CN110178035B - Type 2 diabetes marker and application thereof - Google Patents
Type 2 diabetes marker and application thereof Download PDFInfo
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- CN110178035B CN110178035B CN201780083287.0A CN201780083287A CN110178035B CN 110178035 B CN110178035 B CN 110178035B CN 201780083287 A CN201780083287 A CN 201780083287A CN 110178035 B CN110178035 B CN 110178035B
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- Investigating Or Analysing Biological Materials (AREA)
Abstract
A set of type 2 diabetes markers, said type 2 diabetes markers 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.
Description
PRIORITY INFORMATION
Is composed of
Technical Field
The invention relates to the field of biological detection, in particular to a type 2 diabetes marker and application thereof, and more particularly to a type 2 diabetes marker, a method for diagnosing type 2 diabetes, a system for diagnosing type 2 diabetes, a kit and application of a reagent in preparation of the kit, wherein the kit is used for diagnosing the type 2 diabetes marker.
Background
The current diagnosis of diabetes is primarily by venous plasma blood glucose level detection. The diagnostic criteria commonly used today are the WHO (1999) criteria and the ADA (2003) criteria. The WHO standard mainly divides glucose metabolism into normal blood glucose (IFG), impaired fasting glucose (igg), Impaired Glucose Tolerance (IGT) and diabetes mellitus through Fast Plasma Glucose (FPG) and 2-hour postprandial glucose (2 h-PG). the impaired fasting glucose and impaired glucose tolerance are collectively referred to as Pre-diabetes (Pre-DM), the ADA standard of year 2010 uses glycated hemoglobin (hemoglobin A1c, HbA1c) as one of diabetes diagnosis standards; in addition, a new diagnosis method is developed, which can help to classify the disease pathology and accurately diagnose the disease type, and provides ideas for the research of drug action targets, accurate medication, the research of pathogenesis and the like.
Therefore, the development of a new diagnosis method for disease risk assessment, diagnosis, early diagnosis and pathological staging is of great significance.
Disclosure of Invention
The present application is based on the discovery and recognition by the inventors of the following facts and problems:
aiming at the defects that the existing type 2 diabetes diagnosis method cannot achieve early warning, cannot predict the onset and development trend of type 2 diabetes and the like, the invention provides a biomarker combination (namely a biomarker composition) which can be used for type 2 diabetes diagnosis and disease risk assessment, and a type 2 diabetes diagnosis and disease risk assessment method, which can predict the onset and development trend of type 2 diabetes and are applied to disease pathological typing.
In a first aspect of the invention, the invention provides a set of type 2 diabetes markers. According to an embodiment of the 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 lipid molecules of the body are the basis of life activities, and the change of the state of diseases and the function of the body can inevitably cause the change of the metabolism of endogenous small molecules in the body. The inventors found that there is a significant difference between the plasma lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group, and screened to obtain the related biomarkers. The type 2 diabetes marker is combined with lipid metabolite spectrum data of biomarkers of type 2 diabetes populations and non-diabetes populations to serve as a training set, and disease risk assessment and early diagnosis of type 2 diabetes can be accurately carried out.
According to an embodiment of the present invention, the 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 table parameters:
the parameters were obtained in mass spectrometry with the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolventizing temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, leucine enkephalin is adopted as locking mass.
In a second aspect of the invention, a method of diagnosing type 2 diabetes is presented. According to an embodiment of the invention, the method comprises: (1) determining the relative content of the markers in a sample of a subject to be diagnosed; (2) determining a diagnostic result of the subject based on the marker content obtained in step (1). Compared with the conventional diagnosis method, the method has the characteristics of no wound, convenience, quickness, high sensitivity and good specificity.
In a third aspect of the invention, a system for diagnosing type 2 diabetes is presented. According to an embodiment of the invention, comprising: an assay device for determining the relative amount of the marker of claim 1 in a sample of a subject to be diagnosed; a determination means for determining a diagnostic result of the subject based on the relative amounts of the markers obtained in the determination means. The type 2 diabetes marker is combined with lipid metabolite spectrum data of biomarkers of type 2 diabetes populations and non-diabetes populations to serve as a training set, and disease risk assessment and early diagnosis can be accurately performed on type 2 diabetes. The system has the characteristics of no wound, convenience, quickness, high sensitivity and good specificity.
