WO2017028308A1 - Biomarkers for coronary heart disease - Google Patents

Biomarkers for coronary heart disease Download PDF

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Publication number
WO2017028308A1
WO2017028308A1 PCT/CN2015/087664 CN2015087664W WO2017028308A1 WO 2017028308 A1 WO2017028308 A1 WO 2017028308A1 CN 2015087664 W CN2015087664 W CN 2015087664W WO 2017028308 A1 WO2017028308 A1 WO 2017028308A1
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biomarker
chd
subject
composition according
mass spectrometry
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French (fr)
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Qiang FENG
Zhipeng Liu
Jun Wang
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BGI Shenzhen Co Ltd
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BGI Shenzhen Co Ltd
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Priority to CN201580082502.6A priority Critical patent/CN108027354B/en
Priority to HK18108482.8A priority patent/HK1248813B/en
Priority to PCT/CN2015/087664 priority patent/WO2017028308A1/en
Priority to EP16836681.3A priority patent/EP3339858A4/en
Priority to PCT/CN2016/096090 priority patent/WO2017028817A1/en
Priority to CN201680047238.7A priority patent/CN108027361B/en
Priority to HK18108736.2A priority patent/HK1249180B/en
Publication of WO2017028308A1 publication Critical patent/WO2017028308A1/en
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    • 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
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • 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
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • 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

Definitions

  • the present invention relates to plasma biomarkers and methods for predicting the risk of a disease related to metabolites, in particular coronary heart disease.
  • Coronary heart disease is the top risk factor in modern society with annual mortality rate overpassing the sum of all types of cancers.
  • the majority of cardiovascular deaths occurrence are related to the extent of people’s awareness of their own medical conditions and are due to lackness of in-time treatment as demonstrated by a five-year follow-up study by MaGiCAD cohort (Graninger, D.J. &Mosedale, D.E. Metabolomics in coronary heart disease. Heart. Metab. 55, 8–12 (2012) , incorporated herein by reference) .
  • the challenge for early diagnosis and prevention of CHD lies in the lackness of reliable non-invasive biomarkers.
  • the "gold standard" for diagnosis of CHD is still coronary angiography which is invasive and accompanied by many deadly side effects, this limited the large population screening and the CHD risk prediction at early stage.
  • Cardiovascular diseases like coronary heart disease and cardiac failure undergo a “metabolic shift” as a consequence of both intrinsic and extrinsic perturbations.
  • Increased low-density lipoprotein cholesterol (LDL-C) has previously been considered as one of the major risk factors for CHD.
  • LDL-C low-density lipoprotein cholesterol
  • Metabolomics is an innovative and high-throughput bioanalytical method aiming to identify and quantify small molecules (molecular weight less than 1500 Daltons) present in any biological system or any specific physiological state.
  • Two major analytical techniques nuclear magnetic resonance (NMR) and mass spectrometry (MS) , have been widely used in endogenous compounds measurement at an exponential increasing rate in last decade.
  • MS-based techniques have made rapid progress and have been used more frequently compared with NMR since 2005 because of the following advantages: higher sensitivity, more coverage of the metabolome, improved metabolites identification and discrimination capability, and modularity to perform compound-class-specific analysis (Griffiths, W.J. et al. Targeted metabolomics for biomarker discovery. Angew. Chem. Int. Ed. 49, 5426–5445 (2010) , incorporated herein by reference) .
  • MS is mostly used in conjunction with chromatography, such as gas chromatography mass spectrometry (GC–MS) and liquid chromatography mass spectrometry (LC–MS
  • the present invention aims to provide a biomarker composition and a method for evaluation of the risk of CHD or diagnosis or early diagnosis of CHD, and specifically comprises the following aspects.
  • the first aspect of the invention relates to a biomarker composition, which comprises one or more selected from the group consisting of:
  • Biomarker 1 for which m/z is 518.32 ⁇ 1.00, retention time (RT) is 12.71 ⁇ 1.00min;
  • Biomarker 2 for which m/z is 480.34 ⁇ 1.00, retention time (RT) is 13.48 ⁇ 1.00min;
  • Biomarker 3 for which m/z is 482.32 ⁇ 1.00, retention time (RT) is 12.92 ⁇ 1.00min;
  • Biomarker 4 for which m/z is 496.33 ⁇ 1.00, retention time (RT) is 13.19 ⁇ 1.00min;
  • Biomarker 5 for which m/z is 468.3 ⁇ 1.00, retention time (RT) is 12.69 ⁇ 1.00min;
  • Biomarker 6 for which m/z is 494.32 ⁇ 1.00, retention time (RT) is 12.82 ⁇ 1.00min;
  • Biomarker 7 for which m/z is 524.36 ⁇ 1.00, retention time (RT) is 13.69 ⁇ 1.00min;
  • Biomarker 8 for which m/z is 516.31 ⁇ 1.00, retention time (RT) is 13.21 ⁇ 1.00min;
  • Biomarker 9 for which m/z is 590.31 ⁇ 1.00, retention time (RT) is 12.86 ⁇ 1.00min;
  • Biomarker 10 for which m/z is 546.35 ⁇ 1.00, retention time (RT) is 14.22 ⁇ 1.00min;
  • Biomarker 11 for which m/z is 570.35 ⁇ 1.00, retention time (RT) is 13.05 ⁇ 1.00min;
  • Biomarker 12 for which m/z is 518.32 ⁇ 1.00, retention time (RT) is 13.19 ⁇ 1.00min;
  • Biomarker 13 for which m/z is 524.36 ⁇ 1.00, retention time (RT) is 14.27 ⁇ 1.00min;
  • Biomarker 14 for which m/z is 522.35 ⁇ 1.00, retention time (RT) is 13.33 ⁇ 1.00min;
  • Biomarker 15 for which m/z is 181.07 ⁇ 1.00, retention time (RT) is 9.05 ⁇ 1.00min;
  • Biomarker 16 for which m/z is 544.33 ⁇ 1.00, retention time (RT) is 13.34 ⁇ 1.00min;
  • Biomarker 17 for which m/z is 175.11 ⁇ 1.00, retention time (RT) is 1.83 ⁇ 1.00min;
  • Biomarker 18 for which m/z is 324.04 ⁇ 1.00, retention time (RT) is 9.33 ⁇ 1.00min.
  • Biomarker 1 ⁇ 18 are shown in Table 1.
  • the biomarker composition comprises Biomarker 1 and one or more selected from the group consisting of Biomarker 2 ⁇ Biomarker 18.
  • the biomarker composition comprises Biomarker 2 and one or more selected from the group consisting of Biomarker 1 and Biomarker 3 ⁇ 18.
  • the biomarker composition comprises Biomarker 3 and one or more selected from the group consisting of Biomarker 1 ⁇ 2 and Biomarker 4 ⁇ 18.
  • the biomarker composition comprises Biomarker 4 and one or more selected from the group consisting of Biomarker 1 ⁇ 3 and Biomarker 5 ⁇ 18.
  • the biomarker composition comprises Biomarker 5 and one or more selected from the group consisting of Biomarker 1 ⁇ 4 and Biomarker 6 ⁇ 18.
  • the biomarker composition comprises Biomarker 6 and one or more selected from the group consisting of Biomarker 1 ⁇ 5 and Biomarker 7 ⁇ 18.
  • the biomarker composition comprises Biomarker 7 and one or more selected from the group consisting of Biomarker 1 ⁇ 6 and Biomarker 8 ⁇ 18.
  • the biomarker composition comprises Biomarker 1 ⁇ 7 and one or more selected from the group consisting of Biomarker 8 ⁇ 18.
  • the biomarker composition comprises one or more selected from the group consisting of:
  • Biomarker 1 for which m/z is 518.32 ⁇ 1.00, retention time (RT) is 12.71 ⁇ 1.00min;
  • Biomarker 2 for which m/z is 480.34 ⁇ 1.00, retention time (RT) is 13.48 ⁇ 1.00min;
  • Biomarker 3 for which m/z is 482.32 ⁇ 1.00, retention time (RT) is 12.92 ⁇ 1.00min;
  • Biomarker 4 for which m/z is 496.33 ⁇ 1.00, retention time (RT) is 13.19 ⁇ 1.00min;
  • Biomarker 5 for which m/z is 468.3 ⁇ 1.00, retention time (RT) is 12.69 ⁇ 1.00min;
  • Biomarker 6 for which m/z is 494.32 ⁇ 1.00, retention time (RT) is 12.82 ⁇ 1.00min;
  • Biomarker 7 for which m/z is 524.36 ⁇ 1.00, retention time (RT) is 13.69 ⁇ 1.00min.
  • the biomarker composition comprises Biomarker 1 and one or more selected from the group consisting of Biomarker 2 ⁇ Biomarker 7.
  • the biomarker composition comprises Biomarker 2 and one or more selected from the group consisting of Biomarker 1 and Biomarker 3 ⁇ Biomarker 7.
  • the biomarker composition comprises Biomarker 3 and one or more selected from the group consisting of Biomarker 1 ⁇ 2 and Biomarker 4 ⁇ 7.
  • the biomarker composition comprises Biomarker 4 and one or more selected from the group consisting of Biomarker 1 ⁇ 3 and Biomarker 5 ⁇ 7.
  • the biomarker composition comprises Biomarker 5 and one or more selected from the group consisting of Biomarker 1 ⁇ 4 and Biomarker 6 ⁇ 7.
  • the biomarker composition comprises Biomarker 6 and one or more selected from the group consisting of Biomarker 1 ⁇ 5 and Biomarker 7.
  • the biomarker composition comprises Biomarker 7 and one or more selected from the group consisting of Biomarker 1 ⁇ 6.
  • the biomarker composition comprises Biomarker 1 ⁇ 7.
  • Biomarker 1 is LysoPC (18: 3 (6Z, 9Z, 12Z) ) ;
  • Biomarker 2 is LysoPC (P-16: 0) ;
  • Biomarker 3 is LysoPC (15: 0) ;
  • Biomarker 4 is 1-Palmitoylglycerophosphocholine
  • Biomarker 5 is LysoPC (14: 0) ;
  • Biomarker 6 is LysoPC (16: 1 (9Z) ) ;
  • Biomarker 7 is LysoPC (0: 0/18: 0) ;
  • Biomarker 8 is LysoPC (18: 4 (6Z, 9Z, 12Z, 15Z) ) ;
  • Biomarker 9 is LysoPC (22: 6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z) ) ;
  • Biomarker 10 is LysoPC (20: 3 (5Z, 8Z, 11Z) ) ;
  • Biomarker 11 is LysoPC (22: 5 (4Z, 7Z, 10Z, 13Z, 16Z) ) ;
  • Biomarker 12 is LysoPC (18: 3 (9Z, 12Z, 15Z) ) ;
  • Biomarker 13 is LysoPC (18: 0) ;
  • Biomarker 14 is 1-Oleoylglycerophosphocholine
  • Biomarker 15 is Paraxanthine
  • Biomarker 16 is LysoPC (20: 4 (5Z, 8Z, 11Z, 14Z) ) ;
  • Biomarker 17 is L-Arginine; and/or
  • Biomarker 18 is N-Acetyl-D-glucosamine 6-phosphate.
  • the biomarker composition comes from a blood, plasma or serum sample.
  • the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  • the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  • each of the biomarker composition is carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the second aspect of the invention relates to a reagent composition, which comprises the reagents used for detection of each of the biomarker composition according to any item of the first aspect of the invention.
  • the reagents comprise substances used in mass spectrometry for detection of the biomarker composition according to any item of the first aspect of the invention.
  • the samples for detection of the biomarkers are selected from blood, plasma and serum.
  • the third aspect of the invention relates to a kit, which comprises the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention.
  • kit further comprises the training dataset of the level of each of the biomarkers in the biomarker composition according to any item of the first aspect of the invention for CHD patients and healthy controls (for example as shown by Table 7) .
  • the fourth aspect of the invention relates to a use of the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention in the preparation of a kit, wherein the kit is used for evaluation of the risk of CHD in a subject, or for use in diagnosis of CHD in a subject.
  • the evaluation or diagnosis comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject; 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ; preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
  • the sample is selected from blood, plasma and serum.
  • the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness; preferably, the multivariate statistical model is random forest model.
  • the subject is determined as being at risk of CHD or having CHD if the probability of illness ⁇ 0.5.
  • the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  • the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further comprises the step of processing the sample before step 1) .
  • kit further comprises the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
  • the fifth aspect of the invention relates to a method for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, wherein the method comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject; 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ; preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
  • the sample is selected from blood, plasma and serum.
  • the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness; preferably, the multivariate statistical model is random forest model.
  • the subject is determined as being at risk of CHD or having CHD if the probability of illness ⁇ 0.5.
  • the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  • the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further comprises the step of processing the samples before step 1) .
  • the method further includes setting up the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
  • the present invention further relates to the biomarker composition according to any item of the first aspect of the invention, for use in a method of evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject.
