.0% B; flow rate was 0.45 mL / min; sample chamber temperature was 4 °C.

[0098] Detection conditions of high-resolution time-of-flight mass spectrometer: The instrument used is Waters SYNAPT G2 HDMS (Waters Corp., Manchester, UK); the ion source is an electrospray ion source; detection modes: positive ion and negative ion detection modes; capillary voltage: 3.0 kV (positive ion detection mode), 2.5 kV (negative ion detection mode); cone voltage: 40 V; extraction cone voltage: 4 V; desolvation gas flow rate: 800 L / h; desolvation gas temperature: 450 °C; cone gas flow rate: 30 L / h; ion source temperature: 100 °C; scan time interval: 0.1 s; delay time: 0.02 s; calibration solution: leucine enkephalin; data collection mass range: m / z 100 - 1200 Da.

[0099] 3. Multivariate statistical analysis of data

[0100] Preprocessing of metabolomics raw data

[0101] Using the MarkerLynx (MassLynx SCN 633) software in MassLynx V4.1, the UPLC-Q-TOF / MS raw data is automatically preprocessed for peak identification, noise filtering, etc. The parameter settings of the processing method are as follows: retention time range: 0.5 - 15 min; mass range: 50 - 1200 amu; mass window: 0.02 Da, peak intensity threshold (count): 300, retention time window: 0.2 s, noise elimination level: 6. A three-dimensional matrix including retention time, exact mass-to-charge ratio, and ion peak intensity / area is obtained.

[0102] Multivariate statistical analysis of metabolomics data

[0103] The data matrix obtained after preprocessing is imported into SIMCA-P software (Umetrics AB, Sweden, 13.0.3 version) for multivariate statistical analysis. The data is preprocessed by mean centering and Pareto scaling, and principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) are carried out in turn.

[0104] ① Principal component analysis (PCA)

[0105] Unsupervised principal component analysis (PCA) is used to perform pattern recognition on the data. It can reflect the original state of the data, be used to examine the separation of the data profiles of each group, and evaluate the explanatory ability R2X and predictive ability Q2 of the established model. However, since PCA cannot ignore the within-group errors and random errors irrelevant to the research purpose, it is not conducive to accurately determining the differences between groups.

[0106] ② Orthogonal partial least squares-discriminant analysis (OPLS-DA)

[0107] Based on the PCA analysis, the data was further analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA), and the explanatory ability R2X and predictive ability Q2 of the model were evaluated. The analysis results were represented by a score plot, S-plot, variable importance in projection (VIP) plot, and loading plot. The grouping information of each group of data could be observed through the score plot; the S-plot reflected the influence of each variable on sample grouping. Each point scattered at both ends of the S curve in the figure represented a variable. The farther the point was from the origin, that is, the greater the degree of dispersion, indicating that it contributed more to the grouping; the VIP value reflected the contribution of each variable to sample grouping. The greater the VIP value, the stronger the contribution of the variable to the grouping; in the loading plot, the farther the variable was from the center, the greater its contribution to the grouping. Through the loading analysis and VIP analysis of the S-plot, variables that made important contributions to distinguishing the differences between groups were found from the established OPLS-DA model.

[0108] II. Experimental Results

[0109] 1. Plasma Metabolic Profile Analysis of Diabetic Populations

[0110] Using the optimized UPLC-Q-TOF / MS metabolomics analysis method, plasma metabolomics analysis was carried out on plasma samples of four groups of people: normal glucose without CVD (NGT nonCVD), normal glucose with CVD (NGT with CVD), non-diabetic without CVD (NDM non CVD), and non-diabetic with CVD (NDM with CVD). The results of principal component analysis (PCA, Figure 1 ) showed that: (1) The plasma metabolic profiles of the four groups of people were distributed in three quadrants. The plasma metabolic profiles of people with normal blood glucose were relatively concentrated and not separated by whether they had CVD or not; (2) The plasma metabolic profiles of diabetic patients were significantly separated from those of people with normal blood glucose, and were affected by CVD. The plasma metabolic profiles of patients with and without CVD were also significantly separated, indicating that in diabetic populations, the presence or absence of CVD had a significant impact on the plasma metabolism of diabetic patients.

