A diagnosis marker combination of gestational diabetes mellitus and application, kit, device, evaluation model thereof

Abstract

The present application belongs to the field of marker detection, and particularly relates to a diagnosis marker combination for gestational diabetes, and application, kit, device and evaluation model thereof. One of the purposes of the present application is to provide application of the detection marker in prediction of gestational diabetes, and the detection marker comprises fructose glycine and / or lysophosphatidyl ethanolamine LysoPE (20:5 (5Z, 8Z, 11Z, 14Z, 17Z) / 0:0). The application comprises: detection of gestational diabetes or related diseases; prediction of the risk of suffering from gestational diabetes or related diseases; or prediction of the prognosis effect of suffering from gestational diabetes or related diseases. The diagnosis marker constructed by the present application is helpful for identification of high-risk GDM population in clinic, and provides important clinical early warning for prevention and treatment of patients with gestational diabetes.

Description

Technical Field

[0001] This invention belongs to the field of biomarker detection, and particularly relates to a combination of diagnostic biomarkers for gestational diabetes mellitus and their applications, reagent kits, devices, and evaluation models. Background Technology

[0002] Gestational diabetes mellitus (GDM) is a pathological condition of abnormal glucose metabolism that first occurs or is discovered during pregnancy. The diagnostic criteria are fasting and / or postprandial blood glucose levels exceeding the gestational threshold. As the most common pregnancy complication globally, 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 significant public health challenge. Epidemiological studies show that with changes in dietary structure, a more sedentary lifestyle, and an increase in the proportion of older mothers, the incidence of GDM has increased significantly in the past decade, further exacerbating 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 fast for 8–12 hours, then ingest 75g of glucose solution within 5 minutes, with three venous blood samples taken: on an empty stomach, 1 hour after glucose ingestion, and 2 hours after ingestion. Although standardized procedures ensure accuracy, the drawbacks of the procedure, such as hunger stress, gastrointestinal reactions, and multiple blood draws, significantly reduce pregnant women's compliance. It is noteworthy that while the glycated hemoglobin (HbA1c) test, widely used in non-pregnant populations, can reflect blood glucose levels over the past 3 months through a single random blood sample, its measurements are systematically low due to changes in erythrocyte metabolic kinetics and blood dilution effects during pregnancy, making it unsuitable for the clinical needs of GDM screening. This technical bottleneck highlights the necessity of developing novel biomarkers, especially establishing a precise testing system that can achieve early risk prediction based on a single random blood sample.

[0004] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the inventors studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features 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-mentioned technical problems, this invention provides a combination of diagnostic biomarkers for gestational diabetes mellitus and their applications, reagent kits, devices, and evaluation models. This invention belongs to the field of biomarker detection.

[0006] One objective of this invention is to provide a diagnostic biomarker combination for gestational diabetes mellitus comprising fructose glycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

[0007] One of the objectives of this invention is to provide the application of a combination of diagnostic biomarkers for gestational diabetes mellitus in the prediction of gestational diabetes mellitus, the diagnostic biomarkers for gestational diabetes mellitus including fructose glycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

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

[0009] Detection of gestational diabetes or related diseases;

[0010] Predicting the risk of developing gestational diabetes or related conditions; 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 this invention is to provide an assessment model for predicting gestational diabetes mellitus, which uses the abundance of fructose glycine and / or lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) as input variables to predict the risk of developing gestational diabetes mellitus.

[0014] According to a preferred embodiment, a high risk of gestational diabetes is predicted when the signal intensity of fructose-glycine, 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 joint predictor is higher than 350000.

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

[0016] The calculated value of the joint predictor is: signal intensity of fructose-glycine * 2.708 + signal intensity of LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) * 6.059 - 9.088.

[0017] One of the objectives of this invention is to provide a kit comprising reagents for detecting the abundance of the aforementioned diagnostic biomarkers.

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

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

[0020] It also includes an information processing module for providing predictions of gestational diabetes based on the abundance of diagnostic biomarkers.

[0021] According to a preferred embodiment, when the signal intensity of fructose-glycine (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 joint predictor factor is higher than 350000, the information processing module provides an assessment result predicting a high risk of gestational diabetes mellitus. Preferably, the information processing module is capable of providing an assessment result diagnosing gestational diabetes mellitus.

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

[0023] The calculated value of the joint predictor is: signal intensity of fructose-glycine * 2.708 + 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] This invention, based on statistical clinical and laboratory test data, identified two factors significantly associated with gestational diabetes mellitus: fructose-glycine and LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0). Positive and negative logistic regression analyses were then performed on these two factors to construct a predictive model using fructose-glycine and LysoPE(20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) as independent risk factors. The model was calculated as A*2.708+B*6.059-9.088 (where A is the signal intensity of fructose-glycine 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 plot were used to evaluate the model's discriminative and calibrated properties. The results showed that the biomarkers had better predictive value for GDM than traditional models. The biomarkers constructed in this invention can help identify high-risk GDM populations in clinical practice and provide important clinical early warning for the prevention and treatment of gestational diabetes mellitus. Attached Figure Description

[0027] Figure 1 Figure A and Figure B are the abundance diagrams of the biomarkers of the present invention, where Figure A and Figure B are the original abundances of fructose glycine and (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0), respectively; Figure C is the calculated value of the joint predictor.

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

[0029] Figure 3 This is the ROC curve analysis of the test set of this invention, namely the distinguishing power of fructose glycine, LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) and its combined predictor on the GDM group and the CON group. Detailed Implementation

[0030] In the description of this invention, terminology is used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly defined.

[0031] The present invention is further illustrated below with reference to specific embodiments. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Furthermore, any methods and materials similar to or equivalent to those described herein may be applied to the methods of the present invention. The preferred embodiments and materials described herein are for illustrative purposes only.

[0032] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0033] The embodiments of the present invention will be specifically explained below with reference to the accompanying drawings. The embodiments of the present invention utilize chromatography-mass spectrometry to detect metabolites in plasma samples and determine diagnostic markers for gestational diabetes mellitus.

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

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

[0036] Those skilled in the art will understand that devices and modules for predicting the likelihood of a subject having diabetes can be implemented in various ways. In some embodiments, the device and modules can be implemented using hardware, software, or a combination of both. Specifically, the hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system (such as a microprocessor or dedicated hardware). Furthermore, the device and modules of the present invention can be implemented using hardware circuits such as very large-scale integrated circuits, gate arrays, logic chips, transistors, field-programmable gate arrays, and programmable logic devices, as well as software executed by various types of processors, or a combination of hardware circuits and software (such as firmware). These methods and devices can be implemented using 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 read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier.

[0037] The subjects in Examples 1 and 2 described below were selected volunteers. All volunteers included in the study signed informed consent forms before plasma sample collection. Eighty-nine patients with gestational diabetes mellitus (GDM) were selected from the GDM database. All participants underwent a 75g OGTT screening for GDM according to standard clinical procedures, and plasma samples were collected from 71 healthy volunteers corresponding to their age and sex ratio. The 89 selected GDM patients and 71 healthy volunteers were randomly assigned to a modeling group and a validation group in a 3:2 ratio.

[0038] Example 1

[0039] 1. Research Subjects

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

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

[0042] 2.1 Instruments, