WO2016049830A1 - 冠心病患者特异性生物标志组合物及其用途 - Google Patents

冠心病患者特异性生物标志组合物及其用途 Download PDF

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WO2016049830A1
WO2016049830A1 PCT/CN2014/087853 CN2014087853W WO2016049830A1 WO 2016049830 A1 WO2016049830 A1 WO 2016049830A1 CN 2014087853 W CN2014087853 W CN 2014087853W WO 2016049830 A1 WO2016049830 A1 WO 2016049830A1
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biomarker
heart disease
training set
coronary heart
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PCT/CN2014/087853
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English (en)
French (fr)
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冯强
刘志鹏
孟楠
王俊
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深圳华大基因科技有限公司
深圳华大基因研究院
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Priority to CN201480082295.XA priority Critical patent/CN107076710B/zh
Priority to EP14903169.2A priority patent/EP3203227B1/en
Priority to US15/515,501 priority patent/US20170227528A1/en
Priority to PCT/CN2014/087853 priority patent/WO2016049830A1/zh
Publication of WO2016049830A1 publication Critical patent/WO2016049830A1/zh

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    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5091—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing the pathological state of an organism
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    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G01N30/02—Column chromatography
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    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N2030/022—Column chromatography characterised by the kind of separation mechanism
    • G01N2030/027—Liquid chromatography
    • G—PHYSICS
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2560/00—Chemical aspects of mass spectrometric analysis of biological material
    • G—PHYSICS
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    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/32—Cardiovascular disorders
    • G01N2800/324—Coronary artery diseases, e.g. angina pectoris, myocardial infarction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers

Definitions

  • the present invention relates to disease-specific metabolite profiles, and in particular to biomarker compositions screened by plasma-specific metabolite profiles in subjects with coronary heart disease.
  • the invention also relates to the use of the biomarker composition for risk assessment, diagnosis, early diagnosis and pathological staging of coronary heart disease, as well as risk assessment, diagnosis, early diagnosis and pathological staging of coronary heart disease.
  • Coronary artery heart disease also known as ischemic heart disease, referred to as coronary heart disease
  • ischemic heart disease is one of the most common types of heart disease, which refers to myocardial infarction caused by coronary artery stenosis and insufficient blood supply.
  • Idiopathic and/or organic lesions also known as ischemic cardiomyopathy (IHD) were the world's leading cause of death in 2012 [1] and one of the leading causes of hospitalization [2].
  • Coronary heart disease can occur at any age, even in children, but the main age of onset is after middle age, and its onset increases with age. Nearly 17 million people worldwide die from atherosclerotic heart disease every year.
  • Metabolomics is a systematic biology discipline developed after genomics and proteomics. The types, quantities and variations of endogenous metabolites are affected by internal or external factors of graduate students. Metabolomics analyzes the entire metabolic profile of organisms and explores the correspondence between metabolites and physiological and pathological changes, thus providing a basis for disease diagnosis. Therefore, the screening of metabolic markers associated with coronary heart disease, especially the combination of multiple metabolic markers, is of great significance for the metabolomics research, clinical diagnosis and treatment of coronary heart disease.
  • the problem to be solved by the present invention is to provide a biomarker combination (ie, a biomarker composition) that can be used for the diagnosis of coronary heart disease and risk assessment of disease, and Diagnosis and risk assessment methods for coronary heart disease.
  • a biomarker combination ie, a biomarker composition
  • the invention adopts an analytical method using liquid chromatography-mass spectrometry to analyze the metabolite profiles of plasma samples of the coronary heart disease group and the control group, and analyzes the metabolite profiles of the coronary heart disease group and the control group group by pattern recognition to determine the specificity.
  • Liquid chromatography mass spectrometry data and related specific biomarkers provide a basis for subsequent theoretical research and clinical diagnosis.
  • a first aspect of the invention relates to a biomarker composition
  • a biomarker composition comprising at least one or more selected from the group consisting of the following biomarkers 1 to 6:
  • Biomarker 1 the mass-to-charge ratio is 310.04 ⁇ 0.4 amu, and the retention time is 611.25 ⁇ 60 s;
  • Biomarker 2 its mass-to-charge ratio is 311.05 ⁇ 0.4 amu, and the retention time is 611.26 ⁇ 60 s;
  • Biomarker 3 its mass-to-charge ratio is 220.00 ⁇ 0.4 amu, and the retention time is 122.77 ⁇ 60 s;
  • Biomarker 4 has a mass-to-charge ratio of 247.09 ⁇ 0.4 amu and a retention time of 146.37 ⁇ 60 s;
  • Biomarker 5 its mass-to-charge ratio is 255.03 ⁇ 0.4 amu, and the retention time is 117.92 ⁇ 60 s;
  • the biomarker 6 has a mass-to-charge ratio of 170.03 ⁇ 0.4 amu and a retention time of 202.18 ⁇ 60 s; for example, one, two, three, four, five or six species are contained therein.
  • the biomarker composition comprises at least biomarkers 1-3 and 6; optionally, biomarker 4 and/or biomarker 5 is also included.
  • the biomarker composition contains biomarkers 1-6.
  • the biomarker composition contains biomarkers 3-6.
  • a second aspect of the invention relates to a reagent composition comprising a reagent for detecting the biomarker composition of any of the first aspects of the invention.
  • the agent for detecting the above biomarker is, for example, a ligand which can bind to a biomarker, such as an antibody; optionally, the reagent for detection may also carry a detectable label.
  • the reagent composition is a combination of all detection reagents.
  • a third aspect of the invention relates to the use of the biomarker composition of any of the first aspects of the invention and/or the reagent composition of any of the second aspects for the preparation of a kit for use in the treatment of coronary heart disease Disease risk assessment, diagnosis, early diagnosis or pathological staging.
  • the kit further comprises training set data for the biomarker composition content of any of the first aspects of the invention in a coronary heart disease subject and a normal subject.
  • the training set data is as shown in Table 2.
  • the invention also relates to a method for risk assessment, diagnosis, early diagnosis or pathological staging of coronary heart disease, the method comprising determining any one of the first aspects of the invention in a sample of a subject (eg plasma, whole blood) The step of the content of each biomarker in the biomarker composition of the item.
