CN113009162A - Serum metabolic marker for diagnosing gestational diabetes and application thereof - Google Patents
Serum metabolic marker for diagnosing gestational diabetes and application thereof Download PDFInfo
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Abstract
The invention provides a serum metabolic marker for gestational diabetes diagnosis and application thereof, and particularly discloses that sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18:2) can be used as biomarkers for gestational diabetes, can be used for evaluating the risk of gestational diabetes or diagnosing gestational diabetes, and have the advantages of high accuracy and strong sensitivity, so that the serum metabolic marker has important clinical application value.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a serum metabolic marker for diagnosing gestational diabetes and application thereof.
Background
Gestational Diabetes Mellitus (GDM) refers to a variable degree of impaired glucose tolerance that occurs or is first discovered during pregnancy, and does not exclude the possibility that impaired glucose tolerance is already present before pregnancy if it occurs in the early stages of pregnancy. GDM is one of the most common complications during pregnancy, and about 3% -8% of pregnant women develop GDM during pregnancy, and particularly, the incidence rate of GDM is continuously increased along with the increase of obesity rate of women of childbearing age.
Gestational diabetes not only has adverse effects on pregnancy, such as spontaneous abortion, malformation and developmental retardation in early pregnancy, delayed lung development and late death in middle pregnancy, but also has a far higher morbidity of the newborn after delivery than the newborn under normal pregnancy conditions, and has a higher probability of suffering from chronic diseases such as obesity, diabetes, cardiovascular diseases and the like after adulthood than the common people, so that the sequelae of gestational diabetes can always accompany the growth of the newborn.
GDM diagnostic criteria have been changing, but there has been a lack of uniform diagnostic criteria worldwide until 2008, Hyperglycemia and Adverse Pregnancy Outcome (HAPO) research published, and international diabetes and pregnancy research group (IADPSG) established new GDM diagnostic criteria. But GDM can be diagnosed only in the late stage of pregnancy 2 or 3, and serological screening of GDM is performed in the week of pregnancy 24-28, which is not favorable for treatment in the later period of pregnancy. Therefore, we need to make early diagnosis of GDM as soon as possible in a limited time before and after pregnancy to achieve sugar control and reach the standard, thereby improving short-term and long-term bad outcomes of mothers and infants.
The occurrence and development of any disease affects the metabolism of the human body, resulting in significant changes in the metabolic substances in the body fluids. By comparing the physiological and disease states of an organism and the metabolite difference of different types and stages of the same disease, a group of Biomarkers related to disease diagnosis and type can be found for disease diagnosis and type. Metabolomics methods allow the detection of high-throughput small molecule substances, many of which have been considered risk factors for disease, by means of standard prepared biological samples of serum, plasma, urine, etc. Metabolic markers associated with cardiovascular disease, hypertension, chronic obstructive pulmonary disease, H1N1 influenza pneumonia have been reported in the literature. In clinical practice, biological samples are obtained through non-invasive or minimally invasive methods, and biomarkers in the samples are detected to achieve the purpose of disease diagnosis. Therefore, the biomarker which is sensitive and specific to gestational diabetes is found, and the biomarker has important significance for early diagnosis, treatment and sequelae of GDM.
Disclosure of Invention
The invention aims to provide a serum metabolic marker for diagnosing gestational diabetes and application thereof, provides basis for prevention and diagnosis of gestational diabetes, and has the advantages of high sensitivity, good specificity, no wound or minimal invasion and the like.
In a first aspect of the invention, there is provided a biomarker panel comprising at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
In another preferred embodiment, the biomarker is used for diagnosing or aiding in the diagnosis of gestational diabetes.
In another preferred example, the biomarker panel comprises sphingomyelin SM (8: 0; 2O/11:0), and oleoyl carnitine CAR (18: 2).
In another preferred embodiment, the individual biomarkers are identified by mass spectrometry, preferably by a combination of chromatographic mass spectrometry, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, the biomarker is used for evaluating the risk of gestational diabetes of a subject to be tested or diagnosing gestational diabetes of the subject to be tested.
In another preferred embodiment, said assessing the risk of gestational diabetes in a subject comprises early screening for gestational diabetes.
In another preferred embodiment, the increase in sphingomyelin SM (8: 0; 2O/11:0) abundance is indicative of a high risk of gestational diabetes.
In another preferred embodiment, the down-regulation of oleoyl carnitine CAR (18:2) abundance is indicative of a high risk of susceptibility to gestational diabetes.
