WO2018191866A1 - Type 2 diabetes marker, and use thereof - Google Patents

Abstract

A type 2 diabetes marker, comprising at least one material selected from the following: 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.

Description

Type 2 diabetes markers and their uses

Priority information

no

Technical field

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 The use of a cartridge and a reagent in a kit for the diagnosis of a type 2 diabetes marker.

Background technique

Type 2 diabetes (T2D) 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. Two recent national epidemiological studies have shown that China has become the country with the most diabetes patients in the world. The data show that the prevalence of total diabetes in Chinese adults rose from 9.7% in 2007 (World Health Organization (WHO) standards in 1999) to 11.6% in 2010 (2010 American Diabetes Association (ADA) standards). In addition, according to these two different screening criteria, the proportion of pre-diabetes adults increased from 15.5% to 50.1%.

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). Impaired glucose tolerance (IGT) and diabetes. Impaired fasting glucose and impaired glucose tolerance are collectively referred to as Prediabetes (Pre-DM). The 2010 ADA standard uses glycosylated hemoglobin (Hemo A1c, HbA1c) as a diagnostic criterion for diabetes. One. 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.

Therefore, develop a new diagnostic method for disease risk assessment, diagnosis, early diagnosis, pathological staging, It is of great significance.

Summary of the invention

This application is based on the discovery and recognition of the following facts and issues by the inventors:

In view of the shortcomings of the existing diagnosis methods for type 2 diabetes that fail to achieve early warning, the inability to predict the onset and development of type 2 diabetes, 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.

In a first aspect of the invention, the invention proposes a panel of type 2 diabetes markers. According to an embodiment of the present invention, 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. By comparing and analyzing the lipid metabolite profiles of type 2 diabetes and non-diabetic groups, the inventors found significant differences in plasma lipid metabolite profiles between type 2 diabetes and non-diabetic groups, and screened for related organisms. landmark. As a training set, 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.

According to an embodiment of the present invention, the above type 2 diabetes marker may further include at least one of the following additional technical features:

According to an embodiment of the invention, the type 2 diabetes marker further comprises at least one of the compounds having the following parameters:

The parameters were obtained in mass spectrometry with 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.

In a second aspect of the invention, the invention provides a method of diagnosing type 2 diabetes. According to an embodiment of the invention, 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. Compared with the currently used diagnostic methods, the method has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.

In a third aspect of the invention, the invention provides a system for diagnosing type 2 diabetes. According to an embodiment of the present invention, 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. As a training set, 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.

In a fourth aspect of the invention, the invention proposes a kit. According to an embodiment of the invention, 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. As mentioned above, 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.

In a fifth aspect of the invention, 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. As described above, the inventors found that the above-mentioned related biomarkers have significant plasma lipid metabolite profiles in 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. difference. 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.

DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

1 is a system for diagnosing type 2 diabetes in accordance with an embodiment of the present invention;

2 is a system for diagnosing type 2 diabetes in accordance with an embodiment of the present invention;

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). 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 model was trained with the training set samples (NGT=70, T2D=70) for the relative ionic strength of all metabolites detected in the anion-cation acquisition mode. 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;

Figures 7 to 9 show the ROC and AUC of the validation set based on a random forest model (28 metabolite markers), Figure 7 shows NGT and T2D (n = 21 and 36), and Figure 8 shows Pre-DM and T2D. (n=76 and 36), Figure 9 shows NGT and Pre-DM (n=21 and 76);

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 12 shows the peak time and LC-MS/MS spectra of the biomarker m/z 508.3406 (RT = 1.83 min) and the standard LysoPC (18:0);

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).

Detailed description of the invention

The embodiments of the present invention are described in detail below, and the examples of the embodiments are illustrated in the drawings, wherein the same or similar reference numerals are used to refer to the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are intended to be illustrative of the invention and are not to be construed as limiting.

It should be noted that the terms "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.

Type 2 diabetes marker

In a first aspect of the invention, the invention proposes a panel of type 2 diabetes markers. According to an embodiment of the present invention, 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. By comparing and analyzing the lipid metabolite profiles of type 2 diabetes and non-diabetic groups, the inventors found significant differences in plasma lipid metabolite profiles between type 2 diabetes and non-diabetic groups, and screened for related organisms. landmark. As a training set, 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.

According to a particular embodiment of the invention, the type 2 diabetes marker further comprises at least one of the compounds having the following parameters:

The parameters were obtained in mass spectrometry with 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 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.