In a fourth aspect of the invention, a kit is provided. According to an embodiment of the invention, the kit comprises reagents 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 described above, the inventors found that there is a significant difference in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group through comparison and analysis of the lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group, and the above-mentioned related biomarkers have a significant difference in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group. The kit provided by the embodiment of the invention can be used for accurately evaluating the risk of the type 2 diabetes and carrying out early diagnosis on the detected individual, and has the characteristics of non-invasiveness, convenience and quickness, and the kit is high in sensitivity and good in specificity.
In a fifth aspect of the invention, the invention proposes the use of a reagent for the preparation of a kit for diagnosing a marker for type 2 diabetes, said reagent being used for the detection of a marker comprising at least one member 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 related biomarkers have a significant difference in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group by comparing and analyzing the metabolite profiles between the type 2 diabetes group and the non-diabetes group. The kit prepared by the reagent can accurately carry out disease risk assessment and early diagnosis on type 2 diabetes, has the characteristics of no wound, convenience and quickness, and has high sensitivity and good specificity.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a system for diagnosing type 2 diabetes according to an embodiment of the present invention;
FIG. 2 is a system for diagnosing type 2 diabetes according to an embodiment of the present invention;
FIG. 3 is a schematic view of the structure of an assay device according to an embodiment of the invention;
figure 4 shows a wien graph plotting significant metabolites compared two by two using the normal glucose tolerance group (NGT), Pre-diabetic group (Pre-DM), type 2 diabetes group (T2D). The number of significant metabolites from the 3 groups compared pairwise is depicted and their coincidences are shown (p < 0.05, Dunn's post test);
fig. 5 shows the error rate distribution of 5-fold cross validation with 10-fold in random forest classifier. The model was trained on the relative ionic strength of all metabolites detected in the zwitterion collection mode using training set samples (NGT 70, T2D 70). The black solid curve represents the average of 5 trials (dashed curve). The grey vertical bars represent the number of metabolites in the selected optimal combination;
FIG. 6 shows the Receiver Operating Curve (ROC) and area under the curve (AUC) for the training set based on the discrimination of T2D and NGT based on the random forest model (28 metabolite markers);
figures 7 to 9 show ROC and AUC for validation sets based on a random forest model (28 metabolite markers), with figure 7 for NGT and T2D (n ═ 21 and 36), figure 8 for Pre-DM and T2D (n ═ 76 and 36), and figure 9 for NGT and Pre-DM (n ═ 21 and 76);
FIG. 10 shows the prediction of 3 subgroups of the Pre-diabetes (Pre-DM), HbA1c, based on a random forest model (28 metabolite markers) 5.7-6.4％ The prevalence of increased, simple and bound IFG/IGT for the development of T2D;
FIG. 11 shows the LC-MS/MS spectrum and the putative chemical structure of biomarker m/z 248.1511;
figure 12 shows the time to peak and LC-MS/MS spectra for biomarker m/z 508.3406(RT ═ 1.83min) and the standard LysoPC (18: 0);
FIG. 13 shows the LC-MS/MS spectra and the putative chemical structures of biomarker m/z 506.3249;
FIG. 14 shows the LC-MS/MS spectrum and the putative chemical structure of biomarker m/z 504.3093;
FIG. 15 shows class 4 metabolite markers (derived from a random forest model) discriminates between T2D and NGT, Receiver Operating Curve (ROC) and area under the curve (AUC); and
figure 16 shows 4 metabolite markers (derived from random forest models) discriminating T2D and non-T2D, Receiver Operating Curve (ROC) and area under the curve (AUC).
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
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 relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. Further, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
In a first aspect of the invention, the invention provides a set of type 2 diabetes markers. According to an embodiment of the 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 lipid molecules of the body are the basis of life activities, and the change of the disease state and the body function can inevitably cause the change of the metabolism of endogenous small molecules in the body. The inventors found that there is a significant difference between the plasma lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group, and screened to obtain the related biomarkers. The type 2 diabetes marker is combined with lipid metabolite spectrum data of biomarkers of type 2 diabetes populations and non-diabetes populations to serve as a training set, and disease risk assessment and early diagnosis of type 2 diabetes can be accurately carried out.
According to a particular embodiment of the invention, said type 2 diabetes markers further comprise at least one of the compounds having the following table parameters:
the parameters were obtained in mass spectrometry with the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolvation temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, and leucine enkephalin is adopted as locking mass.
The inventors find that there is a significant difference between plasma lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group through comparison and analysis of lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group, and further screen to obtain the related biomarkers. The 28 type 2 diabetes markers obtained by screening by the inventor are combined with lipid metabolite spectrum data of biomarkers of type 2 diabetes people and non-diabetes people to serve as a training set, and disease risk assessment and early diagnosis can be further accurately carried out on type 2 diabetes.