In a second aspect, the present invention provides a reagent combination for use in risk assessment or diagnosis of gestational diabetes comprising a reagent for detecting a biomarker according to the first aspect of the present invention.
In another preferred embodiment, the reagent comprises a substance for detecting each biomarker according to the first aspect of the invention by mass spectrometry.
In another preferred embodiment, the agent may be an antibody, including a monoclonal antibody or a polyclonal antibody.
In a third aspect, the present invention provides a kit comprising a biomarker according to the first aspect of the invention and/or a combination of reagents according to the second aspect of the invention.
In another preferred embodiment, each biomarker according to the first aspect of the invention may be used as a standard or control.
In another preferred embodiment, the kit further comprises an instruction which describes reference data for the levels of the biomarkers of the first aspect of the invention derived from individuals with gestational diabetes and/or healthy individuals.
In a fourth aspect, the invention provides a use of a biomarker for preparing a kit for assessing the risk of contracting gestational diabetes in a subject or for diagnosing gestational diabetes in a subject, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
In another preferred embodiment, said evaluating or diagnosing comprises the steps of:
(1) providing a sample from an individual to be tested, and detecting the level of the biomarker in the sample;
(2) comparing the level measured in step (1) with a reference data (e.g., a reference data from a healthy control);
preferably, said reference data comprises the levels of said individual biomarkers derived from gestational diabetic individuals and healthy individuals.
In another preferred embodiment, the sample is selected from the group consisting of: blood, plasma, and serum.
In another preferred embodiment, the comparing the level measured in step (1) with a reference data further comprises the step of establishing a multivariate statistical model to output the probability of disease, and preferably, the multivariate statistical model is determined based on an orthogonal partial least squares regression method.
In another preferred embodiment, the level of each biomarker is detected by mass spectrometry, preferably by a combination of chromatographic mass spectrometry, such as gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS).
In another preferred embodiment, before step (1), the method further comprises a step of processing the sample.
The fifth aspect of the present invention provides a method for evaluating the risk of gestational diabetes of a subject to be tested or diagnosing gestational diabetes of the subject to be tested, comprising the steps of:
(1) providing a sample from an individual to be tested, and detecting the level of each biomarker in the sample, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18: 2);
(2) comparing the level measured in step (1) with a reference data (e.g., a reference data for a healthy individual);
preferably, said reference data comprises the levels of each of said biomarkers derived from gestational diabetes patients and healthy controls.
The sixth aspect of the present invention provides a method for screening a candidate drug for treating gestational diabetes mellitus, comprising the steps of:
(1) administering a test drug to an individual to be tested in a test group, and detecting the level of each biomarker in a sample derived from said individual in the test group V1; in a control group, administering a blank control (including vehicle) to the subject to be tested, and detecting the level of each biomarker V2 in a sample derived from the subject in the control group;
(2) comparing the level V1 and the level V2 detected in the previous step to determine whether the test drug is a candidate for the treatment of gestational diabetes, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
In another preferred embodiment, the subject to be tested is an animal model of gestational diabetes, such as a murine model.
In another preferred example, the subject to be tested is a human gestational diabetes patient.
In another preferred example, when the biomarker to be detected is sphingomyelin SM (8: 0; 2O/11:0), if the level V1 is significantly lower than the level V2, the test drug is indicative of a candidate drug for the treatment of gestational diabetes.
In another preferred embodiment, the phrase "substantially lower than" means that the ratio of level V1/level V2 is 0.8 or less, preferably 0.6 or less, and more preferably 0.4 or less.
In another preferred example, when the biomarker tested is oleoyl carnitine CAR (18:2), if the level V1 is significantly higher than the level V2, it indicates that the test drug is a candidate for the treatment of gestational diabetes.
In another preferred embodiment, said "significantly higher" means that the ratio of level V1/level V2 is ≥ 1.1, preferably ≥ 1.5, more preferably ≥ 2.0.
The seventh aspect of the invention provides a use of a biomarker for screening a candidate drug for treating gestational diabetes and/or for evaluating the therapeutic effect of the candidate drug on gestational diabetes, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), and oleoyl carnitine CAR (18: 2).
In an eighth aspect, the invention provides a method of creating a mass spectrometric model for assessing the risk of or diagnosing gestational diabetes, said method comprising the step of mass spectrometric detection of a biomarker in a model blood sample, wherein said biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), and oleoyl carnitine CAR (18: 2).
