Diagnostic marker combination for gestational diabetes mellitus and application thereof, kit, device and evaluation model

Abstract

The invention belongs to the field of marker detection, and particularly relates to a gestational diabetes diagnosis marker combination and application thereof, a kit, a device and an evaluation model. One purpose of the invention is to provide an application of a detection marker in predicting gestational diabetes mellitus. The detection marker comprises fructose glycine and / or lysophosphatidyl ethanolamine LysoPE (20: 5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0: 0). The application comprises the following steps: detecting gestational diabetes mellitus or related diseases; predicting the risk of suffering from gestational diabetes mellitus or related diseases; or the prognosis effect of gestational diabetes mellitus or related diseases can be predicted. The diagnostic marker constructed by the invention is helpful to clinically identify high-risk GDM people, and provides important clinical early warning for prevention and treatment of gestational diabetes mellitus patients.

Description

Technical Field

[0001] The present invention belongs to the field of marker detection, and in particular relates to a diagnostic marker combination for gestational diabetes and its application, kit, device, and evaluation model. Background Art

[0002] Gestational diabetes mellitus (GDM) refers to a pathological condition characterized by abnormal glucose metabolism that occurs or is first discovered during pregnancy. Its diagnostic criteria are fasting and / or postprandial blood glucose levels exceeding the gestational threshold. As the most common pregnancy complication worldwide, GDM has a prevalence of 14%. It is not only closely associated with perinatal risks such as fetal growth restriction, macrosomia, and preterm birth, but also increases the risk of long-term metabolic abnormalities in offspring and postpartum type 2 diabetes mellitus (T2DM) and cardiovascular disease in mothers, posing a major public health challenge. Epidemiological studies have shown that the incidence of GDM has increased significantly over the past decade, driven by changes in dietary structure, sedentary lifestyles, and an increase in the prevalence of advanced maternal age, further increasing the burden on healthcare systems.

[0003] In clinical practice, the oral glucose tolerance test (OGTT) remains the core basis for GDM screening and diagnosis. This test requires pregnant women to ingest 75g of glucose solution within 5 minutes after a strict fast of 8–12 hours. Venous blood samples are then drawn three times, one on an empty stomach and one and two hours after taking the glucose. Although standardized procedures ensure test accuracy, operational drawbacks such as hunger stress, gastrointestinal reactions, and multiple blood draws significantly reduce maternal compliance. It is worth noting that the glycated hemoglobin (HbA1c) test, widely used in non-pregnant populations, can reflect blood glucose levels over the past three months through a single random blood draw. However, due to altered red blood cell metabolic dynamics during pregnancy and the hemodilution effect, the measured values ​​are systematically low, making it unable to meet the clinical needs of GDM screening. This technical bottleneck highlights the need to develop new biomarkers, particularly the need to establish an accurate detection system that can achieve early risk prediction based on a single random blood sample.

[0004] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the inventor studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0005] Based on the above technical problems, a combination of diagnostic markers for gestational diabetes and its application, kit, device, and evaluation model are provided. The present invention belongs to the field of marker detection.

[0006] One of the objects of the present invention is to provide a diagnostic marker combination for gestational diabetes mellitus, which comprises fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

[0007] One of the purposes of the present invention is to provide a combination of diagnostic markers for gestational diabetes mellitus for use in predicting gestational diabetes mellitus. The diagnostic markers for gestational diabetes mellitus include fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

[0008] According to a preferred embodiment, the application comprises:

[0009] Testing for gestational diabetes or related conditions;

[0010] Predicting the risk of developing gestational diabetes or a related condition; or

[0011] Predicting the prognosis of gestational diabetes or related diseases.

[0012] According to a preferred embodiment, the diagnostic markers for gestational diabetes mellitus are derived from one or more of plasma, serum, and whole blood.

[0013] One of the objectives of the present invention is to provide an assessment model for predicting gestational diabetes, which uses the abundance of fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0) as input variables to predict the risk of gestational diabetes.