  • a subject eg plasma, whole blood
  • the method for determining the content of each biomarker in the biomarker composition of any one of the first aspects of the invention in a sample of a subject is liquid chromatography mass spectrometry The method of joint use.
  • the method further comprises establishing a biomarker combination of any one of the first aspects of the invention of a coronary heart disease subject and a normal subject (control group) sample (eg, plasma, whole blood) The steps of the training set for each biomarker content.
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data for the training set is as shown in Table 2.
  • the method further comprises administering to the subject sample (eg, plasma, whole blood) the amount of each biomarker in the biomarker composition of any of the first aspects of the invention and coronary heart disease The step of comparing the training set data of the biomarker composition of the subject and the normal subject.
  • subject sample eg, plasma, whole blood
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data for the training set is as shown in Table 2.
  • comparing refers to comparing using a receiver operating characteristic curve.
  • the result of the comparison step is determined by the method, if the subject is assumed to be a non-coronary heart disease patient, the probability of non-coronary heart disease patients diagnosed by ROC is less than 0.5 or the probability of suffering from coronary heart disease is greater than 0.5. , indicating that the original hypothetical subject has a high probability of having coronary heart disease, a higher risk, or is diagnosed as a coronary heart disease patient.
  • the method comprises the steps of:
  • the probability of non-coronary heart disease patients with ROC diagnosis is less than 0.5 or the probability of coronary heart disease is greater than 0.5, indicating that the original hypothetical subject has a high probability of suffering from coronary heart disease. Patients with high risk or diagnosed as coronary heart disease.
  • the invention further relates to a biomarker composition according to any of the first aspects of the invention for use in the assessment, diagnosis, early diagnosis or pathological staging of the risk of coronary heart disease.
  • the method for determining the content of each biomarker in the biomarker composition of any one of the first aspects of the invention in a sample of a subject is liquid chromatography mass spectrometry The method of joint use.
  • the method further comprises the step of establishing a training set for each biomarker content in the biomarker composition of any one of the first aspects of the invention in a coronary heart disease subject and a normal subject.
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data for the training set is as shown in Table 2.
  • a subject sample eg, plasma, whole blood
  • the content of each biomarker in the biomarker composition of any one of the first aspects of the invention and the subject of coronary heart disease The step of comparing the training set data of the biomarker composition of the normal subject.
  • the training set is a training set established using a multivariate statistical classification model, such as a random forest model.
  • the data for the training set is as shown in Table 2.
  • the method of comparison refers to a comparison using a method of a receiver operating characteristic curve.
  • the result of the comparison step is determined by the method, if the subject is assumed to be a non-coronary heart disease patient, the probability of non-coronary heart disease patients diagnosed by ROC is less than 0.5 or the probability of suffering from coronary heart disease is greater than 0.5. , indicating that the original hypothetical subject has a high probability of having coronary heart disease, a higher risk, or is diagnosed as a coronary heart disease patient.
  • the amount of each biomarker in the biomarker composition, as well as the biomarker content data in the training set is obtained by the following steps:
  • Sample collection and treatment collect plasma samples from clinical patients or model animals; the samples are subjected to liquid-liquid extraction through organic solvents, including but not limited to ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, and Methyl chloride, acetonitrile, etc.; or protein precipitation, protein precipitation methods include the addition of organic solvents (such as methanol, ethanol, acetone, acetonitrile, isopropanol), various acid-base precipitation, heating precipitation, filtration / ultrafiltration, solid phase Extraction, centrifugation, etc.
  • organic solvents including but not limited to ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, and Methyl chloride, acetonitrile, etc.
  • protein precipitation, protein precipitation methods include the addition of organic solvents (such as methanol, ethanol, acetone, acetonit
  • sample is dried or not dried and then reused with various organic solvents (eg methanol, acetonitrile, isopropanol, chloroform, etc., preferably methanol, acetonitrile) or water (alone or Dissolve in combination, salt-free or salt-free; sample is not derivatized or derivatized with reagents such as trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide, etc. .
  • organic solvents eg methanol, acetonitrile, isopropanol, chloroform, etc., preferably methanol, acetonitrile
  • water alone or Dissolve in combination, salt-free or salt-free
  • reagents such as trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide, etc.
  • the treatment in the step (1) comprises the sample being subjected to liquid-liquid extraction through an organic solvent; or by protein precipitation; the sample is dried or not dried, and the organic solvent or water alone or in combination is used.
  • the dissolution is carried out, the water is salt-free or salt-containing, and the salt includes sodium chloride, phosphate, carbonate, etc.; the sample is not derivatized or derivatized with a reagent.
  • the organic solvent in the step (1) is subjected to liquid-liquid extraction, and the organic solvent includes, but not limited to, ethyl acetate, chloroform, diethyl ether, n-butanol, petroleum ether, dichloromethane, acetonitrile. .
  • the step (1) protein precipitation includes, but is not limited to, adding an organic solvent, various acid-base salt precipitation, heat precipitation, filtration/ultrafiltration, solid phase extraction, centrifugation, alone or in combination.
  • the treatment is carried out in that the organic solvent comprises methanol, ethanol, acetone, acetonitrile, isopropanol.
  • step (1) preferably comprises treatment using a protein precipitation method, preferably using ethanol for protein precipitation.
  • the step (1) sample is dried or not Drying is carried out by dissolving in an organic solvent or methanol, and the organic solvent includes methanol, acetonitrile, isopropanol, chloroform, preferably methanol or acetonitrile.
  • the step (1) sample is subjected to a derivatization treatment using a reagent comprising trimethylsilane, ethyl chloroformate, N-methyltrimethylsilyltrifluoroacetamide .
  • the metabolite spectrum in step (2) is processed to obtain raw data, which is preferably data such as peak height or peak area of each peak and mass and retention time.
  • step (2) peak detection and peak matching are performed on the raw data, and the peak detection and peak matching are preferably performed using XCMS software.
  • the types of mass spectrometry are roughly classified into ion traps, quadrupoles, electrostatic field orbital ion traps, and time-of-flight mass spectrometers.
  • the mass deviations of these four types of analyzers are 0.2 amu, 0.4 amu, 3 ppm, and 5 ppm, respectively.