  • the method of evaluation or diagnosis comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject; 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ; preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
  • the sample is selected from blood, plasma and serum.
  • the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness; preferably, the multivariate statistical model is random forest model.
  • the subject is determined as being at risk of CHD or having CHD if the probability of illness ⁇ 0.5.
  • the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  • the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further comprises the step of processing the samples before step 1) .
  • the present invention further relates to a use of the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention in the preparation of a kit, wherein the kit is used for screening candidate compounds for treatment of CHD in a subject, or for evaluating the effect of the treatment of CHD in a subject.
  • the screening or evaluation comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of the second aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject; 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  • the sample is selected from blood, plasma and serum.
  • the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further comprises the step of processing the samples before step 1) .
  • the present invention further relates to a method for screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject, wherein the method comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject; 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  • the sample is selected from blood, plasma and serum.
  • the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further includes setting up the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
  • the method further comprises the step of processing the samples before step 1) .
  • the present invention further relates to the biomarker composition according to any item of the first aspect of the invention, for use in a method of screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject.
  • the method of screening and evaluation comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject; 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  • the sample is selected from blood, plasma and serum.
  • the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  • the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  • the method further comprises the step of processing the samples before step 1) .
  • the present invention further relates to A method for setting up a mass spectrometry model for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, which comprises the step of identifying the differentially expressed substance in a blood sample between CHD patients and healthy controls, wherein the differentially expressed substance comprises one or more selected from the group consisting of Biomarker 1-18, preferably, from the group consisting of of Biomarker 1-7.
  • coronary heart disease also known as “coronary artery disease (CAD) ”
  • CAD coronary artery disease
  • patients with CHD are diagnosed by coronary angiography techniques.
  • mass spectrometry can be classified into ion traps, quadrupole, orbitrap and time-of-flight mass spectrometry, and the deviations are 0.2amu, 0.4amu, 3ppm, 5ppm, respectively.
  • the MS data is acquired with orbitrap mass spectrometry.
  • the levels of the biomarkers are indicated by peak intensity in MS.
  • mass-to-charge (m/z) and retention time (RT) have the same meaning known by prior art.
  • the unit of m/z is amu, which refers to atomic mass unit, also named as Dalton. Dalton is the standard unit that is used for indicating mass on an atomic or molecular scale (atomic mass) .
  • One unified atomic mass unit is equivalent to 1 g/mol. It is defined as one twelfth of the mass of an unbound neutral atom of carbon-12 in its nuclear and electronic ground state.
  • m/z can vary in the range of ⁇ 3.00, or ⁇ 2.00, or ⁇ 1.00
  • retention time can vary in the range of ⁇ 60s, or ⁇ 45s, or ⁇ 30s, or ⁇ 15s.
  • a reference dataset refers to a training dataset.
  • training datasets and validation datasets have the same meaning known by prior art.
  • the training datasets refer to a collection of data of the levels of biomarkers in biological samples for CHD patients and healthy controls.
  • the validation datasets refer to a collection of data used to test the performance of the training datasets.
  • the levels of biomarkers can be indicated as absolute values or relative values according to the method of determination. For example, when mass spectrometry is used to determine the levels of the biomarkers, the intensity of a peak can represent the levels of the biomarkers, which is a relative value.
  • a reference value refers to a reference value of healthy controls or a normal value.
  • range of normal value (absolute value) of each biomarker in sample can be obtained with test and calculation method well known in the art when sample size is large emough.
  • mass spectrometry for example antibody or ELISA
  • the absolute values of the levels of biomarkers in samples can be directly compared with normal values, in order to evaluate the risk of CHD and diagnosis or early diagnosis of CHD, optionally , statistical methods can be included.
  • a biomarker also named “a biological marker” refers to a measurable indicator of a biological state or condition of a subject.
  • biomarkers can be any substance in the subject, for example, nucleic acid marker (e.g. DNA) , protein marker, cytokine marker, chemokine marker, carbohydrate marker, antigen marker, antibody marker, species marker (species/genus marker) and functions marker (KO/OG marker) and the like, provided that they are in relation with a particular biological state or condition (such as, a disease) of the subject.
  • Biomarkers are often measured and evaluated to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention, and are useful in many scientific fields.
  • a biomarker set refers to a set of biomarkers (that is, a combination of two or more biomarkers) .
  • a subject refers to an animal, particularly a mammal, such as a primate, preferably human.
  • plasma refers to the fluid component of the whole blood. Depending on the separation method used, plasma may be completely free of cellular components, or may contain various amounts of platelets and/or small amounts of other cellular components.
  • the low-molecular-weight metabolites ( ⁇ 1500Da) in the urine samples are extracted for HPLC-MS experiments.
  • the extraction method is well known in the art.
  • organic solvent for example methanol
  • methanol is used to precipitate protein
  • the obtained mixture is centrifuged, and then supernatant is transferred for metabolic profiling by HPLC-MS.
  • Shimadzu Prominence HPLC system (Shimadzu) was coupled to a LTQ Orbitrap Velos instrument (Thermo Fisher Scientific, MA, USA) set at 30000 resolution to acquire HPLC-MS data; an Agilent ZORBAX ODS C18 column (150 mm ⁇ 2.1 mm, 3.5 ⁇ m, Agilent, USA) was used.
  • Sample analysis was performed in positive ion modes with a spray voltage of 4.5 kV and capillary temperature of 350°C; the mass scanning range was 50-1500 m/z; the flow rates of nitrogen sheath gas and nitrogen auxiliary gas were set to 30 L/min and 10 L/min, respectively; the HPLC-MS system was run in binary gradient mode; solvent A was 0.1% (v/v) formic acid/water, and solvent B was 0.1% (v/v) formic acid/methanol; the gradient was as follows: 5%B at 0 min, 5%B at 5 min, 100%B at 8 min, 100%B at 9 min, 5%B at 18 min, and 5%B at 20 min; the flow rate was 0.2 mL/min. To ensure system equilibrium, the pooled “quality control ” (QC) sample was used to monitor the system stability.
  • QC quality control
  • the present invention is based on the following findings by the inventors:
  • Non-invasive and highly accurate approaches to diagnose and predict CHD are urgently needed.
  • non-targeted metabolomics technique is performed to analyze plasma, urine samples and metagenomics technology is applied to further validate the metabolites with potential origin from the fecal metagenomics data of CHD patients and healthy subjects.
  • the workflow is shown in Fig. 1.
  • Statistical and bioinformatics methods are used to identify significantly different mass-to-charge (m/z) that can discriminate CHD cases from healthy controls.
  • Hierarchical cluster analysis (HCA) is performed to identify m/z clusters contributing to phenotype separation and spearman correlation analysis is applied to identify potential biomarkers’ correlations related to abnormal functions.
  • the identified significantly changed metabolites are validated using purchased standards.
  • mannitol was identified as a potential urine biomarker in CHD patients. The fact that no related homo sapiens enzymes are found in the fructose and mannose metabolism so far indicates mannitol should belong to microbial metabolites family. Mannitol was previously reported to be produced by lactic acid bacteria and pseudomonas putida.
  • GlcNAc-6-P the metabolism of GlcNAc-6-P was found to be significantly correlated with Clostridium sp. HGF2 by NagA (EC: 3.5.1.25) and N-acylglucosamine-6-phosphate 2-epimerase (EC: 5.1.3.9) .
  • GlcNAc-6-P can be converted into glucosamine-6-P by NagA (EC: 3.5.1.25) enzyme which plays a central role in microbial cell wall synthesis and glycolysis.
  • microbial metabolites can be used as potential biomarkers for CHD diagnosis along with other traditional metabolites.
  • GlcNAc-6-P in urine exhibited relatively strong CHD diagnostic ability with AUC of 0.88 and showed FN of 0.153 and FP of 0.208 in the ROC analysis of validation dataset.
  • microbial metabolites reflect the abnormalities of the host intestine microbiota, so new strategy for CHD treatments can be developed by adjusting patients gut intestine ecosystem. In the future, microbial species and their associated metabolites could be used as new indexes and targets for diagnosis and treatment of CHD.
  • Fig. 1 The workflow in our study. Firstly, potential biomarkers discovery in plasma and urine had been performed. Secondly, pathway analysis and association analysis of potential biomarkers and gut flora had been applied. Lastly, potential biomarkers associated gut flora species had been discovered.
  • FIG. 3 Potential biomarkers discovery in plasma metabolomics. Cloud plot of plasma metabolites profiles. Upper and under circles indicated metabolites with increased (fold change > 1.2, 196 metabolites) and decreased intensity (fold change ⁇ 0.8, 319 metabolites) in CHD patients’ plasma samples compared with healthy controls. The darkness of color is correlated with adjusted p. value (named as q. value) : color from white to dark black indicated smaller adjusted p. value. The area of circle is correlated with magnitude of intensity change: In the upper part, the bigger the circle was, the more enriched metabolites were in CHD patients’ plasma samples compared with healthy controls’ plasma samples. While in the under part, the bigger the circle was, the more enriched metabolites were in healthy controls’ .
  • Fig. 4 Plasma metabolomics statistical analysis.
  • Veen-plot, Volcano-plot and S-plot were integrated and there were 202 overlapped m/z that were significantly different between CHD patients’ plasma samples and healthy controls’ plasma samples.
  • Fig. 8 Veen diagram of all significant differential metabolites in plasma and urine.
  • ROC analysis of potential biomarkers Receiver operating characteristic (ROC) analysis of potential biomarkers.
  • Example 1 Identifying and validating biomarkers for evaluating risk of CHD related diseases
  • PCI Percutaneous Coronary Intervention
  • Venous blood and midstream urine were collected in the morning before breakfast from all participants. Venous blood collected by Vacuette EDTA blood collection tubes were centrifuged at 2200x g for 5 min at 4°Cto obtain plasma samples. The plasma and urine samples were stored at-80°Cuntil use.
  • low molecular weight metabolites ( ⁇ 1500Da) were extracted from the plasma and urine samples using the following procedure: Plasma samples were mixed with methanol (1: 2 v/v) while urine samples were mixed with methanol (1:1 v/v) to precipitate protein. The plasma and urine sample mixture were centrifuged at 14000x g for 10 min at 4°Cand then supernatant was transferred into a 1.5 mL polypropylene tube for metabolic profiling by HPLC-MS.
  • Solvent A was 0.1% (v/v) formic acid/water
  • solvent B was 0.1% (v/v) formic acid/methanol.
  • the gradient was as follows: 5%B at 0 min, 5%B at 5 min, 100%B at 8 min, 100%B at 9 min, 5%B at 18 min, and 5%B at 20 min.
  • the flow rate was 0.2 mL/min.
  • the pooled QC sample was injected five times at the beginning. QC sample was injected every five samples during samples detection to further monitor the system stability.
  • HPLC-MS data analysis The acquired MS data pretreatments including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts was performed using the same method as our previously published work (Luan, H. et al. Serum metabolomics reveals lipid metabolism variation between coronary artery disease and congestive heart failure: a pilot study. Biomarkers. 18, 314-321 (2013) , incorporated herein by reference) .
  • LC-MS raw data files were converted into mzXML format and then processed by the XCMS and CAMERA toolbox implemented with the R software (v3. 1.1) . Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peaks were recorded and a three dimensional matrix containing arbitrarily assigned peak indices (retention time-m/z pairs) , sample names (observations) and ion intensity information (variables) was generated.
  • R-LSC Robust Loess Signal Correction
  • QC sample standard biological quality control sample
  • the relative standard deviation (RSD) values of metabolites in the QC samples was set at a threshold of 30%which was accepted as a standard in the assessment of repeatability in metabolomics data sets.
  • the nonparametric univariate method (Mann-Whitney-Wilcoxon test) was performed to measure and discover the significantly changed metabolites among the CHD patients and control subjects and then corrected by false discovery rate (FDR) to ensure that metabolite peaks were reproducibly detected.
  • multivariate statistical analysis (PCA, PLS-DA) were performed to discriminate CHD samples from control subjects.
  • a number of metabolites responsible for the difference in the metabolic profile scan of CHD patients and control subjects can be obtained on the basis of variable importance in the projection (VIP) threshold of 1 from the 7-fold cross-validated PLS-DA model.
  • the PLS-DA model was validated at a univariate level using FDR test from the R statistical toolbox with the critical p.
  • Permutation multivariate analysis of variance (PERMANOVA) , a permutation-based version of the multivariate analysis of variance, was performed in R using the “vegan” package to test the statistical significant differences between metabolic profiles and individuals’ phenotypes (Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32-46 (2001) , incorporated herein by reference) . Three dimensional PLS-DA analysis was also implemented to show the difference between CHD samples and control subjects. Phenotype analysis was performed to cluster those significantly distributed metabolites.