Method for diagnosing type 2 diabetes
In a second aspect of the invention, a method of diagnosing type 2 diabetes is presented. According to an embodiment of the invention, the method comprises: (1) determining the relative content (relative ionic strength) of the above markers in a sample of a subject to be diagnosed; (2) determining a diagnostic result of the subject based on the relative amounts of the markers obtained in step (1). Compared with the conventional diagnosis method, the method has the characteristics of no wound, convenience, quickness, high sensitivity and good specificity.
According to a specific embodiment of the present invention, determining the diagnosis result of the subject based on the relative content of the markers obtained in step (1) is achieved by: a marker model having a disease risk value above a predetermined threshold is indicative of the subject having type 2 diabetes. According to a specific embodiment of the present invention, the predetermined threshold is 0.5. According to a specific example of the present invention, the risk of developing is calculated based on a model calculated jointly by 28 characteristic metabolites obtained by random forest screening, the risk of developing is higher than 0.5, namely, the indication of being determined to have type 2 diabetes. Specifically, according to a random forest model, the disease risk probability of the patients in different pathological stages in the Pre-DM group is checked, the trend of increasing prediction probability in different pathological stages is shown, and the type of the patients is increased in HbA1c 5.6-6.4％ The RF model can be used to reflect the molecular typing characteristics of different pre-diabetic pathologic phases, with the lowest median (median of disease probability 0.298), slightly elevated in simple IGT (IGT) (median of disease probability 0.398), and highest in binding IFG/IGT (median of disease probability 0.494).
According to a specific embodiment of the present invention, the sample comprises at least one of blood, skin, hair, saliva and muscle. In particular, the sample is a plasma lipid extract.
According to a specific embodiment of the present invention, in step (1), the content of the marker is determined by a method of liquid chromatography-mass spectrometry.
Specifically, the liquid chromatography analysis was performed under the following conditions:
ultra high performance liquid chromatograph ACQUITY UPLC (Waters, Manchester, USA),
a chromatographic column: waters CSH C18 column (100 mm. times.2.1 mm, 1.7 μm);
mobile phase A: acetonitrile H 2 O60: 40, 0.1% formic acid, 10mM ammonium formate;
mobile phase B: isopropyl alcohol ACN 90: 10, 0.1% formic acid, 10mM ammonium formate;
gradient elution procedure: 2min, 40% B linear gradient increased to 43% B; 0.1min, increasing to 50% B; 3.9min, increasing to 54% B; 0.1min, increasing to 70% B; 1.9min, gradient increased to 99% B; recovering to 40% B after 0.1min, and balancing the chromatographic column for 1.9min before each sample injection;
flow rate: 0.4 mL/min; the injection volume is 10. mu.L.
Specifically, the mass spectrometry is performed under the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolvation temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, and leucine enkephalin is adopted as locking mass.
The invention adopts an analysis method of liquid chromatography-mass spectrometry to analyze a lipid metabolite spectrum of a plasma sample, and based on 28 metabolite markers, a random forest discrimination model is used to discriminate a type 2 diabetes group and a non-diabetes group (including a diabetes prophase and a sugar tolerance normal group) to obtain a disease probability, so that the method is used for disease risk evaluation, diagnosis and early diagnosis of type 2 diabetes, and searching for potential drug targets.
In one embodiment of the invention, the metabolite profiles are processed to obtain raw data, preferably peak height or peak area of each peak, mass number and retention time.
In one embodiment of the invention, the raw data is subjected to peak detection and peak matching, preferably using Progenesis QI software.
The mass spectrometry types are roughly divided into four types, namely an ion trap, a quadrupole rod, an electrostatic field orbit ion trap and a time-of-flight mass spectrometer, and the mass deviation of the four types of analyzers is 0.2amu, 0.4amu, 3ppm and 5ppm respectively. The experimental result obtained by the invention is analyzed by the time-of-flight mass spectrum, so that the method is suitable for all mass spectrometry instruments which take the time-of-flight mass spectrum as a mass analyzer, including the TQS, the TQD and the like of Waters.
In the embodiment of the present invention, the relative content of the biomarker is represented by the peak area (peak intensity) of the mass spectrum.
In the present invention, the mass-to-charge ratio and the retention time have meanings well known in the art.