  • the experimental results obtained by the present invention are analyzed by ion trap, so it is applicable to all mass spectrometers using ion traps and quadrupoles as mass analyzers, including Thermo Fisher's LTQ Orbitrap Velos, Fusion, Elite, etc., Waters' TQS, TQD, etc. , AB Sciex 5500, 4500, 6500, etc., Agilent's 6100, 6490, etc., Bruker's amaZon speed ETD and so on.
  • the peak intensity of the mass spectrum is used to indicate the content of the biomarker.
  • the mass to charge ratio and retention time have the meanings well known in the art.
  • the atomic mass units and retention times of the biomarkers in the biomarker composition of the present invention fluctuate within a certain range;
  • the atomic mass unit may fluctuate within a range of ⁇ 0.4 amu, for example ⁇ 0.2 amu, for example ⁇ 0.1 amu, which may be in the range of ⁇ 60 s, for example ⁇ 45 s, for example ⁇ 30 s, for example ⁇ 15 s. fluctuation.
  • the training set refers to and the test set has a meaning as is known in the art.
  • the training set refers to a data set comprising the content of each biomarker in a coronary heart disease subject and a normal subject test sample comprising a certain number of samples.
  • the test set is a data set used to test the performance of the training set.
  • a training set of biomarkers for coronary heart disease subjects and normal subjects is constructed, and based on this, the biomarker content values of the samples to be tested are evaluated.
  • the data of the training set is as shown in Table 2.
  • the subject may be a human or a model animal.
  • the mass-to-charge ratio unit is amu, and amu refers to the atomic mass unit, also known as Dalton (Daton, Da, D), which is a unit for measuring the mass of an atom or a molecule, which is defined as carbon. 1/12 of 12 atomic mass.
  • one or more of the biomarkers may be selected for risk assessment, diagnosis or pathological staging of coronary heart disease, etc., preferably, at least four of them are selected, namely biomarkers 1 to 3 and The biomarker 6 is evaluated, or the six biomarkers (biomarkers 1 to 6) are simultaneously selected for evaluation to obtain the desired sensitivity and specificity.
  • the normal content range (absolute value) of each biomarker in the sample can be derived using sample detection and calculation methods well known in the art.
  • the absolute value of the detected biomarker content can be compared with the normal content value, optionally It can also be combined with statistical methods to obtain the risk assessment, diagnosis and pathological staging of coronary heart disease.
  • biomarkers are endogenous compounds that are present in the human body.
  • the metabolite profile of the subject's plasma is analyzed by the method of the invention, and the mass values and retention times in the metabolite profile indicate the presence of the corresponding biomarker and the corresponding position in the metabolite profile.
  • the biomarkers of the coronary heart disease population exhibit a range of content values in their metabolite profiles.
  • the present invention shows that plasma metabolite profiles exist in coronary heart disease group and control group. obvious difference.
  • the invention compares and analyzes the metabolite profiles of the coronary heart disease group and the control group, and obtains a plurality of related biomarkers, and combines high-quality coronary heart disease patient population and normal population biomarker metabolite data as a training set. Accurate risk assessment, early diagnosis and pathological staging of coronary heart disease. Compared with the currently used diagnostic methods, the method has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.
  • FIG. 1 PLS-DA score graph.
  • the prismatic shape (white) represents the normal group and the triangle (black) represents the coronary heart disease group.
  • FIG. 1 Principal component analysis load map.
  • a triangle (black) represents a variable with a VIP value greater than one.
  • Figure 4 Volcano-plot diagram. Above the horizontal dashed line are the differential metabolites, where the two sides of the two vertical dashed lines (black triangles) are metabolites with fold-change greater than 1.2 and Q-value less than 0.05, and substances between two vertical dashed lines (grey balls) Type) is a metabolite with a fold-change less than 0.8 and a Q-value less than 0.05.
  • FIG. 1 S-plot diagram.
  • the prism (black) is a variable with a VIP greater than one.
  • Figure 6 Principal component analysis score map.
  • the prismatic shape (white) represents the normal group and the triangle (black) represents the coronary heart disease group.
  • Principal component analysis was performed on 83 test set data using the found markers.
  • Figure 9.6 Random combination selection plot of potential markers.
  • the left side of the vertical line mark is the four markers that need to be detected at least.
  • the plasma samples of coronary heart disease and normal subjects of the present invention are from Guangdong Provincial People's Hospital.
  • ESI ion source data collected in positive ion mode, scan quality m / z 50 ⁇ 1000.
  • the ion source parameter ESI sheath gas is 10, auxiliary gas is 5, capillary temperature is 350 ° C, and cone hole voltage is 4.5 KV.
  • Peak detection and peak matching were performed on the original data using XCMS software (for example, from http://metlin.scripps.edu/xcms/), and the PLS-DA (partial least squares-discriminant analysis) was used to quantify the coronary heart disease group by R software.
  • the spectrum of the spectrum (Fig. 1a) and the normal group of metabolites (Fig. 1b) were used for pattern recognition analysis of differential variables to establish PLS-DA. mathematical model.
  • the plasma metabolite profiles of patients with coronary heart disease were compared by comparing the plasma metabolite profiles of the normal group and the coronary heart disease group (Fig. 1). The results showed that the plasma metabolite profiles of the normal group and the coronary heart disease group were significantly different.
  • ESI ion source data collected in positive ion mode, scan quality m / z 50 ⁇ 1000.
  • the ion source parameter ESI sheath gas is 10, auxiliary gas is 5, capillary temperature is 350 ° C, and cone hole voltage is 4.5 KV.
  • the original data was pre-processed by XCMS software to obtain two-dimensional matrix data, and the statistical difference of peaks of wilcox-test metabolites was analyzed. Partial least squares-discriminant analysis (PLS-DA) was used. Differential metabolic variables were performed on the metabolite profiles of the coronary heart disease group (Fig. 1a) and the normal group metabolite profiles (Fig. 1b). Identification analysis, combined with VIP, Volcano-plot and S-plot maps to screen for potential biomarkers.
  • PLS-DA Partial least squares-discriminant analysis
  • the PLS-DA method was used to distinguish between the normal group and the coronary heart disease group, and the potential markers were further screened by VIP values (Principal Component Analysis Loading-plot) (Fig. 3), Volcano-plot (Fig. 4) and S-plot (Fig. 5). .