Method for diagnosing type 2 diabetes

In a second aspect of the invention, the invention provides a method of diagnosing type 2 diabetes. According to an embodiment of the invention, 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. Compared with the currently used diagnostic methods, the method has the characteristics of non-invasive, convenient and fast, and has high sensitivity and good specificity.

According to a specific embodiment of the present invention, 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. According to a particular embodiment of the invention, the predetermined threshold is 0.5. According to a specific example of the present invention, 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. Specifically, according to the random forest model, 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.

According to a particular embodiment of the invention, the sample comprises at least one of blood, skin, hair, saliva and muscle. Specifically, the sample is a plasma lipid extract.

According to a specific embodiment of the present invention, in the step (1), the content of the marker is determined by a method of liquid chromatography-mass spectrometry.

Specifically, the liquid chromatography analysis is carried out under the following conditions:

Ultra Performance Liquid Chromatograph ACQUITY UPLC (Waters, Manchester, USA),

Column: Waters CSH C18 column (100 mm x 2.1 mm, 1.7 μm);

Mobile phase A: acetonitrile: H ₂ O = 60: 40, 0.1% formic acid, 10 mM ammonium formate;

Mobile phase B: isopropanol: ACN = 90: 10, 0.1% formic acid, 10 mM ammonium formate;

Gradient elution procedure: 2 min, 40% B linear gradient increased to 43% B; 0.1 min, increased to 50% B; 3.9 min, increased to 54% B; 0.1 min, increased to 70% B; 1.9 min, gradient increased Up to 99% B; 0.1 min, return to 40% B, equilibrate the column for 1.9 min before each injection;

Flow rate: 0.4 mL/min; injection volume 10 μL.

Specifically, 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.

In a specific embodiment of the invention, 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.

In one embodiment of the invention, 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.

In an embodiment of the invention, the relative intensity of the biomarker is expressed by the peak intensity of the mass spectrum. the amount.

In the present invention, the mass to charge ratio and retention time have the meanings well known in the art.

It is well known to those skilled in the art that when different liquid chromatography mass spectrometry devices and different detection methods are employed, the atomic mass units and retention times of the biomarkers in the biomarker composition of the present invention fluctuate within a certain range; Wherein 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.

In the present invention, the use of random forest models and ROC curves is well known in the art (Drogan D, Dunn WB, Lin W, Buijsse B, Schulze MB, Langenberg C, Brown M, Floegel a., Dietrich S, Rolandsson O). , Wedge DC, Goodacre R, Forouhi NG, Sharp SJ, Spranger J, Wareham NJ, Boeing H: Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2Diabetes Mellitus in a Prospective, Nested Case Control Study. Clin Chem 2015, 61:487- 497.;Mihalik SJ,Michaliszyn SF,de las Heras J,Bacha F,Lee S,Chace DH,DeJesus VR,Vockley J,Arslanian SA:Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2diabetes:evidence For enhanced mitochondrial oxidation. Diabetes Care 2012, 35: 605-611., the entire disclosure of which is hereby incorporated by reference in its entirety in its entirety in the entire disclosure.

In the present invention, the training set and verification set have the meanings well known in the art. In an embodiment of the invention, 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.

In the present invention, 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.

In the present invention, the data of the training set is as shown in Table 1.

In the present invention, the non-diabetic subject is a glucose tolerant normal subject and/or a pre-diabetic subject.

In the present invention, the subject can be a human.

In the present invention, 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. For example, the mass of a metabolite A isotope = 118 Da, the mass measured by the instrument = 118.001 Da; the deviation = 0.001 amu; the error [deviation / accurate mass × 10 ⁶ ] = 8.47 ppm.

Those skilled in the art will recognize that when further expanding the sample size, 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.

Without wishing to be bound by any theory, the inventors indicate that these 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. At the same time, the biomarkers of the type 2 diabetes population exhibit a range of content values in their metabolite profiles.

System for diagnosing type 2 diabetes

In a third aspect of the invention, the invention provides a system for diagnosing type 2 diabetes. According to an embodiment of the present invention, referring to FIG. 1, 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. As a training set, 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.

According to a particular embodiment of the invention, the sample is a plasma lipid extract. Specifically, referring to FIG. 2, the system further includes an extracting device 300 connected to the measuring device 100 for extracting plasma lipids of a subject to be diagnosed.