It is well known to those skilled in the art that the atomic mass units and retention times of the biomarkers in the biomarker compositions of the present invention may fluctuate within a certain range when different liquid chromatography-mass spectrometry combined equipment and different detection methods are used; wherein the atomic mass unit may fluctuate within a range of ± 10ppm, such as ± 5ppm, such as ± 3ppm, and the retention time may fluctuate within a range of ± 60s, such as ± 45s, such as ± 30s, such as ± 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, Goodare R, Forouhi NG, Sharp SJ, Spranger J, Wareham NJ, Boeing H: Untared metallic filters associated with Type 2 Diabetes mellitis in a promoter, New Case Control study. 611 Clin Chem 2015, 61: 487 497-, the parameter setting and adjustment can be performed by those skilled in the art according to specific situations.
In the present invention, the training set and validation set have meanings well known in the art. In an embodiment of the present invention, the training set refers to a data set comprising a certain number of samples of the content of each biomarker in a test sample of type 2 diabetic subjects and non-diabetic subjects. The validation set is an independent data set used to test the performance of the training set.
In the invention, a training set of the biomarkers of the type 2 diabetes mellitus subjects and the non-diabetes mellitus subjects is constructed, and the biomarker content value of the sample to be tested is evaluated by taking the training set as a reference.
In the present invention, the data of the training set is shown in table 1.
In the present invention, the non-diabetic subject is a normal glucose tolerant subject and/or a pre-diabetic subject.
In the present invention, the subject may be a human.
In the present invention, the unit of mass to charge ratio is amu, which refers to the unit of atomic mass, also known as daltons (Dalton, Da, D), which is a measure of the mass of an atom or molecule, and is defined as 1/12 of the mass of a carbon 12 atom. The allowable mass resolution (error) for metabolite identification in the present invention is 10ppm, i.e., parts per million. For example, the isotopically accurate mass of a certain metabolite a is 118Da, and the instrumentally measured mass is 118.001 Da; deviation is 0.001 amu; error [ deviation/exact mass x 10 6 ]＝8.47ppm。
One skilled in the art knows that when further expanding the sample size, the normal content value interval (absolute value) of each biomarker in the sample can be derived using sample detection and calculation methods well known in the art. Thus, when the content of the biomarker is detected by methods other than mass spectrometry (for example, by using an antibody, an ELISA method and the like), the absolute value of the content of the biomarker obtained by detection can be compared with the normal content value, and optionally, statistical methods can be combined to obtain the risk evaluation, diagnosis and the like of the type 2 diabetes.
Without wishing to be bound by any theory, the inventors indicate that these biomarkers are endogenous compounds and/or food-borne compounds present in the human body. The analysis of the metabolite profile of the plasma of the subject, preferably the lipid metabolites of the plasma, by the methods described herein, the mass number values and retention times in the metabolite profile indicate the presence and corresponding positions of the respective biomarkers in the metabolite profile. At the same time, the biomarkers of the type 2 diabetes population exhibit a range of values in their metabolite profile.
System for diagnosing type 2 diabetes
In a third aspect of the invention, a system for diagnosing type 2 diabetes is provided. According to an embodiment of the invention, with reference to fig. 1, it comprises: a measuring device 100, wherein the measuring device 100 is used for determining the content of the marker in a sample of a subject to be diagnosed; a determination means 200, said determination means 200 being adapted to determine a diagnostic result of said subject based on said marker content obtained in said assay means. The type 2 diabetes marker is combined with metabolite spectrum data of biomarkers of type 2 diabetes populations and non-diabetes populations to serve as a training set, and disease risk assessment and early diagnosis of type 2 diabetes can be accurately carried out. The system has the characteristics of no wound, convenience, quickness, 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 extraction device 300, wherein the extraction device 300 is connected with the measuring device 100 and is used for extracting plasma lipid of a subject to be diagnosed.
According to still another embodiment of the present invention, referring to fig. 3, the assay device 100 comprises a liquid chromatography unit 110 and a mass spectrometry unit 120.
Reagent kit
In a fourth aspect of the invention, a kit is provided. According to an embodiment of the invention, the kit comprises reagents 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 described above, the inventors found that there is a significant difference in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group through comparison and analysis of the metabolite profiles between the type 2 diabetes group and the non-diabetes group, and the related biomarkers have a significant difference in plasma lipid metabolite profiles between the type 2 diabetes group and the non-diabetes group. The kit provided by the embodiment of the invention can be used for accurately carrying out disease risk assessment and early diagnosis on type 2 diabetes, has the characteristics of no wound, convenience and quickness, and is high in sensitivity and good in specificity.