  • VIP values Principal Component Analysis Loading-plot
  • Fig. 3 Volcano-plot
  • Fig. 5 S-plot
  • Fig. 3 and Fig. 4 there are obvious differential metabolites in the normal group and the coronary heart disease group.
  • each point in the S-plot diagram represents a variable, and the S-plot diagram indicates the dependence of the variable on the model.
  • the black prismatic markers have variables with a VIP greater than one, which have large deviations and have good correlation with the model, see Figures 2 and 5.
  • the potential markers are screened, and the variables with the VIP value greater than 1 are extracted in the PLS-DA model, and the Volcano-plot map and the S-plot map are further selected according to the load map. And related variables, as well as variables with a P value of less than 0.05 and a Q value of less than 0.05, yielded 6 potential biomarkers, as shown in Table 1.
  • PCA is a non-supervised pattern recognition method that can visually describe differences between samples in a multidimensional space. Using the 6 different markers obtained for 83 obese groups and controls The group samples were analyzed by PCA. From Fig. 6, it can be seen that in the PCA model, the two groups were substantially separated in the direction of the first principal component, indicating that there was a significant difference in the plasma metabolic profiles between the normal group and the coronary heart disease group.
  • Random challenge forest model [7] and receiver response characteristic curve (ROC, also called receiver operating characteristic curve) [8] for 6 potential markers in the normal group and coronary heart disease group Discrimination.
  • the random forest model was used to calculate the typing ability of the six potential biomarkers for the obese and normal groups.
  • the results of the typing ability (from high to low) are shown in Table 3.
  • the markers in the table should be at least the front.
  • the four markers were tested ( Figure 9) to maintain high sensitivity and specificity.

Abstract

提供一种疾病特异性代谢物谱，特别是涉及由冠心病患者血浆特异性代谢物谱筛选得到的生物标志组合物。以及所述生物标志组合物用于冠心病的患病风险评估、诊断、早期诊断以及病理分期的用途，以及冠心病的患病风险评估、诊断、早期诊断以及病理分期方法。提供的生物标志组合物可用于早期诊断冠心病，并且灵敏度高、特异性好，具有良好的应用前景。

Description

冠心病患者特异性生物标志组合物及其用途 技术领域
本发明涉及疾病特异性代谢物谱，特别是涉及由冠心病受试者血浆特异性代谢物谱筛选得到的生物标志组合物。本发明还涉及所述生物标志组合物用于冠心病的患病风险评估、诊断、早期诊断以及病理分期的用途，以及冠心病的患病风险评估、诊断、早期诊断以及病理分期方法。
背景技术
冠状动脉性心脏病(英语：coronary artery heart disease，CAHD)，又称缺血性心脏病，简称冠心病，是一种最常见的心脏病，是指因冠状动脉狭窄、供血不足而引起的心肌机能障碍和(或)器质性病变，故又称缺血性心肌病(IHD)，在2012年是全球第一大死因[1]，也是人们住院的主要原因之一[2]。冠心病可发生于任何年龄，甚至于儿童，但是主要发病年龄为中年以后，并且其发病随着年龄的增长而增加。全球每年有将近1700万人死于动脉粥样硬化心脏病，2020年预计死亡增加50％，达2500万/年，占全球死亡人数的1/3，排第一位。我国每年死于心血管疾病的人数达250万，新发心肌梗死50万人，冠心病的发生有较显著的地区差异，北方城市普遍高于南方城市，在男女性别上也有显著性差异，男女比例为2-5：1，数据显示全球冠心病患者分布也有类似差异[3]。目前对于冠心病的诊断缺乏比较统一的标准并且现有的诊断方法如心电图、心电图负荷试验、动态心电图、核素心肌显像、超声心动图、血液学检查、冠状动脉CT、冠状动脉造影及血管内成像技术等都具有一定的缺陷，例如，症状的观察、超声心动图检查则主观性太强，冠状动脉CT、冠状动脉造影及血管内成像技术等均为侵入式诊断，给患者带来额外的痛苦；现已发现的血液中单一的标志物诊断则存在敏感性和特异性差，假阳性率较高等缺点，开发一种无创、特异性、准确的冠心病诊断方法具有重要的意义[4、5]。