[0014] According to a preferred embodiment, when the signal intensity of fructoseglycine, i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is higher than 5600; the signal intensity of LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0), i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is higher than 54000; and / or the calculated value based on the combined predictive factors is higher than 350000, the risk of gestational diabetes mellitus is predicted to be high.

[0015] Preferably, the calculation formula is as follows:

[0016] The calculated value of the combined prediction factor = the signal intensity of fructoseglycine*2.708+the signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)*6.059-9.088.

[0017] One of the objects of the present invention is also to provide a kit comprising reagents for detecting the abundance of the above-mentioned diagnostic markers.

[0018] One of the purposes of the present invention is to provide a diagnostic device for gestational diabetes mellitus, comprising:

[0019] An information acquisition module is used to obtain the abundance of diagnostic markers in the subject sample, wherein the diagnostic markers include fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0);

[0020] and an information processing module for providing a prediction result of gestational diabetes based on the abundance of the diagnostic markers.

[0021] According to a preferred embodiment, when the signal intensity of fructoseglycine, i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is greater than 5600; the signal intensity of LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0), i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is greater than 54,000; and / or the calculated value based on the combined predictive factor is greater than 350,000, the information processing module provides an assessment result predicting a high risk of gestational diabetes. Preferably, the information processing module can provide an assessment result diagnosing gestational diabetes.

[0022] Preferably, the calculation formula is as follows:

[0023] The calculated value of the combined prediction factor = the signal intensity of fructoseglycine*2.708+the signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)*6.059-9.088.

[0024] According to a preferred embodiment, the gestational diabetes diagnostic device can be used to detect gestational diabetes or related diseases; predict the risk of developing gestational diabetes or related diseases; or predict the prognosis of developing gestational diabetes or related diseases.

[0025] The beneficial effects of this technical solution are as follows:

[0026] Based on statistical clinical and laboratory test data, the present invention identified two factors significantly associated with gestational diabetes: fructoseglycine and lysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)]. Positive and negative logistic regression analysis was then performed on the two factors, and a prediction method was constructed using fructoseglycine and lysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) as independent risk factors. The model was constructed using A*2.708+B*6.059-9.088 (A is the signal intensity of fructoseglycine, and B is the signal intensity of lysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)). The area under the receiver operating characteristic curve (AUC) and calibration plots were used to evaluate the discrimination and calibration of the model. The results showed that the markers have better predictive value for GDM than traditional models. The markers constructed by the present invention are helpful in identifying high-risk GDM populations in clinical practice and provide important clinical warnings for the prevention and treatment of gestational diabetes mellitus. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 : is an abundance graph of the markers of the present invention, wherein Panel A and Panel B are the raw abundances of fructoseglycine and (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), respectively; Panel C is the calculated value of the combined predictor;

[0028] Figure 2 is the ROC curve analysis of the training set in a specific embodiment of the present invention;

[0029] Figure 3 It is the ROC curve analysis of the test set of the present invention, that is, the discriminative efficacy of fructoseglycine, LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0) and their combined predictors for the GDM group and the CON group. DETAILED DESCRIPTION

[0030] In the description of the present invention, terms are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0031] The present invention is further described below with reference to specific examples. Experimental methods in the following examples, where specific conditions are not specified, generally follow conventional conditions or the conditions recommended by the manufacturer. Unless otherwise defined, all professional and scientific terms used herein have the same meanings as those familiar to professionals in the field. In addition, any methods and materials similar or equivalent to those described herein can be applied to the present invention. The preferred embodiments and materials described herein are for illustrative purposes only.

[0032] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0033] The following is a detailed description of the embodiments of the present invention with reference to the accompanying drawings. The embodiments of the present invention use chromatography-mass spectrometry to detect metabolites in plasma samples and determine diagnostic markers for gestational diabetes.

[0034] It should be noted that the information acquisition module in the gestational diabetes diagnostic device of the present invention can be used to obtain the abundance of diagnostic markers in a subject's sample. The information acquisition module can also be used to obtain conventional characteristics of the subject, such as age, BMI, height, weight, etc.