According to still another embodiment of the present invention, referring to FIG. 3, the assay device 100 includes a liquid chromatography analysis unit 110 and a mass spectrometry unit 120.

Kit

In a fourth aspect of the invention, the invention proposes a kit. According to an embodiment of the invention, 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. As mentioned above, 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.

Use of reagents in preparation kits

In a fifth aspect of the invention, 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. As described above, 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.

The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, however, the following examples are intended to illustrate the invention and are not intended to limit the scope of the invention. Those who do not specify the specific conditions in the examples are carried out according to the conventional conditions or the conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are conventional products that can be obtained commercially.

Plasma samples of type 2 diabetes and non-diabetic subjects of the present invention are from the Suzhou Center for Disease Control and Prevention.

Example 1

1.1 Sample collection: The fasting morning blood plasma of the volunteers was collected and immediately stored in a -80 ° C low temperature refrigerator. A total of 98 plasma samples were collected from the normal glucose tolerance group (NGT). A total of 81 plasma samples were collected from the pre-diabetes group (Pre-DM) and 114 plasma samples were collected from the type 2 diabetes group (T2D). Among them, the Pre-DM group can be further divided into four subgroups: a) HbA1c increased type _5.7-6.4% (WHO-2011 diagnostic criteria, HbA1c ranged from 5.7-6.4% and FPG<6.1mmol/L and 2h-PG< 7.8mmol/L, n=15); b) simple IFG (FPG between 6.1-7.0mmol/L and 2h-PG<7.8mmol/L, n=7); c) simple IGT (referred to as iIGT, FPG) <6.1 mmol/L and 2h-PG between 7.8 and 11.0 mmol/L, n=35); d) Binding IFG/IGT (FPG between 6.1-7.0 mmol/l and 2h-PG between 7.8- 11.0 mmol/L, n=24).

1.2 Lipid extraction: Plasma samples were thawed on ice and lipids were extracted using isopropanol (IPA). Briefly, 40 μL of plasma was extracted with 120 μL of pre-cooled IPA, vortexed for 1 min, incubated for 10 min at room temperature, and then the extract mixture was placed at -20 ° C overnight. Centrifuge at 4000g for 20min, transfer the supernatant to a new 96-well plate with IPA/acetonitrile (ACN) /H2O (2:1:1, V:V:V) is diluted 1:10, the sample name and positive and negative ions are marked with a marker, and stored at -80 °C before analysis by liquid chromatography-mass spectrometer. spare. In addition, 10 ul of mixing was taken as a QC quality control sample in each sample to be tested.

1.3 Liquid chromatography-mass spectrometry analysis

equipment

Ultra Performance Liquid Chromatograph ACQUITY UPLC (Waters, Manchester, USA), Mass Spectrometer Waters Xevo ^TM G2-XS Qtof (Waters, USA)

Chromatographic conditions

Column: Waters CSH C18 column (100 mm x 2.1 mm, 1.7 μm); mobile phase A: ACN (acetonitrile): H ₂ O = 60: 40, v/v, 0.1% formic acid (FA), 10 mM ammonium formate; Phase B: IPA (isopropanol): ACN = 90: 10, v/v, 0.1% FA, 10 mM ammonium formate.

Gradient elution procedure: 2 min, 40% B linear gradient increased to 43% B; 0.1 min, increased to 50% B; 3.9 min, increased to 54% B; 0.1 min, increased to 70% B; 1.9 min, gradient increased Up to 99% B; 0.1 min, return to 40% B, equilibrate the column for 1.9 min before each injection. Flow rate: 0.4 mL/min; injection volume 10 μL.

Mass spectrometry condition

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, and the capillary pressure and cone voltage are 2.0 KV (+) / 1.5 KV (-), respectively. And 30V. The leucine enkephalin (molecular weight (MW) = 555.62; 200 pg/μL, dissolved in 1:1 ACN:H2O) was used as the locking mass and calibrated with a 0.5 mM sodium formate solution. All samples were randomly ordered by first injecting 10 QC samples to adjust the column, followed by injection of 1-2 QC samples per 10 samples to investigate data repeatability.