Use of reagent in preparation of kit
In a fifth aspect of the invention, the invention proposes the use of a reagent for the preparation of a kit for diagnosing a marker for type 2 diabetes, said reagent being used for the detection of a marker comprising at least one member 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 related biomarkers have a significant difference in plasma lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group by comparing and analyzing the lipid metabolite profiles of the type 2 diabetes group and the non-diabetes group. The kit prepared by the reagent can accurately evaluate the risk of the disease of the type 2 diabetes and diagnose the type 2 diabetes at early stage, has the characteristics of no wound, convenience and quickness, and has high sensitivity and good specificity.
Embodiments of the present invention will be described in detail below with reference to examples, but those skilled in the art will appreciate that the following examples are only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products commercially available.
The plasma samples of type 2 diabetic and non-diabetic subjects of the present invention were obtained from the centers for disease prevention and control in suzhou city.
Example 1
1.1 sample collection: fasting morning blood plasma from volunteers was collected and immediately stored in a-80 ℃ freezer. 98 plasma samples were collected in the normal glucose tolerance group (NGT), 81 plasma samples were collected in the Pre-diabetic group (Pre-DM), and 114 plasma samples were collected in the type 2 diabetes group (T2D). Wherein the Pre-DM group can be further divided into four subgroups: a) HbA1c increasing type 5.7-6.4％ (WHO-2011 diagnostic standard, HbA1c between 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 (iIGT for short, FPG is less than 6.1mmol/L, 2h-PG is between 7.8 and 11.0mmol/L, and n is 35); d) binding IFG/IGT (FPG between 6.1-7.0mmol/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 isopropyl alcohol (IPA). Briefly, 40. mu.L of plasma was extracted with 120. mu.L of pre-cooled IPA, vortexed for 1min, incubated at room temperature for 10min, and the extraction mixture was then left at-20 ℃ overnight. Centrifuging at 4000g for 20min, transferring the supernatant to a new 96-well plate, diluting with IPA/Acetonitrile (ACN)/H2O (2: 1, V: V) at a ratio of 1: 10, labeling sample name and positive and negative ions with a marker pen, and storing at-80 deg.C for use before analysis by a liquid chromatography-mass spectrometer. In addition, 10ul of each sample to be tested was mixed to serve as a QC quality control sample.
1.3 liquid chromatography-mass spectrometry
Instrumentation and equipment
Ultra high performance liquid chromatograph ACQUITY UPLC (Waters, Manchester, USA), Mass spectrometer Waters Xevo TM G2-XS Qtof(Waters，USA)
Chromatographic conditions
A chromatographic column: waters CSH C18 column (100mm x 2.1mm,1.7 μm); a mobile phase A: ACN (acetonitrile):H 2 O60: 40, v/v, 0.1% Formic Acid (FA), 10mM ammonium formate; mobile phase B: IPA (isopropanol): ACN 90: 10, v/v, 0.1% FA, 10mM ammonium formate.
Gradient elution procedure: 2min, 40% B linear gradient increased to 43% B; 0.1min, increasing to 50% B; 3.9min, increasing to 54% B; 0.1min, increasing to 70% B; 1.9min, gradient increased to 99% B; 0.1min, 40% B, equilibrate the column for 1.9min before each injection. Flow rate: 0.4 mL/min; the injection volume is 10. mu.L.
Conditions of Mass Spectrometry
ESI ion source, positive/negative ion mode data collection, mass range m/z 50 ~ 2000, every second (s)/time. The ion source temperature is 120 ℃, the desolvation temperature is 600 ℃, the mobile phase gas is nitrogen, the gas flow is 800L/h, and the capillary hole voltage and the cone hole voltage are respectively 2.0KV (+)/1.5KV (-) and 30V. Leucine enkephalin (molecular weight (MW) ═ 555.62; 200 pg/. mu.l in 1: 1 ACN: H2O) was used as the lock mass and calibrated with 0.5mM sodium formate solution. All samples were randomly ordered by injecting 10 QC samples first to condition the column, followed by 1-2 QC samples per 10 samples injected to investigate the reproducibility of the data.