In another preferred example, the model is a murine model, a dog model, or a monkey model.
It is to be understood that within the scope of the present invention, the above-described features of the present invention and those specifically described below (e.g., in the examples) may be combined with each other to form new or preferred embodiments. Not to be reiterated herein, but to the extent of space.
Drawings
FIG. 1 Quality Control (QC) and PCA 2D score plots for samples.
Figure 2 data distribution plots of positive and negative ion pattern data for GDM and control before and after normalization.
Figure 3 volcano plot of serum metabolites: expression in GDM and control groups.
Fig. 4 PCA analysis score: a significantly altered lipid metabolite.
FIG. 5 PCA 2D score plots of GDM and control samples.
Fig. 6 five main components of R2 and Q2.
FIG. 7 distribution diagram of principal components: distribution of samples among the main components PC1 and PC 2.
FIG. 8 hierarchical clustering plots of differential metabolites.
Figure 9 KEGG pathways for different metabolites.
FIG. 10 sphingomyelin SM (8: 0; 2O/11:0) ROC curve.
FIG. 11 oleoyl carnitine CAR (18:2) ROC curve.
Detailed Description
The present inventors have conducted extensive and intensive studies and have unexpectedly found biomarkers for gestational diabetes for the first time. Specifically, the sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18:2) can be used as biomarkers of gestational diabetes, can be used for evaluating the risk of gestational diabetes or diagnosing the gestational diabetes, and has the advantages of high accuracy and strong sensitivity, so that the sphingomyelin has important clinical application value. On the basis of this, the present invention has been completed.
Term(s) for
The terms used herein have meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present invention, some definitions and related terms are explained as follows:
according to the present invention, the term "gestational diabetes mellitus" (GDM) refers to a variable degree of impaired glucose tolerance that occurs or is first discovered during pregnancy, and does not exclude the possibility that impaired glucose tolerance is already present before pregnancy if it occurs in the early stages of pregnancy.
According to the present invention, the term "biomarker", also referred to as "biological marker", refers to a measurable indicator of the biological state of an individual. Such biomarkers can be any substance in an individual as long as they are related to a particular biological state (e.g., disease) of the subject, e.g., nucleic acid markers (e.g., DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers), and the like. Biomarkers are measured and evaluated, often to examine normal biological processes, pathogenic processes, or therapeutic intervention pharmacological responses, and are useful in many scientific fields.
According to the present invention, the term "individual" refers to an animal, in particular a mammal, such as a primate, preferably a human.
According to the present invention, the term "plasma" refers to the liquid component of whole blood. Depending on the separation method used, the plasma may be completely free of cellular components and may also contain varying amounts of platelets and/or small amounts of other cellular components.
According to the present invention, "mass spectrometry" (MS) can be divided into ion trap mass spectrometry, quadrupole mass spectrometry, orbitrap mass spectrometry and time-of-flight mass spectrometry with deviations of 0.2amu, 0.4amu, 3ppm and 5ppm, respectively. In the present invention, MS data is obtained using time-of-flight mass spectrometry.
According to the invention, the level of the biomarker is indicated by a mass spectrometry signal area normalization value.
In one embodiment of the invention, the reference value refers to a reference value or normal value of a healthy control. It will be clear to those skilled in the art that, in the case of a sufficiently large number of samples, a range of normal values (absolute values) for each biomarker can be obtained by testing and calculation methods. Thus, when the levels of biomarkers are detected by methods other than mass spectrometry, the absolute values of the levels of these biomarkers can be directly compared to normal values to assess the risk of having gestational diabetes, as well as to diagnose or early diagnose gestational diabetes. Statistical methods may also be used in the present invention.
According to the present invention, terms such as "a," "an," and "the" do not refer only to a singular entity, but also include the general class that may be used to describe a particular embodiment.
It should be noted that the explanation of the terms provided herein is only for the purpose of better understanding the present invention by those skilled in the art, and is not intended to limit the present invention.
The main advantages of the invention are:
the biomarker for rapidly diagnosing gestational diabetes mellitus and the application thereof are used for analyzing according to serum metabolites of different gestational diabetes mellitus individuals, diagnosing the gestational diabetes mellitus from a microscopic metabolite angle, are simple to operate, have accurate, objective and reliable results, can well distinguish gestational diabetes mellitus groups from normal groups, provide convenience for accurate clinical diagnosis, can be carried out only by providing blood samples without other tissue samples, greatly improve the possibility and feasibility of clinical application, and provide reference basis for early accurate diagnosis and early intervention schemes of clinicians.
