CN116087485B

CN116087485B - Biomarkers for early prediction of cardiovascular disease (CVD) in diabetics and uses thereof

Info

Publication number: CN116087485B
Application number: CN202211510543.9A
Authority: CN
Inventors: 陈燕燕; 钱鑫; 何思垚; 邹忠梅; 贾红梅; 于猛
Original assignee: Fuwai Hospital of CAMS and PUMC
Current assignee: Fuwai Hospital of CAMS and PUMC
Priority date: 2021-11-08
Filing date: 2021-11-08
Publication date: 2023-10-20
Anticipated expiration: 2041-11-08
Also published as: CN116087487A; CN116087489A; CN116087488A; CN116087486B; CN114113573B; CN114113573A; CN116087486A; CN116087485A

Abstract

The present application relates to a kit for predicting whether a subject suffers from a cardiovascular disease, in particular a cardiovascular event, and the use of a reagent for determining biomarker levels in a biological sample in the preparation of a kit. The biomarkers of the application are useful for predicting the risk of cardiovascular disease, particularly cardiovascular events (e.g., acute myocardial infarction, acute heart failure, acute cerebral infarction, and sudden death) in a subject (e.g., a subject suffering from diabetes).

Description

Biomarkers for early prediction of cardiovascular disease (CVD) in diabetics and uses thereof

The divisional application is based on the original Chinese patent application with the application number of CN202111315640.8, the application date of 2021, 11 months and 8 days, and the application name of biomarker for early prediction of cardiovascular disease (CVD) of diabetics and application thereof.

Technical Field

The application relates to the field of medicine. The application relates in particular to a kit for predicting whether a diabetic subject is at risk of suffering from a cardiovascular disease, in particular a cardiovascular event, and the use of a reagent for determining the level of a biomarker in a biological sample in the preparation of a kit.

Background

Diabetes is a major chronic disease that is a hazard to human health. Diabetic patients have a higher, earlier risk of developing cardiovascular disease (CVD) than non-diabetic patients, and the risk of mortality is also increased by a factor of 2-4. At present, the exact molecular biological mechanism of diabetes for promoting the occurrence of cardiovascular diseases is not clear, and early and effective prediction of the cardiovascular disease risk of a diabetes patient group is difficult.

Early warning of diabetes complicated with cardiovascular and cerebrovascular diseases risks can most effectively apply limited medical resources to high risk groups of cardiovascular diseases, and accurate treatment is achieved. Previous studies have shown that lipid intracellular acyl carnitine is associated with CVD risk. In prospective cohort studies and case cohort studies, the lipidomic profile and plasma choline pathway metabolites are associated with major cardiovascular events. However, there has been no report of related metabolites that predict early stages of cardiovascular disease, particularly cardiovascular events, in diabetes.

The high performance liquid chromatography tandem mass spectrometry detection technology is characterized in that liquid chromatography and mass spectrometry are connected in series, the relative molecular mass and structural information of metabolites are provided while different polar components of a sample are separated, and the method is suitable for analysis and determination of trace and trace substances in a complex biological system, and has the characteristics of high sensitivity, high separation efficiency, high analysis speed, no influence of sample volatility and stability and the like.

The method utilizes factors other than traditional risk factors to predict cardiovascular diseases, especially cardiovascular event risks, improves the capability of early predicting cardiovascular high risk groups, and is particularly important to early warn the high risk groups to provide early intervention.

Disclosure of Invention

Through a great deal of experiments and repeated fumbling, the inventor obtains the biomarker which can be used for representing the cardiovascular diseases related to hyperglycemia, in particular to cardiovascular events through analysis of 4 groups of different samples. Thus, the biomarkers of the application are useful for predicting cardiovascular disease, particularly the risk of cardiovascular events, in diabetics.

Thus, in a first aspect, the present application provides the use of an agent for determining the level of a biomarker in a biological sample in the manufacture of a kit for predicting whether a subject suffering from diabetes is at risk for a cardiovascular disease, in particular a cardiovascular event; wherein the biomarker is selected from dihydroxyacetone phosphate (Dihydroxyacetone phosphate), guanyl taurine (Taurocyamine), lactose ceramide (d18:1/12:0) (lactosyerceramide (d18:1/12:0)), glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), palmitoyl sphingomyelin (Palmitoyl sphingomyelin), glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), diglycerides (22:1 n9/0:0/22:6n 3) (Diacylglycerol DAG (22:1 n9/0:0/22:6n 3)), glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)), glucose ceramide (d18:1/18:0)), glycerophosphorylcholine (18:4/24:1/2) (Diacylglycerol DAG:564/24:1/24), stearoyl phosphatidylinositol (24:1/12:1), stearoyl phosphatidylinositol (24:1/6 n 3), or any combination thereof.

Specifically, the CAS number of the dihydroxyacetone phosphate (Dihydroxyacetone phosphate) is CAS 10030-20-3; the CAS number of the amidino taurine (Taurocyamine) is CAS 543-18-0; the CAS number of lactose ceramide (d18:1/12:0) (Lactosylate (d18:1/12:0)) is CAS number 474943-80-1; the HMDB ID number of the glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) is HMDB:0008103; the CAS number of the palmitoyl sphingomyelin (Palmitoyl sphingomyelin) is CAS 6254-89-3; the HMDB ID number of the glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) is HMDB:0008270; the diglyceride (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) has an HMDB ID number of HMDB0056266; the HMDB ID number of the glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) is HMDB:0008788; the CAS number of the Glucosylceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) is CAS number 85305-87-9; the diglyceride (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) has an HMDB ID number of HMDB:0007355; the CAS number of the sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) is CAS 94359-13-4; the CAS number of the 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) is CAS (CAS: 327620-46-2); the CAS number of stearoyl carnitine (stearoyl carnitine) is CAS 25597-09-5.

In another aspect, the application provides a method for predicting whether a subject suffering from diabetes is at risk of cardiovascular disease, in particular cardiovascular event, the method comprising:

(1) Detecting the level of a biomarker in a biological sample from the subject, wherein the biomarker is selected from dihydroxyacetone phosphate (Dihydroxyacetone phosphate), guanyl taurate (taurocyanine), lactose ceramide (d 18:1/12:0)) (lactosylynamide (d 18:1/12:0)), glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), palmitoyl sphingomyelin (Palmitoyl sphingomyelin), glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), diglyceride (22:1 n9/0:0/22:6n 3) (Diacylglycerol DAG (22:1 n9/0:0/22:6n 3)), glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)), glucose ceramide (d 18:1/18:0) (glyceroglycoside (d 18:1/18:0)), glycerophosphorylcholine (18:4/24:4/24:561), stearoyl phosphatidylcholine (24:1/24:2), stearoyl phosphatidylinositol (24:1/2), stearoyl phosphatidylinositol (24:1/3), stearoyl phosphatidyl (24:1/2), stearoyl phosphatidyl (2), stearoyl cholesterol (d 1/6:1/6:1:3);

(2) Comparing the level of the biomarker in the biological sample of the subject with the level of the corresponding biomarker in a control sample, wherein an alteration in the level of the biomarker is capable of being a predictor that the subject is at risk for cardiovascular disease, particularly cardiovascular events.

In certain embodiments, an increase in the level of a biomarker selected from dihydroxyacetone phosphate (Dihydroxyacetone phosphate), lactose ceramide (d 18:1/12:0) (Lactosylceramide (d 18:1/12:0)), glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), palmitoyl sphingomyelin (Palmitoyl sphingomyelin), glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), diglycerides (22:1 n9/0:0/22:6n 3) (Diacylglycerol DAG (22:1 n9/0:0/22:6n 3)), glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)), glucose ceramide (d 18:1/18:0) (glyceroglycol (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), stearoyl phosphatidylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), stearoyl phosphatidylcholine (22:1/24:1/14:2)), stearoyl phosphatidylcholine (24:1/14:2), and the like, as an indicator of the risk of cardiovascular disease, particularly cardiovascular event, in the subject, compared to the level of the biomarker in a control sample.

In certain embodiments, a decrease in the level of a biomarker selected from the group consisting of guanyltaurine (Taurocyamine) in the subject as compared to the level of the biomarker in a control sample can be used as an indicator of predicting the risk of the subject having a cardiovascular disease.

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.

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

In certain embodiments, the biomarker is selected from the group consisting of any one or a combination of more of the following groups (1) to (7):

(1) Glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), and glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0));

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

(3) Diglycerides (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) and diglycerides (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0));

(4) Lactose ceramide (d18:1/12:0) (Lactosylceramide (d18:1/12:0)) and glucose ceramide (d18:1/18:0) (glucosyl ceramide (d18:1/18:0));

(5) Dihydroxyacetone phosphate (Dihydroxyacetone phosphate);

(6) Guanyl taurate (Taurocyamine);

(7) 1-stearoyl phosphatidylinositol (1-stearoyl glycidoxyphosphoinositol) and stearoyl carnitine (stearoyl carnitine).

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

In certain embodiments, the subject is a mammal, e.g., a human.

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

In certain embodiments, 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.

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

In certain embodiments, the reagents and/or consumables for chromatography are selected from a chromatographic column, an aqueous acetonitrile solution, ammonium acetate, ammonium formate, formic acid, or any combination thereof.

In another aspect, the application provides a kit for predicting whether a subject suffering from diabetes has cardiovascular disease, in particular cardiovascular events, comprising reagents for determining the level of a biomarker selected from dihydroxyacetone phosphate (Dihydroxyacetone phosphate), guanyl taurine (taurocyyamine), lactose ceramide (d18:1/12:0) (Lactosylceramide (d 18:1/12:0)), glycerophosphoryl choline (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)), palmitoyl sphingomyelin (Palmitoyl sphingomyelin), glycerophosphoryl choline (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), diglycerides (22:1 n9/0:0/22:6n 3) (Diacylglycerol DAG (22:1 n9/0:0/22:6n 3)), glycerophosphoryl choline (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0) (glycosphingosine (d 18:1/18:1)), glycerophosphoryl choline (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)), stearoyl phosphatidylcholine (20:1/18:2) (35:1/14:2), stearoyl phosphatidylinositol (24:24:1/14:0) (14:24:2), stearoyl phosphatidylinositol (24:24/24:1/2), stearoyl phosphatidylinositol (24:24/2), or any combination thereof in a biological sample from the subject.

(5) Dihydroxyacetone phosphate (Dihydroxyacetone phosphate);

(6) Guanyl taurate (Taurocyamine);

In certain embodiments, the subject is a mammal, e.g., a human.

Definition of terms

In the present invention, unless otherwise indicated, scientific and technical terms used herein have the meanings commonly understood by one of ordinary skill in the art. Further, the procedures of molecular genetics, nucleic acid chemistry, molecular biology, biochemistry, cell culture, microbiology, cell biology, genomics and recombinant DNA, etc., as used herein, are all conventional procedures widely used in the corresponding field. Meanwhile, in order to better understand the present invention, definitions and explanations of related terms are provided below.

As used herein, the term "Biomarker" refers to a biochemical marker that can label systems, organs, tissues, cells, and subcellular structures or alterations thereof, with very broad utility. Biomarkers can be used for prediction of disease risk, for judging disease stage or for evaluating the safety and effectiveness of new drugs or new therapies in a target population.

As used herein, the term "metabolite" or "metabolite" refers to a substance produced during the human chemical or physical process. It includes any chemical or biochemical product of a metabolic process, such as any compound produced by processing, cleavage or consumption of biomolecules. Such molecules include, but are not limited to: acids and related compounds; mono-, di-and tricarboxylic acids (saturated, unsaturated aliphatic, aryl, alkylaryl); aldehyde acids and keto acids; a lactone form; gibberellin; 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 and butanone; anthraquinone; a carbohydrate; monosaccharides, disaccharides, trisaccharides; alkaloids, amines and other bases; pyridine (including niacin, nicotinamide); pyrimidine (including cytosine, thymine); purine (including guanine, adenine, xanthine/hypoxanthine, kinetin); pyrrole; quinoline (including isoquinoline); morphinans, tropanes, cinchonines (cinchonans), nucleotides, oligonucleotides, derivatives and related compounds; guanosine, cytosine, adenosine, thymidine, inosine; amino acids, oligopeptides, derivatives and related compounds; an ester; phenols and related compounds; heterocyclic compounds and derivatives; pyrrole, tetrapyrrole; flavonoids; an indole; lipids (including fatty acids and triglycerides), derivatives and related compounds; carotenoids, phytoene and sterols, isoprenoids, including terpenes; and any modified form of the above molecules. In some embodiments, the metabolite is a product of the metabolism of the endogenous substance. In some embodiments, the metabolite is a product of the metabolism of the exogenous material. In some embodiments, the metabolite is a product of the metabolism of the endogenous and exogenous substances.