1.4 data processing
The raw data for LC-MS were processed using commercial software Progenetics QI 2.0 software (N.C. Nonlinear Dynamics, UK) and included raw data input, adduct ion selection, peak comparison, detection, deconvolution, low mass peak filtration, data noise correction, peak identification and normalized relative quantification of peak intensity. The specific analysis parameters are as follows: 1) selection of [ M + H] + ，[M+H-H2O] + ，[M+Na] + And [ M + K] + A cationic ionization mode adduct; selection of [ M-H] - Is an anionic ionomeric adduct; 2) the retention time of the ion peak is 0.5-9 min; 3) the peak width is 1-30 s; 4) the maximum allowable error of the mass number of the parent ion is 10 ppm; 5) the maximum allowable error of the mass number of the theoretical ion fragment of the parent ion is 10ppm, and the accuracy of metabolite identification is improved through the strict parameters. Adopting MetaX software to checkAnd (5) determining the peak intensity for normalization. Peaks appearing in less than 50% QC samples or less than 80% plasma test samples were considered low mass peaks and removed; the missing values are filled in the sample using the nearest neighbor rule. After the above analysis, a total of 12,000 high quality metabolites were produced, of which 923 were identified as anion patterns and 11,077 as cation patterns. The low-mass outlier samples in the anion and cation mode are respectively detected by PCA (principal components analysis) analysis and removed, and 20 parts of the outlier samples (comprising 7 cases of NGT, 8 cases of T2D and 5 cases of Pre-DM) are removed in the step. And (3) correcting the difference caused by signal fluctuation among batches by adopting QC-RLSC (quality control-based robust route signal correction) for the high-quality data set subjected to the multiple strict filtering. After correction, characteristic peaks with relative standard deviation still > 30% are rejected. Metabolites were annotated using Progenesis Metascope against three database alignments of HMDB 3.6 (http:// www.hmdb.ca /), LIPID MAPS (http:// www.lipidmaps.org /) and LipidMass (http:// fiehnlab. ucdavis. edu/projects/LipidMass), with the maximum allowable error for both parent and theoretical ion fragment mass numbers being 10 ppm. Metabolites satisfying the above matching conditions were determined according to CSHC adopted for the experiment by Waters Corp 18 The UPLC system provides the operating guidelines for filtering the matching results based on retention time characteristics of the different lipids. Wherein, in the cation ionization mode: 1) lysophospholipids having a retention time in the range of 0.5-4 minutes include lysophosphatidylcholine (LysoPC); lysophosphatidylethanolamine (LysoPE); lysophosphatidylglycerol (LysoPG); lysophosphatidylserine (LysoPS); lysophosphatidic acid (LysoPA) and lysophosphatidylinositol (lysophosphatidylinositol, LysoPI); 2) sphingomyelin is available with a retention time of 3-8.1 minutes and includes Sphingomyelin (SM), ceramide (ceramide, Cer), lactosylceramide (LacCer), glucosylceramide (GluCer) and galactosylceramide (GalCer); 3) the retention time is 4-7.8 minutesCholine, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylserine, phosphatidic acid, phosphatidylinositol; 4) the long-chain ester has retention time of 7.8-9.5min, and is selected from Diglyceride (DG), Triglyceride (TG), and cholesterol ester (ChE); anion ionization mode: 1) lysophospholipids and free fatty acids have a retention time of 0.5-4 minutes; 2) phospholipids are present for 4-9 minutes. After retention time screening, metabolites of aliphatic compounds that matched the molecular framework levels of LIPID MAPS, the lipidplast database, or the HMDB database were classified as lipids or lipid analogs. And selecting the target metabolites in the subsequent analysis, performing secondary identification by adopting a data-dependent tandem mass spectrometry (DDA) combined standard, and classifying and reporting the metabolites according to the metabonomics standard plan (MSI) standard.
1.5 Metabolic profiling and potential biomarkers
1.5.1 univariate comparative analysis
First, 1590 metabolites were screened by the Kruskal-Wallis test for significant differences in relative intensities among the three groups of samples (p < 0.05, block Kruskal-Wallis test). Further comparison of differential metabolite analysis between pairs of groups (p < 0.05, Dunn's post-test) on the basis of differential metabolites As demonstrated by the Wehn diagram (FIG. 4), the number and type of differential metabolites between groups (p < 0.05, post-test) are different: with the largest number of differential metabolites between the NGT group and the T2D group followed by the NGT group and the Pre-DM group, with the relative fewest differential metabolites between the Pre-DM group and the T2D group.
1.5.2 screening of potential biomarkers for development of T2D Using random forest (ROC/AUC)
In order to further screen plasma lipid metabolites closely related to diseases, the invention adopts a random forest classifier to screen biomarkers to carry out disease risk prediction modeling on NGT and T2D crowds, and adopts independent untrained crowds to finish verification on the prediction model. The method comprises the following steps: from the total NGT and T2D population (91 NGTs, 106T 2D), 140 samples (70 NGTs and 70T 2D) were randomly selected as training sets, and the remaining samples were used as validation sets. Inputting all 12,000 metabolites into a random forest classifier, performing 10-fold cross validation and 10-fold repetition on a test set for 5 times, calculating the T2D risk of each individual by using the relative intensity of the metabolites screened by an RF model, drawing an operation characteristic (ROC) curve of a subject, and calculating the area under the curve (AUC) as a judgment model efficiency evaluation parameter. And selecting the combination with the optimal distinguishing efficiency of the combination with the marker combination number less than 30 in 10 repeated results as the combination of the invention. The selection frequency of each metabolite is output in the model, and the higher the frequency, the higher the importance of distinguishing the metabolite from T2D and NGT is represented.
