WO2018191866A1 - Marqueur du diabète de type 2 et utilisation associée - Google Patents

Marqueur du diabète de type 2 et utilisation associée Download PDF

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WO2018191866A1
WO2018191866A1 PCT/CN2017/080954 CN2017080954W WO2018191866A1 WO 2018191866 A1 WO2018191866 A1 WO 2018191866A1 CN 2017080954 W CN2017080954 W CN 2017080954W WO 2018191866 A1 WO2018191866 A1 WO 2018191866A1
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lysopc
diabetes
type
marker
mass
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PCT/CN2017/080954
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English (en)
Chinese (zh)
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钟焕姿
方超
李俊桦
任华慧
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深圳华大基因研究院
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Priority to CN201780083287.0A priority Critical patent/CN110178035B/zh
Priority to PCT/CN2017/080954 priority patent/WO2018191866A1/fr
Publication of WO2018191866A1 publication Critical patent/WO2018191866A1/fr

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    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • 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
    • G01N33/64—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving ketones
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work

Definitions

  • the present invention relates to the field of biological detection, and in particular to the type 2 diabetes marker and its use, and more particularly, to a type 2 diabetes marker, a method for diagnosing type 2 diabetes, a system for diagnosing type 2 diabetes, and a reagent
  • a cartridge and a reagent in a kit for the diagnosis of a type 2 diabetes marker.
  • Type 2 diabetes is the most common type of diabetes, accounting for about 90% of all diabetes.
  • Type 2 diabetes is a complex disorder of progressive metabolic disorders characterized by hyperglycemia, especially in glucose and lipid metabolism disorders.
  • the pathological features are mainly characterized by insulin resistance accompanied by a deficiency in insulin beta-cell function leading to a relative decrease in insulin.
  • WHO World Health Organization
  • ADA 2010 American Diabetes Association
  • the current diagnosis of diabetes is mainly through the detection of venous plasma blood glucose levels.
  • the currently used diagnostic criteria are the WHO (1999) standard and the ADA (2003) standard.
  • the WHO standard mainly uses fast plasma glucose (FPG) and 2-hour postprandial glucose (2h-PG) to divide glucose metabolism into normal blood glucose and impaired fasting glucose (IFG).
  • FPG fast plasma glucose
  • 2h-PG 2-hour postprandial glucose
  • IGF impaired fasting glucose
  • IGT impaired fasting glucose
  • ITT impaired fasting glucose
  • Pre-DM Prediabetes
  • the 2010 ADA standard uses glycosylated hemoglobin (Hemo A1c, HbA1c) as a diagnostic criterion for diabetes.
  • Hemo A1c, HbA1c glycosylated hemoglobin
  • the existing diagnostic criteria are only for the disease that has been developed, can not be early warning, can not predict the incidence and development of type 2 diabetes; another development of a new diagnostic method can help the pathological classification of disease, help to accurately diagnose the disease Types provide ideas for drug target research, precise drug use, and pathogenesis research.
  • the present invention provides a combination of biomarkers that can be used for the diagnosis and risk assessment of type 2 diabetes (ie, organisms).
  • the marker composition as well as the diagnosis and disease risk assessment methods for type 2 diabetes, can predict the onset and development of type 2 diabetes and apply to pathological classification of diseases.
  • the invention proposes a panel of type 2 diabetes markers.
  • the type 2 diabetes marker comprises at least one selected from the group consisting of LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric acid, N-(3) -oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38:1), PI ( O-38: 2), LysoPC (18:1), PS (38:1), LysoPC (24:1 (15Z)), Carotenes, and 5,6-dichloro-tetradecanoic acid.
  • the body's lipid molecules are the basis of life activities, and the changes in the state of the disease and the function of the body will inevitably cause changes in the metabolism of endogenous small molecules in the body.
  • the inventors found significant differences in plasma lipid metabolite profiles between type 2 diabetes and non-diabetic groups, and screened for related organisms. landmark.
  • the type 2 diabetes markers combined with lipid metabolite data of biomarkers in type 2 diabetes and non-diabetic populations can accurately assess the risk and early diagnosis of type 2 diabetes.
  • the above type 2 diabetes marker may further include at least one of the following additional technical features:
  • the type 2 diabetes marker further comprises at least one of the compounds having the following parameters:
  • ESI ion source data collected in positive/negative ion mode, mass range m/z 50 ⁇ 2000, s/times per second, ion source temperature is 120°C, desolvation temperature is 600°C, mobile phase gas is nitrogen, gas flow is 800L /h, capillary pressure and cone voltage were 2.0 KV (+) / 1.5 KV (-) and 30 V, respectively, using leucine enkephalin as the locking mass.
  • the invention provides a method of diagnosing type 2 diabetes.
  • the method comprises: (1) determining a relative content of the markers in a sample of the object to be diagnosed; (2) determining the content based on the marker content obtained in step (1) The diagnosis result of the object.
  • the method has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.
  • the invention provides a system for diagnosing type 2 diabetes.
  • a system for diagnosing type 2 diabetes comprising: an assay device for determining a relative content of the marker according to claim 1 in a sample of a subject to be diagnosed; determining means for determining based on the assay device The relative content of the markers obtained in the determination of the diagnostic result of the subject.
  • the type 2 diabetes markers combined with lipid metabolite data of biomarkers in type 2 diabetes and non-diabetic populations can accurately assess the risk and early diagnosis of type 2 diabetes.
  • the system has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.
  • the invention proposes a kit.
  • the kit comprises a reagent for detecting at least one selected from the group consisting of LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric Acid, N-(3-oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38) :1), PI (O-38: 2), LysoPC (18:1), PS (38:1), LysoPC (24:1 (15Z)), Carotenes, and 5,6-dichloro-tetradecanoic acid.
  • the inventors found a significant difference in the plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetic group by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetic group.
  • Biomarkers have significant differences in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetic group.
  • the kit according to the embodiment of the invention can accurately assess the risk of early detection of type 2 diabetes and early diagnosis, and has the characteristics of non-invasive, convenient and fast, and the kit has high sensitivity and good specificity.
  • the invention provides the use of an agent for the preparation of a kit for diagnosing a type 2 diabetes marker, the reagent for detecting comprising at least one selected from the group consisting of: LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric acid, N-(3-oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38:1), PI (O-38:2), LysoPC (18:1), PS (38:1), LysoPC (24:1 (15Z)), Carotenes, and 5,6-dichloro- Tetradecanoic acid.
  • LysoPC (18:0
  • Hydroxybutyrylcarnitine 3-oxo-4-pentenoic acid
  • Ajoene Hydroxybutyric acid
  • the kit prepared by the above reagent can accurately assess the risk of early diagnosis and early diagnosis of type 2 diabetes, and has the characteristics of non-invasive, convenient and quick, and the kit has high sensitivity and good specificity.
  • 1 is a system for diagnosing type 2 diabetes in accordance with an embodiment of the present invention
  • FIG. 3 is a schematic structural view of an assay device according to an embodiment of the present invention.
  • Figure 4 shows a Venn diagram plotting significant metabolites obtained using a comparison between the normal group of glucose tolerance (NGT), the pre-diabetic group (Pre-DM), and the type 2 diabetes group (T2D).
  • NTT normal group of glucose tolerance
  • Pre-DM pre-diabetic group
  • T2D type 2 diabetes group
  • the figure depicts the number of significant metabolites obtained from the comparison of the three groups and shows their coincident parts (p ⁇ 0.05, Dunn's post-test);
  • Figure 5 shows the distribution of error rates for five 10-fold cross-validations in a random forest classifier.
  • the black solid curve represents the average of 5 trials (dashed curves).
  • Gray vertical lines represent the number of metabolites in the best combination selected;
  • Figure 6 shows the discrimination of T2D and NGT based on a random forest model (28 metabolite markers), the receiver operating curve (ROC) and the area under the curve (AUC) of the training set;
  • Figure 10 shows the prediction of three sub-groups of pre-diabetes (Pre-DM) based on a random forest model (28 metabolite markers), ie, HbA1c 5.7-6.4% increased, simple IGT and combined IFG/IGT development The probability of becoming a T2D disease;
  • Figure 11 shows the LC-MS/MS spectrum of the biomarker m/z 248.1511 and the predicted chemical structure
  • Figure 13 shows the LC-MS/MS spectrum of the biomarker m/z 506.3249 and the predicted chemical structure
  • Figure 14 shows the LC-MS/MS spectrum of the biomarker m/z 504.3093 and the deduced chemical structure
  • Figure 15 shows four types of metabolite markers (derived from a random forest model) that discriminate between T2D and NGT, receiver operating curve (ROC) and area under the curve (AUC);
  • Figure 16 shows four metabolite markers (derived from a random forest model) that discriminate between T2D and non-T2D, receiver operating curve (ROC) and area under the curve (AUC).