Some metabolites are indicated by CAS numbers. Some metabolites are represented by ID numbers of HMDB (human metabolomics database, human Metabolome Database).

As described above, the "CAS number" refers to a CAS number assigned to each substance by the American society for chemical abstracts of lower hierarchy (Chemical Abstracts Service, CAS for short). In the biochemical field, CAS numbers are a representation of unique identification codes for substances, i.e., each CAS number corresponds to a unique substance. Similarly, the ID number of HMDB refers to a unique ID number corresponding to each substance in the human metabonomics database (Human Metabolome Database).

As used herein, the term "complications" refers to the occurrence of one disease in the course of its development that causes another disease or condition in the same patient, the latter being the former complication.

As used herein, the term "cardiovascular event" refers to the first occurrence of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, hospitalization for heart failure, or sudden death. Cardiovascular events generally refer to serious consequences of cardiovascular disease, including sudden death, acute myocardial infarction, acute heart failure, acute cerebral infarction, etc., which are the leading causes of disability or death in patients. Acute myocardial infarction refers to an acute condition in which the coronary arteries are blocked, the heart muscle is necrotized due to lack of blood supply, and the heart function is damaged, possibly endangering life. Acute heart failure refers to a clinical syndrome that acute heart congestion, pulmonary edema and tissue and organ hypoperfusion and cardiogenic shock occur due to acute attack or aggravated left heart dysfunction, such as heart contractility decrease, acute heart discharge sudden drop, pulmonary circulation pressure increase and peripheral circulation resistance increase, and pulmonary circulation congestion is caused, and is most common in left heart failure. Acute heart failure can be aggravated acutely or suddenly on the basis of original chronic heart failure, and the acute heart failure is often life-threatening and needs urgent rescue. Acute cerebral infarction refers to brain tissue necrosis caused by sudden interruption of cerebral blood supply, and is mainly caused by atherosclerosis and thrombosis of arteries supplying cerebral blood, and causes stenosis and even occlusion of a lumen, resulting in occurrence of focal acute cerebral ischemia.

As used herein, the term "change in level" refers to a value that is changed relative to a control level or normal level. The normal level is a history-based normal control sample or a normal control sample tested in the same experiment. The specific "normal" value will depend, for example, on the type of assay (e.g., ELISA, enzymatic activity, immunohistochemistry, PCR, spectroscopy), the sample to be tested (e.g., cell type and culture conditions, sample to be tested), and other considerations known to those skilled in the art. The control sample may be used to define a threshold between normal and abnormal.

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 particular condition or not.

As used herein, the term "control sample" refers to any clinically relevant control sample, including, for example, a sample from a healthy subject not suffering from a cardiovascular disease, or from an earlier point in time, for example, prior to treatment, or from a subject at an early stage of treatment or disease. The control sample may be a purified sample, e.g., protein, nucleic acid, and/or lipid extracted with a kit. Such control samples may be diluted, e.g., serially diluted, to allow for quantitative determination of the analyte in the test sample. The control sample may comprise samples from one or more subjects. The control sample may also be a sample from an animal model, or 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 may be determined, for example, from any suitable statistical measure, such as, for example, a measure of concentration trend (including average, median or modal value), before onset, at an earlier point in time, at an earlier stage of the disease, or before administration of a treatment or a portion of a treatment for cardiomyopathy or other disorder, particularly with an agent known to induce cardiomyopathy (e.g., type 2 diabetes drug, chemotherapeutic agent treatment).

In one embodiment, the control is a standardized control, e.g., a control predetermined using an average of the expression levels of one or more markers from a population without cardiovascular disease. In yet another embodiment of the invention, the control level of the marker is the level of the marker in a normal sample from the subject.

As used herein, the term "biological sample" is a body fluid or tissue in which a marker of interest 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 rinsing, or gynecological fluid. In one embodiment, the subject sample is a blood sample or a component thereof (e.g., serum). The sample may be a tissue sample from a subject, such as a heart tissue sample from a subject. In certain embodiments, the tissue is selected from the group consisting of bone, connective tissue, cartilage, lung, liver, kidney, muscle tissue, heart, pancreas, and skin. The cell sample or samples from laboratory animals may be used in many of the same experimental methods provided herein for use in human biological or subject samples.

Advantageous effects of the application

Compared with the prior art, the biomarker provided by the application can be used as a marker for characterizing hyperglycemia-related CVD. Prediction of whether a diabetic patient is at risk for cardiovascular disease, in particular cardiovascular events, is further enabled by these biomarkers. Thus, the biomarkers of the application are useful for predicting the risk of cardiovascular disease, particularly cardiovascular events (e.g., acute myocardial infarction, acute heart failure, acute cerebral infarction, and sudden death) in a subject (e.g., a subject suffering from diabetes). The biomarker can be effectively used as a marker for predicting the cardiovascular disease risk of a diabetic patient, and has the advantages of noninvasive property, accuracy, early stage and high accuracy.

Embodiments of the present application will be described in detail below with reference to the accompanying drawings and examples, but it will be understood by those skilled in the art that the following drawings and examples are only for illustrating the present application and are not to be construed as limiting the scope of the present application. Various objects and advantageous aspects of the present application will become apparent to those skilled in the art from the following detailed description of the preferred embodiments and the accompanying drawings.

Drawings

FIG. 1 shows the results of analysis of principal components of plasma metabolites of different populations, wherein "NDM (non-CVD)" represents a diabetic and CVD-free sample; "NDM (with CVD)" means a sample of diabetes combined with CVD; "NGT (non CVD)" means a normoglycemic and CVD-free sample; "NGT (with CVD)" means a sample of euglycemic pooled CVD. CVD refers to the occurrence of cardiovascular events such as: myocardial infarction, cerebral infarction or admission due to heart failure.

FIG. 2 shows the results of OPLS-DA analysis of plasma metabolic differences between the diabetic-associated CVD population and the normoglycemic CVD-free population in the positive ion detection mode.

FIG. 3 shows the results of OPLS-DA analysis of plasma metabolic difference products in a diabetic-combined CVD population and a normoglycemic CVD-free population in an anion detection mode.

FIG. 4 shows the results of OPLS-DA analysis of plasma metabolic differences between diabetic and normoglycemic CVD-free populations in positive ion detection mode.