The results show that the RF classifier obtained in the present invention contains 28 metabolites (fig. 5, tables 1-1, 1-2, 1-3, and the metabolite numbers are the same as in table 3), and the discrimination performance for the training set samples is: AUC 90.23%, 95% confidence interval CI 84.95-95.52% (fig. 6), the results indicate that the resulting metabolite combination of this model can be used as a potential biomarker to distinguish T2D from NGT.
1.5.3 validation of the biomarkers screened Using the validation set data
The model is verified by using an independent population, and the disease probability (RP) is more than or equal to 0.5 to predict that the individual is at risk of suffering from type 2 diabetes or suffers from type 2 diabetes.
Based on the model:
for independent validation set 1(T2D ═ 36 and NGT ═ 21), the discrimination AUC of the model was 86.24% (95% CI ═ 76.05-96.43%); accuracy 80.70% (fig. 7, table 2);
for independent validation set 2(T2D ═ 36 and Pre-DM ═ 76), the discriminatory AUC of the model was 71.77% (95% CI ═ 61.95-81.58%); accuracy 66.07% (fig. 8, table 2);
for independent validation set 3(Pre-DM 76 and NGT 21), the discrimination AUC of the model was 68.08% (95% CI 54.87-81.28%), and the accuracy was 63.91% (fig. 9), table 2.
3-batch verification results show that the model distinguishes people with diabetes and people with normal glucose tolerance in high efficiency; simultaneously, the medicine can be used for treating diabetes and diabetes in the early stage; the diabetes mellitus is distinguished from the person with normal glucose tolerance in the early stage.
The inventors further examined the probability of disease risk for the Pre-DM group of patients with different pathological stages (fig. 7), and the results also showed a trend of increasing predicted probability for different pathological stages, lowest in the 5.6-6.4% of the increased HbA1c types (median of disease probability of 0.298), slightly higher in the simple IGT (IGT) (median of disease probability of 0.398), and highest in the combined IFG/IGT (median of disease probability of 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 contained a total of 28 potential biomarkers as shown in table 3. Details of the 28 potential biomarkers described above (based on the 273 population sample described above) are listed in table 3, including Retention Time (RT), parent ion (m/z), best-matched compound, P-value, fold change, VIP-value. Table 4 lists the AUC values (based on the 273 population sample above) for 28 metabolites individually identified T2D and NGT, T2D and non-T2D (including Pre-DM and NGT), T2D and Pre-DM, and Pre-DM and NGT, respectively. Table 5 lists details of two-by-two comparisons of 28 biomarkers in T2D, NGT, Pre-DM sets (based on the 273 population sample described above).
The potential biomarkers of 28 random forests were further identified by data-dependent mass spectrometry (DDA) to give 4 compounds (FIG. 11-FIG. 14) identified together at MSI2 (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 hydroxybutyryl carnitine (hydroxybutyryl carnitine) in the population showed a significant increase with the disease progression, particularly in the lowest NGT group, significantly higher than NGT in the Pre-DM group, and the highest and significantly higher than Pre-DM group in the T2D group (Table 5). The lysophospholipid compounds of LysoPC (18:0), LysoPC (18:1) and LysoPC (18:2) have no significant difference in the contents of NGT group and Pre-DM group, but are all significantly higher than T2D group (Table 5). Further, markers 2 and 7(m/z 508.3406 and m/z 508.3404) were identified as lysoPC (18:0) by alignment of the standard lysoPC (18:0) (purchased from Avanti Polar Lipids Inc (Alabaster, AL) with retention time and plasma sample. The 4 potential biomarker combinations were able to significantly identify T2D and NGT as well as T2D and non-T2D (based on the validation set population samples described above), with discriminatory power (AUC) reaching 0.784 (95% CI 0.703-0.849) (fig. 15, table 6) and 0.723 (95% CI 0.654-0.771) (fig. 16, table 6), respectively (note: modeling was performed using only m/z 508.3406 for marker 2).