  • HMDB database http: //www. hmdb. ca
  • KEGG database www. genome. jp/kegg/
  • Lysophosphatidylcholine LPCs
  • 2 glycerophosphocholines L-Arginine
  • N-Acetyl-D-glucosamine 6-phosphate GlcNAc-6-P
  • paraxanthine as listed in Table 1 .
  • the MS/MS spectrums of LysoPC (0: 0/18: 0) , LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (16: 1 (9Z) ) , LysoPC (15: 0) , LysoPC (P-16: 0) , and LysoPC (14: 0) are shown in Fig. 14.
  • LPCs Lysophosphatidylcholine
  • 2 glycerophosphocholines were lower in CHD patients (as shown in Fig. 5) .
  • LysoPC (0: 0/18: 0)
  • LysoPC (16: 1 (9Z)
  • VIP > 1 adjusted p. value produced by Mann-Whitney-Wilcoxon test after FDR correction ⁇ 0.05, fold change > 1.2 or ⁇ 0.8
  • 391 m/z were found to be significantly changed in CHD group by intersection of 558 m/z and 559 m/z in S-plot and Volcano-plot, respectively, as is shown in Veen plot (Fig. 7) .
  • the 391 m/z were aligned and annotated using the HMDB and KEGG database.
  • the intensity of 96 metabolites were increased while that of 64 metabolites were decreased in CHD patients.
  • These 160 metabolites were used to perform phenotype analysis for the 102 samples.
  • the CHD patients’ metabolism was obviously different from healthy controls.
  • 4 metabolites were verified and the results were listed in Table 2.
  • the level of GlcNAc-6-P and mannitol were increased with fold change of 165.99 and 8.45 in CHD patients respectively. Meanwhile, the level of creatine and phytosphigosine were decreased with fold changes of 0.41 and 0.39 respectively.
  • GlcNAc-6-P The level of GlcNAc-6-P was found to be increased in both plasma and urine samples. GlcNAc-6-P could be produced by human body enzymes and gut bacterial enzymes, so its origin need to be further determined. Similar pattern of GlcNAc-6-P increasing in urine and blood suggested interactions of host and gut flora were important in the development of CHD. Mannitol is a polyol or sugar alcohol produced by several microorganisms. The fact that no related homo sapiens enzymes are found in the fructose and mannose metabolism so far indicates mannitol might belong to microbial metabolites family.
  • Mannitol could be produced by many microorganisms, such as lactic acid bacteria (Carvalheiro, F., Moniz, P., Duarte, L.C., Esteves, M.P. &G ⁇ rio, F.M. Mannitol production by lactic acid bacteria grown in supplemented carob syrup. J. Ind Microbiol. Biotechnol. 38, 221-7 (2011) , incorporated herein by reference) and pseudomonas putida (Kets, E.P. Galinski, E.A., de Wit, M., de Bont, J.A. &Heipieper, H.J.
  • pseudomonas putida Kets, E.P. Galinski, E.A., de Wit, M., de Bont, J.A. &Heipieper, H.J.
  • Phytosphingosine is a phospholipid and a major component of mammalian tissue biological membranes.
  • the synthesis of phytosphingosine can be performed by human body and intestinal microbiota in the sphingosine metabolism.
  • Phytosphingosine could induce caspase-independent apoptosis in human T-cell lymphoma and non-small cell lung cancer cells.
  • the decrease in urine phytosphingosine suggested sphingolipid metabolism was abnormal in CHD patients.
  • metabolites Seven significantly changed metabolites (Supplementary Table 2) , including GlcNAc-6-P, were found both in plasma and urine on the condition that retention time error was less than 1 min and m/z error was less than 0.01 Dalton with MS/MS comparison.
  • a Veen diagram exhibiting the common metabolites among plasma and urine significantly changed metabolites is provided in Fig. 8. Two metabolites (m/z : 185.04, 202.04) were decreased in CHD patients while other five metabolites (m/z : 125.01, 309.05, 310.04, 311.05, 324.04) were increased in CHD patients.
  • ROC receiver operating characteristic analysis
  • GlcNAc-6-P and mannitol exhibited AUC of 0.88, 0.81 and fold change at 36.91 and 2.62 respectively (as shown in Fig. 9h ⁇ 9i and Table 4) .
  • creatine and phytosphingosine did not show good diagnostic ability in both training and validation datasets.
  • the existence of GlcNAc-6-P and mannitol in urine indicates the interaction of gut flora activity and host metabolism.
  • GlcNAc-6-P appeared the most discriminative biomarker which showed relatively good diagnostic ability with false negative (FN) of 0.051, 0.153 and false positive (FP) of 0.047, 0.208 in the training datasets and validation datasets respectively.
  • LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (P-16: 0) , LysoPC (15: 0) , 1-Palmitoylglycerophosphocholine, LysoPC (14: 0) , LysoPC (16: 1 (9Z) ) , LysoPC (0: 0/18: 0) and mannitol exhibited diagnostic ability with FN of 0.271, 0.169, 0.136, 0.068, 0.119, 0.119, 0.085, 0.153 and FP of 0.233, 0.163, 0.256, 0.233, 0.209, 0.140, 0.279, 0.093 respectively in the training datasets, showed diagnostic ability with FN of 0.013, 0, 0, 0.013, 0.013, 0.013, 0, 0.135 and FP of 0.582, 0.755, 0.673, 0.694, 0.612, 0.684, 0.714, 0.416 respectively in the validation datasets.
  • the FN and FP of 7 choline metabolites and 2 urine metabolites were all analysed with R using the “randomForest” and “pROC” packages based on the intensity of training datasets shown in Table 7 and Table 8 respectively.
  • the randomForest model classification output prediction results (probability of illness; cutoff was 0.5, and if the probability of illness ⁇ 0.5, the subject was at risk of CHD) .
  • the FN and FP were (0.016, 0) and (0.121, 0.225) in urine training and validation datasets respectively.
  • the FN and FP of 7 choline metabolites combination and 2 urine metabolites combination were all analysed with R using the “randomForest” and “pROC” packages based on the intensity of training datasets shown in Table 7 and Table 8 respectively.
  • the randomForest model classification output prediction results (probability of illness; cutoff was 0.5, and if the probability of illness ⁇ 0.5, the subject was at risk of CHD) .
  • the two potential urine biomarkers, GlcNAc-6-P and mannitol exhibited strong negative correlations with CHOL, HDLC, TP and APOB (q. value ⁇ 0.01) .
  • CKMB creatine kinase MB
  • ALB albumin
  • ALT Alanine aminotransferase
  • TP Total Protein
  • AST Aspartate transaminase
  • CREA creatinine
  • HBDH hydroxy low-density lipoprotein
  • CHOL cholesterol
  • HDLC high-density lipoprotein
  • APOB apolipoprotein (b)
  • APOA apolipoprotein (a)
  • LPA lipoprotein (a) .
  • FC mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls
  • FC was large CHD patients.
  • FC was lower than 1, it represented that the biomarker was enriched in healthy individuals.
  • VIP Very Importance for Projection
  • FC mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls
  • FC mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls
  • FC was large CHD patients.
  • FC was lower than 1, it represented that the biomarker was enriched in healthy individuals .
  • ⁇ VIP Very Importance for Projection
  • the inventors have identified and validated 7 plasma metabolites and 2 urine metabolites for early and non-invasive diagnosis of CHD by a random forest model based on the associated metabolites. And the inventors have constructed a method to evaluate the risk of CHD based on these associated metabolites.

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Abstract

Biomarkers and methods for predicting the risk of a disease related to metabolites, in particular coronary heart disease.

Description

BIOMARKERS FOR CORONARY HEART DISEASE
CROSS-REFERENCE TO RELATED APPLICATION
None
FIELD
The present invention relates to plasma biomarkers and methods for predicting the risk of a disease related to metabolites, in particular coronary heart disease.
BACKGROUND
Coronary heart disease (CHD) is the top risk factor in modern society with annual mortality rate overpassing the sum of all types of cancers. The majority of cardiovascular deaths occurrence are related to the extent of people’s awareness of their own medical conditions and are due to lackness of in-time treatment as demonstrated by a five-year follow-up study by MaGiCAD cohort (Graninger, D.J. &Mosedale, D.E. Metabolomics in coronary heart disease. Heart. Metab. 55, 8–12 (2012) , incorporated herein by reference) . The challenge for early diagnosis and prevention of CHD lies in the lackness of reliable non-invasive biomarkers. The "gold standard" for diagnosis of CHD is still coronary angiography which is invasive and accompanied by many deadly side effects, this limited the large population screening and the CHD risk prediction at early stage.
Many researches point the fatty acids play important roles in the heart metabolism； they are predominant substrates, accounting for 60-90％cardiac ATP synthesis, for cardiac ATP generation by mitochondrial oxidative phosphorylation under normal physiological conditions. Cardiovascular diseases (CVD) like coronary heart disease and cardiac failure undergo a “metabolic shift” as a consequence of both intrinsic and extrinsic perturbations. Increased low-density lipoprotein cholesterol (LDL-C) has previously been considered as one of the major risk factors for CHD. The fact that core defects in cardiovascular disease are lipids metabolism (Fernandez, C. et al. Plasma lipid composition and risk of developing cardiovascular disease. PLoS ONE 8, e71846 (2013) , incorporated herein by reference) makes  metabolomics a particularly promising method to study these types of diseases.
Metabolomics is an innovative and high-throughput bioanalytical method aiming to identify and quantify small molecules (molecular weight less than 1500 Daltons) present in any biological system or any specific physiological state. Two major analytical techniques, nuclear magnetic resonance (NMR) and mass spectrometry (MS) , have been widely used in endogenous compounds measurement at an exponential increasing rate in last decade. MS-based techniques have made rapid progress and have been used more frequently compared with NMR since 2005 because of the following advantages: higher sensitivity, more coverage of the metabolome, improved metabolites identification and discrimination capability, and modularity to perform compound-class-specific analysis (Griffiths, W.J. et al. Targeted metabolomics for biomarker discovery. Angew. Chem. Int. Ed. 49, 5426–5445 (2010) , incorporated herein by reference) . MS is mostly used in conjunction with chromatography, such as gas chromatography mass spectrometry (GC–MS) and liquid chromatography mass spectrometry (LC–MS) .
Contents of the Invention
The present invention aims to provide a biomarker composition and a method for evaluation of the risk of CHD or diagnosis or early diagnosis of CHD, and specifically comprises the following aspects.
The first aspect of the invention relates to a biomarker composition, which comprises one or more selected from the group consisting of:
Biomarker 1, for which m/z is 518.32±1.00, retention time (RT) is 12.71±1.00min；
Biomarker 2, for which m/z is 480.34±1.00, retention time (RT) is 13.48±1.00min；
Biomarker 3, for which m/z is 482.32±1.00, retention time (RT) is 12.92±1.00min；
Biomarker 4, for which m/z is 496.33±1.00, retention time (RT) is 13.19±1.00min；
Biomarker 5, for which m/z is 468.3±1.00, retention time (RT) is 12.69±1.00min；
Biomarker 6, for which m/z is 494.32±1.00, retention time (RT) is 12.82±1.00min；
Biomarker 7, for which m/z is 524.36±1.00, retention time (RT) is 13.69±1.00min；
Biomarker 8, for which m/z is 516.31±1.00, retention time (RT) is 13.21±1.00min；
Biomarker 9, for which m/z is 590.31±1.00, retention time (RT) is 12.86±1.00min；
Biomarker 10, for which m/z is 546.35±1.00, retention time (RT) is 14.22±1.00min；
Biomarker 11, for which m/z is 570.35±1.00, retention time (RT) is 13.05±1.00min；
Biomarker 12, for which m/z is 518.32±1.00, retention time (RT) is 13.19±1.00min；
Biomarker 13, for which m/z is 524.36±1.00, retention time (RT) is 14.27±1.00min；
Biomarker 14, for which m/z is 522.35±1.00, retention time (RT) is 13.33±1.00min；
Biomarker 15, for which m/z is 181.07±1.00, retention time (RT) is 9.05±1.00min；
Biomarker 16, for which m/z is 544.33±1.00, retention time (RT) is 13.34±1.00min；
Biomarker 17, for which m/z is 175.11±1.00, retention time (RT) is 1.83±1.00min； and
Biomarker 18, for which m/z is 324.04±1.00, retention time (RT) is 9.33±1.00min.
In an embodiment of the invention, Biomarker 1～18 are shown in Table 1.
In an embodiment of the invention, the biomarker composition comprises Biomarker 1 and one or more selected from the group consisting of Biomarker 2～Biomarker 18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 2 and one or more selected from the group consisting of Biomarker 1 and Biomarker 3～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 3 and one or more selected from the group consisting of Biomarker 1～2 and Biomarker 4～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 4 and one or more selected from the group consisting of Biomarker 1～3 and Biomarker 5～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 5 and one or more selected from the group consisting of Biomarker 1～4 and Biomarker 6～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 6 and one or more selected from the group consisting of Biomarker 1～5 and Biomarker 7～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 7 and one or more selected from the group consisting of Biomarker 1～6 and Biomarker 8～18.
In an embodiment of the invention, the biomarker composition comprises Biomarker 1～7 and one or more selected from the group consisting of Biomarker 8～18.
In an embodiment of the invention, the biomarker composition comprises one or more selected from the group consisting of:
Biomarker 1, for which m/z is 518.32±1.00, retention time (RT) is 12.71±1.00min；
Biomarker 2, for which m/z is 480.34±1.00, retention time (RT) is 13.48±1.00min；
Biomarker 3, for which m/z is 482.32±1.00, retention time (RT) is 12.92±1.00min；
Biomarker 4, for which m/z is 496.33±1.00, retention time (RT) is 13.19±1.00min；
Biomarker 5, for which m/z is 468.3±1.00, retention time (RT) is 12.69±1.00min；
Biomarker 6, for which m/z is 494.32±1.00, retention time (RT) is 12.82±1.00min； and
Biomarker 7, for which m/z is 524.36±1.00, retention time (RT) is 13.69±1.00min.
In an embodiment of the invention, the biomarker composition comprises Biomarker 1 and one or more selected from the group consisting of Biomarker 2～Biomarker 7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 2 and one or more selected from the group consisting of Biomarker 1 and Biomarker 3～Biomarker 7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 3 and one or more selected from the group consisting of Biomarker 1～2 and Biomarker 4～7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 4 and one or more selected from the group consisting of Biomarker 1～3 and Biomarker 5～7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 5 and one or more selected from the group consisting of Biomarker 1～4 and Biomarker 6～7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 6 and one or more selected from the group consisting of Biomarker 1～5 and Biomarker 7.
In an embodiment of the invention, the biomarker composition comprises Biomarker 7 and one or more selected from the group consisting of Biomarker 1～6.
In an embodiment of the invention, the biomarker composition comprises Biomarker 1～7.
In an embodiment of the invention, wherein,
Biomarker 1 is LysoPC (18: 3 (6Z, 9Z, 12Z) ) ；
Biomarker 2 is LysoPC (P-16: 0) ；
Biomarker 3 is LysoPC (15: 0) ；
Biomarker 4 is 1-Palmitoylglycerophosphocholine；
Biomarker 5 is LysoPC (14: 0) ；
Biomarker 6 is LysoPC (16: 1 (9Z) ) ；
Biomarker 7 is LysoPC (0: 0/18: 0) ；
Biomarker 8 is LysoPC (18: 4 (6Z, 9Z, 12Z, 15Z) ) ；
Biomarker 9 is LysoPC (22: 6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z) ) ；
Biomarker 10 is LysoPC (20: 3 (5Z, 8Z, 11Z) ) ；
Biomarker 11 is LysoPC (22: 5 (4Z, 7Z, 10Z, 13Z, 16Z) ) ；
Biomarker 12 is LysoPC (18: 3 (9Z, 12Z, 15Z) ) ；
Biomarker 13 is LysoPC (18: 0) ；
Biomarker 14 is 1-Oleoylglycerophosphocholine；
Biomarker 15 is Paraxanthine；
Biomarker 16 is LysoPC (20: 4 (5Z, 8Z, 11Z, 14Z) ) ；
Biomarker 17 is L-Arginine； and/or
Biomarker 18 is N-Acetyl-D-glucosamine 6-phosphate.
In an embodiment of the invention, the biomarker composition comes from a blood, plasma or serum sample.
In an embodiment of the invention, wherein, as compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein, as compared with a reference value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein the identification of each of the biomarker composition is carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
The second aspect of the invention relates to a reagent composition, which comprises the reagents used for detection of each of the biomarker composition according to any item of the first aspect of the invention.
In an embodiment of the invention, wherein the reagents comprise substances used in mass spectrometry for detection of the biomarker composition according to any item of the  first aspect of the invention.
In an embodiment of the invention, the samples for detection of the biomarkers are selected from blood, plasma and serum.
The third aspect of the invention relates to a kit, which comprises the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention.
In an embodiment of the invention, wherein the kit further comprises the training dataset of the level of each of the biomarkers in the biomarker composition according to any item of the first aspect of the invention for CHD patients and healthy controls (for example as shown by Table 7) .
The fourth aspect of the invention relates to a use of the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention in the preparation of a kit, wherein the kit is used for evaluation of the risk of CHD in a subject, or for use in diagnosis of CHD in a subject.
In an embodiment of the invention, wherein the evaluation or diagnosis comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
In an embodiment of the invention, wherein the sample is selected from blood, plasma and serum.
In an embodiment of the invention, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness； preferably, the multivariate statistical model is random forest model.
In an embodiment of the invention, wherein the subject is determined as being at risk of  CHD or having CHD if the probability of illness ≥0.5.
In an embodiment of the invention, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein, when compared with a reference value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein the method further comprises the step of processing the sample before step 1) .
In an embodiment of the invention, wherein the kit further comprises the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
The fifth aspect of the invention relates to a method for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, wherein the method comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
In an embodiment of the invention, wherein the sample is selected from blood, plasma and serum.
In an embodiment of the invention, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to  output the probability of illness； preferably, the multivariate statistical model is random forest model.
In an embodiment of the invention, wherein the subject is determined as being at risk of CHD or having CHD if the probability of illness ≥0.5.
In an embodiment of the invention, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein, when compared with a reference value, the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein the method further comprises the step of processing the samples before step 1) .
In an embodiment of the invention, wherein the method further includes setting up the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
The present invention further relates to the biomarker composition according to any item of the first aspect of the invention, for use in a method of evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject.
In an embodiment of the invention, wherein the method of evaluation or diagnosis comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for  example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of the first aspect of the invention in a sample from CHD patients and healthy controls.
In an embodiment of the invention, wherein the sample is selected from blood, plasma and serum.
In an embodiment of the invention, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness； preferably, the multivariate statistical model is random forest model.
In an embodiment of the invention, wherein the subject is determined as being at risk of CHD or having CHD if the probability of illness ≥0.5.
In an embodiment of the invention, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein, when compared with a reference value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein, the method further comprises the step of processing the samples before step 1) .
The present invention further relates to a use of the biomarker composition according to any item of the first aspect of the invention and/or the reagent composition according to any item of the second aspect of the invention in the preparation of a kit, wherein the kit is used for screening candidate compounds for treatment of CHD in a subject, or for evaluating the effect of the treatment of CHD in a subject.
In an embodiment of the invention, wherein the screening or evaluation comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of the second aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
In an embodiment of the invention, wherein the sample is selected from blood, plasma and serum.
In an embodiment of the invention, wherein the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein the method further comprises the step of processing the samples before step 1) .
The present invention further relates to a method for screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject, wherein the method comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
In an embodiment of the invention, wherein the sample is selected from blood, plasma  and serum.
In an embodiment of the invention, wherein the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein the method further includes setting up the training dataset of the levels of the biomarker composition according to any item of the first aspect of the invention for CHD patients and/or healthy controls (for example as shown by Table 7) .
In an embodiment of the invention, wherein the method further comprises the step of processing the samples before step 1) .
The present invention further relates to the biomarker composition according to any item of the first aspect of the invention, for use in a method of screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject.
In an embodiment of the invention, wherein the method of screening and evaluation comprises the following steps: 1) determining the levels of the biomarkers of the biomarker composition according to any item of the first aspect of the invention in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
In an embodiment of the invention, wherein the sample is selected from blood, plasma  and serum.
In an embodiment of the invention, wherein the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
In an embodiment of the invention, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
In an embodiment of the invention, wherein the method further comprises the step of processing the samples before step 1) .
The present invention further relates to A method for setting up a mass spectrometry model for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, which comprises the step of identifying the differentially expressed substance in a blood sample between CHD patients and healthy controls, wherein the differentially expressed substance comprises one or more selected from the group consisting of Biomarker 1-18, preferably, from the group consisting of of Biomarker 1-7.
Terms used herein have meanings as commonly understood by a person of ordinary skill in the fields to which the present invention is relevant. However, in order to better understand the invention, the definitions and explanations of the relevant terms are provided as follows.
According to the invention, the term “coronary heart disease (CHD) ” , also known as “coronary artery disease (CAD) ” , is a group of diseases that includes: stable angina, unstable angina, myocardial infarction, and sudden coronary death, etc. In the present invention, patients with CHD are diagnosed by coronary angiography techniques.
According to the invention, mass spectrometry (MS) can be classified into ion traps,  quadrupole, orbitrap and time-of-flight mass spectrometry, and the deviations are 0.2amu, 0.4amu, 3ppm, 5ppm, respectively. In the present invention, the MS data is acquired with orbitrap mass spectrometry.
According to the invention, the levels of the biomarkers are indicated by peak intensity in MS.
According to the invention, mass-to-charge (m/z) and retention time (RT) have the same meaning known by prior art. In the present invention, the unit of m/z is amu, which refers to atomic mass unit, also named as Dalton. Dalton is the standard unit that is used for indicating mass on an atomic or molecular scale (atomic mass) . One unified atomic mass unit is equivalent to 1 g/mol. It is defined as one twelfth of the mass of an unbound neutral atom of carbon-12 in its nuclear and electronic ground state.
The person skilled in the art knows that the values of m/z and retention time will vary within a certain range when different detection methods or LC-MS instruments are used. For example, m/z can vary in the range of ±3.00, or ±2.00, or ±1.00, and retention time can vary in the range of ±60s, or ±45s, or ±30s, or ±15s. In an embodiment of the invention, a reference dataset refers to a training dataset.
According to the invention, training datasets and validation datasets have the same meaning known by prior art. In an embodiment of the invention, the training datasets refer to a collection of data of the levels of biomarkers in biological samples for CHD patients and healthy controls. In an embodiment of the invention, the validation datasets refer to a collection of data used to test the performance of the training datasets. In an embodiment of the invention, the levels of biomarkers can be indicated as absolute values or relative values according to the method of determination. For example, when mass spectrometry is used to determine the levels of the biomarkers, the intensity of a peak can represent the levels of the biomarkers, which is a relative value.
In an embodiment of the invention, a reference value refers to a reference value of healthy controls or a normal value. The person skilled in the art knows that range of normal value (absolute value) of each biomarker in sample can be obtained with test and calculation method well known in the art when sample size is large emough. Thus, when other methods except mass spectrometry (for example antibody or ELISA) are applied to detect the levels of  biomarkers, the absolute values of the levels of biomarkers in samples can be directly compared with normal values, in order to evaluate the risk of CHD and diagnosis or early diagnosis of CHD, optionally , statistical methods can be included.
According to the invention, the term “a biomarker” , also named “a biological marker” , refers to a measurable indicator of a biological state or condition of a subject. Such biomarkers can be any substance in the subject, for example, nucleic acid marker (e.g. DNA) , protein marker, cytokine marker, chemokine marker, carbohydrate marker, antigen marker, antibody marker, species marker (species/genus marker) and functions marker (KO/OG marker) and the like, provided that they are in relation with a particular biological state or condition (such as, a disease) of the subject. Biomarkers are often measured and evaluated to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention, and are useful in many scientific fields.
According to the invention, the term “a biomarker set” refers to a set of biomarkers (that is, a combination of two or more biomarkers) .
According to the invention, the term “a subject” refers to an animal, particularly a mammal, such as a primate, preferably human.
According to the invention, the term “plasma” refers to the fluid component of the whole blood. Depending on the separation method used, plasma may be completely free of cellular components, or may contain various amounts of platelets and/or small amounts of other cellular components.
According to the invention, the low-molecular-weight metabolites (＜1500Da) in the urine samples are extracted for HPLC-MS experiments. The extraction method is well known in the art. In an embodiment of the invention, organic solvent (for example methanol) is used to precipitate protein, the obtained mixture is centrifuged, and then supernatant is transferred for metabolic profiling by HPLC-MS.
In an embodiment of the invention, Shimadzu Prominence HPLC system (Shimadzu) was coupled to a LTQ Orbitrap Velos instrument (Thermo Fisher Scientific, MA, USA) set at 30000 resolution to acquire HPLC-MS data； an Agilent ZORBAX ODS C18 column (150 mm×2.1 mm, 3.5 μm, Agilent, USA) was used. Sample analysis was performed in positive ion modes with a spray voltage of 4.5 kV and capillary temperature of 350℃； the mass  scanning range was 50-1500 m/z； the flow rates of nitrogen sheath gas and nitrogen auxiliary gas were set to 30 L/min and 10 L/min, respectively； the HPLC-MS system was run in binary gradient mode； solvent A was 0.1％ (v/v) formic acid/water, and solvent B was 0.1％ (v/v) formic acid/methanol； the gradient was as follows: 5％B at 0 min, 5％B at 5 min, 100％B at 8 min, 100％B at 9 min, 5％B at 18 min, and 5％B at 20 min； the flow rate was 0.2 mL/min. To ensure system equilibrium, the pooled “quality control ” (QC) sample was used to monitor the system stability.