The present invention will be described in further detail with reference to the following examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Experimental procedures for conditions not specified in detail in the following examples are generally carried out under conventional conditions such as those described in molecular cloning, A laboratory Manual (Huang Petang et al, Beijing: scientific Press, 2002) by Sambrook. J, USA, or under conditions recommended by the manufacturer. Unless otherwise indicated, percentages and parts are by weight. The test materials and reagents used in the following examples are commercially available without specific reference.
The invention discloses metabolites, combinations thereof, markers and applications thereof, and can be realized by appropriately improving process parameters by referring to the contents in the text by a person skilled in the art. It is expressly intended that all such similar substitutes and modifications which would be obvious to one skilled in the art are deemed to be included in the invention. While the methods and applications of this invention have been described in terms of preferred embodiments, it will be apparent to those of ordinary skill in the art that variations and modifications in the methods and applications described herein, as well as other suitable variations and combinations, may be made to implement and use the techniques of this invention without departing from the spirit and scope of the invention.
Example 1
The inventors identified metabolic markers based on the measured metabolite results of serum samples from a total of 21 Gestational Diabetes Mellitus (GDM) patients, 22 healthy pregnant women age-matched to the GDM group.
1. Patient grouping and sample collection
The peripheral blood samples are from a memorial hospital of any xian in the area of wine in Guangzhou city, pregnant women are recruited in Guangzhou in the year 2019 and 2020, and mothers who suffered from obvious diabetes before pregnancy are excluded. After written consent, a history of structural disease was recorded.
All pregnant women brought into the study are in the perinatal medicine outpatient service card of the memorial hospital of any xian and are followed by the clinic according to the period, the card meets the standard of nao and Rou, and basic information data of patients, such as height, weight, age and the like, are collected by a specially-assigned person. The pre-pregnancy weight index (BMI) is calculated as body weight (kg) divided by the square of body height (m). All participants underwent a standardized 75g oral glucose tolerance test (OGCT) at 24-28 weeks gestation, according to the american diabetes association standard, 75g OGCT test: before the test, the patient needs to have a fasting state for eight hours, then 75g of glucose is taken within five minutes, the blood glucose is respectively detected for 1 hour and 2 hours, and any blood glucose value reaches or exceeds the following standard, so that the GDM can be diagnosed. (fasting: 5.1 mmol/L; 1 hour after meal: 10.0 mmol/L; 2 hours after meal: 8.5mmol/L)
After the subjects carry out OGTT screening at the 24 th week, 21 GDM patients are screened out, and 22 healthy pregnant women matched with the age of the GDM group are found out according to a random principle and serve as a control group for further study. The blood sampling time is in the early morning with empty stomach. All samples were serum obtained by centrifugation and immediately transferred to centrifuge tubes for storage at-80 ℃ for testing.
TABLE 1 clinical and demographic characteristics of study population
The parameters recorded between GDM and normal delivery women are shown in table 1.
The age of the GDM mothers was significantly increased (greater than 30 years, p < 0.05), and the BMI of the GDM mothers was significantly increased (p < 0.01) compared to the control group of non-GDM mothers. GDM has no statistical significance for the differences of fetal times, fasting blood sugar and gestational age of women with normal childbirth. Infants in the GDM group weighed higher than the normal group, consistent with the BMI of the pre-pregnant mother. Differences in neonatal gender and Apgar scores were not statistically significant.
2. Metabolite extraction
The sample was thawed at 4 ℃ and vortexed for 10S, 100uL of the sample was placed in a 1.5ml lep tube, 400uL of methanol acetonitrile (1:1, v/v) was added, the mixed solution was vortexed for 30S, allowed to stand at-20 ℃ for 60min, and then centrifuged at 17000g at high speed at 4 ℃ for 15 min. Vacuum-drying 250uL of supernatant at 35 deg.C, dissolving with 150uL of acetonitrile and water (1:1, v/v), vortexing for 30s, sonicating for 10min, and centrifuging at 170000g at 4 deg.C for 15 min. 100uL of the supernatant was placed in an internal cannula and 20uL of each sample was mixed for QC.