  • ROC receiver operating curve
  • AUC area under the curve
  • first and second are used for descriptive purposes only, and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defining “first” and “second” may include one or more of the features either explicitly or implicitly. Further, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specified.
  • the invention proposes a panel of type 2 diabetes markers.
  • the type 2 diabetes marker comprises at least one selected from the group consisting of LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric acid, N-(3) -oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38:1), PI ( O-38: 2), LysoPC (18:1), PS (38:1), LysoPC (24:1 (15Z)), Carotenes, and 5,6-dichloro-tetradecanoic acid.
  • the body's lipid molecules are the basis of life activities, and the changes in the state of the disease and the function of the body will inevitably cause changes in the metabolism of endogenous small molecules in the body.
  • the inventors found significant differences in plasma lipid metabolite profiles between type 2 diabetes and non-diabetic groups, and screened for related organisms. landmark.
  • the type 2 diabetes markers combined with lipid metabolite data of biomarkers in type 2 diabetes and non-diabetic populations can accurately assess the risk and early diagnosis of type 2 diabetes.
  • the type 2 diabetes marker further comprises at least one of the compounds having the following parameters:
  • ESI ion source data collected in positive/negative ion mode, mass range m/z 50 ⁇ 2000, s/times per second, ion source temperature is 120°C, desolvation temperature is 600°C, mobile phase gas is nitrogen, gas flow is 800L /h, capillary pressure and cone voltage were 2.0 KV (+) / 1.5 KV (-) and 30 V, respectively, using leucine enkephalin as the locking mass.
  • the inventors found a significant difference in the plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetic group by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetic group, and further screening for the above related Biomarkers.
  • the above-mentioned 28 types of type 2 diabetes markers screened by the inventors combined with lipid metabolite data of biomarkers of type 2 diabetes and non-diabetic populations as training sets can further accurately assess the risk of type 2 diabetes. And early diagnosis.
  • the invention provides a method of diagnosing type 2 diabetes.
  • the method comprises: (1) determining a relative content (relative ionic strength) of the marker in a sample of the object to be diagnosed; (2) based on the marker obtained in step (1) The relative content of the object determines the diagnosis of the subject.
  • the method has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.
  • determining the diagnosis result of the object based on the relative content of the marker obtained in the step (1) is achieved by the disease risk value of the marker model being higher than a predetermined threshold Is an indication that the subject has type 2 diabetes.
  • the predetermined threshold is 0.5.
  • a model calculated by a total of 28 characteristic metabolites obtained by random forest screening is used to calculate the risk of disease, and the risk of disease is higher than 0.5, that is, an indication of having type 2 diabetes is determined.
  • the probability of disease risk in patients with different pathological stages in the Pre-DM group was tested, and the trend of increasing the probability of prediction in different pathological periods was the lowest among the HbA1c increase type 5.6-6.4% (median probability of disease) 0.298), slightly elevated in simple IGT (iIGT) (median probability of disease is 0.398), highest in binding IFG/IGT (median probability of disease is 0.494), the RF model It can be used to reflect the molecular typing characteristics of different pre-diabetic pathological stages.
  • the sample comprises at least one of blood, skin, hair, saliva and muscle.
  • the sample is a plasma lipid extract.
  • the content of the marker is determined by a method of liquid chromatography-mass spectrometry.
  • liquid chromatography analysis is carried out under the following conditions:
  • the mass spectrometry was carried out under the following conditions:
  • ESI ion source data collected in positive/negative ion mode, mass range m/z 50 ⁇ 2000, s/times per second, ion source temperature is 120°C, desolvation temperature is 600°C, mobile phase gas is nitrogen, gas flow is 800L /h, capillary pressure and cone voltage were 2.0 KV (+) / 1.5 KV (-) and 30 V, respectively, using leucine enkephalin as the locking mass.