[0111] 2. Screening of Specific Markers

[0112] (1) Inter-group Differential Metabolites Related to Diabetes with CVD

[0113] Using multivariate statistical analysis, the plasma metabolic profile data of people with normal blood glucose and no CVD and people with diabetes with CVD collected were subjected to OPLS-DA analysis ( Figure 2 , Figure 3)。The screening criteria for group - difference variables in S - Plot: VIP>1, p[1]P>0, p(corr)>±0.6 as the screening threshold. 304 group - difference variables were obtained in the positive - ion mode, and 119 group - difference variables were obtained in the negative - ion mode.

[0114] (2) Group - difference metabolites related to diabetes

[0115] Using multivariate statistical analysis, the plasma metabolic profile data of the normoglycemic and CVD - free population and the diabetic and CVD - free population collected were subjected to OPLS - DA analysis ( Figure 4 , Figure 5 ). The screening criteria for group - difference variables in S - Plot: VIP>1, p[1]P>0, p(corr)>±0.6 as the screening threshold. 786 group - difference variables were obtained in the positive - ion mode, and 164 group - difference variables were obtained in the negative - ion mode.

[0116] (3) Group - difference metabolites related to CVD

[0117] Using multivariate statistical analysis, the plasma metabolic profile data of the normoglycemic and CVD - free population and the normoglycemic and CVD - combined population collected were subjected to OPLS - DA analysis ( Figure 6 , Figure 7 ). The screening criteria for group - difference variables in S - Plot: VIP>1, p[1]P>0, p(corr)>±0.6 as the screening threshold. 54 group - difference variables were obtained in the positive - ion mode, and 86 group - difference variables were obtained in the negative - ion mode.

[0118] (4) Specific biomarkers for diabetes - related CVD

[0119] The plasma metabolomics data of the diabetic and CVD - combined population, the diabetic and CVD - free population, the normoglycemic and CVD - combined population, and the normoglycemic and CVD - free population were compared respectively, and three groups of group - difference metabolite groups were obtained, namely, group - difference metabolites related to hyperglycemia with concomitant CVD (A), metabolites related to diabetes (B), and metabolites related only to CVD (C). Further, by the method of deduction, after excluding the effects of simple diabetes factors and CVD factors on the body, 72 specific biomarkers for diabetes - related CVD were finally obtained (60 were screened from the positive - ion detection mode, 13 from the negative - ion detection mode, and 1 metabolite was detected in both detection modes). With the screening threshold of VIP value greater than 1, relative peak area greater than 1000, and change - fold greater than 5, 13 metabolites (Table 1) were finally obtained. They can be used as plasma biomarkers for characterizing hyperglycemia - induced CVD, with the potential for assisting clinical diagnosis and the function of clinical CVD risk warning.

[0120] Table 1. List of Biomarkers Specifically Associated with Diabetes-Induced CVD

[0121]

[0122] Example 3. Detection of the Relative Content of Biomarker Dihydroxyacetone Phosphate in Plasma of Different Populations Figure 8

[0123] In this example, taking the biomarker dihydroxyacetone phosphate as an example, it is detected whether it can be used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above can all predict the risk of cardiovascular disease in diabetic subjects according to the method described in this example.

[0124] Experimental samples: Plasma samples of 3 groups of clinical patients (fasting plasma), including: 60 samples of normal blood glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0125] Experimental reagents and consumables: Chromatographic grade methanol, acetonitrile solvent (Merck), chromatographic grade formic acid (Thermo Fisher Scientific), 1.5 ml and 2.0 ml EP tubes.

[0126] Instrumentation: Ultra-high performance liquid chromatography (Waters Acquity TM UPLC system), high-resolution time-of-flight mass spectrometer Waters SYNAPT G2 HDMS (Waters Corp., Manchester, UK), cryogenic refrigerated centrifuge (Thermo Fisher), micropipette (Eppendorf), ultra-low temperature freezer (Thermo Fisher).