3. LC-MS analysis
1) The column temperature is 35 ℃, and the sample loading amount is 1 mu L;
2) water, acetonitrile, formic acid (4:6), acetonitrile, isopropanol (1: 9);
3) the Agilent6545A QTOF mass spectrometer carries out primary and secondary mass spectrum Data Acquisition based on an Auto MS/MS mode under the control of control software (LC/MS Data Acquisition, Version B.08.00), and the mass scanning range m/z (50-1100). Respectively adopting positive and negative ion modes for collection; the ESI ion source parameters were set as follows: ion source drying Gas temperature (Gas Temp): 320 ℃, nitrogen Flow (Gas Flow): 8L/min, sheath gas flow rate (SheatGasflow): 12L/min, sheath gas temperature (SheatGasTemp): 350 ℃; capillary voltage (VCap): 3500V (negative ion mode), 4000V (positive ion mode).
4. Data processing
The data of the off-line is firstly converted into a format of abf by using an Analysis Base File Converter, then data processing such as peak searching, peak alignment and the like is carried out on the abf File after conversion by using MSDIAL software (version 4.24), and simultaneously a Lipid Blast database is searched based on a primary map and a secondary map to obtain an identification result.
For the data identified by MSDIAL alignment, the sample index CV value is controlled to be less than 30% by QC samples, then the ion peak with deletion value in the group being more than 50% is deleted, and the normalization is carried out by adopting an Auto-scaling method. Single-factor statistical analysis of metabolites was performed using the Fold change analysis and T-test. Differential metabolite screening criteria: p value is less than 0.05, fold change is more than 2 times, PLS-DA VIP value is more than 1. Multivariate statistical analysis was performed. The GDM and normal samples were analyzed by Principal Component Analysis (PCA) using an unsupervised statistical model. And establishing PLS-DA models of the GDM group and the normal group, and obtaining evaluation parameters of the models through interactive verification. The prediction effect of R2 and Q2 is better than 0.5. And carrying out hierarchical clustering on each group of samples, and clustering the expressions of qualitatively different metabolites and obviously different metabolites. Enrichment analysis was performed on the KEGG pathway. Analysis was performed using Metabo analysis 4.0 software.
5. Results
Principal Component Analysis (PCA) shows that QC samples are tightly clustered, experiment repeatability is good, and an instrument analysis system is stable and reliable (figure 1).
After normalization, the distribution of positive and negative ion mode data of the GDM and the control group is substantially normal (fig. 2).
Single-factor statistical analysis of metabolites fold change analysis and t-test were used. As shown in fig. 3, there were 167 lipid metabolites significantly changed in the serum of GDM patients compared to normal, of which 158 lipid metabolites were down-regulated and 9 lipid metabolites were up-regulated.
TABLE 2 167 different metabolites leading to a difference in metabolic profile between gestational diabetes patients and healthy persons
PCA analysis showed that the cumulative contribution of the five principal components was 61.7%, indicating that lipid metabolites could well separate GDM from normal components (fig. 4 and 5). The sample distribution of the principal components PC1 and PC2 showed that GDM was separated from the normal group of samples (fig. 6).
R2 and Q2 of 5 principal components in the GDM and normal group PLS-DA models are both >0.5, indicating that the prediction effect of the principal components is good (FIG. 7).
Hierarchical clustering showed that significantly different metabolites can divide GDM and normal samples into two distinct clusters (fig. 8). Thus, there were significant differences in metabolites between the two groups.
Analysis of 167 significantly altered lipid metabolites by KEGG pathway enrichment showed that these differential metabolites were involved in sphingolipid metabolism and glycerophospholipid metabolism (fig. 9).
Example 2
The diagnostic performance of two differential metabolites was further considered using Receiver Operating (ROC) curves, which are plotted according to a series of two different classification approaches (cut-off or decision threshold) with true positive rate (sensitivity) as ordinate and false positive rate (1-specificity) as abscissa. The closer the ROC curve is to the upper left corner, the higher the diagnostic accuracy of the marker, the point of the ROC curve closest to the upper left corner being the best threshold with the least error and the least total number of false positives and false negatives. Calculating the area under the ROC curve (AUC) of each potential marker can judge the diagnostic value of the potential marker, and the diagnostic value is higher when the AUC is higher.
The AUC values for the 2 differential metabolites were: sphingomyelin SM (8: 0; 2O/11: 0): AUC 0.775, 95% CI: 0.636-0.913, P ═ 0.002 (fig. 10); oleoyl carnitine CAR (18: 2): AUC ═ 0.996, 95% CI: 0.984-1.000, P < 0.00019 (FIG. 11). Therefore, these 2 differential metabolites can be used to diagnose GDM.