FIG. 5 shows the results of OPLS-DA analysis of plasma metabolic difference products in a diabetic CVD-free population versus a normoglycemic CVD-free population in an anion detection mode.

FIG. 6 shows the results of OPLS-DA analysis of plasma metabolic differences between normoglycemic and CVD populations and normoglycemic and CVD-free populations in the positive ion detection mode.

FIG. 7 shows the results of OPLS-DA analysis of plasma metabolic difference products in a normoglycemic and CVD-free population and a normoglycemic and CVD-free population in the negative ion detection mode.

A in fig. 8 shows a graph comparing the relative amounts of the biomarker dihydroxyacetone phosphate (Dihydroxyacetone phosphate) in different groups, wherein "NGT-CVD" in the abscissa indicates a normoglycemic and CVD-free sample, "NDM-CVD" indicates a diabetic and CVD-free sample, and "ndm+cvd" indicates a diabetic and CVD-free sample; the ROC analysis results are shown in B in fig. 8.

A in fig. 9 shows a graph comparing the relative amounts of the biomarker guanyltaurine (Taurocyamine) in different groups, wherein "NGT-CVD" in the abscissa indicates a normoglycemic and CVD-free sample, "NDM-CVD" indicates a diabetic and CVD-free sample, and "ndm+cvd" indicates a diabetic and CVD-free sample; b in fig. 9 shows ROC analysis results.

FIG. 10A shows a comparison of the relative amounts of the biomarker Lactosylceramide (d18:1/12:0) (Lactosylceramide (d18:1/12:0)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, and "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 10.

FIG. 11A shows a graph comparing the relative amounts of the biomarker glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 11.

A in fig. 12 shows a graph comparing the relative amounts of the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin in different groups, wherein "NGT-CVD" in the abscissa represents a normoglycemic and CVD-free sample, "NDM-CVD" represents a diabetic and CVD-free sample, and "ndm+cvd" represents a diabetic and CVD-free sample; the ROC analysis results are shown in B in fig. 12.

FIG. 13A shows a graph comparing the relative amounts of the biomarker glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 13.

FIG. 14A shows a graph comparing the relative amounts of the biomarker diglycerides (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 14.

FIG. 15A shows a graph comparing the relative amounts of the biomarker glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 15.

FIG. 16A shows a comparison of the relative amounts of the biomarker Glucosylceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 16.

A in fig. 17 shows a comparison of the relative amounts of the biomarker diglycerides (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) in different groups, wherein "NGT-CVD" in the abscissa indicates a normoglycemic and CVD-free sample, "NDM-CVD" indicates a diabetic and CVD-free sample, and "ndm+cvd" indicates a diabetic and CVD-free sample. B in fig. 17 shows ROC analysis results.

FIG. 18A shows a graph comparing the relative amounts of the biomarker sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) in different groups, wherein "NGT-CVD" in the abscissa indicates normoglycemic and CVD-free samples, "NDM-CVD" indicates diabetic and CVD-free samples, and "NDM+CVD" indicates diabetic and CVD-free samples; the ROC analysis results are shown in B in fig. 18.

FIG. 19A shows a graph comparing the relative amounts of the biomarker 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) in different groups, wherein "NGT-CVD" in the abscissa indicates a normoglycemic and CVD-free sample, "NDM-CVD" indicates a diabetic and CVD-free sample, and "NDM+CVD" indicates a diabetic and CVD-free sample; the ROC analysis results are shown in B in fig. 19.

A in fig. 20 shows a graph comparing the relative amounts of the biomarker stearoyl carnitine (stearoyl carnitine) in different groups, wherein "NGT-CVD" in the abscissa indicates a normoglycemic and CVD-free sample, "NDM-CVD" indicates a diabetic and CVD-free sample, and "ndm+cvd" indicates a diabetic and CVD-free sample. The ROC analysis results are shown in B in fig. 20.

Fig. 21 shows ROC analysis results of the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin in different samples.

Detailed Description

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

The experiments and methods described in the examples were performed substantially in accordance with conventional methods well known in the art and described in various references unless specifically indicated. For example, for the conventional techniques of immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics and recombinant DNA used in the present invention, reference may be made to Sambrook (Sambrook), friech (Fritsch) and manitis (Maniatis), molecular cloning: laboratory Manual (MOLECULAR CLONING: A LABORATORY MANUAL), edit 2 (1989); the handbook of contemporary molecular biology (CURRENT PROTOCOLS IN MOLECULAR BIOLOGY) (edited by f.m. ausubel (f.m. ausubel) et al, (1987)); series (academic publishing company) of methods in enzymology (METHODS IN ENZYMOLOGY): PCR2: practical methods (PCR 2: A PRACTICAL APPROACH) (M.J. MaxPherson (M.J. MacPherson), B.D. Thoms (B.D. Hames) and G.R. Taylor (G.R. Taylor) editions (1995)), and animal cell CULTURE (ANIMAL CELL CULTURE) (R.I. French Lei Xieni (R.I. Freshney) editions (1987)).

In addition, the specific conditions are not specified in the examples, and the process is carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention. Those skilled in the art will appreciate that the examples describe the invention by way of example and are not intended to limit the scope of the invention as claimed. All publications and other references mentioned herein are incorporated by reference in their entirety.

Example 1 Experimental sample and article

Experimental samples: 240, 4 groups of clinical patient plasma samples (fasting plasma) were selected, including: a normoglycemic CVD-free sample (NGT non CVD), a normoglycemic CVD-free sample (NGT with CVD), a diabetic CVD-free sample (NDM non CVD), a diabetic CVD-combined sample (NDM with CVD).

Experimental reagent and consumable: chromatographic grade methanol, acetonitrile solvent (merck company), chromatographic grade formic acid (sammer technology) 1.5ml, 2.0ml EP tube.

Instrument apparatus: ultra performance liquid chromatograph (Waters acquisition) ^TM UPLC system), high resolution time of flight mass spectrometer Waters SYNAPT G2 HDMS (Waters Corp., manchester, UK), cryogenic refrigerated centrifuge (Sieimer flied), micropipette (Ai Bende), ultra low temperature refrigerator (Sieimer flied).