[0127] Experimental method:

[0128] 1. The extraction of metabolites is carried out according to the method described in Example 2. The detection conditions of the liquid chromatography-mass spectrometry are according to the method described in Example 2.

[0129] Experimental results:

[0130] Based on the established detection method, the relative contents of the biomarker dihydroxyacetone phosphate in samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) are detected and analyzed. As Figure 8As shown in A of [the figure], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of dihydroxyacetone phosphate in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), with a change multiple of about 587 times. The verification experiment results showed that the content of the biomarker dihydroxyacetone phosphate could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.6042, 95% CI (0.4973, 0.7111), with a relatively high prediction accuracy ( Example 4. Detection of the Relative Content of Biomarker Taurocyamine in Plasma of Different Populations in B of [the figure]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0131] Figure 9 Figure 9

[0132] In this embodiment, taking the biomarker taurocyamine as an example, it was detected whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0133] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal blood glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0134] The experimental reagents, consumables, and instrument equipment were the same as those in Example 3.

[0135] Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography - mass spectrometry were according to the method described in Example 2.

[0136] Experimental results:

[0137] Based on the established detection method, the relative contents of the biomarker taurocyamine in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As Example 5. Detection of the Relative Content of Biomarker Lactosylceramide (d18:1 / 12:0) in Plasma of Different PopulationsAs shown in A of [reference], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of taurocyamine in the samples of diabetes with CVD (NDM with CVD) was significantly lower than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was 5. The verification experiment results showed that the content of the biomarker taurocyamine could distinguish whether diabetic patients had concomitant CVD events. Through ROC curve analysis, the area under the curve was 0.8708, 95% CI (0.8045, 0.9371), with strong prediction accuracy ( Figure 10 as shown in B of [reference]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0138] Figure 10 Example 6. Detection of the Relative Content of Biomarker Glycerophosphocholines PC (18:1 / 18:1) in Plasma of Different Populations

[0139] In this example, the biomarker lactosylceramide (d18:1 / 12:0) was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this example.

[0140] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0141] Experimental reagents, consumables and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2.

[0142] Experimental results:

[0143] Based on the established detection method, the relative contents of the biomarker lactosylceramide (d18:1 / 12:0) in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 11As shown in A above, by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of lactosylceramide (d18:1 / 12:0) (Lactosylceramide (d18:1 / 12:0)) in samples of diabetes with CVD (NDM with CVD) was significantly higher than that in samples of diabetes without CVD (NDM non CVD), and the change multiple was 41 times. The verification experiment results showed that the content of the biomarker lactosylceramide (d18:1 / 12:0) (Lactosylceramide (d18:1 / 12:0)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.8574, 95% CI (0.7856, 0.9291), with strong prediction accuracy ( Figure 11 in B), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0144] Example 7. Detection of the Relative Content of Biomarker Palmitoylsphingomyelin in Plasma of Different Populations Figure 12

[0145] In this embodiment, taking the biomarker glycerophosphocholine (18:1 / 18:1) (Glycerophosphocholines PC (18:1 / 18:1)) as an example, it was detected whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0146] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0147] Experimental reagents, consumables and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2

[0148] Experimental results:

[0149] Based on the established inspection method, the relative contents of the biomarker glycerophosphocholine (18:1 / 18:1) (Glycerophosphocholines PC (18:1 / 18:1)) in samples of diabetes with CVD (NDM with CVD) and samples of diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 12As shown in A of [specific reference], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of glycerophosphocholine (18:1 / 18:1) (Glycerophosphocholines PC(18:1 / 18:1)) in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was > 10,000 times. The verification experiment results showed that the content of the biomarker glycerophosphocholine (18:1 / 18:1) (Glycerophosphocholines PC(18:1 / 18:1)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.6944, 95% CI (0.5968, 0.7921), with good prediction accuracy. Example 8. Detection of the Relative Content of Biomarker Glycerophosphocholines PC (20:0 / 18:2) in Plasma of Different Populations as shown in B of [specific reference], it can be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0150] Figure 13 Figure 13