代谢组学是继基因组学和蛋白质组学之后发展起来的一门系统生物学学科，研究生物体在内在或者外在因素影响后其内源性代谢物种类、数量以及变化规律。代谢组学对有机体的整个代谢谱进行分析，探寻代谢物与生理病理变化之间的对应关系，从而为疾病诊断提供依据。因此，筛选与冠心病相关的代谢标志物，特别是多个代谢标志物的联合使用，对冠心病的代谢组学研究、临床诊断和治疗具有重大意义
发明内容
针对现有冠心病诊断方法的有创伤性，侵入性等缺点，本发明所要解决的问题是提供能够用于冠心病诊断和患病风险评估的生物标志物组合(即生物标志组合物)，以及冠心病的诊断和患病风险评估方法。
本发明采用液相色谱质谱联用的分析方法，分析冠心病群体和对照组群体的血浆样本的代谢物谱，并用模式识别进行分析比较冠心病群体和对照组群体的代谢物谱，确定特异性液相色谱质谱数据以及相关特异性生物标志物，为后续理论研究和临床诊断提供依据。
本发明第一方面涉及生物标志组合物，其至少含有选自以下生物标志物1～6中的一种或数种：
生物标志物1，其质荷比为310.04±0.4amu，保留时间为611.25±60s；
生物标志物2，其质荷比为311.05±0.4amu，保留时间为611.26±60s；
生物标志物3，其质荷比为220.00±0.4amu，保留时间为122.77±60s；
生物标志物4，其质荷比为247.09±0.4amu，保留时间为146.37±60s；
生物标志物5，其质荷比为255.03±0.4amu，保留时间为117.92±60s；
生物标志物6，其质荷比为170.03±0.4amu，保留时间为202.18±60s；例如含有其中的1种、2种、3种、4种、5种或6种。
在本发明的实施方案中，上述6种生物标志物的特征如表1所示。
在本发明的一个实施方案中，所述生物标志组合物至少含有生物标志物1～3和6；任选地，还含有生物标志物4和/或生物标志物5。
在本发明的一个实施方案中，所述生物标志组合物含有生物标志物1～6。
在本发明的一个实施方案中，所述生物标志组合物含有生物标志物3～6。
本发明第二方面涉及试剂组合物，其含有用于检测本发明第一方面任一项的生物标志组合物的试剂。
在本发明中，用于检测上述生物标志物的试剂例如为可以与生物标志物结合的配体，例如抗体；任选地，所述用于检测的试剂还可以带有可检测的标记。所述试剂组合物为所有检测试剂的组合。
本发明第三方面涉及本发明第一方面任一项的生物标志组合物和/或第二方面任一项的试剂组合物用于制备试剂盒的用途，所述试剂盒用于冠心病的患病风险评估、诊断、早期诊断或病理分期。
在本发明的实施方案中，所述试剂盒还包括冠心病受试者和正常受试者的本发明第一方面任一项的生物标志组合物含量的训练集数据。
在本发明的一个实施方案中，其中所述的训练集数据如表2所示。
本发明还涉及一种用于冠心病的患病风险评估、诊断、早期诊断或病理分期的方法，所述方法包括测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的步骤。
在本发明的一个实施方案中，其中测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
在本发明的一个实施方案中，所述方法还包括建立冠心病受试者和正常受试者(对照组)样本(例如血浆、全血)的本发明第一方面任一项的生物标志组合物中各生物标示物含量的训练集的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2所示。
在本发明的一个实施方案中，所述方法还包括将受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与冠心病受试者和正常受试者的生物标志组合物的训练集数据进行比较的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2所示。
在本发明的一个实施方案中，其中所述进行比较是指采用受试者工作特征曲线进行比较。
在本发明的一个实施方案中，其中比较步骤的结果判定方法为，若假定受试者为非冠心病患者，进行ROC诊断得到的非冠心病患者的概率小于0.5或者患冠心病的概率大于0.5，则表明原假定的受试者患冠心病的概率大、风险较高或者诊断为冠心病患者。
在本发明的具体实施方案中，所述方法包括以下步骤：
1)利用液相色谱质谱联用的方法测定受试者血浆中本发明第一方面任一项的生物标志组合物中各生物标志物的含量；
2)利用液相色谱质谱联用的方法测定冠心病受试者和正常受试者血浆中的本发明第一方面任一项的生物标志组合物的含量，并利用随机森林模型建立生物标志组合物含量的训练集(例如表2所示)；
3)采用ROC曲线，将受试者血浆中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与冠心病受试者和正常受试者的生物标志组合物的训练集数据进行比较；
4)若假定受试者为非冠心病患者，进行ROC诊断得到的非冠心病患者的概率小于0.5或者患冠心病的概率大于0.5，则表明原假定的受试者患冠心病的概率大、风险较高或者诊断为冠心病患者。
本发明还涉及本发明第一方面任一项的生物标志组合物，用于冠心病的患病风险评估、诊断、早期诊断或病理分期。
在本发明的一个实施方案中，其中测定受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
在本发明的一个实施方案中，还包括建立冠心病受试者和正常受试者的本发明第一方面任一项的生物标志组合物中各生物标志物含量的训练集的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2所示。
在本发明的一个实施方案中，还包括将受试者样本(例如血浆、全血)中本发明第一方面任一项的生物标志组合物中各生物标志物的含量与冠心病受试者和正常受试者的生物标志组合物的训练集数据进行比较的步骤。
在本发明的一个实施方案中，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
在本发明的一个实施方案中，其中所述训练集的数据如表2所示。
在本发明的一个实施方案中，其中所述进行比较的方法是指采用受试者工作特征曲线的方法进行比较。