[0035] The information processing module can be used to predict the likelihood of the subject having diabetes based on the concentration of at least one of the diagnostic markers using the information processing module.

[0036] Those skilled in the art will appreciate that the apparatus and its modules for predicting the likelihood of a subject having diabetes can be implemented in a variety of ways. In certain embodiments, the apparatus and its modules can be implemented by hardware, software, or a combination of hardware and software. Specifically, the hardware portion can be implemented by dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system (such as a microprocessor or specially designed hardware). In addition, the apparatus and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits, gate arrays, logic chips, transistors, field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software (such as firmware). These methods and apparatus can be implemented by computer-executable instructions and / or contained in processor control code. For example, such code can be placed on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier.

[0037] The subjects for Examples 1 and 2 below were selected from screened volunteers. All enrolled volunteers signed informed consent forms before plasma sample collection. A total of 89 patients with gestational diabetes (GDM) were screened from a gestational diabetes sample library. All participants completed a 75g oral glucose tolerance test (OGTT) according to standard clinical procedures for GDM. Plasma samples were also collected from 71 healthy volunteers corresponding to their age and sex ratio. The 89 screened patients with GDM and 71 healthy volunteers were randomly divided into a modeling group and a validation group in a 3:2 ratio.

[0038] Example 1

[0039] 1. Research subjects

[0040] The samples in this example were derived from gestational diabetes patients and healthy volunteers in the modeling group.

[0041] 2. LC-MS metabolomics analysis of plasma samples

[0042] 2.1 Instruments, materials, and reagents

[0043] The instruments used were an Acquity I Class ultra-high performance liquid chromatograph and a Xevo G2S QTOF mass spectrometer from Waters (Milford, Massachusetts, USA); a VIBRAX VXR vortex mixer (IKA, Germany); an AL204 electronic balance (METTLERTOLEDO), a Labconco CentriVap vacuum centrifugal concentrator, and an Eppendorf 5920R centrifuge (Hamburg, Germany).

[0044] Formic acid, ammonium formate, methanol, and acetonitrile (chromatographic grade, Merck, Germany); and pure water were prepared by a Nanopure purification system (Barnstead, USA).

[0045] 2.2 Plasma sample pretreatment

[0046] Samples to be tested: 200 μL of plasma sample was added to 1 mL of a methanol-acetonitrile mixture (1:1, v / v) for protein precipitation. After centrifugation at 12,000 rpm at 4°C for 5 minutes in an Eppendorf 5920R centrifuge (Hamburg, Germany), the supernatant was transferred to a 1.5 mL centrifuge tube, dried in a Labconco CentriVap vacuum dryer (Missouri, USA) at 40°C, and reconstituted with 100 μL of 5% methanol.

[0047] The solution was centrifuged again at 4°C, 12,000 rpm for 5 minutes, and 80 μL of the supernatant was transferred to an LC-MS injection vial. Quality control (QC) samples were prepared by mixing equal amounts of each test sample.

[0048] 2.3 Liquid chromatography-mass spectrometry detection

[0049] Chromatographic conditions:

[0050] Chromatographic column: ACQUITY UPLC T3 column (2.1×100 mm, 1.7 μm) and VanGuard T3 guard column (2.1×5 mm, 1.8 μm); column temperature, 40°C; flow rate, 0.4 mL / min; injection volume, 5 μL; mobile phase A: 0.1% (v / v) formic acid and 5 mM ammonium formate in water; mobile phase B: ACN; column temperature 40°C, injection volume 5.00 μL, flow rate 0.4 mL / min, gradient elution 22.5 min.

[0051] The liquid chromatography gradient elution program is as follows: from 0 to 1 min, the proportion of mobile phase A decreases from 98% to 95%, and the proportion of mobile phase B increases from 2% to 5%; from 1 to 2 min, the proportion of mobile phase A decreases from 95% to 60%, and the proportion of mobile phase B increases from 5% to 40%; from 2 to 14 min, the proportion of mobile phase A decreases from 60% to 2%, and the proportion of mobile phase B increases from 40% to 98%; from 14 to 20 min, the proportion of mobile phase A decreases from 2% to 1%, and the proportion of mobile phase B increases from 98% to 99%; from 20 to 20.1 min, the proportion of mobile phase A increases from 1% to 98%, and the proportion of mobile phase B decreases from 98% to 2%, and is maintained for 2.4 min.