[0151] In this embodiment, the biomarker palmitoyl sphingomyelin is taken as an example to detect whether it can be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above can all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0152] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0153] Experimental reagents, consumables, and equipment are the same as in Example 3. Experimental method: 1. The extraction of metabolites is carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography - mass spectrometry are according to the method described in Example 2. Experimental results:

[0154] Based on the established detection method, the relative contents of the biomarker palmitoyl sphingomyelin in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) are detected and analyzed. As Example 9. Detection of the Relative Content of Biomarker Diacylglycerol DAG (22:1n9 / 0:0 / 22:6n3) in Plasma of Different PopulationsAs shown in A of [Figure 0], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of Palmitoyl sphingomyelin in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was 34 times. The verification experiment results showed that the content of the biomarker Palmitoyl sphingomyelin could distinguish whether diabetic patients had concomitant CVD events. Through ROC curve analysis, the area under the curve was 0.9167, 95% CI (0.8586, 0.9747), with strong prediction accuracy ( Figure 14 as shown in B of [Figure 1]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0155] Figure 14 Example 10. Detection of the Relative Content of Biomarker Glycerophosphocholines PC (24:1 / 14:0) in Plasma of Different Populations

[0156] In this embodiment, taking the biomarker Glycerophosphocholines PC(20:0 / 18:2) as an example, it was detected whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0157] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0158] Experimental reagents, consumables, and instrument equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography - mass spectrometry instrument were according to the method described in Example 2. Experimental results:

[0159] Based on the established detection method, the relative contents of the biomarker Glycerophosphocholines PC(20:0 / 18:2) in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 15As shown by A in [reference], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of glycerophosphocholine (20:0 / 18:2) (Glycerophosphocholines PC(20:0 / 18:2)) in samples of diabetes with CVD (NDM with CVD) was significantly higher than that in samples of diabetes without CVD (NDM non CVD), and the change multiple was 35 times. The verification experiment results showed that the content of the biomarker glycerophosphocholine (20:0 / 18:2) (Glycerophosphocholines PC(20:0 / 18:2)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.7589, 95% CI (0.6619, 0.8559), with strong prediction accuracy( Figure 15 as shown by B in [reference]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0160] Example 11. Detection of the Relative Content of Biomarker Glucosylceramide (d18:1 / 18:0) in Plasma of Different Populations Figure 16

[0161] In this embodiment, the biomarker diacylglycerol (22:1n9 / 0:0 / 22:6n3) (Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3)) was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0162] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0163] Experimental reagents, consumables and instruments were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2. Experimental results:

[0164] Based on the established inspection method, the relative contents of the biomarker diacylglycerol (22:1n9 / 0:0 / 22:6n3) (Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3)) in samples of diabetes with CVD (NDM with CVD) and samples of diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 16As shown by A in [figure], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of diacylglycerol (22:1n9 / 0:0 / 22:6n3) (Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3)) in samples with diabetes and CVD (NDM with CVD) was significantly higher than that in samples with diabetes but without CVD (NDM non CVD), with a change multiple of 1074 times. The verification experiment results showed that the content of the biomarker diacylglycerol (22:1n9 / 0:0 / 22:6n3) (Diacylglycerol DAG(22:1n9 / 0:0 / 22:6n3)) could distinguish whether diabetic patients had concomitant CVD events. Through ROC curve analysis, the area under the curve was 0.8158, 95% CI (0.7354, 0.8963), with strong prediction accuracy( Example 12. Detection of the Relative Content of Biomarker Diacylglycerol DAG (18:4 / 24:1 / 0:0) in Plasma of Different Populations as shown by B in [figure]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0165] Figure 17 Figure 17

[0166] In this example, the biomarker glycerophosphocholine (24:1 / 14:0) (Glycerophosphocholines PC(24:1 / 14:0)) was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this example.