在本发明的一个实施方案中，其中比较步骤的结果判定方法为，若假定受试者为非冠心病患者，进行ROC诊断得到的非冠心病患者的概率小于0.5或者患冠心病的概率大于0.5，则表明原假定的受试者患冠心病的概率大、风险较高或者诊断为冠心病患者。
在本发明的实施方案中，所述生物标志组合物中各生物标志物的含量，以及所述训练集中各生物标志物含量数据的获得，是通过以下步骤：
(1)样本的收集与处理：收集临床病人或者模型动物的血浆样本；样本经过有机溶剂进行液液萃取，有机溶剂包括但不限于乙酸乙酯、氯仿、乙醚、正丁醇、石油醚、二氯甲烷、乙腈等；或者经过蛋白沉淀，蛋白沉淀方法包括加入有机溶剂(例如甲醇、乙醇、丙酮、乙腈、异丙醇)、各类酸碱盐沉淀、加热沉淀、过滤/超滤、固相萃取，离心等方法单独或者综合的方式进行处理；样本进行干燥或者不进行干燥再利用各种有机溶剂(例如甲醇，乙腈，异丙醇，氯仿等，优选为甲醇、乙腈)或者水(单独或者组合，不含盐或者含盐)溶解；样本不进行衍生化或者利用试剂(例如三甲基硅烷，氯甲酸乙酯，N-甲基三甲基硅基三氟乙酰胺等)进行衍生化处理。
(2)液相色谱质谱分析测定(HPLC-MS)：采用基于液相色谱和质谱的方法得到血浆中的代谢物谱，代谢物谱经过处理得到各个峰的峰高或者峰面积(peak intensity)以及质荷比和保留时间(retention time)等数据，其中的峰面积即代表生物标志物的含量。
在本发明的一个具体实施方式中，步骤(1)中的处理包括样本经过有机溶剂进行液液萃取；或者经过蛋白沉淀；样本进行干燥或者不进行干燥，再利用单独或者组合的有机溶剂或者水进行溶解，所述水不含盐或者含盐，盐包括氯化钠，磷酸盐，碳酸盐等；样本不进行衍生化或者利用试剂进行衍生化处理。
在本发明的一个具体实施方式中，步骤(1)有机溶剂进行液液萃取中，所述有机溶剂包括但不限于乙酸乙酯、氯仿、乙醚、正丁醇、石油醚、二氯甲烷、乙腈。
在本发明的一个具体实施方式中，步骤(1)蛋白沉淀中，包括但不限于加入有机溶剂、各类酸碱盐沉淀、加热沉淀、过滤/超滤、固相萃取、离心方法单独或者组合的方式进行处理，其中所述有机溶剂包括甲醇、乙醇、丙酮、乙腈、异丙醇。
在本发明的一个具体实施方式中，步骤(1)中优选地包括使用蛋白沉淀方法进行处理，优选地使用乙醇进行蛋白沉淀。
在本发明的一个具体实施方式中，步骤(1)样本进行干燥或者不 进行干燥，再利用有机溶剂或者水溶解中，所述有机溶剂包括甲醇、乙腈、异丙醇、氯仿，优选为甲醇、乙腈。
在本发明的一个具体实施方式中，步骤(1)样本利用试剂进行衍生化处理中，所述试剂包括三甲基硅烷，氯甲酸乙酯，N-甲基三甲基硅基三氟乙酰胺。
在本发明的一个具体实施方式中，步骤(2)中代谢物谱经过处理得到原始数据，所述原始数据优选地是各个峰的峰高或者峰面积以及质量数和保留时间等数据。
在本发明的一个具体实施方式中，步骤(2)中，对原始数据进行峰检测和峰匹配，优选地采用XCMS软件进行所述峰检测和峰匹配。
质谱分析类型大致分为离子阱、四级杆、静电场轨道离子阱、飞行时间质谱四类，这四类分析器的的质量偏差分别为0.2amu、0.4amu、3ppm、5ppm。本发明得到的实验结果是离子阱分析的，所以适用于所有以离子阱和四级杆为质量分析器的质谱仪器，包括Thermo Fisher的LTQ Orbitrap Velos、Fusion、Elite等，Waters的TQS、TQD等，AB Sciex的5500、4500、6500等，Agilent的6100、6490等，Bruker的amaZon speed ETD等。
在本发明的实施方案中，用质谱的峰面积(peak intensity)表示生物标志物的含量。
在本发明中，所述的质荷比和保留时间具有本领域公知的含义。
本领域技术人员公知，当采用不同的液相色谱质谱联用设备以及不同的检测方法时，本发明的生物标志组合物中各生物标志物的原子质量单位和保留时间会在一定范围内波动；其中，所述原子质量单位可以在±0.4amu，例如±0.2amu，例如±0.1amu的范围内波动，所述保留时间可以在±60s，例如±45s，例如±30s，例如±15s的范围内波动。
在本发明中，随机森林模型和ROC曲线的使用方法为本领域所公知(参见参考文献[7]和[8])，本领域技术人员可以根据具体情况进行 参数设置和调整。
在本发明中，所述训练集是指和测试集具有本领域公知的含义。在本发明的实施方案中，所述训练集是指包含一定样本数的冠心病受试者和正常受试者待测样本中的各生物标志物的含量的数据集合。所述测试集是用来测试训练集性能的数据集合。
在本发明中，构建了冠心病受试者和正常受试者的生物标志物的训练集，并以此为基准，对待测样本的生物标志物含量值进行评估。
在本发明的实施方案中，所述训练集的数据如表2所示。
在本发明中，所述受试者可以为人或者模型动物。
在本发明中，质荷比的单位为amu，amu是指原子质量单位，也称为道尔顿(Dalton，Da，D)，是用来衡量原子或分子质量的单位，它被定义为碳12原子质量的1/12。
在本发明中，可以选用生物标志物中的一种或多种进行冠心病的患病风险评估、诊断或病理分期等，优选地，至少选取其中的四种，即生物标志物1～3和生物标志物6进行评估，或者同时选用这6种生物标志物(生物标志物1～6)进行评估，以获得理想的灵敏度和特异性。
本领域技术人员知晓，当进一步扩大样本量时，利用本领域公知的样本检测和计算方法，可以得出每种生物标志物在样本中的正常含量值区间(绝对数值)。这样当采用除质谱以外的其它方法对生物标志物的含量进行检测时(例如利用抗体和ELISA方法等)，可以将检测得到的生物标志物含量的绝对值与正常含量值进行比较，任选地，还可以结合统计学方法，以得出冠心病的患病风险评、诊断以及病理分期等。
不希望受任何理论的限制，发明人指出这些生物标志物是存在于人体中的内源性化合物。通过本发明所述的方法对受试者血浆的代谢物谱进行分析，代谢物谱中的质量数值以及保留时间指示相应生物标志物的存在及在代谢物谱中的对应位置。同时，冠心病群体的所述生物标志物在其代谢物谱中表现出一定的含量范围值。
机体内源性小分子是生命活动的基础，疾病的状态与机体功能的变化必然会引起内源性小分子在体内代谢的变化，本发明表明，冠心病组和对照组的血浆代谢物谱存在明显的差异。本发明通过对冠心病组和对照组代谢物谱的比较和分析，得到多种相关的生物标志物，结合高质量的冠心病人群和正常人群生物标志物的代谢物谱数据作为训练集，能够准确地对冠心病进行患病风险评估、早期诊断和病理分期。该方法与目前常用的诊断方法相比，具有无创、方便、快捷的特点，且灵敏度高，特异性好。
附图说明
图1.冠心病患者组(a)和正常人组(b)质谱总离子流图.