  • the invention adopts an analytical method using liquid chromatography-mass spectrometry to analyze the lipid metabolite spectrum of plasma samples, and uses a random forest discriminant model to identify type 2 diabetes groups and non-diabetic groups based on 28 metabolite markers (including pre-diabetes and Glucose tolerance group), obtain the probability of disease, use it for risk assessment, diagnosis, early diagnosis of type 2 diabetes, and find potential drug targets.
  • metabolite markers including pre-diabetes and Glucose tolerance group
  • the metabolite profile is processed to obtain raw data, which is preferably data such as peak height or peak area of each peak as well as mass and retention time.
  • peak detection and peak matching are performed on the raw data, preferably using the Progenesis QI software for peak detection and peak matching.
  • the types of mass spectrometry are roughly classified into ion traps, quadrupoles, electrostatic field orbital ion traps, and time-of-flight mass spectrometers.
  • the mass deviations of these four types of analyzers are 0.2 amu, 0.4 amu, 3 ppm, and 5 ppm, respectively.
  • the experimental results obtained by the present invention are time-of-flight mass spectrometry, and are therefore applicable to all mass spectrometers using time-of-flight mass spectrometry as mass analyzers, including Waters' TQS, TQD, and the like.
  • the relative intensity of the biomarker is expressed by the peak intensity of the mass spectrum. the amount.
  • the mass to charge ratio and retention time have the meanings well known in the art.
  • the atomic mass units and retention times of the biomarkers in the biomarker composition of the present invention fluctuate within a certain range;
  • the atomic mass unit may fluctuate within a range of ⁇ 10 ppm, for example ⁇ 5 ppm, for example ⁇ 3 ppm, which may fluctuate within a range of ⁇ 60 s, for example ⁇ 45 s, for example ⁇ 30 s, for example ⁇ 15 s.
  • the training set and verification set have the meanings well known in the art.
  • the training set refers to a data set comprising the content of each biomarker in a sample of a type 2 diabetes and a sample of a non-diabetic subject comprising a certain number of samples.
  • the verification set is an independent data set used to test the performance of the training set.
  • a training set of biomarkers for type 2 diabetic subjects and non-diabetic subjects is constructed, and based on this, the biomarker content values of the samples to be tested are evaluated.
  • the data of the training set is as shown in Table 1.
  • the non-diabetic subject is a glucose tolerant normal subject and/or a pre-diabetic subject.
  • the subject can be a human.
  • the mass-to-charge ratio unit is amu, and amu refers to the atomic mass unit, also known as Dalton (Daton, Da, D), which is a unit for measuring the mass of an atom or a molecule, which is defined as carbon. 1/12 of 12 atomic mass.
  • the allowable mass resolution (error) for the identification of metabolites of the present invention is 10 ppm, ppm is one millionth.
  • the normal content range (absolute value) of each biomarker in the sample can be derived using sample detection and calculation methods well known in the art. In this way, when using mass spectrometry When other methods are used to detect the content of the biomarker (for example, by using an antibody and an ELISA method, etc.), the absolute value of the detected biomarker content can be compared with the normal content value, and optionally, the statistics can also be combined. Study methods to obtain the risk assessment, diagnosis, etc. of type 2 diabetes.
  • biomarkers are endogenous and/or foodborne compounds present in the human body.
  • the metabolite profile of the subject's plasma is analyzed by the method of the invention, preferably, the lipid metabolite of the plasma is analyzed, the mass value in the metabolite profile and the retention time indicate the presence of the corresponding biomarker and Corresponding position in the metabolite spectrum.
  • the biomarkers of the type 2 diabetes population exhibit a range of content values in their metabolite profiles.
  • the invention provides a system for diagnosing type 2 diabetes.
  • a system for diagnosing type 2 diabetes comprising: an assay device 100 for determining a content of the marker in a sample of a subject to be diagnosed; a determining device 200, the determining device 200 for The content of the marker obtained in the assay device determines the diagnosis result of the subject.