1.4 Data Processing

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. After the above analysis, a total of 12,000 high-quality metabolites were produced, of which 923 were identified by anion mode and 11,077 by cationic mode. PCA (Principal components analysis) analysis was used to detect and remove low-quality outlier samples in anion-cation mode, and 20 parts of outlier samples were removed (including 7 cases of NGT, 8 cases of T2D and 5 cases of Pre-DM). For the above multi-rigid filtered high-quality data sets, QC-RLSC (quality control-based robust LOESS signal correction) is used to correct the difference caused by signal fluctuations between batches. After calibration, the characteristic peaks with a relative standard deviation of >30% were removed. Using Progenesis Metascope via HMDB 3.6 (http://www.hmdb.ca/), LIPID MAPS (http://www.lipidmaps.org/) and LipidBlast (http://fiehnlab.ucdavis.edu/projects/LipidBlast) The three database alignments annotated the metabolites, and the maximum allowable error for both the parent ion mass and the theoretical ion fragment mass was 10 ppm. Metabolites meeting the above matching conditions were filtered according to the operating guidelines provided by Waters Corporation for the CSHC ₁₈ UPLC system used in this experiment, based on the retention time characteristics of the different lipids. Among them, in the cation ionization mode: 1) 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 phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylserine, phosphatidic acid, phosphatidylinositol; 4) long-chain ester retention time in the range of 7.8-9.5 min is Diacylglycerol (DG), Triacylglycerol (TG), cholesterol ester (Chester), anion Mode from: 1) class of lysophospholipids and free fatty acids with a retention time of 0.5-4 minutes; 2) Phospholipids 4-9 minutes. After screening by retention time, 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.

1.5 Metabolic profiling and potential biomarkers

1.5.1 univariate comparative analysis

First, 1590 metabolites were screened by Kruskal-Wallis test, and the relative intensities of the three groups were significantly different (p<0.05, block Kruskal-Wallis test). Further comparison of differential metabolites between the two groups on the basis of differential metabolites (p<0.05, Dunn's post-test), such as the Wayne diagram (Figure 4), shows the number and type of differential metabolites between the different groups (p< 0.05, post-test) differences: The number of differential metabolites between the NGT group and the T2D group was the largest, followed by the NGT group and the Pre-DM group, and the differential metabolites between the Pre-DM group and the T2D group were relatively least.

1.5.2 Using Random Forest (ROC/AUC) to Screen Potential Biomarkers for T2D Development

In order to further screen plasma lipid metabolites closely related to diseases, 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. Among the 10 repeated results, 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 results show that the RF classifier obtained by the present invention contains 28 metabolites (Fig. 5, Tables 1-1, 1-2, 1-3, and the metabolite number are the same as Table 3), and the discriminant efficacy of the above training set samples is: AUC=90.23%, 95% confidence interval CI=84.95-95.52% (Fig. 6), the results show that the metabolite combination obtained by this model can be used as a potential biomarker to distinguish between T2D and NGT.

1.5.3 Biomarkers obtained by using validation set data verification screening

In the present invention, 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.

Based on this model:

For independent verification set 1 (T2D=36 and NGT=21), the model's discriminant AUC=86.24% (95% CI=76.05-96.43%); accuracy=80.70% (Fig. 7, Table 2);

For independent verification set 2 (T2D=36 and Pre-DM=76), the model's discriminant AUC=71.77% (95% CI= 61.95-81.58%); accuracy = 66.07% (Figure 8, Table 2);

For independent verification set 3 (Pre-DM=76 and NGT=21), the model's discriminant AUC=68.08% (95% CI=54.87-81.28%), accuracy=63.91% (Fig. 9), Table 2.

The results of 3 batches of validation showed that the model can effectively distinguish between diabetic and normal glucose tolerance patients; at the same time, it can differentiate between diabetes and pre-diabetes; pre-diabetes and normal glucose tolerance.

According to the random forest model, 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).