[0167] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0168] The experimental reagents, consumables, and instrument equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2. Experimental results:

[0169] Based on the established detection method, the relative contents of the biomarker glycerophosphocholine (24:1 / 14:0) (Glycerophosphocholines PC(24:1 / 14:0)) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes but without CVD (NDM non CVD) were detected and analyzed. As Example 13. Detection of the Relative Content of Biomarker Sphingomyelins SM (d18:1 / 24:1) in Plasma of Different PopulationsAs shown in A of [Figure / Table], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of glycerophosphocholine (24:1 / 14:0) (Glycerophosphocholines PC(24:1 / 14:0)) in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was 67 times. The verification experiment results showed that the content of the biomarker glycerophosphocholine (24:1 / 14:0) (Glycerophosphocholines PC(24:1 / 14:0)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.7810, 95% CI (0.6937, 0.8682), with strong prediction accuracy. Figure 18 as shown in B of [Figure / Table], it can be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0170] Figure 18 ​

[0171] In this example, the biomarker glucosylceramide (d18:1 / 18:0) (Glucosylceramide(d18:1 / 18:0)) was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above can all predict the cardiovascular disease risk of diabetic subjects according to the method described in this example.

[0172] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0173] The experimental reagents, consumables, and instruments and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2. Experimental results:

[0174] Based on the established detection method, the relative contents of the biomarker glucosylceramide (d18:1 / 18:0) (Glucosylceramide(d18:1 / 18:0)) in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As ​As shown in A of ​ , by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of glucosylceramide (d18:1 / 18:0) (Glucosylceramide (d18:1 / 18:0)) in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was 1192 times. The verification experiment results showed that the content of the biomarker glucosylceramide (d18:1 / 18:0) (Glucosylceramide (d18:1 / 18:0)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.8019, 95% CI (0.7189, 0.8849), with strong prediction accuracy ( ​ in B of ) and could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0175] ​ ​ ​

[0176] In this example, diacylglycerol (18:4 / 24:1 / 0:0) (Diacylglycerol DAG (18:4 / 24:1 / 0:0)) was taken as an example of a biomarker to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this example.

[0177] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0178] Experimental reagents, consumables and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2.

[0179] Experimental results:

[0180] Based on the established inspection method, the relative contents of the biomarker diacylglycerol (18:4 / 24:1 / 0:0) (Diacylglycerol DAG (18:4 / 24:1 / 0:0)) in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As​ As shown in A of , by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of diacylglycerol (18:4 / 24:1 / 0:0) (Diacylglycerol DAG(18:4 / 24:1 / 0:0)) in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was >10,000 times. The verification experiment results showed that the content of the biomarker diacylglycerol (18:4 / 24:1 / 0:0) (Diacylglycerol DAG(18:4 / 24:1 / 0:0)) could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.7447, 95% CI (0.6525, 0.837), with strong prediction accuracy ( ​ as shown in B of ), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0181] ​ ​

[0182] In this example, the biomarker sphingomyelin (d18:1 / 24:1) (sphingomyelins SM(d18:1 / 24:1)) was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this example.

[0183] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal blood glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0184] The experimental reagents, consumables, and instruments and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were carried out according to the method described in Example 2. Experimental results:

[0185] Based on the established inspection method, the relative contents of the biomarker sphingomyelin (d18:1 / 24:1) (sphingomyelins SM(d18:1 / 24:1)) in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As ​As shown in A above, by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of sphingomyelins SM(d18:1 / 24:1) in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was >10,000 times. The verification experiment results showed that the content of the biomarker sphingomyelins SM(d18:1 / 24:1) could distinguish whether diabetic patients had concomitant CVD events. Through ROC curve analysis, the area under the curve was 0.7519, 95% CI(0.6606, 0.8433), with strong prediction accuracy( ​ as shown in B), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0186] Example 14. Detection of the relative content of biomarker 1-stearoylglycerophosphoinositol in plasma of different populations stearoylglycerophosphoinositol

[0187] In this embodiment, the biomarker 1-stearoylglycerophosphoinositol was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0188] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0189] Experimental reagents, consumables, and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2.