  • the type 2 diabetes marker combined with the metabolite data of biomarkers in type 2 diabetes and non-diabetic population can accurately assess the risk of early diagnosis of type 2 diabetes.
  • the system has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.
  • the sample is a plasma lipid extract.
  • the system further includes an extracting device 300 connected to the measuring device 100 for extracting plasma lipids of a subject to be diagnosed.
  • the assay device 100 includes a liquid chromatography analysis unit 110 and a mass spectrometry unit 120.
  • the invention proposes a kit.
  • the kit comprises a reagent for detecting at least one selected from the group consisting of LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric Acid, N-(3-oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38) :1), PI (O-38: 2), LysoPC (18:1), PS (38:1), LysoPC (24:1 (15Z)), Carotenes, and 5,6-dichloro-tetradecanoic acid.
  • the inventors found a significant difference in the plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetic group by comparing and analyzing the metabolite profiles of the type 2 diabetes group and the non-diabetic group. There were significant differences in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetic group.
  • the kit according to the embodiment of the present invention can accurately assess the risk of early diagnosis and early diagnosis of type 2 diabetes, and has the characteristics of non-invasive, convenient and rapid, and the kit has high sensitivity and good specificity.
  • the invention provides the use of an agent for the preparation of a kit for diagnosing a type 2 diabetes marker, the reagent for detecting comprising at least one selected from the group consisting of: LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyric acid, N-(3-oxo-octanoyl)-homoserine lactone, PC (42:8), TG (62:9), LysoPC (P-16:0), LysoPC (18:2), PI (P-38:1), PI (O-38:2), LysoPC (18:1), PS (38:1), LysoPC (24 : 1 (15Z)), Carotenes and 5,6-dichloro-tetradecanoic acid.
  • LysoPC (18:0
  • Hydroxybutyrylcarnitine 3-oxo-4-pentenoic acid
  • Ajoene Hydroxybutyric acid
  • the inventors found that the plasma lipid metabolite profiles of the above-mentioned related biomarkers in the type 2 diabetes group and the non-diabetic group existed by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetic group. obvious difference.
  • the kit prepared by the above reagent can accurately diagnose and diagnose the risk of type 2 diabetes, and has the characteristics of non-invasive, convenient and quick, and the kit has high sensitivity and specificity.
  • Plasma samples of type 2 diabetes and non-diabetic subjects of the present invention are from the Suzhou Center for Disease Control and Prevention.
  • iIGT simple IGT
  • IPA isopropanol
  • ESI ion source data acquisition in positive/negative ion mode, mass range m/z 50 to 2000, (s) per second.
  • the ion source temperature is 120 ° C
  • the desolvation temperature is 600 ° C
  • the mobile phase gas is nitrogen
  • the gas flow rate is 800 L / h
  • the capillary pressure and cone voltage are 2.0 KV (+) / 1.5 KV (-), respectively.
  • the raw data was processed by liquid chromatography mass spectrometry using commercial software Progenesis QI 2.0 software (Nonlinear Dynamics, Newcastle, UK), including raw data input, adduct ion selection, peak alignment, detection, deconvolution, low quality. Peak filtration, data noise correction, peak identification, and normalization of peak intensity are relatively quantitative.
  • the specific analysis parameters are: 1) Select [M+H] + , [M+H-H2O] + , [M+Na] + and [M+K] + cation ionization mode adduct; select [MH] - Anion ionization mode adduct; 2) ion peak retention time is 0.5-9min; 3) peak width is 1-30s; 4) maximum allowable error of parent ion mass is 10ppm; 5) maximum allowable mass of theoretical ion fragment of parent ion The error is 10 ppm, and the above strict parameters are used to improve the accuracy of metabolite identification.
  • the peak intensity of the identification was normalized using MetaX software.
  • Peaks appearing in less than 50% QC samples or below 80% of plasma test samples are considered as low quality peaks and removed; the missing values are filled in the samples using the nearest neighbor rule.