The potential biomarkers of 28 random forests were identified by further data-dependent mass spectrometry (DDA). At MSI level 2, four compounds were identified (Figure 11-14), namely hydroxybutyrylcarnitine ( Marker 3, m/z 248.1511), LysoPC (18:0) ( markers 2 and 7, m/z 508.3406 and m/z 508.3404), LysoPC (18:1) (marker 19, m/z 506.3249) , LysoPC (18:2) (marker 17, m/z 504.3093) (Table 3). Among them, the relative plasma content of hydroxybutyrylcarnitine in the population increased significantly with the development of the disease, which was the lowest in the NGT group, significantly higher in the Pre-DM group than in the NGT group, and highest in the T2D group. Pre-DM group (Table 5). The LysoPC (18:0), LysoPC (18:1), LysoPC (18:2) three types of lysophospholipids were not significantly different in the NGT group and the Pre-DM group, but were significantly higher than the T2D group (Table). 5). Further, by reference to the standard LysoPC (18:0) (purchased from Avanti Polar Lipids Inc (Alabaster, AL), catalog number: 855775P), the alignment and retention time versus plasma samples, markers 2 and 7 (m/z 508.3406) And m/z 508.3404) were identified as LysoPC (18:0). Four potential biomarker combinations were able to significantly identify T2D and NGT as well as T2D and non-T2D (based on the above-mentioned validation set population samples) with an identification capacity (AUC) of 0.784 (95% CI=0.703-0.849), respectively (Figure 15, Table 6). And 0.723 (95% CI = 0.654-0.771) (Figure 16, Table 6) (Note: Modeling was performed using only m/z 508.3406 for marker 2).

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

The above results indicate that the 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.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means a specific feature described in connection with the embodiment or example. A structure, material or feature is included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms is not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, various embodiments or examples described in the specification, as well as features of various embodiments or examples, may be combined and combined.

Although the embodiments of the present invention have been shown and described, it is understood that the above-described embodiments are illustrative and are not to be construed as limiting the scope of the invention. The embodiments are subject to variations, modifications, substitutions and variations.

Claims (16)

1. A group of type 2 diabetes markers, characterized by 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.
2. The type 2 diabetes marker according to claim 1, further comprising at least one of the compounds having the following parameters:

   

   The parameters were obtained in mass spectrometry with 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.
3. A method for diagnosing type 2 diabetes, comprising:

   (1) determining the relative content of the marker according to claim 1 or 2 in the sample of the object to be diagnosed;

   (2) The diagnosis result of the subject is determined based on the relative content of the marker obtained in the step (1).
4. The method according to claim 2, wherein the diagnosis result of the object is determined based on the relative content of the marker obtained in the step (1) by:

   The disease risk value of the marker model is above a predetermined threshold and is an indication that the subject has type 2 diabetes.
5. The method of claim 3 wherein said predetermined threshold is 0.5.
6. The method of claim 4 wherein said sample comprises at least one of blood, skin, hair, saliva, and muscle.
7. The method of claim 6 wherein said sample is a plasma lipid extract.
8. The method according to claim 3, wherein in the step (1), the relative content of the marker is determined by a liquid chromatography-mass spectrometry analysis method.
9. The method according to claim 8, wherein said liquid chromatography is carried out under the following conditions:

   Ultra Performance Liquid Chromatograph ACQUITY UPLC (Waters, Manchester, USA),

   Column: Waters CSH C18 column (100 mm x 2.1 mm, 1.7 μm);

   Mobile phase A: acetonitrile: H ₂ O = 60: 40, 0.1% formic acid, 10 mM ammonium formate;

   Mobile phase B: isopropanol: ACN = 90: 10, 0.1% formic acid, 10 mM ammonium formate;

   Gradient elution procedure: 2 min, 40% B linear gradient increased to 43% B; 0.1 min, increased to 50% B; 3.9 min, increased to 54% B; 0.1 min, increased to 70% B; 1.9 min, gradient increased Up to 99% B; 0.1 min, return to 40% B, equilibrate the column for 1.9 min before each injection;

   Flow rate: 0.4 mL/min; injection volume 10 μL.
10. The method of claim 8 wherein said mass spectrometry is performed 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.
11. A system for diagnosing type 2 diabetes, comprising:

    An assay device for determining a relative content of the marker of claim 1 in a sample of the subject to be diagnosed;

    Determining means for determining a diagnosis result of the subject based on a relative content of the markers obtained in the assay device.
12. The system of claim 11 wherein said sample is a plasma lipid extract.
13. The system according to claim 12, further comprising: extracting means, said extracting device Connected to the assay device for extracting plasma lipids from the subject to be diagnosed.
14. The method according to claim 11, wherein said measuring means comprises a liquid chromatography analyzing unit and a mass spectrometry unit.
15. A kit characterized by comprising 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,

    Optionally, the reagent further comprises at least one of a compound for detecting a parameter having the following:

    

    The parameters were obtained in mass spectrometry with 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.
16. Use of an agent in 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,

    Optionally, the reagent further comprises at least one of a compound for detecting a parameter having the following:

    

    The parameters were obtained in mass spectrometry with 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.

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