[0190] Experimental results:

[0191] Based on the established inspection method, the relative contents of the biomarker 1-stearoylglycerophosphoinositol in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 19As shown in A of [Figure / Chart / Diagram] [0], by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of 1-stearoylglycerophosphoinositol in samples of diabetes with CVD (NDM with CVD) was significantly higher than that in samples of diabetes without CVD (NDM non CVD), and the change multiple was 8 times. The verification experiment results showed that the content of the biomarker 1-stearoylglycerophosphoinositol could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.6830, 95% CI (0.5775, 0.7884), with strong prediction accuracy ( Figure 19 as shown in B of [Figure / Chart / Diagram] [0]), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0192] Example 15. Detection of the relative content of biomarker Stearoylcarnitine in plasma of different populations relative content detection

[0193] In this embodiment, taking the biomarker Stearoylcarnitine as an example, it was detected whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations as described above could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0194] Experimental samples: 3 groups of clinical patient plasma samples (fasting plasma), including: 60 samples of normal glucose without CVD (NGT non CVD), 60 samples of diabetes without CVD (NDM non CVD), and 60 samples of diabetes with CVD (NDM with CVD).

[0195] Experimental reagents, consumables, and instrument equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2. Experimental results:

[0196] Based on the established detection method, the relative contents of the biomarker Stearoylcarnitine in samples of diabetes with CVD (NDM with CVD) and samples of diabetes without CVD (NDM non CVD) were detected and analyzed. As Figure 20As shown in A of , by comparing the peak areas of this biomarker in the above two groups of samples, the verification experiment found that the content of Stearoylcarnitine in the samples of diabetes with CVD (NDM with CVD) was significantly higher than that in the samples of diabetes without CVD (NDM non CVD), and the change multiple was 11 times. The verification experiment results showed that the content of the biomarker Stearoylcarnitine could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, the area under the curve was 0.7258, 95% CI (0.6286, 0.8230), with strong prediction accuracy ( Figure 20 in B of ), and it could be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0197] Example 16. Detection of the content of biomarker Palmitoylsphingomyelin in plasma of different populations content detection

[0198] In this embodiment, Palmitoyl sphingomyelin, a biomarker, was taken as an example to detect whether it could be used as a biomarker for predicting the cardiovascular disease risk of diabetic subjects. Other biomarkers or combinations could all predict the cardiovascular disease risk of diabetic subjects according to the method described in this embodiment.

[0199] Experimental samples: 450 clinical patient plasma samples (fasting plasma) were selected, including 179 samples of diabetes with CVD (NDM with CVD) and 271 samples of diabetes without CVD (NDM non CVD).

[0200] Experimental reagents, consumables and equipment were the same as those in Example 3. Experimental method: 1. The extraction of metabolites was carried out according to the method described in Example 2, and the detection conditions of the liquid chromatography-mass spectrometry were according to the method described in Example 2.

[0201] 2. Content determination

[0202] 2.1 Preparation of standard solution:

[0203] Weigh an appropriate amount of Palmitoyl sphingomyelin precisely and dissolve it in a methanol-acetonitrile (4:1) solvent to prepare a stock solution with a concentration of 200 μg / mL.

[0204] 2.2 Establishment of standard curve

[0205] Dilute the stock solution into standard solutions with contents of 2.5, 5, 10, 20, 25, 40, 60, and 80 μg / mL respectively. Precisely pipette the reference solution under item "2.1" and determine it according to the LC-MS detection conditions in Example 2. Using the reference concentration (mg / L) (X) as the abscissa and the peak area (Y) as the ordinate, plot the standard curve and establish the regression equation. y = 1817.2x - 4743.6, r 2 = 0.9992, the linear range is 2.5 - 80 μg / mL, the lowest detection limit LOD is 1.0 μg / mL, and the lowest quantification limit LOQ is 2.5 μg / mL.