EXAMPLE 2 extraction and analysis of metabolites

1. Experimental method

1. Sample solution preparation

Before the experiment, the sample was taken out of the-80 ℃ ultra-low temperature refrigerator and thawed at room temperature. mu.L of methanol-acetonitrile (v/v=4:1) was added to 200. Mu.L of plasma sample, centrifuged at 4 ℃,12,000g for 30min, and the supernatant was taken and analyzed by a liquid chromatography-mass spectrometer.

2. Detection condition of liquid chromatography-mass spectrometer

Ultra-high performance liquid chromatography detection conditions: the instrument was Waters AcquityTM Ultra Performance LC System (Waters Corporation, milford, MA, USA); chromatographic column: waters Acquity BEH C18 column (100 mm. Times.2.1 mm,1.7 μm); mobile phase: phase A: 0.1% aqueous formic acid+2 mM ammonium formate; and B phase: 0.1% acetonitrile formate solution+2 mM ammonium formate; elution gradient flow: 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, 1.0% B elution balance; the flow rate is 0.45mL/min; the sample chamber temperature was 4 ℃.

High resolution time-of-flight mass spectrometer detection conditions: the instrument used was a Waters synpt G2HDMS (Waters corp., manchester, UK); the ion source is an electrospray ion source; detection mode: positive and negative ion detection modes; capillary voltage: 3.0kV (positive ion detection mode), 2.5kV (negative ion detection mode); taper hole voltage: 40V; extraction cone voltage: 4V; desolventizing gas flow rate: 800L/h; desolventizing gas temperature: 450 ℃; taper hole gas flow rate: the ion source temperature is 100 ℃ at 30L/h; the scanning time interval is 0.1s; delay time: 0.02s; correction fluid: leucine enkephalin; data collection quality range: m/z is 100-1200Da.

3. Data multivariate statistical analysis

Metabonomics raw data preprocessing

And automatically completing preprocessing such as spectrum peak identification, noise filtering and the like on UPLC-Q-TOF/MS original data by using marker Lynx (MassLynx SCN 633) software in MassLynx V4.1. The parameters of the processing method are set as follows: retention time range: 0.5-15min; the mass range is as follows: 50-1200amu; and (3) a quality window: 0.02Da, peak intensity threshold (count): 300, retention time window: 0.2s, and the noise level is eliminated by 6. A three-dimensional matrix is obtained that includes retention time, exact mass-to-charge ratio, and ion peak intensity/area composition.

Metabonomics data multivariate statistical analysis

The data matrix obtained after the pretreatment is imported into SIMCA-P software (Umetrics AB, sweden,13.0.3 version) for multi-element statistical analysis, and the data is subjected to average automation and Pareto scale pretreatment, and Principal Component Analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) are sequentially carried out.

(1) Principal Component Analysis (PCA)

The data is subjected to pattern recognition by using an unsupervised Principal Component Analysis (PCA), which can reflect the original state of the data, and is used for examining the separation condition of each group of data profiles and evaluating the interpretation ability R2X and the prediction ability Q2 of the established model. However, PCA is not conducive to accurate determination of inter-group differences because it cannot ignore intra-group errors and random errors that are not relevant for the purpose of the study.

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

Based on PCA analysis, the data were further analyzed by an orthogonal partial least squares-discriminant analysis (OPLS-DA) method, and the interpretation ability R2X and the prediction ability Q2 of the model were evaluated. The analysis results are represented by Score plot (Score plot), S-plot, variable projection importance (VIP) plot, and Loading plot (Loading plot). The grouping information of each group of data can be observed through the score graph; s-plot reflects the influence of each variable on sample grouping, each point scattered at two ends of an S curve in the graph represents a variable, and the farther from the origin, namely, the greater the degree of dispersion, the greater the contribution of the point to the grouping; the VIP value reflects the contribution of each variable to the sample grouping, with a greater VIP value indicating a stronger contribution of the variable to the grouping; the farther a variable is from the center in the load map (Loading plot), the greater its contribution to the packet. And (3) through the loading analysis and the VIP analysis of the S-plot, finding out the variables which have important contributions to the difference among the distinguishing groups from the established OPLS-DA model.

2. Experimental results

1. Plasma metabolic profile analysis of diabetic population

Plasma metabonomics analysis was performed on plasma samples from normoglycemic non-CVD (NGT non-CVD), normoglycemic co-CVD (NGT with CVD), diabetic non-CVD (NDM non-CVD) and diabetic co-CVD (NDM with CVD) 4 groups of people using an optimized UPLC-Q-TOF/MS metabonomics analysis method. The results of principal component analysis (PCA, FIG. 1) show that (1) the plasma metabolic profile of the 4 groups of people is distributed in three quadrants, and the plasma metabolic profile of the normoglycemic people is relatively concentrated and separated without being affected by the combination of CVD; (2) The plasma metabolism profile of the diabetic patient is obviously separated from that of the normoglycemic person, and the plasma metabolism profile of the patient with or without CVD is also obviously separated due to the influence of CVD, which indicates that whether the CVD has obvious influence on the plasma metabolism of the diabetic patient in the diabetic population.

2. Screening of specific markers

(1) Inter-group differential metabolites associated with diabetes combined CVD

The collected plasma metabolic profile data of normoglycemic and CVD-free populations and diabetic co-CVD populations were subjected to OPLS-DA analysis using multivariate statistical analysis (fig. 2, fig. 3). Screening standard by S-Plot inter-group variance variable: VIP >1, p 1P >0, p (corr) > + -0.6 screening threshold, 304 inter-group difference variables were obtained in positive ion mode, and 119 inter-group difference variables were obtained in negative ion mode.

(2) Inter-group differential metabolites associated with diabetes

The collected plasma metabolic profile data of normoglycemic and CVD-free populations and diabetic CVD-free populations were subjected to OPLS-DA analysis using multivariate statistical analysis (fig. 4, fig. 5). Screening standard by S-Plot inter-group variance variable: VIP >1, p 1P >0, p (corr) > + -0.6 are screening thresholds, 786 inter-group difference variables are obtained in positive ion mode, and 164 inter-group difference variables are obtained in negative ion mode.

(3) CVD-related group differential metabolites

The collected plasma metabolic profile data of normoglycemic and CVD-free populations and normoglycemic combined CVD populations were subjected to OPLS-DA analysis using multivariate statistical analysis (fig. 6, fig. 7). Screening standard by S-Plot inter-group variance variable: VIP >1, p 1P >0, p (corr) > + -0.6 are screening thresholds, 54 inter-group difference variables are obtained in positive ion mode, and 86 inter-group difference variables are obtained in negative ion mode.

(4) Specific markers for diabetes-related CVD

Comparing plasma metabonomics data of the diabetic co-CVD population, the diabetic non-CVD population, and the normoglycemic co-CVD population with the normoglycemic non-CVD population, respectively, to obtain 3 groups of inter-group differential metabolites, which are respectively an inter-group differential metabolite (a) associated with hyperglycemia with CVD, a metabolite (B) associated with diabetes, and a metabolite (C) associated with CVD alone. And finally obtaining 72 specific biomarkers of diabetes-related CVD after eliminating the influence of simple diabetes factors and CVD factors on organisms by a deduction method (60 biomarkers are obtained from positive ion detection modes by screening, 13 negative ion detection modes are obtained, and 1 metabolite is detected in both detection modes). The VIP value is larger than 1, the relative peak area is larger than 1000, the change multiple is larger than 5, and 13 metabolites (table 1) are finally obtained, can be used as plasma biomarkers for representing the CVD caused by hyperglycemia, and have the potential of assisting clinical diagnosis and the function of clinical CVD risk early warning.

TABLE 1 list of biomarkers specifically related to diabetes-induced CVD

EXAMPLE 3 biomarker Dihydroxyacetone phosphate Dihydroxyacetone phosphate is used for blood of different people Detection of relative content in pulp

This example takes the biomarker dihydroxyacetone phosphate Dihydroxyacetone phosphate as an example, and detects whether it can be used as a biomarker for predicting the risk of cardiovascular diseases in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

Experimental samples: group 3 clinical patient plasma samples (fasting plasma) comprising: 60 normal blood glucose non-CVD samples (NGT non-CVD), 60 diabetes non-CVD samples (NDM non-CVD), 60 diabetes combined CVD samples (NDM with CVD).

The experimental method comprises the following steps:

1. The extraction of metabolites was performed as described in example 2. The LC-MS detection conditions were as described in example 2.

Experimental results:

based on established examination methods, the relative amounts of the biomarker dihydroxyacetone phosphate Dihydroxyacetone phosphate in samples with diabetes and CVD (NDM with CVD) and samples without diabetes and CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 8, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiment show that the content of dihydroxyacetone phosphate Dihydroxyacetone phosphate in the samples with diabetes and CVD (NDM with CVD) is significantly higher than that in the samples without diabetes and CVD (NDM non CVD), and the change factor is about 587 times. The verification experiment result shows that the content of the biomarker dihydroxyacetone phosphate Dihydroxyacetone phosphate can distinguish whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.6042 and 95% CI (0.4973,0.7111) through ROC curve analysis, so that the biomarker has higher prediction accuracy (B in figure 8) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

Example 4 relative content of biomarker Taurocyamine, guanyl taurate in plasma of different populations Measuring

This example takes the biomarker Taurocyamine as an example, and detects 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 may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

The experimental reagents and consumables and the instrument equipment are the same as in example 3.

The experimental method comprises the following steps: 1. metabolite extraction following the procedure described in example 2, the LC-MS detection conditions were as described in example 2.

Experimental results:

based on established examination methods, the relative content of the biomarker Taurocyamine in diabetic and CVD-bearing samples (NDM with CVD) and diabetic and CVD-free samples (NDM non CVD) was examined and analyzed. As shown in a in fig. 9, by comparing the peak areas of the biomarker in the two groups of samples, the results of the verification experiment find that the content of Taurocyamine amidine taurine in the samples with diabetes and CVD (NDM with CVD) is significantly lower than that in the samples without diabetes and CVD (NDM non CVD), and the change multiple is 5. The verification experiment result shows that the content of the biomarker guanyltaurine Taurocyamine can distinguish whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.8708 and 95% CI (0.8045 0.9371) through ROC curve analysis, so that the biomarker guanyltaurine Taurocyamine has stronger prediction accuracy (B in fig. 9) and can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject.

Example 5 biomarker lactose ceramide (d18:1/12:0) (Lactosylceramide (d18:1/12: 0) Detection of the relative content in the plasma of different populations

This example exemplifies the biomarker Lactosylceramide (d18:1/12:0) (Lactosylceramide (d18:1/12:0)) which is used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

The experimental reagents and consumables and the instrument equipment are the same as in example 3. The experimental method comprises the following steps: 1. metabolite extraction following the procedure described in example 2, the LC-MS detection conditions were as described in example 2.

Experimental results:

based on established examination methods, the relative amounts of the biomarker Lactosylceramide (d18:1/12:0) (Lactosylceramide (d18:1/12:0)) in samples with diabetes and with CVD (NDM with CVD) and in samples with diabetes and without CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 10, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiment found that the content of Lactosylceramide (d18:1/12:0) (lactosylamide (d18:1/12:0)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD) by a factor of 41. The verification experiment result shows that the content of the biomarker lactose ceramide (d18:1/12:0) (Lactosylceramide (d18:1/12:0)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the biomarker can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject through ROC curve analysis, wherein the area under the curve is 0.8574, 95% CI (0.7856,0.9291), and the biomarker has stronger prediction accuracy (B in FIG. 10).

EXAMPLE 6 biomarker glycerophosphochloride (18:1/18:1) (Glycerol phosphatidylcholine PC) (18:1/18:1)) in the plasma of different populations

This example exemplifies the biomarker glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) which is used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

The experimental reagents and consumables and the instrument equipment are the same as in example 3. The experimental method comprises the following steps: 1. metabolite extraction following the procedure described in example 2, and LC-MS detection conditions following the procedure described in example 2

Experimental results:

based on established examination methods, the relative amounts of the biomarker glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes and no CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 11, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiments found that the content of glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples without diabetes and CVD (NDM non CVD), by a factor > 10000. The verification experiment result shows that the content of the biomarker glycerophosphorylcholine (18:1/18:1) (Glycerophosphocholines PC (18:1/18:1)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.6944 and 95% CI (0.5968,0.7921) through ROC curve analysis, so that the biomarker has better prediction accuracy (B in FIG. 11) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

Example 7 biomarker palmitoyl sphingomyelin palmitoyl sphingomylin in plasma of different people Relative content detection of (2)

This example takes the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin as an example to examine 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 may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

The experimental reagents and consumables and the instrument equipment are the same as in example 3. The experimental method comprises the following steps: 1. metabolite extraction following the procedure described in example 2, the LC-MS detection conditions were as described in example 2. Experimental results:

based on established examination methods, the relative amounts of the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin in diabetic and CVD-bearing samples (NDM with CVD) and diabetic and CVD-free samples (NDM non CVD) were examined and analyzed. As shown in a in fig. 12, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiment found that the content of palmitoyl sphingomyelin Palmitoyl sphingomyelin in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples without diabetes and CVD (NDM non CVD), by a factor of 34. The verification experiment result shows that the content of the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin can distinguish whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.9167 and 95% CI (0.8586,0.9747) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in fig. 12) and can be used as a biomarker for judging the cardiovascular disease risk of the diabetic subject.