  • PCA Principal components analysis
  • QC-RLSC quality control-based robust LOESS signal correction
  • the lysophospholipid retention time range is 0.5-4 minutes, including lysophosphatidylethanolamine (LysoPC); lysophosphatidylethanolamine (LysoPE); lysophosphatidylglycerol (lysophosphatidylglycerol, LysoPG); lysophosphatidylserine (LysoPS); lysophosphatidic acid (LysoPA) and lysophosphatidylinositol (LysoPI); 2) sphingomyelin for retention time 3-8.1 minutes, including sphingomyelin (sphingomyelin, SM), ceramide (Cer), lactosylceramide (LacCer), glucosylceramide (GluCer) and galactosylceramide (GalCer); 3) retention time 4-7.8 minutes Is phosphatid
  • metabolites that match the aliphatic compounds in the LIPID MAPS, LipidBlast database, or HMDB database molecular framework levels are classified as lipids or lipid analogs.
  • the target metabolites selected in the subsequent analysis will be identified by data-dependent tandem mass spectrometry (DDA) in combination with the standard, and the metabolites will be classified according to the Metabolomics Standards Program (MSI) standard.
  • DDA data-dependent tandem mass spectrometry
  • MSI Metabolomics Standards Program
  • the present invention uses a random forest classifier to screen biomarkers for disease risk prediction modeling of NGT and T2D populations, and uses independent untrained populations to complete the prediction model. verification.
  • the specific approach is as follows: 140 samples (70 NGT and 70 T2D) were randomly selected from the NGT and T2D population (91 NGT, 106 T2D) as the training set, and the remaining samples were used as the validation set. All 12,000 metabolites were input into a random forest classifier, and the test set was subjected to 5 10-fold cross-validation, 10 replicates, and the relative intensity of the metabolites screened by the RF model was used to calculate the risk of T2D for each individual, and the subjects were drawn.
  • the receiver operation characteristic (ROC) curve is calculated, and the area under the curve (AUC) is calculated as the parameter evaluation parameter of the discriminant model.
  • the combination number of markers was ⁇ 30, and the combination with the best efficacy was the combination of the present invention.
  • the selection frequency of each metabolite is output in the model, and the higher the frequency, the higher the importance of the metabolite to discriminate between T2D and NGT.
  • the model is verified using an independent population, and the probability of disease (RP) ⁇ 0.5 predicts that the individual has a risk of developing type 2 diabetes or has type 2 diabetes.
  • the inventors further tested the risk of disease risk in patients with different pathological stages in the Pre-DM group (Fig. 7).
  • the results also showed a trend of increasing the probability of prediction in different pathological periods, in the HbA1c increase type 5.6-6.4%.
  • the lowest (median probability of disease is 0.298), slightly elevated in simple IGT (iIGT) (median probability of disease is 0.398), highest in combined IFG/IGT (probability of disease)
  • the number of bits is 0.494), indicating that the RF model can also be used to reflect the molecular typing characteristics of different pre-diabetic pathological stages.
  • the RF classifier contains a total of 28 potential biomarkers, as shown in Table 3.
  • Table 3 lists the details of the above 28 potential biomarkers (based on the 273 population sample above), including retention time (RT), parent ion (m/z), best matching compound, P value, change factor, VIP value.
  • Table 4 lists the AUC values for 28 metabolites to identify T2D and NGT, T2D and non-T2D (including Pre-DM and NGT), T2D and Pre-DM, and Pre-DM and NGT, respectively (based on the 273 population sample above) .
  • Table 5 lists the detailed information of 28 biomarkers in the T2D, NGT, and Pre-DM groups (based on the 273 population samples above).
  • LysoPC obtained from Avanti Polar Lipids Inc (Alabaster, AL), catalog number: 855775P
  • markers 2 and 7 m/z 508.3406
  • m/z 508.3404 were identified as LysoPC (18:0).
  • markers 2 and 7 were identified as LysoPC (18:0).
  • markers 2 and 7 were identified as LysoPC (18:0).
  • markers 2 and 7 were identified as LysoPC (18:0).
  • AUC identification capacity
  • Table 6 predicts the risk of type 2 diabetes or the risk of type 2 diabetes in T2D and NGT and T2D and non-T2D samples based on four metabolite markers
  • biomarkers disclosed in the present invention have high accuracy and specificity, and have good prospects for development as a diagnostic method, thereby assessing, diagnosing, and early diagnosis of type 2 diabetes, and searching for potential drugs.