Terms such as “a” , “an” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration.
It would be appreciated by those skilled in the art that the terminology herein is provided for better understanding of the present invention, but is not intended to delimit the invention, except as outlined in the claims.
The present invention is based on the following findings by the inventors:
Non-invasive and highly accurate approaches to diagnose and predict CHD are urgently needed. To explore potential characteristic metabolites signatures associated with CHD, non-targeted metabolomics technique is performed to analyze plasma, urine samples and metagenomics technology is applied to further validate the metabolites with potential origin from the fecal metagenomics data of CHD patients and healthy subjects. The workflow is shown in Fig. 1. Statistical and bioinformatics methods are used to identify significantly different mass-to-charge (m/z) that can discriminate CHD cases from healthy controls. Hierarchical cluster analysis (HCA) is performed to identify m/z clusters contributing to phenotype separation and spearman correlation analysis is applied to identify potential biomarkers’ correlations related to abnormal functions. The identified significantly changed metabolites are validated using purchased standards. Several significantly different metabolites are correlated with intestine flora on ECs, KOs and species levels. This study proves the power of potential noninvasive biomarkers discovery in biofluids of patients and the integrated analysis of metabolomics and metagenomics would pave the way to reveal the interactions between host and gut microbiobes.
In this invention, plasma and urine samples from CHD patients and control healthy people  were analyzed using untargeted metabolomics technique. In plasma, 18 significantly changed metabolites (13 LPCs, 2 glycerophosphocholines, L-Arginine, GlcNAc-6-P and paraxanthine) were identified as potential biomarkers. In urine, 4 significantly changed metabolites (GlcNAc-6-P, mannitol, creatine, phytosphigosine) were identified as potential biomarkers in CHD patients. To access the clinical relevance of these potential biomarker, the diagnostic capability of these 22 metabolites was evaluated by ROC. GlcNAc-6-P appears the most discriminative biomarker which shows relatively good diagnostic ability with FN of 0.153 and FP of 0.208. Correlation analysis between potential biomarkers and biochemical clinical data suggest plasma LPCs are significantly positive correlated with cholesterol (CHOL) , high-density lipoprotein (HDLC) , and total protein (TP) , while GlcNAc-6-P and L-arginine exhibit negative correlation with CHOL, HDLC, and TP. This suggests the metabolites may potentially influence the normal metabolic pathways in our body. Among these 59 CHD patients, 32 patients had undergone Percutaneous Coronary Intervention (PCI) before but no difference had been observed between these 32 postoperative patients group and those 27 patients group with no surgery (as shown in PCA score plots in Fig. 2) , suggesting the PCI did not influence the whole metabolic pattern in patients.
As estimated, over 30％of metabolites in human body originate from intestinal microbes and may contribute to host diseases. In this study, metabolomics and metagenomics techniques were integrated and evidence that microbial species and their associated metabolites were involved in CHD diseases were uncovered for the first time. Firstly, mannitol was identified as a potential urine biomarker in CHD patients. The fact that no related homo sapiens enzymes are found in the fructose and mannose metabolism so far indicates mannitol should belong to microbial metabolites family. Mannitol was previously reported to be produced by lactic acid bacteria and pseudomonas putida. In current study, spearman correlation analysis of, KOs, species and mannitol indicates that three gut flora species, Clostridium sp. HGF2, Streptococcus sp. M334, and Streptococcus sp. M143, play important roles in metabolism of mannitol. This was further validated by mannose-specific IIB component of PTS system (EC: 2.7.1.69) which was found to be the common enzyme in all three CHD enriched gut microbiota species. Secondly, GlcNAc-6-P, an endogenous and microbial metabolites, was identified in both plasma and urine samples of CHD patients.  GlcNAc-6-P participates in sugar metabolism with dual functions in regulating host cardiovascular activity. Previous literature shows that Escherichia coli could metabolize anhydro-N-acetylmuramic acid obtained either from the environment or its own cell wall to N-acetylglucosamine-phosphate (Uehara, T., Suefuji, K., Jaeger, T., Mayer, C. &Park, J.T. Mur Q etherase is required by Escherichia coli in order to metabolize anhydro-N-acetylmuramic acid obtained either from the environment or from its own cell wall. J. Bacteriol. 188, 1660-1662 (2006) , incorporated herein by reference) , which would then be converted to GlcNAc-6-P in amino sugar and nucleotide sugar metabolism. In our study, the metabolism of GlcNAc-6-P was found to be significantly correlated with Clostridium sp. HGF2 by NagA (EC: 3.5.1.25) and N-acylglucosamine-6-phosphate 2-epimerase (EC: 5.1.3.9) . GlcNAc-6-P can be converted into glucosamine-6-P by NagA (EC: 3.5.1.25) enzyme which plays a central role in microbial cell wall synthesis and glycolysis.
The discovery of these two microbial metabolites (Mannitol and GlcNAc-6-P) and their correlated microbiota in CHD patients has two important implications. First, it confirmed that microbial metabolites can be used as potential biomarkers for CHD diagnosis along with other traditional metabolites. For instance, GlcNAc-6-P in urine exhibited relatively strong CHD diagnostic ability with AUC of 0.88 and showed FN of 0.153 and FP of 0.208 in the ROC analysis of validation dataset. Second, microbial metabolites reflect the abnormalities of the host intestine microbiota, so new strategy for CHD treatments can be developed by adjusting patients gut intestine ecosystem. In the future, microbial species and their associated metabolites could be used as new indexes and targets for diagnosis and treatment of CHD.
In summary, non-targeted metabolomics technology and metagenomics technology were integrated for CHD study in this study. This combinational work not only lead to important biological candidate biomarkers discovery in CHD, but also bridge the gap between our human systems and intestine microbiota and provide novel insights into the microbial species related to CHD.
The present invention is further exemplified in the following non-limiting Examples. Unless otherwise stated, parts and percentages are by weight and degrees are Celsius. The agents as used were all commercially available. As apparent to one of ordinary skill in the art,  these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only.
BRIEF DISCRIPTION OF DRAWINGS
These and other aspects and advantages of the present disclosure will become apparent and more readily appreciated from the following descriptions taken in conjunction with the drawings, in which:
Fig. 1 | The workflow in our study. Firstly, potential biomarkers discovery in plasma and urine had been performed. Secondly, pathway analysis and association analysis of potential biomarkers and gut flora had been applied. Lastly, potential biomarkers associated gut flora species had been discovered.
Fig. 2 | PCA Scores plot of 32 postoperative patients and 27 no operative patients with 43 healthy controls. (a) The plasma samples of postoperative CHD patients and no operative CHD patients gathered together showing no significant difference among them. (b) The scattered distribution of urine samples in postoperative CHD patients and no operative CHD patients also suggested no significant difference among them.
Fig. 3 | Potential biomarkers discovery in plasma metabolomics. Cloud plot of plasma metabolites profiles. Upper and under circles indicated metabolites with increased (fold change > 1.2, 196 metabolites) and decreased intensity (fold change < 0.8, 319 metabolites) in CHD patients’ plasma samples compared with healthy controls. The darkness of color is correlated with adjusted p. value (named as q. value) : color from white to dark black indicated smaller adjusted p. value. The area of circle is correlated with magnitude of intensity change: In the upper part, the bigger the circle was, the more enriched metabolites were in CHD patients’ plasma samples compared with healthy controls’ plasma samples. While in the under part, the bigger the circle was, the more enriched metabolites were in healthy controls’ .
Fig. 4 | Plasma metabolomics statistical analysis. In Veen-plot, Volcano-plot and S-plot were integrated and there were 202 overlapped m/z that were significantly different between CHD patients’ plasma samples and healthy controls’ plasma samples.
Fig. 5 | Boxplot of 15 choline metabolites.
Fig. 6 | Cloud plot of urine metabolites profiles.
Fig. 7 | Urine metabolomics statistical analysis. In Veen-plot, the overlapped 391 m/z were significantly different metabolites between CHD urine samples and control urine samples by the integration of Volcano-plot and S-plot analysis.
Fig. 8 | Veen diagram of all significant differential metabolites in plasma and urine.
Fig. 9 | Receiver operating characteristic (ROC) analysis of potential biomarkers. ROC analysis and boxplots of 7 identified plasma potential biomarkers (a-g) and 2 identified urine potential biomarkers (h-i) with good diagnostic capability among 176 additional plasma samples (98 controls vs 78 CHD patients) and 395 additional urine samples (173 controls vs 222 CHD patients) respectively.
Fig. 10 | ROC analysis of 7 identified plasma potential biomarkers with good diagnostic capability among plasma validation datasets (78 CHD patients plasma samples VS 98 healthy controls plasma samples) .
Fig. 11 | ROC analysis of 2 identified urine potential biomarkers with good diagnostic capability among urine validation datasets (222 CHD patients urine samples VS 173 healthy controls urine samples) .
Fig 12 | MS/MS spectrums of LysoPC (0: 0/18: 0) , LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (16: 1 (9Z) ) , LysoPC (15: 0) , LysoPC (P-16: 0) , and LysoPC (14: 0) .
EXAMPLES
Example 1. Identifying and validating biomarkers for evaluating risk of CHD related diseases
1.1 Materials. Formic acid and methanol (HPLC grade) was purchased from Fisher Scientific Corporation (Loughborough, UK) . Water used in the experiments was obtained from a Milli-Q Ultra-pure water system (Millipore, Billerica, MA) . An Agilent ZORBAX ODS C-18 column (150 mm×2.1 mm, 3.5 μm, Agilent, USA) was used for all analysis.
1.2 Clinical samples. All patients with CHD diagnosed by coronary angiography techniques were recruited from the Guangdong General Hospital. All control people enrolled in our study were free of clinically evident CHD at medical examination during the same period.
Paired plasma, urine and fecal samples of CHD patients (n＝59) and healthy controls  (n＝43) were obtained from the Guangdong General Hospital. Coronary angiography techniques were performed to diagnose CHD patients recruited in this study. The healthy control had underwent physical examination in the same hospital. Patients and controls did not receive probiotics or antibiotics within one month before sample collection. Among these 59 CHD patients, 32 patients had undergone Percutaneous Coronary Intervention (PCI) before. The participants’ clinical information was provided in Supplementary Table 1. Besides, 176 additional plasma samples (98 controls vs 78 CHD patients) and 395 additional urine samples (173 controls vs 222 CHD patients) were included for potential biomarkers diagnostic capability analysis. Venous blood and midstream urine were collected in the morning before breakfast from all participants. Venous blood collected by Vacuette EDTA blood collection tubes were centrifuged at 2200x g for 5 min at 4℃to obtain plasma samples. The plasma and urine samples were stored at-80℃until use.
The corresponding fresh stool samples were collected from CHD patients (n＝59) and healthy controls (n＝43) on the same day at hospital. Samples were mechanically homogenized with a sterile spatula, and then aliquots containing 1g of stool in a 12ml sterile cryovial were made using the Sarstedt stool sampling system (Sarstedt, Nümbrecht, Germany) . The aliquots were then stored in freezers at -20℃and transported to the laboratory with ice pack within 48 h after collection. After that, fecal samples were stored at-80℃until use. These protocols were reviewed by the Institutional Review Board of BGI-Shenzhen. Before collecting samples, patients were informed and written consent were obtained from them.
Samples preparations for HPLC-MS experiments. To extract the low-molecular-weight metabolites (<1500Da) in the plasma and urine samples, some modifications were made to the procedures reported previously (Luan, H. et al. Serum metabolomics reveals lipid metabolism variation between coronary artery disease and congestive heart failure: a pilot study. Biomarkers. 18, 314-321 (2013) , incorporated herein by reference) . Before experiment, all plasma and urine samples were thawed on ice and a “quality control” (QC) sample was made by mixing and blending equal volumes (10μL) from each plasma or urine samples which was applied to estimate a “mean” profile representing all the analytes encountered during analysis. Then low molecular weight metabolites (<1500Da) were extracted from the plasma and urine samples using the following procedure: Plasma  samples were mixed with methanol (1: 2 v/v) while urine samples were mixed with methanol (1:1 v/v) to precipitate protein. The plasma and urine sample mixture were centrifuged at 14000x g for 10 min at 4℃and then supernatant was transferred into a 1.5 mL polypropylene tube for metabolic profiling by HPLC-MS.
HPLC-MS experiments. Shimadzu Prominence HPLC system (Shimadzu) was coupled to a LTQ Orbitrap Velos instrument (Thermo Fisher Scientific, MA, USA) set at 30000 resolution to acquire HPLC-MS data. An Agilent ZORBAX ODS C18 column (150 mm×2.1 mm, 3.5 μm, Agilent, USA) was used. Sample analysis was performed in positive ion modes with a spray voltage of 4.5 kV and capillary temperature of 350℃. The mass scanning range was 50-1500 m/z. The flow rates of nitrogen sheath gas and nitrogen auxiliary gas were set to 30 L/min and 10 L/min, respectively. The HPLC-MS system was run in binary gradient mode. Solvent A was 0.1％ (v/v) formic acid/water, and solvent B was 0.1％ (v/v) formic acid/methanol. The gradient was as follows: 5％B at 0 min, 5％B at 5 min, 100％B at 8 min, 100％B at 9 min, 5％B at 18 min, and 5％B at 20 min. The flow rate was 0.2 mL/min. To ensure system equilibrium, the pooled QC sample was injected five times at the beginning. QC sample was injected every five samples during samples detection to further monitor the system stability.
HPLC-MS data analysis. The acquired MS data pretreatments including peak picking, peak grouping, retention time correction, second peak grouping, and annotation of isotopes and adducts was performed using the same method as our previously published work (Luan, H. et al. Serum metabolomics reveals lipid metabolism variation between coronary artery disease and congestive heart failure: a pilot study. Biomarkers. 18, 314-321 (2013) , incorporated herein by reference) . LC-MS raw data files were converted into mzXML format and then processed by the XCMS and CAMERA toolbox implemented with the R software (v3. 1.1) . Each ion was identified by combining retention time (RT) and m/z data. Intensities of each peaks were recorded and a three dimensional matrix containing arbitrarily assigned peak indices (retention time-m/z pairs) , sample names (observations) and ion intensity information (variables) was generated.
The obtained matrix was further reduced by removing peaks with more than 80％missing values (ion intensity ＝ 0) and those with isotope ions from each groups in order to  obtain consistent results. As a quality assurance strategy in metabolic profiling, all retained peaks were normalized to the QC sample using Robust Loess Signal Correction (R-LSC) based on the periodic analysis of a standard biological quality control sample (QC sample) together with the real plasma and urine samples to ensure that the data are of high quality within an analytical run (Dunn, W.B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060-1083 (2011) , incorporated herein by reference) . The relative standard deviation (RSD) values of metabolites in the QC samples was set at a threshold of 30％which was accepted as a standard in the assessment of repeatability in metabolomics data sets.
The nonparametric univariate method (Mann-Whitney-Wilcoxon test) was performed to measure and discover the significantly changed metabolites among the CHD patients and control subjects and then corrected by false discovery rate (FDR) to ensure that metabolite peaks were reproducibly detected. And multivariate statistical analysis (PCA, PLS-DA) were performed to discriminate CHD samples from control subjects. A number of metabolites responsible for the difference in the metabolic profile scan of CHD patients and control subjects can be obtained on the basis of variable importance in the projection (VIP) threshold of 1 from the 7-fold cross-validated PLS-DA model. The PLS-DA model was validated at a univariate level using FDR test from the R statistical toolbox with the critical p. value set to not higher than 0.05. Permutation multivariate analysis of variance (PERMANOVA) , a permutation-based version of the multivariate analysis of variance, was performed in R using the “vegan” package to test the statistical significant differences between metabolic profiles and individuals’ phenotypes (Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Aust. Ecol. 26, 32-46 (2001) , incorporated herein by reference) . Three dimensional PLS-DA analysis was also implemented to show the difference between CHD samples and control subjects. Phenotype analysis was performed to cluster those significantly distributed metabolites. Spearman correlation analysis was implemented among those significantly changed plasma metabolites, urine metabolites and clinical data of CHD patients and control subjects and correlations of metabolites was profiled with Cytoscape software 3.0.2. In addition, receiver operating characteristic (ROC) analysis was used to evaluate  diagnostic capability of identified potential biomarkers with the online tool-ROCCET (http: //www. roccet. ca) (Xia, J.G., Broadhurst, D.I., Wilson, M. &Wishart, D.S. Translational Biomarker Discovery in Clinical Metabolomics: An Introductory Tutorial . Metabolomics 9, 280-299 (2012) , incorporated herein by reference) .
Metabolites identification and validation. The online HMDB database (http: //www. hmdb. ca) and KEGG database (www. genome. jp/kegg/) were used to identify the metabolites by matching the exact molecular mass data (m/z) of samples with those from database. If a mass difference between observed and the database value was less than 10 ppm, the metabolite would be identified and the molecular formula of metabolites would further be validated by the isotopic distribution measurements. Reference standards were purchased and used to validate and confirm those significantly changed metabolites by comparing their MS/MS spectra and retention time.
Metabolic profiles of plasma and urine samples
Untargeted metabolomics analysis was performed for the plasma and urine samples from 59 CHD patients and 43 healthy controls. The participants’ clinical information was listed in Supplementary Table 1. The detailed workflow was illustrated in Fig. 1. A total of 1347 m/z (mass-to-charge ratio, 93.67％) and 2858 m/z (96.68％) were obtained in plasma and urine samples respectively. The stability and reproducibility of current data was evaluated by the QC samples measured during the whole experimental period. Principle component analysis (PCA) scores plot representation of QC samples for plasma and urine samples were obtained respectively. No drift in the metabolites profiles obtained in positive ion modes, were observed demonstrating good stability and reproducibility in our current metabolomics data set.
Results for Plasma Samples
For plasma samples, cloud plot analysis of the total 1347 m/z (Fig. 3) showed that the intensity of 196 m/z (14.55％) were increased in CHD patients’ plasma samples (fold change > 1.2) and 23.68％of them (319 m/z) were decreased in CHD patients’ (fold change < 0.8) . Both PCA scores plot and three-dimensional partial least squares–discriminant analysis (PLS-DA) (Triba, M. N. et al. PLS/OPLS models in metabolomics: impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol. Biosyst. 11, 13-19 (2015) ,  incorporated herein by reference) scores plot of these plasma samples showed that there were significant differences between 59 CHD patient samples and 43 healthy control samples. CHD patients’ plasma samples were apart from healthy control’s samples with PC1, PC2, PC3 as 15.32％, 10.62％, 13.73％respectively. The permutation multivariate analysis of variance (PERMANOVA) was implemented to test the relation of individual’s phenotypes with their metabolite characteristics, and we found CHD status had significant impacts on the metabolic profiling (P < 0.001, 1000 permutations) in positive ion mode.
S-plot analysis was used for selection of potentially interesting metabolites biomarkers (Miao, H. et al. Urinary Metabolomics on the biochemical profiles in diet-induced hyperlipidemia rat using ultra-performance liquid chromatography coupled with quadrupole time-of-flight SYNAPT high-definition mass spectrometry. J. Anal. Methods. Chem. 2014, ID184162 (2014) , incorporated herein by reference) . Using the criteria that variable importance in the projection (VIP) was larger than 1, 230 variables were selected in S-plot. On the condition that adjusted p. value < 0.05, fold change > 1.2 or < 0.8, 414 variables were retained in Volcano-plot. Combing these two results, 202 shared m/z were obtained (Fig. 4) . And a total of 109 potential metabolites (20 increased and 89 decreased in CHD patients) were identified by aligning the exactly significant peaks’ molecular mass data (m/z) with online database: HMDB and KEGG.
To further identify potential metabolites from 109 m/z, both HMDB and HMDB SERUM databases were searched using accurate mass and mass spectrometric fragmentation patterns (Chen, J. et al. Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information. Anal. Chem. 80, 1280-1289 (2008) , incorporated herein by reference) . We found 18 matched metabolites from the above database, including 13 Lysophosphatidylcholine (LPCs) , 2 glycerophosphocholines, L-Arginine, N-Acetyl-D-glucosamine 6-phosphate (GlcNAc-6-P) and paraxanthine (as listed in Table 1) . The MS/MS spectrums of LysoPC (0: 0/18: 0) , LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (16: 1 (9Z) ) , LysoPC (15: 0) , LysoPC (P-16: 0) , and LysoPC (14: 0) are shown in Fig. 14.
The intensity of 13 Lysophosphatidylcholine (LPCs) ， 2 glycerophosphocholines and  paraxanthine were lower in CHD patients (as shown in Fig. 5) .
To investigate latent relationships of those 109 significantly changed metabolites, spearman correlation analysis was also performed. Significantly changed plasma metabolites with smaller adjusted p. value either in CHD enriched metabolites or in control enriched metabolites had a relatively stronger correlation. Analysis among those 18 identified metabolites showed that LysoPC (18: 0) had strong positive correlations with the following metabolites: LysoPC (18: 0) vs LysoPC (P-16: 0) (rho ＝ 0.861, q. value ＝ 0) , LysoPC (20: 3 (5Z, 8Z, 11Z) ) (rho ＝ 0.831, q. value ＝ 0) , LysoPC (0: 0/18: 0) (rho ＝ 0.802, q. value ＝ 0) . LysoPC (16: 1 (9Z) ) had strong positive correlations with LysoPC (14: 0) (rho ＝ 0.854, q. value ＝ 0) and LysoPC (18: 0) (rho ＝ 0.815, q. value ＝ 0) . On the other hand, L-Arginine negatively correlated with 1-Palmitoylglycerophosphocholine (rho ＝ -0.558, q. value ＝ 1.07E-08) .
Results for Urine Samples
In the urine cloud plot (Fig. 6) , there were 870 m/z (30.44％) with increased intensity in CHD patients (fold change > 1.2) while the level of 557 m/z (19.49％) were decreased (fold change < 0.8) . PCA and PLS-DA models were used and the analysis results were shown in PCA scores plot and three-dimensional PLS-DA scores plot (PC1 (4.34％) , PC2 (8.25％) , PC3 (2.99％) ) . This result indicated the CHD patients’ urine samples were significantly different from healthy subjects’ . PERMANOVA analysis demonstrated CHD had a significant impact on metabolic profile. Furthermore, S-plot analysis and Volcano-plot analysis were applied for potential biomarkers discovery. Using these criteria (VIP > 1, adjusted p. value produced by Mann-Whitney-Wilcoxon test after FDR correction < 0.05, fold change > 1.2 or < 0.8) , 391 m/z were found to be significantly changed in CHD group by intersection of 558 m/z and 559 m/z in S-plot and Volcano-plot, respectively, as is shown in Veen plot (Fig. 7) .
The 391 m/z were aligned and annotated using the HMDB and KEGG database. Among the 160 annotated metabolites, the intensity of 96 metabolites were increased while that of 64 metabolites were decreased in CHD patients. These 160 metabolites were used to perform phenotype analysis for the 102 samples. The CHD patients’ metabolism was obviously different from healthy controls. By comparing MS/MS spectra and retention time with  commercially available reference standards, 4 metabolites were verified and the results were listed in Table 2. The level of GlcNAc-6-P and mannitol were increased with fold change of 165.99 and 8.45 in CHD patients respectively. Meanwhile, the level of creatine and phytosphigosine were decreased with fold changes of 0.41 and 0.39 respectively.
The level of GlcNAc-6-P was found to be increased in both plasma and urine samples. GlcNAc-6-P could be produced by human body enzymes and gut bacterial enzymes, so its origin need to be further determined. Similar pattern of GlcNAc-6-P increasing in urine and blood suggested interactions of host and gut flora were important in the development of CHD. Mannitol is a polyol or sugar alcohol produced by several microorganisms. The fact that no related homo sapiens enzymes are found in the fructose and mannose metabolism so far indicates mannitol might belong to microbial metabolites family. Mannitol could be produced by many microorganisms, such as lactic acid bacteria (Carvalheiro, F., Moniz, P., Duarte, L.C., Esteves, M.P. &Gírio, F.M. Mannitol production by lactic acid bacteria grown in supplemented carob syrup. J. Ind Microbiol. Biotechnol. 38, 221-7 (2011) , incorporated herein by reference) and pseudomonas putida (Kets, E.P. Galinski, E.A., de Wit, M., de Bont, J.A. &Heipieper, H.J. Mannitol, a novel bacterial compatible solute in Pseudomonas putida S12. J. Bacteriol. 178, 6665-6670 (1996) , incorporated herein by reference) . Creatine was also found to be decreased in CHD patient’s urine sample. It is a nitrogenous organic acid naturally produced by the human body from amino acids. In biosystem, creatine can elevate creatine phosphate levels and improve maintenance of ATP content during tissue oxygen depletion period, and it also has the capacity to scavenge free radicals and reduce oxidative stress. Decreased creatine in CHD patients’ urine indicated that obvious energy disturbance, more free radicals and more serious oxidative stress existed in CHD patients. Reduced phytosphingosine was also found in CHD patients’ urine. Phytosphingosine is a phospholipid and a major component of mammalian tissue biological membranes. The synthesis of phytosphingosine can be performed by human body and intestinal microbiota in the sphingosine metabolism. Phytosphingosine could induce caspase-independent apoptosis in human T-cell lymphoma and non-small cell lung cancer cells. The decrease in urine phytosphingosine suggested sphingolipid metabolism was abnormal in CHD patients.