图2.PLS-DA得分图。棱形(白色)代表正常组，三角形(黑色)代表冠心病组。
图3.主成分分析荷载图。三角形(黑色)代表VIP值大于1的变量。
图4.Volcano-plot图。水平虚线以上部分是差异代谢物，其中两条竖直虚线两侧的物质(黑色三角形)是fold-change大于1.2且Q-value小于0.05的代谢物，两条竖直虚线间的物质(灰色球型)是fold-change小于0.8且Q-value小于0.05的代谢物。
图5.S-plot图。棱形(黑色)是VIP大于1的变量。
图6.主成分分析得分图。棱形(白色)代表正常组，三角形(黑色)代表冠心病组。使用发现的标记物对83个测试集数据进行主成分分析。
图7.随机森林模型(Randomforest)模型的ROC图。Training ROC是基于训练集，AUC＝1；Test ROC是基于测试集，AUC＝1。
图8.随机去掉训练集中的310.04和311.05质荷比的ROC测试集图，AUC＝0.8851。
图9.6个潜在标记物的随机组合挑选图。竖直线标记处左侧是至少需要检测的4个标记物。
具体实施方式
下面将结合实施例对本发明的实施方案进行详细描述，但是本领域技术人员将会理解，下列实施例仅用于说明本发明，而不应视为限定本发明的范围。实施例中未注明具体条件者，按照常规条件或制造商建议的条件进行。所用试剂或仪器未注明生产厂商者，均为可以通过市购获得的常规产品。
本发明的冠心病和正常受试者的血浆样本来自广东省人民医院。
实施例1
1.1样本收集：收集志愿者的晨血，立即置于-80℃低温冰箱中储存。正常组共收集52份血浆样本，冠心病组共收集40份血浆样本。
1.2样本的处理：冰冻的样本置于室温下解冻，取血浆样本500μL至于2.0mL离心管中，加入甲醇1000μL稀释，10000rpm离心5min，备用。
1.3液相色谱质谱联用分析
仪器设备
HPLC-MS-LTQ Orbitrap Discovery(Thermo，Germany)
色谱条件
色谱柱：C18柱(150mm×2.1mm，5μm)；流动相A：0.1％甲酸水溶液，流动相B：0.1％甲酸乙腈溶液；梯度洗脱程序：0～3min，5％B，3～36min，5％～80％B，36～40min，80％～100％B，40～45min，100％B，45～50min，100％～5％B，50～60min，5％B；流速：0.2mL/min；进样体积20μL。
质谱条件
ESI离子源，正离子模式采集数据，扫描质量m/z 50～1000。离子源参数ESI：鞘气为10，辅气为5，毛细管温度为350℃，锥孔电压4.5KV。
1.4数据处理
采用XCMS软件(例如得自http：//metlin.scripps.edu/xcms/)对原始数据进行峰检测和峰匹配，采用R软件采用PLS-DA(partial least squares-discriminant analysis)对冠心病组代谢物谱(图1a)和正常组代谢物谱(图1b)进行差异性变量进行模式识别分析，建立PLS-DA 数学模型。
1.5比较和确定特征性代谢物谱
通过比较正常组与冠心病组的血浆代谢物谱图，建立冠心病患者血浆代谢物谱(图1)，结果表明，正常组和冠心病组的血浆代谢物谱图是有明显差异的。
实施例2
2.1样本收集：收集志愿者的晨尿，立即置于-80℃低温冰箱中储存。正常组共收集52份血浆样本，冠心病组共收集40份血浆样本。
2.2样本的处理：冰冻的样本置于室温下解冻，取血浆样本500μL至于2.0mL离心管中，加入甲醇1000μL稀释，10000rpm离心5min，备用。
2.3液相色谱质谱联用分析
仪器设备
HPLC-MS-LTQ Orbitrap Discovery(Thermo，Germany)
色谱条件
色谱柱：C18柱(150mm×2.1mm，5μm)；流动相A：0.1％甲酸水溶液，流动相B：0.1％甲酸乙腈溶液；梯度洗脱程序：0～3min，5％B，3～36min，5％～80％B，36～40min，80％～100％B，40～45min，100％B，45～50min，100％～5％B，50～60min，5％B；流速：0.2mL/min；进样体积20μL。
质谱条件
ESI离子源，正离子模式采集数据，扫描质量m/z 50～1000。离子源参数ESI：鞘气为10，辅气为5，毛细管温度为350℃，锥孔电压4.5KV。
2.4数据处理
采用XCMS软件对原始数据进行相关前处理，得到二维矩阵数据，wilcox-test统计代谢物峰的显著性差异；采用正交偏最小二乘法判别分析(PLS-DA，partial least squares-discriminant analysis)对冠心病组代谢物谱(图1a)和正常组代谢物谱(图1b)进行差异性变量进行模 式识别分析，结合VIP、Volcano-plot图和S-plot图筛选出潜在的生物标志物。
2.5代谢谱分析和潜在的生物标志物
2.5.1正交偏最小二乘法判别分析(PLS-DA)
采用PLS-DA方法来区分正常组和冠心病组，进一步通过VIP值(主成分分析Loading-plot)(图3)、Volcano-plot(图4)和S-plot(图5)筛选潜在标志物。从图3、图4可知，正常组和冠心病组存在明显的差异性代谢物。如图5所示，S-plot图中每个点代表一个变量，S-plot图表明变量与模型的相关性。黑色的棱形标记的变量为VIP大于1的变量，它们具有较大的偏差并且与模型有良好的相关性，见图2、5。
2.5.2潜在生物标记物
根据模式识别模型PLS-DA的VIP值筛选潜在标志物，在PLS-DA模型中提取VIP值大于1的变量，并进一步根据荷载图，Volcano-plot图和S-plot图进一步选择具有较大偏差和相关性的变量，以及结合P值小于0.05，Q值小于0.05的变量，得到6个潜在的生物标记物，如表1所示。
表1潜在的生物标记物
Figure PCTCN2014087853-appb-000001
2.5.3主成分分析(PCA)
PCA是一种无师监督模式识别方法，可以直观地在多维空间上描述样本间的差异。使用得到的6个差异的标记物对83个肥胖组和对照 组样本进行PCA分析，从图6可知，在PCA模型中，两组在第一主成分方向上基本分开，表明正常组和冠心病组的血浆代谢谱存在明显的区别。
2.5.4受试者诊断曲线(ROC)
使用随机森林模型(RandomForest)[7]和受试者诊断曲线(reveiver operating characteristic curve，ROC，也叫受试者工作特征曲线)[8]对6个潜在标记物进行正常组与冠心病组的判别。通过选取92个正常组与冠心病组代谢物谱峰面积数据采用ROC建模作为训练集(参见参考文献[7]和[8])(表2)，另外选取83个测试样本(含冠心病的样本38个，正常对照样本45个)作为测试集，测试结果为AUC＝1，FN(假阴性)＝0，FP(假阳性)＝0(图7)，具有较高的准确度和特异性，具有良好的开发为诊断方法的前景，从而为冠心病疾病的诊断提供依据。
Figure PCTCN2014087853-appb-000002
Figure PCTCN2014087853-appb-000003
Figure PCTCN2014087853-appb-000004
Figure PCTCN2014087853-appb-000005
Figure PCTCN2014087853-appb-000006
利用随机森林模型计算这6个潜在的生物标记物对于肥胖组和正常组的分型能力，分型能力结果(从高往低排列)如表3所示，表中的标记物至少要采用前面的4种标记物进行检测(图9)，这样才能保持较高的灵敏度和特异性。
表3潜在标记物的分型能力
Figure PCTCN2014087853-appb-000007
若随机去掉训练集中的质荷比，比如310.04和311.05，得到ROC测试集(上述83个测试集样本)的AUC＝0.8851，AUC明显降低，FN＝0.184和FP＝0.200，FN和FP明显增大(图8)，冠心病诊断能力明显降低。
参考文献：
[1]Finegold，JA；Asaria，P；Francis，DP.Mortality from ischaemic heart disease by country，region，and age：Statistics from World Health Organisation and United Nations.International journal of cardiology.4December 2012，168(2)：934-45.