  • the target provides the basis.

Abstract

L'invention concerne un marqueur du diabète de type 2, comprenant au moins un matériau choisi parmi les suivants : LysoPC(18:0), hydroxybutyrylcarnitine, acide 3-oxo-4-penténoïque, ajoène, acide hydroxybutyrique, N-(3-oxo-octanoyl)-homosérine lactone, PC(42:8), TG(62:9), LysoPC(P-16:0), LysoPC(18:2), PI(P-38:1), PI(O-38:2), LysoPC(18:1), PS(38:1), LysoPC(24:1(15Z)), carotènes et acide 5,6-dichloro-tétradécanoïque.
PCT/CN2017/080954 2017-04-18 2017-04-18 Marqueur du diabète de type 2 et utilisation associée WO2018191866A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114354827A (zh) * 2022-03-21 2022-04-15 天津云检医疗器械有限公司 代谢标志物及其在制备2型糖尿病的风险预测试剂盒中的应用和试剂盒
CN115266972A (zh) * 2022-07-19 2022-11-01 四川格林豪斯生物科技有限公司 组合标志物及筛选方法、诊断试剂或者试剂盒及其应用
US11780331B2 (en) 2020-06-10 2023-10-10 Mercedes-Benz Group AG Methods and systems for displaying visual content on a motor vehicle and method for providing a motor vehicle

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112730638B (zh) * 2020-11-25 2022-07-29 首都医科大学附属北京朝阳医院 糖尿病合并心梗代谢标志物、检测试剂及试剂盒

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201438187U (zh) * 2009-07-28 2010-04-14 北京怡成生物电子技术有限公司 一种糖尿病检测分析装置
CN104769434A (zh) * 2012-08-13 2015-07-08 亥姆霍兹慕尼黑中心德国研究健康与环境有限责任公司 用于2型糖尿病的生物标志物
CN105096225A (zh) * 2014-05-13 2015-11-25 深圳华大基因研究院 辅助疾病诊疗的分析系统、装置及方法
CN105166085A (zh) * 2015-07-08 2015-12-23 蚌埠市江淮粮油有限公司 一种抗氧化保健米糠油及其制备方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7648825B2 (en) * 2003-06-20 2010-01-19 University Of Florida Research Foundation, Inc. Biomarkers for differentiating between type 1 and type 2 diabetes
JP2017516085A (ja) * 2014-04-08 2017-06-15 メタボルン インコーポレーティッド 疾患診断および健康評価のための個々の対象の低分子生化学プロファイリングの方法
WO2017003166A1 (fr) * 2015-06-29 2017-01-05 고려대학교 산학협력단 Composition pour le diagnostic précoce du diabète par analyse métabolomique

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201438187U (zh) * 2009-07-28 2010-04-14 北京怡成生物电子技术有限公司 一种糖尿病检测分析装置
CN104769434A (zh) * 2012-08-13 2015-07-08 亥姆霍兹慕尼黑中心德国研究健康与环境有限责任公司 用于2型糖尿病的生物标志物
CN105096225A (zh) * 2014-05-13 2015-11-25 深圳华大基因研究院 辅助疾病诊疗的分析系统、装置及方法
CN105166085A (zh) * 2015-07-08 2015-12-23 蚌埠市江淮粮油有限公司 一种抗氧化保健米糠油及其制备方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
NING, MENG ET AL.: "Metabolomics Study of Rat Serum of Early stage type 2 Diabetic Model", BIOMEDICAL ENGINEERING AND CLINICAL MEDICINE, vol. 20, no. 2, 31 March 2016 (2016-03-31), pages 131 - 132 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11780331B2 (en) 2020-06-10 2023-10-10 Mercedes-Benz Group AG Methods and systems for displaying visual content on a motor vehicle and method for providing a motor vehicle
CN114354827A (zh) * 2022-03-21 2022-04-15 天津云检医疗器械有限公司 代谢标志物及其在制备2型糖尿病的风险预测试剂盒中的应用和试剂盒
CN115266972A (zh) * 2022-07-19 2022-11-01 四川格林豪斯生物科技有限公司 组合标志物及筛选方法、诊断试剂或者试剂盒及其应用

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