To evaluate correlation among 160 annotated urine metabolites, spearman correlation  analysis was performed. The results showed that urine metabolites which were significantly changed (with smaller adjusted p. value) had relatively stronger correlations compared with plasma significant metabolites. In addition, among those 4 validated metabolites, mannitol showed a relatively high positive correlation with GlcNAc-6-P (rho ＝ 0.775, q. value ＝ 9.40E-21) and this could be a signal for disturbed intestine microbiota.
Correlations between plasma and urine significant metabolites.
To apply metabolomics to clinical diagnosis, treatments or pathophysiology research, physiological function of metabolites and relationships between them needed to be illustrated, and correlation networks of all potential biomarkers needed to be built (Liu, P. et al. Biomarkers of primary dysmenorrhea and herbal formula intervention: an exploratory metabonomics study of blood plasma and urine. Mol. BioSyst. 9, 77—87 (2013) , incorporated herein by reference) . For this purpose, the significantly changed plasma and urine metabolites were connected according to spearman correlation analysis profiled with Cytoscape software. These 109 annotated significantly changed plasma metabolites and 160 annotated significantly changed urine metabolites were involved in different pathways and can be divided into 8 categories: carbohydrate metabolism, lipids metabolism, amino acids metabolism, bile acids metabolism, purine/pyrimidine metabolism, vitamins metabolism, microbial related metabolism and others. Lipids metabolism showed significantly negatively correlations with microbial related metabolism while other 6 metabolism categories were in strong positive correlation with microbial related metabolism.
Seven significantly changed metabolites (Supplementary Table 2) , including GlcNAc-6-P, were found both in plasma and urine on the condition that retention time error was less than 1 min and m/z error was less than 0.01 Dalton with MS/MS comparison. A Veen diagram exhibiting the common metabolites among plasma and urine significantly changed metabolites is provided in Fig. 8. Two metabolites (m/z : 185.04, 202.04) were decreased in CHD patients while other five metabolites (m/z : 125.01, 309.05, 310.04, 311.05, 324.04) were increased in CHD patients.
To evaluate the correlation among 7 common metabolites, spearman correlation analysis was implemented using the criteria that the coefficient was larger than 0.90. First, correlations among plasma metabolites were obtained. m/z 311.05 showed strong correlation  with m/z 309.05 (rho ＝ 0.929, q. value ＝ 9.31E-45) , m/z 310.04 (rho ＝ 0.911, q. value ＝ 5.70E-40) , m/z 324.04 (rho ＝ 0.900, q. value ＝ 1.53E-37) ； m/z 309.05 also strongly correlated with m/z 310.04 (rho ＝ 0.929, q. value ＝ 9.31E-45) . Second, correlations among urine metabolites were obtained， GlcNAc-6-P (m/z 324.04) was strongly correlated with m/z 310.04 (rho ＝ 0.933, q. value ＝ 1.26E-45) , m/z 311.05 (rho ＝ 0.910, q. value ＝ 1.17E-39) , m/z 125.01 (rho ＝ 0.903, q. value ＝ 3.43E-38) , while m/z 125.01 also showed strong correlation with urine metabolite (m/z 310.04 (rho ＝ 0.918, q. value ＝ 1.28E-41) . In addition, correlations of these metabolites in plasma and urine were also evaluated. The results showed that plasma metabolites have strong positive correlations with the same metabolites in urine (Supplementary Table3) . Among them, validated GlcNAc-6-P (324.04) showed very strong positive correlation with itself (rho ＝ 0.747, q. value ＝ 5.60E-19) .
Clinical relevance of plasma and urine potential metabolites
Receiver operating characteristic analysis
To evaluate the potential of the identified metabolites (18 plasma and 4 urine ones) as biomarkers, receiver operating characteristic analysis (ROC) was applied to 176 additional plasma samples (98 healthy controls vs 78 CHD patients) and 395 additional urine samples (173 healthy controls vs 222 CHD patients) .
In plasma validation datasets, 6 LPCs and 1 glycerophosphocholine metabolites showed area under curve (AUC) larger than 0.80 and were significantly different in CHD patients (Table 3, Fig. 12A-F) . As shown in Fig. 9a-g, The levels of LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (P-16: 0) , LysoPC (15: 0) , 1-Palmitoylglycerophosphocholine, LysoPC (14: 0) , LysoPC (16: 1 (9Z) ) , LysoPC (0: 0/18: 0) were decreased in CHD patients with fold change at 0.26, 0.58, 0.51, 0.65, 0.49, 0.62, 0.42 respectively and AUC of 0.91, 0.88, 0.88, 0.88, 0.84, 0.83, 0.83 respectively. On the other hand, other 9 plasma potential biomarkers exhibited the same enrichment direction except that LysoPC (20: 3 (5Z, 8Z, 11Z) ) became normal and GlcNAc-6-P even became undetected (data shown in Table 3) . These results validated that LPCs could become biomarkers and targets for CHD diagnosis and therapies in the future.
In urine validation datasets, GlcNAc-6-P and mannitol exhibited AUC of 0.88, 0.81 and fold change at 36.91 and 2.62 respectively (as shown in Fig. 9h、 9i and Table 4) . However, creatine and phytosphingosine did not show good diagnostic ability in both training and  validation datasets. The existence of GlcNAc-6-P and mannitol in urine indicates the interaction of gut flora activity and host metabolism. These results also validated that GlcNAc-6-P and mannitol could become biomarkers and targets for CHD diagnosis and therapies in the future.
Among these 7 choline metabolites and 2 urine metabolites with AUC larger than 0.80, GlcNAc-6-P appeared the most discriminative biomarker which showed relatively good diagnostic ability with false negative (FN) of 0.051, 0.153 and false positive (FP) of 0.047, 0.208 in the training datasets and validation datasets respectively. LysoPC (18: 3 (6Z, 9Z, 12Z) ) , LysoPC (P-16: 0) , LysoPC (15: 0) , 1-Palmitoylglycerophosphocholine, LysoPC (14: 0) , LysoPC (16: 1 (9Z) ) , LysoPC (0: 0/18: 0) and mannitol exhibited diagnostic ability with FN of 0.271, 0.169, 0.136, 0.068, 0.119, 0.119, 0.085, 0.153 and FP of 0.233, 0.163, 0.256, 0.233, 0.209, 0.140, 0.279, 0.093 respectively in the training datasets, showed diagnostic ability with FN of 0.013, 0, 0, 0.013, 0.013, 0.013, 0, 0.135 and FP of 0.582, 0.755, 0.673, 0.694, 0.612, 0.684, 0.714, 0.416 respectively in the validation datasets. The FN and FP of 7 choline metabolites and 2 urine metabolites were all analysed with R using the “randomForest” and “pROC” packages based on the intensity of training datasets shown in Table 7 and Table 8 respectively. The randomForest model classification output prediction results (probability of illness； cutoff was 0.5, and if the probability of illness ≥0.5, the subject was at risk of CHD) .
7 potential plasma biomarkers were combined to perform ROC analysis in plasma training datasets (59 CHD patients plasma samples VS 43 healthy control plasma samples) and plasma validation datasets (78 CHD patients plasma samples VS 98 healthy controls plasma samples, Fig. 10, Table 9, Table 11) with R package–pROC. The FN and FP were (0, 0) and (0, 0.724) in plasma training and validation datasets respectively. 2 potential urine biomarkers were combined to perform ROC analysis in urine training datasets (59 CHD patients urine samples VS 43 healthy control urine samples) and urine validation datasets (222 CHD patients urine samples VS 173 healthy controls urine samples, Fig. 11, Table 10, Table 11) with R package-pROC. The FN and FP were (0.016, 0) and (0.121, 0.225) in urine training and validation datasets respectively. The FN and FP of 7 choline metabolites combination and 2 urine metabolites combination were all analysed with R using the “randomForest” and “pROC” packages based on the intensity of training datasets shown in  Table 7 and Table 8 respectively. The randomForest model classification output prediction results (probability of illness； cutoff was 0.5, and if the probability of illness ≥0.5, the subject was at risk of CHD) .
Table 7 Ion intensity of 7 plasma biomarkers in training datasets
Figure PCTCN2015087664-appb-000001
Figure PCTCN2015087664-appb-000002
Figure PCTCN2015087664-appb-000003
Figure PCTCN2015087664-appb-000004
Figure PCTCN2015087664-appb-000005
Table 9 Ion intensity of 7 plasma biomarkers in two samples of validation datasets
Figure PCTCN2015087664-appb-000006
Table 10 Ion intensity of 2 urine biomarkers in two samples of validation datasets
Figure PCTCN2015087664-appb-000007
Table 11 Probability of illness of validation datasets predicted by 7 plasma biomarkers and 2 urine biomarkers respectively
Figure PCTCN2015087664-appb-000008
Figure PCTCN2015087664-appb-000009
Figure PCTCN2015087664-appb-000010
Figure PCTCN2015087664-appb-000011
Figure PCTCN2015087664-appb-000012
Figure PCTCN2015087664-appb-000013
Figure PCTCN2015087664-appb-000014
Example 2. Association of potential metabolic biomarkers with clinical phenotypes
To access the effects of patients’ covariates (such as age and clinical biochemical factors) on metabolic profiles, PERMANOVA analysis was performed. Albumin (ALB) , alanine aminotransferase (ALT) , total protein (TP) , low-density lipoprotein (LDLC) , cholesterol (CHOL) , high-density lipoprotein (HDLC) , apolipoprotein b (APOB) and apolipoprotein a (APOA) were found to be significantly different in CHD patients (Supplementary Table 1) .
Besides, spearman correlation analysis was performed among 18 potential plasma biomarkers and 4 potential urine biomarkers with individual phenotypes. CHOL, HDLC and TP showed significantly positive correlations with plasma LPCs (Supplementary Table 4) .
LysoPC (18: 0) was correlated with CHOL (rho ＝ 0.518, q. value ＝ 7.89E-07) , HDLC (rho ＝ 0.548, q. value ＝ 1.29E-07) and TP (rho ＝ 0.573, q. value ＝ 5.16E-08) . LysoPC (P-16: 0) was positively correlated with HDLC (rho ＝ 0.561, q. value ＝ 7.39E-08) . Meanwhile, the two potential urine biomarkers, GlcNAc-6-P and mannitol, exhibited strong negative correlations with CHOL, HDLC, TP and APOB (q. value < 0.01) . These results indicated significantly abnormal LPCs metabolism in CHD patients, and thus we speculated that it could be beneficial to reduce CHD occurrence by properly increasing intake of these extra LPCs which were significantly decreased in CHD patients.
Supplementary Table 1 | The characteristics of CHD samples and control samples in the study
Figure PCTCN2015087664-appb-000015
CKMB, creatine kinase MB； ALB, albumin； ALT, Alanine aminotransferase； TP, Total Protein； AST, Aspartate transaminase； CREA, creatinine； HBDH, hydroxy low-density lipoprotein； CHOL, cholesterol； HDLC, high-density lipoprotein； APOB, apolipoprotein (b) ； APOA, apolipoprotein (a) ； LPA, lipoprotein (a) .
＊ P. value calculated by Student’s t-test. 
Figure PCTCN2015087664-appb-000016
Permuted p. value calculated by PERMANOVA analysis (permutation＝1000) , permuted p. value less than 0.05 indicat Significant clinical biochemical indicators affected metabolic profiles of CHD patients (p. value and permutated p. value less than 0.05)
Table 1 | Potential plasma biomarkers for discriminating CHD patients from control subjects
Figure PCTCN2015087664-appb-000017
Figure PCTCN2015087664-appb-000018
＊ Retention time. 
Figure PCTCN2015087664-appb-000019
Fold change (FC＝mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls) . When FC was large CHD patients. When FC was lower than 1, it represented that the biomarker was enriched in healthy individuals. 
Figure PCTCN2015087664-appb-000020
Adjusted p. value calculated by the two-taile correction. §VIP (Variable Importance for Projection) , one indicator reflecting the capability of the variables to explain Y. ||Metabolites matched with the online peaks but mismatched retention time with commercial available reference standards.
Table 2 | Potential urine biomarkers for discriminating CHD patients from control subjects
Figure PCTCN2015087664-appb-000021
＊Retention time. 
Figure PCTCN2015087664-appb-000022
Fold change (FC＝mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls) . When FC was large CHD patients. When FC was lower than 1, it represented that the biomarker was enriched in healthy individuals . 
Figure PCTCN2015087664-appb-000023
Adjusted p. value calculated by the two-taile correction. §VIP (Variable Importance for Projection) , one indicator reflecting the capability of the variables to explain Y. #Metabolites matched with commercia matched characteristic peaks but mismatched retention time with commercial available reference standards.
Supplementary Table 2 | Seven common potential biomarkers in plasma and urine samples
Figure PCTCN2015087664-appb-000024
＊ Retention time. 
Figure PCTCN2015087664-appb-000025
 Fold change (FC＝mean of metabolite ion intensity in CHD patients/mean of metabolite ion intensity in healthy controls) . When FC was larg enriched in CHD patients. When FC was lower than 1, it represented that the biomarker was enriched in healthy individuals. 