[0052] Mass spectrometry conditions: positive ion mode, using MS E Centered acquisition mode, mass scan range 50–1200 m / z, cycle time 0.5 s. MS1 collision energy 0 eV, MS2 30–60 eV. Mass spectrometer parameters: capillary voltage 3 kV, source offset voltage 80 V, cone voltage 30 V, ion source temperature 120°C, desolvation temperature 350°C, cone gas flow 50 L / h, desolvation gas flow 800 L / h.

[0053] 3. Analysis results

[0054] The raw mass spectrometry data were normalized for total ions using Progenesis QI software from Waters and then exported. The test sample data were corrected using the QC sample data using support vector regression to reduce batch effects (7). The normalized mass spectrometry data are detailed in the attached file. Mass spectrometry features (retention time-m / z pairs) with >20% missing values ​​in the test samples were removed, and the missing values ​​of the remaining features were filled with 1 / 5 of the minimum value of the feature across all samples. The data after the above processing were used for subsequent analysis.

[0055] The cleaned data in positive and negative ion modes were uploaded to the statistical analysis module of MetaboAnalyst 6.0 database (https: / / www.metaboanalyst.ca).

[0056] Further filter the data by the following criteria:

[0057] (1) relative standard deviation > 25%; (2) variance filtering - interquartile range 40%; (3) mean intensity value 0%, followed by automatic scaling.

[0058] Principal component analysis (PCA) was used to visualize sample similarity. Screening criteria were set as follows: false discovery rate (FDR) <0.05, fold change >2, and variable projection importance (VIP) >1 in orthogonal partial least squares discriminant analysis (OPLS-DA). Selected mass spectral features were identified using Progenesis QI software, based on m / z or molecular weight, isotopic similarity, and fragmentation pattern matching against the Human Metabolome Database (HMDB version 5.0). The mass tolerance for both precursor and product ions was set to 50 ppm. When a single mass spectral feature matched multiple metabolites, the one with the highest score was selected; features that did not match any metabolites in the HMDB were not further investigated.

[0059] Each data set was tested for normality before statistical analysis. Depending on the data distribution, Student's t-test or Mann-Whitney test was used for intergroup comparisons, and Fisher's exact test was used for comparisons of categorical variables. Pearson, Spearman, or Kendall's Tau-b correlation tests were used to assess associations between variables, depending on the data set characteristics. A p-value < 0.05 was considered statistically significant. Logistic regression was used to identify predictors and to calculate combined predictors to distinguish between the two groups. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the predictors. Intergroup comparisons were performed using GraphPad Prism 10 (macOS version), and correlation and regression analyses were performed using IBM SPSS Statistics (macOS version, 29.0.2.0). Two plasma metabolites were identified using the above procedures: fructoseglycine and lysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

[0060] like Figure 1 As shown in the figure, the raw abundances (area under the curve of liquid chromatography-mass spectrometry signal) of fructoseglycine (cutoff value 5600) and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) (cutoff value 54000) detected in the GDM group and the CON group, and the distribution of the calculated value of the joint predictor (A*2.708+B*6.059-9.088) in the two groups (cutoff value 350000) indicated that the raw abundances of fructoseglycine and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) were significantly different in the healthy group and the gestational diabetes group.

[0061] Figure 2 The results showed that in the modeling group, fructoseglycine and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) as markers can effectively identify patients with gestational diabetes.

[0062] Example 2

[0063] The research subjects of this example were derived from gestational diabetes patients and healthy volunteers in the validation group.

[0064] Combined predictor model: calculated value of the combined predictor = signal intensity of fructoseglycine*2.708+signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)*6.059-9.088.