[0206] 2.3 Precision test

[0207] Precisely pipette 4 μL of the reference solution prepared under item "2.1" and continuously inject samples 6 times according to the LC-MS detection conditions in Example 2. Calculate the relative standard deviation RSD% of the peak area, which is the within-day precision; continuously measure the above standard solution for three days (inject samples 3 times a day for 3 consecutive days), calculate the relative standard deviation RSD% of the peak area, which is the between-day precision.

[0208] 2.4 Repeatability test

[0209] Take 1 sample, parallelly prepare 6 test solutions according to the extraction and analysis method of metabolites in Example 2, determine them according to the LC-MS detection conditions in Example 2, and calculate the content and the relative standard deviation RSD%.

[0210] 2.5 Stability test

[0211] Take 1 sample, prepare the test solution according to the extraction and analysis method of metabolites in Example 2, determine it according to the LC-MS detection conditions in Example 2, inject samples at 0, 2, 4, 8, 12, and 24 h after preparation respectively, measure the peak area, and calculate the relative standard deviation RSD% of the peak area.

[0212] 2.6 Spiked recovery test

[0213] Precisely weigh 9 portions of the standard reference substance, add the standard product at 3 levels of 80%, 100%, and 120% of the known content respectively, determine it according to the LC-MS detection conditions in Example 2, and calculate the spiked recovery rate and the relative standard deviation RSD%.

[0214] 3 Content determination results

[0215] By content determination, the contents of the biomarker Palmitoyl sphingomyelin in the samples of diabetes with CVD (NDM with CVD) and diabetes without CVD (NDM non CVD) were 22.1±15.5 μg / mL and 8.6±4.6 μg / mL, respectively. The verification experiment results showed that the content of the biomarker Palmitoyl sphingomyelin could distinguish whether diabetic patients were accompanied by CVD events. Through ROC curve analysis, using the plasma concentration of Palmitoyl sphingomyelin to predict CVD, the area under the curve was 0.8264, 95% CI (0.808, 0.875). Through analysis, it was clear that the value with the best sensitivity and specificity was the optimal diagnostic cut-off value, which was Palmitoyl sphingomyelin exceeding 11.19 μg / mL in the population of this study. The sensitivity and specificity for predicting the risk of CVD were 79.7% and 69.3%, respectively, with strong predictive value( Figure 21 ), and it can be used as a biomarker for judging the cardiovascular disease risk of diabetic subjects.

[0216] Although the specific embodiments of the present invention have been described in detail, those skilled in the art will understand that: according to all the teachings that have been published, various modifications and changes can be made to the details, and these changes are all within the protection scope of the present invention. The entire scope of the present invention is given by the appended claims and any equivalents thereof.

Claims

1. Use of a reagent for determining the level of a biomarker in a biological sample in the preparation of a kit for predicting whether a subject with diabetes has: a risk of cardiovascular disease; wherein, the biomarker is a combination of 1-stearoyl phosphatidylinositol and stearoyl carnitine, and the biological sample is selected from whole blood, serum, plasma obtained from the subject, or any combination thereof.

2. The use according to claim 1, wherein, the cardiovascular disease is a cardiovascular event.

3. The use according to claim 1 or 2, wherein, the subject is a mammal.

4. The use according to claim 1 or 2, wherein, the subject is a human.

5. The use according to claim 1 or 2, wherein, the reagent determines the level of the biomarker in the biological sample by: determination by chromatography and / or mass spectrometry, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near-infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and turbidimetry.

6. The use according to claim 5, wherein, the reagent determines the level of the biomarker in the biological sample by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography coupled with mass spectrometry.

7. The use according to claim 5, wherein, the kit further comprises reagents and / or consumables for chromatography.

8. The use according to claim 7, wherein, the chromatography is liquid chromatography.

9. The use according to claim 7, wherein, the reagents and / or consumables for chromatography are selected from chromatographic columns, aqueous acetonitrile solutions, ammonium acetate, ammonium formate, formic acid, or any combination thereof.

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