Figure PCTCN2015087664-appb-000026
Adjusted p. value calculated by t discovery rate correction. §VIP (Variable Importance for Projection) , one indicator reflecting the capability of the variables to explain Y. || Metabolites matched Metabolites matched characteristic peaks but mismatching retention time with commercial available reference standards.
Supplementary Table 3 | Spearman correlation analysis of the 7 common metabolites
Figure PCTCN2015087664-appb-000027
＊ P. value calculated by spearman correlation analysis.
Figure PCTCN2015087664-appb-000028
Adjusted p. value calculated by false discovery rate correction.
Table 3 | AUC results of plasma training and validation datasets
Figure PCTCN2015087664-appb-000029
Figure PCTCN2015087664-appb-000030
*AUC calculated by online tool–ROCCET (http: //www. roccet. ca) . 
Figure PCTCN2015087664-appb-000031
P. value calculated by T-test. 
Figure PCTCN2015087664-appb-000032
Fold change.
Table 4 | AUC results of urine training and validation datasets
Figure PCTCN2015087664-appb-000033
*AUC calculated by online tool–ROCCET (http: //www. roccet. ca) . 
Figure PCTCN2015087664-appb-000034
P. value calculated by T-test. 
Figure PCTCN2015087664-appb-000035
Fold change.
Supplementary Table 4 | Spearman correlation analysis of clinical data and identified biomarkers
Figure PCTCN2015087664-appb-000036
＊ P. value calculated by spearman correlation analysis.
Figure PCTCN2015087664-appb-000037
Adjusted p. value calculated by false discovery rate correction.
Thus the inventors have identified and validated 7 plasma metabolites and 2 urine metabolites for early and non-invasive diagnosis of CHD by a random forest model based on the associated metabolites. And the inventors have constructed a method to evaluate the risk of CHD based on these associated metabolites.
Although explanatory embodiments have been shown and described in detailed, it would be appreciated by those skilled in the art that the above embodiments are illustrative, and are not intended to limit the present disclosure in any way, and that changes, alternatives, and modifications can be made to the embodiments without departing from the spirit, principles and scope of the present disclosure.

Claims (53)

  1. A biomarker composition, which comprises one or more selected from the group consisting of :
    Biomarker 1, for which m/z is 518.32±1.00, retention time (RT) is 12.71±1.00min；
    Biomarker 2, for which m/z is 480.34±1.00, retention time (RT) is 13.48±1.00min；
    Biomarker 3, for which m/z is 482.32±1.00, retention time (RT) is 12.92±1.00min；
    Biomarker 4, for which m/z is 496.33±1.00, retention time (RT) is 13.19±1.00min；
    Biomarker 5, for which m/z is 468.3±1.00, retention time (RT) is 12.69±1.00min；
    Biomarker 6, for which m/z is 494.32±1.00, retention time (RT) is 12.82±1.00min；
    Biomarker 7, for which m/z is 524.36±1.00 retention time (RT) is 13.69±1.00min；
    Biomarker 8, for which m/z is 516.31±1.00, retention time (RT) is 13.21±1.00min；
    Biomarker 9, for which m/z is 590.31±1.00, retention time (RT) is 12.86±1.00min；
    Biomarker 10, for which m/z is 546.35±1.00, retention time (RT) is 14.22±1.00min；
    Biomarker 11, for which m/z is 570.35±1.00, retention time (RT) is 13.05±1.00min；
    Biomarker 12, for which m/z is 518.32±1.00, retention time (RT) is 13.19±1.00min；
    Biomarker 13, for which m/z is 524.36±1.00, retention time (RT) is 14.27±1.00min；
    Biomarker 14, for which m/z is 522.35±1.00, retention time (RT) is 13.33±1.00min；
    Biomarker 15, for which m/z is 181.07±1.00, retention time (RT) is 9.05±1.00min；
    Biomarker 16, for which m/z is 544.33±1.00, retention time (RT) is 13.34±1.00min；
    Biomarker 17, for which m/z is 175.11±1.00, retention time (RT) is 1.83±1.00min； and
    Biomarker 18, for which m/z is 324.04±1.00, retention time (RT) is 9.33±1.00min.
  2. The biomarker composition according to claim 1, which comprises one or more selected from the group consisting of:
    Biomarker 1, for which m/z is 518.32±1.00, retention time (RT) is 12.71±1.00min；
    Biomarker 2, for which m/z is 480.34±1.00, retention time (RT) is 13.48±1.00min；
    Biomarker 3, for which m/z is 482.32±1.00, retention time (RT) is 12.92±1.00min；
    Biomarker 4, for which m/z is 496.33±1.00, retention time (RT) is 13.19±1.00min；
    Biomarker 5, for which m/z is 468.3±1.00, retention time (RT) is 12.69±1.00min；
    Biomarker 6, for which m/z is 494.32±1.00, retention time (RT) is 12.82±1.00min； and
    Biomarker 7, for which m/z is 524.36±1.00, retention time (RT) is 13.69±1.00min；
  3. The biomarker composition according to claim 1, wherein,
    Biomarker 1 is LysoPC (18: 3 (6Z, 9Z, 12Z)) ；
    Biomarker 2 is LysoPC (P-16: 0) ；
    Biomarker 3 is LysoPC (15: 0) ；
    Biomarker 4 is 1-Palmitoylglycerophosphocholine；
    Biomarker 5 is LysoPC (14: 0) ；
    Biomarker 6 is LysoPC (16: 1 (9Z)) ；
    Biomarker 7 is LysoPC (0: 0/18: 0) ；
    Biomarker 8 is LysoPC (18: 4 (6Z, 9Z, 12Z, 15Z)) ；
    Biomarker 9 is LysoPC (22: 6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)) ；
    Biomarker 10 is LysoPC (20: 3 (5Z, 8Z, 11Z)) ；
    Biomarker 11 is LysoPC (22: 5 (4Z, 7Z, 10Z, 13Z, 16Z)) ；
    Biomarker 12 is LysoPC (18: 3 (9Z, 12Z, 15Z)) ；
    Biomarker 13 is LysoPC (18: 0) ；
    Biomarker 14 is 1-Oleoylglycerophosphocholine；
    Biomarker 15 is Paraxanthine；
    Biomarker 16 is LysoPC (20: 4 (5Z, 8Z, 11Z, 14Z)) ；
    Biomarker 17 is L-Arginine； and/or
    Biomarker 18 is N-Acetyl-D-glucosamine 6-phosphate.
  4. A reagent composition, which comprises the reagents used for detection of each of the biomarker composition according to any item of claims 1-3.
  5. The reagent composition according to claim 4, wherein the reagents comprise substances used in mass spectrometry for detection of the biomarker composition according to any item of claims 1-3.
  6. A kit, which comprises the biomarker composition according to any item of claims 1-3 and/or the reagent composition according to claim 4 or 5.
  7. A use of the biomarker composition according to any item of claims 1-3 and/or the reagent composition according to claim 4 or 5 in the preparation of a kit, wherein the kit is used for evaluation of the risk of CHD in a subject, or for use in diagnosis of CHD in a subject.
  8. The use according to claim 7, wherein the evaluation or diagnosis comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of claims 1-3 in a sample from CHD patients and healthy controls.
  9. The use according to claim 8, wherein the sample is selected from blood, plasma and serum.
  10. The use according to claim 8 or 9, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness； preferably, the multivariate statistical model is random forest model.
  11. The use according to claim 10, wherein the subject is determined as being at risk of CHD or having CHD if the probability of illness ≥0.5.
  12. The use according to claim 8 or 9, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  13. The use according to claim 8 or 9, wherein, when compared with a reference value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  14. The use according to claim 8 or 9, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  15. The use according to claim 8, wherein the method further comprises the step of processing the sample before step 1) .
  16. A method for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, wherein the method comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of claims 1-3 in a sample from CHD patients and healthy controls.
  17. The method according to claim 16, wherein the sample is selected from blood, plasma and serum.
  18. The method according to claim 16 or 17, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness； preferably, the multivariate statistical model is random forest model.
  19. The method according to claim 16 or 17, wherein the subject is determined as being at risk of CHD or having CHD if the probability of illness ≥0.5.
  20. The method according to claim 16 or 17, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  21. The method according to claim 16 or 17, wherein, when compared with a reference  value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  22. The method according to claim 16 or 17, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  23. The method according to claim 16 or 17, wherein the method further comprises the step of processing the samples before step 1) .
  24. The biomarker composition according to any item of claims 1-3 or the reagent composition according to claim 4 or 5, for use in a method of evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject.
  25. The biomarker composition according to claim 24, wherein the method of evaluation or diagnosis comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject； 2) comparing the level of step 1) with a reference dataset or a reference value (for example a reference value of healthy controls) ； preferably, the reference dataset comprises the level of the biomarker of the biomarker composition according to any item of claims 1-3 in a sample from CHD patients and healthy controls.
  26. The biomarker composition according to claim 25, wherein the sample is selected from blood, plasma and serum.
  27. The biomarker composition according to claim 25 or 26, wherein the comparing the level of step 1) with a reference dataset further comprises the step of executing a multivariate statistical model to output the probability of illness； preferably, the multivariate statistical model is random forest model.
  28. The biomarker composition according to claim 25 or 26, wherein the subject is determined as being at risk of CHD or having CHD if the probability of illness ≥0.5.
  29. The biomarker composition according to claim 25 or 26, wherein, when compared with a reference value, the decrease of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the subject is in the risk of CHD or has CHD.
  30. The biomarker composition according to claim 25 or 26, wherein, when compared with a reference value, the increase of Biomarker 17 and/or Biomarker 18 indicates that the subject is in the risk of CHD or has CHD.
  31. The biomarker composition according to claim 25 or 26, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  32. The biomarker composition according to claim 25 or 26, wherein the method further comprises the step of processing the samples before step 1) .
  33. A use of the biomarker composition according to any item of claims 1-3 and/or the reagent composition according to claim 4 or 5 in the preparation of a kit, wherein the kit is used for screening candidate compounds for treatment of CHD in a subject, or for evaluating the effect of the treatment of CHD in a subject.
  34. The use according to claim 33, wherein the screening or evaluation comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  35. The use according to claim 34, wherein the sample is selected from blood, plasma and serum.
  36. The use according to claim 34 or 35, wherein the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  37. The use according to claim 34 or 35, wherein the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  38. The use according to claim 34 or 35, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  39. The use according to claim 34 or 35, wherein the method further comprises the step of processing the samples before step 1) .
  40. A method for screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject, wherein the method  comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  41. The method according to claim 40, wherein the sample is selected from blood, plasma and serum.
  42. The method according to claim 40 or 41, wherein the increase of one or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  43. The method according to claim 40 or 41 the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  44. The method according to claim 40 or 41, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  45. The method according to claim 40 or 41, wherein the method further comprises the step of processing the samples before step 1) .
  46. The biomarker composition according to any item of claims 1-3 or the reagent composition according to claim 4 or 5, for use in a method of screening candidate compounds for treatment of CHD in a subject or for evaluation of the effect of the treatment of CHD in a subject.
  47. The biomarker composition according to claim 46, wherein the method of screening and evaluation comprises the following steps: 1) determining the level of each of the biomarkers of the biomarker composition according to any item of claims 1-3 in a sample from the subject after administering the candidate compounds or the treatment to the subject； 2) comparing the level of step 1) with the level of the above mentioned biomarker before administering the candidate compounds or the treatment to the subject.
  48. The biomarker composition according to claim 47, wherein the sample is selected from blood, plasma and serum.
  49. The biomarker composition according to claim 47 or 48, wherein the increase of one  or more Biomarkers selected from the group consisting of Biomarker 1-16, preferably, from the group consisting of Biomarker 1-7, indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  50. The biomarker composition according to claim 47 or 48, wherein the decrease of Biomarker 17 and/or Biomarker 18 indicates that the compound is a candidate compound for treatment of CHD in a subject or the treatment of CHD in the subject is effective.
  51. The biomarker composition according to claim 47 or 48, wherein the step of determining the level of each of the biomarkers are carried out by mass spectrometry, preferably, mass spectrometry in conjunction with chromatography, such as gas chromatography mass spectrometry (GC-MS) or liquid chromatography mass spectrometry (LC-MS) .
  52. The biomarker composition according to claim 47 or 48, wherein the method further comprises the step of processing the samples before step 1) .
  53. A method for setting up a mass spectrometry model for evaluation of the risk of CHD in a subject or for diagnosis of CHD in a subject, which comprises the step of identifying the differentially expressed substance in a blood sample between CHD patients and healthy controls, wherein the differentially expressed substance comprises one or more selected from the group consisting of Biomarker 1-18, preferably, from the group consisting of of Biomarker 1-7.
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CN109507337B (en) * 2018-12-29 2022-02-22 上海交通大学医学院附属新华医院 Novel method for predicting mechanism of Gandi capsule for treating diabetic nephropathy based on metabolites in hematuria
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