[0065] ROC curves of the two markers and combined predictor models ( Figure 3 ), calculate the area under the curve, that is, AUC area. Figure 3 The results showed that both markers [fructosylglycine and LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0)] demonstrated excellent diagnostic efficacy both independently and using the evaluation model. Specifically, the area under the curve (AUC) for each individual marker exceeded 0.9, while the AUC for the combined predictor constructed based on the two markers reached or exceeded 0.98 in both data sets. Test results predicting a high risk of gestational diabetes met the following criteria: the signal intensity of fructoseglycine, defined as the area under the curve (AUC) of the liquid chromatography-mass spectrometry signal, was greater than 5600; the signal intensity of LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0), defined as the area under the curve (AUC) of the liquid chromatography-mass spectrometry signal, was greater than 54,000; and the calculated value of the combined predictor was greater than 350,000.

[0066] It should be noted that the above-described specific embodiments are illustrative only. Those skilled in the art may devise various solutions based on the disclosure of the present invention, and such solutions fall within the scope of the present invention and are intended to be protected by the present invention. Those skilled in the art should understand that the present description and its accompanying drawings are intended to be illustrative only and are not intended to limit the scope of the claims. The scope of protection of the present invention is defined by the claims and their equivalents.

Claims

1. A diagnostic marker combination for gestational diabetes, characterized in that: Contains fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

2. Use of a combination of diagnostic markers for gestational diabetes mellitus in predicting gestational diabetes mellitus, characterized in that: The diagnostic markers for gestational diabetes mellitus include fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0).

3. The use according to claim 2, characterized in that The application includes: Testing for gestational diabetes or related conditions; Predicting the risk of developing gestational diabetes or a related condition; or Predicting the prognosis of gestational diabetes or related diseases.

4. The use according to claim 2, characterized in that The diagnostic marker for gestational diabetes mellitus is derived from one or more of plasma, serum, and whole blood.

5. A diagnostic and evaluation model for gestational diabetes, characterized in that: The evaluation model uses the abundance of fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) as input variables to predict the risk of gestational diabetes.

6. The diagnostic and evaluation model for gestational diabetes mellitus according to claim 3, wherein: When the signal intensity of fructoseglycine appears, that is, the area under the curve of the liquid chromatography-mass spectrometry signal is higher than 5600; The signal intensity of LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0), i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is greater than 54,000; and / or When the calculated value based on the combined predictor is higher than 350,000, the risk of gestational diabetes mellitus is predicted to be high, among which, The calculation formula is as follows: The calculated value of the combined prediction factor = the signal intensity of fructoseglycine*2.708+the signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)*6.059-9.

088.

7. A diagnostic kit for gestational diabetes, characterized in that: The gestational diabetes diagnostic kit comprises reagents for detecting the abundance of the biomarkers according to claim 1.

8. A diagnostic device for gestational diabetes mellitus, characterized in that: include: An information acquisition module, configured to obtain the abundance of diagnostic markers in a sample from a subject, wherein the diagnostic markers include fructoseglycine and / or lysophosphatidylethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0); and an information processing module for providing a prediction result of gestational diabetes based on the abundance of the diagnostic marker.

9. The gestational diabetes diagnostic device according to claim 8, characterized in that: When the signal intensity of fructoseglycine appears, that is, the area under the curve of the liquid chromatography-mass spectrometry signal is higher than 5600; The signal intensity of LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0), i.e., the area under the curve of the liquid chromatography-mass spectrometry signal, is greater than 54,000; and / or When the calculated value of the combined predictor factor is higher than 350,000, the information processing module gives an assessment result predicting a high risk of gestational diabetes mellitus, where: The calculation formula is as follows: The calculated value of the combined prediction factor = the signal intensity of fructoseglycine*2.708+the signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0)*6.059-9.

088.

10. The gestational diabetes diagnostic device according to claim 8, characterized in that: The gestational diabetes diagnostic device can be used to detect gestational diabetes or related diseases; predict the risk of gestational diabetes or related diseases; or predict the prognosis of gestational diabetes or related diseases.

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