Example 8 biomarker glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) in the plasma of different populations

This example exemplifies the biomarker glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) which is used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative amounts of the biomarker glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes and no CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 13, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiment found that the content of glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD), with a fold change of 35 times. The verification experiment result shows that the content of the biomarker glycerophosphorylcholine (20:0/18:2) (Glycerophosphocholines PC (20:0/18:2)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.7589 and 95% CI (0.6619,0.8559) through ROC curve analysis, so that the biomarker has high prediction accuracy (B in FIG. 13) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

Example 9 biomarker diglycerides (22:1 n9/0:0/22:6n 3) (Diacylglycerol DAG (22: 1n 9/0:0/22:6n3)) in the plasma of different people

This example exemplifies the biomarker diglyceride (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) that could be used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative amounts of the biomarkers diglyceride (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) in samples with diabetes and with CVD (NDM with CVD) and samples without diabetes and CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 14, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiments found that the content of diglyceride (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) was significantly higher in the samples with diabetes and CVD (NDM with CVD) than in the samples without diabetes and CVD (NDM non CVD), by a factor of 1074. The verification experiment result shows that the content of the biomarker diglyceride (22:1n9/0:0/22:6n3) (Diacylglycerol DAG (22:1n9/0:0/22:6n3)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.8158, 95% CI (0.7354,0.8963) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in fig. 14) and can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject.

EXAMPLE 10 biomarker glycerophosphochloride (24:1/14:0) (Glycerol phosphatidylcholine PC) (24:1/14:0)) in the plasma of different populations

This example exemplifies the biomarker glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) which is used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative amounts of the biomarker glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes and no CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 15, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiment found that the content of glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples without diabetes and CVD (NDM non CVD), with a fold change of 67 times. The verification experiment result shows that the content of the biomarker glycerophosphorylcholine (24:1/14:0) (Glycerophosphocholines PC (24:1/14:0)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.7810 and 95% CI (0.6937,0.8682) through ROC curve analysis, so that the biomarker has high prediction accuracy (B in FIG. 15) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

EXAMPLE 11 biomarker glucose ceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1 18:0)) in the plasma of different populations

This example takes the biomarker Glucosylceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) as an example, and detects 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 may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative content of the biomarker Glucosylceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) in samples with diabetes and with CVD (NDM with CVD) and in samples with diabetes and without CVD (NDM non CVD) was examined and analyzed. As shown in a in fig. 16, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiment found that the content of Glucosylceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD), by a factor of 1192. The verification experiment result shows that the content of the biomarker glucose ceramide (d18:1/18:0) (glucopyranosyl ceramide (d18:1/18:0)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.8019 and 95% CI (0.7189,0.8849) through ROC curve analysis, so that the biomarker glucose ceramide has stronger prediction accuracy (B in FIG. 16) and can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject.

EXAMPLE 12 biomarker diglyceride (18:4/24:1/0:0) (diacetylglycol) DAG(18:4/ 24:1/0:0)) in the plasma of different people

This example exemplifies the biomarker diglyceride (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) that could be used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

Experimental results:

based on established examination methods, the relative amounts of the biomarkers diglyceride (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) in samples with diabetes and CVD (NDM with CVD) and samples without diabetes and CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 17, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiments found that the content of diglyceride (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD), the fold change was >10000 times. The verification experiment result shows that the content of the biomarker diglyceride (18:4/24:1/0:0) (Diacylglycerol DAG (18:4/24:1/0:0)) can distinguish whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.7447 and 95% CI (0.6525,0.837) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in FIG. 17) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

EXAMPLE 13 biomarker sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) Detection of relative amounts in plasma of different populations

This example takes the biomarker sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) as an example, and detects 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 may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative amounts of the biomarker sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes and no CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 18, by comparing peak areas of the biomarker in the two groups of samples, the results of the validation experiment found that the content of sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) in samples with diabetes and CVD (NDM with CVD) was significantly higher than that in samples with diabetes and no CVD (NDM non CVD), by a factor > 10000. The verification experiment result shows that the content of the biomarker sphingomyelin (d18:1/24:1) (sphingomyelins SM (d18:1/24:1)) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.7519 and 95% CI (0.6606,0.8433) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in fig. 18) and can be used as a biomarker for judging the cardiovascular disease risk of a diabetic subject.

EXAMPLE 14 biomarker 1-stearoyl phosphatidylinositol (1- stearoyl glycosporinopool) relative levels in plasma of different populations

This example exemplifies the biomarker 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) which is tested for its ability to be a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations as described above may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

Experimental results:

based on established examination methods, the relative amounts of the biomarker 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) in samples with diabetes and with CVD (NDM with CVD) and samples with diabetes and without CVD (NDM non CVD) were examined and analyzed. As shown in a in fig. 19, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiments found that the content of 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD) by a factor of 8. The verification experiment result shows that the content of the biomarker 1-stearoyl phosphatidylinositol (1-stearoyl phosphatidylinositol) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.6830 and 95% CI (0.5775,0.7884) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in FIG. 19) and can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject.

Example 15 phase of the biomarker stearoyl carnitine (Stearoylcannine) in plasma of different people For content detection

This example takes the biomarker stearoyl carnitine (stearoyl carnitine) as an example, and detects 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 may be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

based on established examination methods, the relative content of the biomarker stearoyl carnitine (stearoyl carnitine) in samples with diabetes and CVD (NDM with CVD) and samples with diabetes and no CVD (NDM non CVD) was examined and analyzed. As shown in a in fig. 20, by comparing peak areas of the biomarker in the two groups of samples, the results of the verification experiment found that the content of stearoyl carnitine (steroyl carnitine) in the samples with diabetes and CVD (NDM with CVD) was significantly higher than that in the samples with diabetes and CVD (NDM non CVD) by a factor of 11. The verification experiment result shows that the content of the biomarker stearoyl carnitine (stearoyl carnitine) can be used for distinguishing whether a diabetic patient is accompanied with a CVD event, and the area under the curve is 0.7258 and 95% CI (0.6286,0.8230) through ROC curve analysis, so that the biomarker has stronger prediction accuracy (B in fig. 20) and can be used as a biomarker for judging the risk of cardiovascular diseases of a diabetic subject.