Table 6 predicts the risk of developing type 2 diabetes or the probability of developing type 2 diabetes for T2D and NGT and T2D and non-T2D samples based on 4 metabolite markers
The results show that the biomarker disclosed by the invention has higher accuracy and specificity and has good prospect of being developed into a diagnosis method, thereby providing basis for disease risk assessment, diagnosis and early diagnosis of type 2 diabetes and searching for potential drug targets.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and not to be construed as limiting the present invention, and that changes, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (16)
1. A set of type 2 diabetes markers comprising: LysoPC (18:0), Hydroxybutyrylcarnitine, LysoPC (18:2), LysoPC (18:1) and LysoPC (P-16: 0).
2. The type 2 diabetes marker according to claim 1, further comprising at least one selected from the group consisting of: 3-oxo-4-pentenoic acid, Ajoene, N- (3-oxo-octanoyl) -homoserine lactone, PC (42:8), TG (62:9), PI (P-38:1), PI (O-38:2), PS (38:1), LysoPC (24:1(15Z)), Carotenes, Hydroxybutyl acid and 5, 6-dichoro-tetracanoic acid.
3. The type 2 diabetes marker according to claim 1, comprising:
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.
4. The type 2 diabetes marker according to any one of claims 1-3, characterized in that it further comprises at least one of the compounds having the parameters of the following table:
the parameters were obtained in mass spectrometry with the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolventizing temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, leucine enkephalin is adopted as locking mass.
5. A system for diagnosing type 2 diabetes, comprising:
an assay device for determining the relative amount of the marker of claim 1 in a sample of a subject to be diagnosed;
a determination means for determining a diagnostic result of the subject based on the relative amount of the marker obtained in the determination means.
6. The system of claim 5, wherein the sample is a plasma lipid extract.
7. The system of claim 5, further comprising: and the extraction device is connected with the measuring device and is used for extracting the plasma lipid of the object to be diagnosed.
8. The system of claim 5, wherein the assay device comprises a liquid chromatography unit and a mass spectrometry unit.
9. A kit comprising reagents for detecting: LysoPC (18:0), Hydroxybutyrylcarnitine, LysoPC (18:2), LysoPC (18:1) and LysoPC (P-16: 0).
10. The kit of claim 9, wherein the reagents are further for detecting a condition comprising at least one selected from the group consisting of: 3-oxo-4-pentenoic acid, Ajoene, N- (3-oxo-octanoyl) -homoserine lactone, PC (42:8), TG (62:9), PI (P-38:1), PI (O-38:2), PS (38:1), LysoPC (24:1(15Z)), Carotenes, Hydroxybutyl acid and 5, 6-dichoro-tetracanoic acid.
11. The kit of claim 9, wherein the reagents are used to detect: 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.
12. The kit of any one of claims 9 to 11, wherein the reagents further comprise reagents for detecting at least one of the compounds having the parameters of the following table:
the parameters were obtained in mass spectrometry with the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolventizing temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, leucine enkephalin is adopted as locking mass.
13. Use of a reagent for the manufacture of a kit for diagnosing a type 2 diabetes marker, for detecting: LysoPC (18:0), Hydroxybutyrylcarnitine, LysoPC (18:2), LysoPC (18:1) and LysoPC (P-16: 0).
14. The use according to claim 13, wherein the reagent is further for detecting a condition comprising at least one selected from the group consisting of: 3-oxo-4-pentenoic acid, Ajoene, N- (3-oxo-octanoyl) -homoserine lactone, PC (42:8), TG (62:9), PI (P-38:1), PI (O-38:2), PS (38:1), LysoPC (24:1(15Z)), Carotenes, Hydroxybutyl acid and 5, 6-dichoro-tetracanoic acid.
15. Use according to claim 13, characterized in that the reagent is used for detecting: LysoPC (18:0), Hydroxybutyrylcarnitine, 3-oxo-4-pentenoic acid, Ajoene, Hydroxybutyrinic 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-dichoro-tetradecanoic acid.
16. Use according to any one of claims 13 to 15, wherein said reagents further comprise reagents for detecting at least one of the compounds having the parameters of the following table:
the parameters were obtained in mass spectrometry with the following conditions:
ESI ion source, positive/negative ion mode data acquisition, mass range m/z 50-2000, s/time per second, ion source temperature of 120 ℃, desolventizing temperature of 600 ℃, mobile phase gas of nitrogen, gas flow of 800L/h, capillary hole voltage and taper hole voltage of 2.0KV (+)/1.5KV (-) and 30V respectively, leucine enkephalin is adopted as locking mass.
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