[2]World Health Organization Department of Health Statistics and Informatics in the Information，Evidence and Research Cluster.The global burden of disease 2004update.Geneva：WHO.2004.ISBN 92-4-156371-0.
[3]Elizabeth Barrett-Connor.Gender differences and disparities in all-cause and coronary heart disease mortality：epidemiological  aspects.Best Pract Res Clin Endocrinol Metab.2013Aug；27(4)：481-500.
[4]Madjid M，Willerson JT.Inflammatory markers in coronary heart disease.Br Med Bull.2011；100：23-38.doi：10.1093/bmb/ldr043.Epub 2011Oct 18.
[5]Spoletini I1，Vitale C，Rosano GM.Biomarkers for predicting postmenopausal coronary heart disease.Biomark Med.2011Aug；5(4)：485-95.doi：10.2217/bmm.11.51.
[6]Kishore Kumar Pasikanti，Kesavan Esuvaranathan，Paul C.Ho，et al.Noninvasive urinary metabonomic diagnosis of human bladder cancer.Journal of Proteome Research，2010，9，2988-2995.
[7]Liaw，Andy&Wiener，Matthew.Classification and Regression by randomForest，R News(2002)，Vol.2/3p.18.
[8]Jianguo Xia，David I.Broadhurst，Michael Wilson，David S.Wishart.Translational biomarker discovery in clinical metabolomics：an introductory tutorial.Metabolomics(2013)9：280-299.

Claims (27)

  1. 生物标志组合物，其至少含有选自以下生物标志物1～6中的一种或数种：
    生物标志物1，其质荷比为310.04±0.4amu，保留时间为611.25±60s；
    生物标志物2，其质荷比为311.05±0.4amu，保留时间为611.26±60s；
    生物标志物3，其质荷比为220.00±0.4amu，保留时间为122.77±60s；
    生物标志物4，其质荷比为247.09±0.4amu，保留时间为146.37±60s；
    生物标志物5，其质荷比为255.03±0.4amu，保留时间为117.92±60s；
    生物标志物6，其质荷比为170.03±0.4amu，保留时间为202.18±60s。
  2. 权利要求1的生物标志组合物，其至少含有生物标志物1～3和6；任选地，还含有生物标志物4和/或生物标志物5。
  3. 权利要求1的生物标志组合物，其含有生物标志物1～6。
  4. 试剂组合物，其含有用于检测权利要求1-3任一项的生物标志组合物的试剂。
  5. 权利要求1-3任一项的生物标志组合物和/或权利要求4的试剂组合物用于制备试剂盒的用途，所述试剂盒用于冠心病的患病风险评估、诊断、早期诊断或病理分期。
  6. 权利要求5的用途，所述试剂盒还包括冠心病受试者和正常受试者的权利要求1-3任一项的生物标志组合物含量的训练集数据。
  7. 权利要求6的用途，其中所述的训练集数据如表2所示。
  8. 一种用于冠心病的患病风险评估、诊断、早期诊断或病理分期 的方法，所述方法包括测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的步骤。
  9. 权利要求8的方法，其中测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
  10. 权利要求8的方法，所述方法还包括建立冠心病受试者和正常受试者样本(例如血浆、全血)的权利要求1-3任一项的生物标志组合物含量的训练集的步骤。
  11. 权利要求10的方法，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  12. 权利要求11的方法，其中所述训练集的数据如表2所示。
  13. 权利要求7-12任一项的方法，所述方法还包括将受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量与训练集数据进行比较的步骤，所述训练集是指冠心病受试者和正常受试者样本的权利要求1-3任一项的生物标志组合物含量的训练集。
  14. 权利要求13的方法，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  15. 权利要求14的方法，其中所述训练集的数据如表2所示。
  16. 权利要求13-15任一项的方法，其中所述进行比较是指采用受试者工作特征曲线进行比较。
  17. 权利要求16的方法，其中比较步骤的结果判定方法为，若假定受试者为非冠心病患者，进行ROC诊断得到的非冠心病患者的概率小于0.5或者患冠心病的概率大于0.5，则表明原假定的受试者患冠心病的概率大、风险较高或者诊断为冠心病患者。
  18. 权利要求1-3任一项的生物标志组合物，用于冠心病的患病风险评估、诊断、早期诊断或病理分期。
  19. 权利要求18的生物标志组合物，其中测定受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量的方法为液相色谱质谱联用的方法。
  20. 权利要求18的生物标志组合物，还包括建立冠心病受试者和正常受试者的权利要求1-3任一项的生物标志组合物含量的训练集的步骤。
  21. 权利要求20的生物标志组合物，其中所述训练集是利用多元统计分类模型(例如随机森林模型)建立的训练集。
  22. 权利要求21的生物标志组合物，其中所述训练集的数据如表2所示。
  23. 权利要求18-22任一项的生物标志组合物，还包括将受试者样本(例如血浆、全血)中权利要求1-3任一项的生物标志组合物中各生物标志物的含量与训练集数据进行比较的步骤，所述训练集是指冠心病受试者和正常受试者样本的权利要求1-3任一项的生物标志组合物含量的训练集。
  24. 权利要求23的生物标志组合物，其中所述训练集是利用多元 统计分类模型(例如随机森林模型)建立的训练集。
  25. 权利要求24的生物标志组合物，其中所述训练集的数据如表2所示。
  26. 权利要求23-25任一项的生物标志组合物，其中所述进行比较是指采用受试者工作特征曲线进行比较。
  27. 权利要求26的生物标志组合物，其中比较步骤的结果判定方法为，若假定受试者为非冠心病患者，进行ROC诊断得到的非冠心病患者的概率小于0.5或者患冠心病的概率大于0.5，则表明原假定的受试者患冠心病的概率大、风险较高或者诊断为冠心病患者。
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