Example 16 biomarker palmitoyl sphingomyelin palmitoyl sphingomylin in plasma of different populations Content detection of (2)

This example takes the biomarker palmitoyl sphingomyelin Palmitoyl sphingomyelin as an example to examine whether it can be used as a biomarker for predicting the risk of cardiovascular disease in diabetic subjects. Other biomarkers or combinations can be used in accordance with the methods described in this example to predict cardiovascular disease risk in a diabetic subject.

Experimental samples: 450 clinical patient plasma samples (fasting plasma) were selected, including 179 diabetic and CVD-bearing samples (NDM with CVD) and 271 diabetic and CVD-free samples (NDM non CVD).

2. Content determination

2.1 preparation of standard solution:

an appropriate amount of Palmitoyl sphingomyelin was precisely weighed and dissolved in methanol-acetonitrile (4:1) solvent to prepare a stock solution at a concentration of 200. Mu.g/mL.

2.2 establishment of standard curve

Stock solutions were diluted to standard solutions at levels of 2.5,5, 10, 20, 25, 40, 60 and 80. Mu.g/mL, respectively, and the control solution under item "2.1" was precisely aspirated and determined according to the LC-MS detection conditions in example 2. The peak surface takes the concentration of the reference substance (mg/L) (X) as the abscissa The product (Y) is taken as the ordinate, a standard curve is drawn, and a regression equation is established. y= 1817.2x-4743.6, r ² = 0.9992, linear range 2.5-80 μg/mL, lowest limit of detection LOD1.0 μg/mL, lowest limit of quantification LOQ 2.5 μg/mL.

2.3 precision test

Precisely sucking 4 mu L of the control solution prepared under the item "2.1", continuously sampling for 6 times according to the LC-MS detection condition in the example 2, and calculating the relative standard deviation RSD% of the peak area to obtain the intra-day precision; the standard solution is continuously measured for three days (3 times per day of sample injection and 3 continuous days), and the relative standard deviation RSD of the peak area is calculated, namely the daytime precision.

2.4 repeatability test

1 sample was taken, 6 test solutions were prepared in parallel with the metabolite extraction and analysis method in example 2, and the content and relative standard deviation RSD% were calculated as determined by LC-MS detection conditions in example 2.

2.5 stability test

1 sample was taken, a sample solution was prepared according to the method for extracting and analyzing the metabolite in example 2, and samples were taken at 0,2,4,8, 12, 24 hours after the preparation, respectively, as determined according to the LC-MS detection conditions in example 2, and peak areas were measured, and the peak area relative standard deviation RSD% was calculated.

2.6 sample recovery test

9 parts of standard reference substance are precisely weighed, and the standard substance is added according to 80%,100% and 120% of 3 levels of known content, and the sample addition recovery rate and the relative standard deviation RSD% are calculated according to LC-MS detection conditions in the example 2.

3 content measurement results

The content of the biomarker Palmitoyl sphingomyelin in the samples with diabetes and CVD (NDM with CVD) and the samples without diabetes and CVD (NDM non CVD) was 22.1.+ -. 15.5. Mu.g/mL and 8.6.+ -. 4.6. Mu.g/mL, respectively, as determined by the content. The results of the validation experiments showed that the biomarker Palmitoyl sphingomyelin content can distinguish whether diabetic patients are accompanied by CVD events, and by ROC curve analysis, CVD was predicted at Palmitoyl sphingomyelin plasma concentration with an area under the curve of 0.8264, 95% ci (0.808,0.875). By analysis, the best value of sensitivity and specificity is determined as the best diagnostic threshold, palmitoyl sphingomyelin exceeds 11.19 mug/mL in the study population, the sensitivity and specificity of the predicted CVD risk are 79.7% and 69.3% respectively, and the predicted value is high (figure 21) and can be used as a biomarker for judging the risk of cardiovascular diseases of diabetic subjects.

Although specific embodiments of the invention have been described in detail, those skilled in the art will appreciate that: many modifications and variations of details may be made to adapt to a particular situation and the invention is intended to be within the scope of the invention. The full scope of the invention is given by the appended claims together with any equivalents thereof.

Claims

1. Use of an agent for determining the level of a biomarker in a biological sample in the manufacture of a kit for predicting whether a subject suffering from diabetes is at risk for cardiovascular disease; wherein the biomarker is guanyl taurine.

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

3. The use of claim 1, wherein the cardiovascular disease is selected from coronary heart disease, ischemic stroke, peripheral arterial disease or heart failure, and the diabetes is a related disease caused by hyperglycemia.

4. The use of claim 2, wherein 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.

5. The use of claim 3, wherein the diabetes is type 1 diabetes or type 2 diabetes.

6. The use of claim 4, wherein the diabetes is type 1 diabetes or type 2 diabetes.

7. The use of any one of claims 1-6, wherein the biological sample is selected from whole blood, serum, plasma, or any combination thereof obtained from a subject.

8. The use of any one of claims 1-6, wherein the subject is a mammal.

9. The use of any one of claims 1-6, wherein the subject is a human.

10. The use of any one of claims 1-6, wherein the reagent determines the level of a biomarker in the biological sample by: chromatographic and/or mass spectrometry, fluorometry, electrophoresis, immunoaffinity, hybridization, immunochemistry, ultraviolet spectroscopy, radiochemical analysis, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, and nephelometry.

11. The use of claim 10, wherein the reagent determines the level of a biomarker in the biological sample by spectroscopy, liquid or gas chromatography, mass spectrometry, liquid or gas chromatography combined with mass spectrometry.

12. The use of claim 10, wherein the kit further comprises reagents and/or consumables for chromatography.

13. The use of claim 12, wherein the chromatography is liquid chromatography.

14. The use of claim 12, wherein the reagent and/or consumable for chromatography is selected from a chromatographic column, an aqueous acetonitrile solution, ammonium acetate, ammonium formate, formic acid, or any combination thereof.