TABLE 3 diagnostic potential analysis
The details of the 2 disease markers are shown in table 4. Sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18:2) are expressed abnormally in GDM. Compared to normal, oleoyl carnitine expression was significantly down-regulated in GDM, while sphingomyelin expression was significantly up-regulated.
Table 42 marker information
KEGG pathway analysis of 2 disease markers indicated that SM was involved in the glycerophospholipid metabolic pathway (map00564), ether lipid metabolic pathway (map00565) and glycerolipid metabolic pathway (map00561), linoleic acid metabolic pathway (map00591), sphingolipid metabolic pathway (map00600), arachidonic acid metabolic pathway (map00590) and alpha-linolenic acid metabolic pathway (map 00592).
In conclusion, the metabolite provided by the invention is a potential biomarker for identifying GDM, can be applied to the clinical diagnosis of the gestational diabetes mellitus, and has a good application prospect.
Example 3 test validation
100 blood samples and 100 healthy samples, which have been clinically diagnosed as GDM, are randomly selected. The 200 samples were tested for sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18:2) levels according to the method of example 1.
As a result, it was found that in 19 samples, the level of sphingomyelin SM (8: 0; 2O/11:0) was abnormally up-regulated, and in 9 samples, the level of oleoyl carnitine CAR (18:2) was abnormally down-regulated, wherein in 5 samples, the level of sphingomyelin SM (8: 0; 2O/11:0) was abnormally up-regulated and the level of oleoyl carnitine CAR (18:2) was abnormally down-regulated.
The 23 abnormal samples were obtained from GDM patients after reviewing the sample sources.
All documents referred to herein are incorporated by reference into this application as if each were individually incorporated by reference. Furthermore, it should be understood that various changes and modifications of the present invention can be made by those skilled in the art after reading the above teachings of the present invention, and these equivalents also fall within the scope of the present invention as defined by the appended claims.
Claims (10)
1. A biomarker, comprising at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
2. A reagent set for risk assessment or diagnosis of gestational diabetes comprising a reagent for detecting the biomarker of claim 1.
3. A reagent combination according to claim 2, wherein the reagents comprise materials for mass spectrometric detection of the respective biomarkers of claim 1.
4. A kit comprising a biomarker according to claim 1 and/or a combination of reagents according to claim 3.
5. Use of a biomarker for the preparation of a kit for assessing the risk of contracting gestational diabetes in a subject or for the diagnosis of gestational diabetes in a subject, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
6. A method of assessing the risk of contracting gestational diabetes in a test subject or diagnosing gestational diabetes in a test subject, comprising the steps of:
(1) providing a sample from an individual to be tested, and detecting the level of each biomarker in the sample, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0) and oleoyl carnitine CAR (18: 2);
(2) comparing the level measured in step (1) with a reference data (e.g., a reference data for a healthy individual);
preferably, said reference data comprises the levels of each of said biomarkers derived from gestational diabetes patients and healthy controls.
7. A method of screening a candidate drug for the treatment of gestational diabetes comprising the steps of:
(1) administering a test drug to an individual to be tested in a test group, and detecting the level of each biomarker in a sample derived from said individual in the test group V1; in a control group, administering a blank control (including vehicle) to the subject to be tested, and detecting the level of each biomarker V2 in a sample derived from the subject in the control group;
(2) comparing the level V1 and the level V2 detected in the previous step to determine whether the test drug is a candidate for the treatment of gestational diabetes, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
8. The method of claim 7, wherein the subject is an animal model of gestational diabetes, such as a murine model;
preferably, when the biomarker detected is sphingomyelin SM (8: 0; 2O/11:0), if the level V1 is significantly lower than the level V2, the test drug is indicated as a candidate drug for the treatment of gestational diabetes; and/or
When the biomarker tested was oleoyl carnitine CAR (18:2), if the level V1 was significantly higher than the level V2, it was suggested that the test drug was a candidate for the treatment of gestational diabetes.
9. Use of a biomarker for screening a candidate drug for the treatment of gestational diabetes and/or for evaluating the therapeutic effect of a candidate drug on gestational diabetes, wherein the biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
10. A method for establishing a mass spectrometric model for assessing the risk of or diagnosing gestational diabetes, comprising the step of mass spectrometric detection of a biomarker in a model blood sample, wherein said biomarker comprises at least one of sphingomyelin SM (8: 0; 2O/11:0), oleoyl carnitine CAR (18: 2).
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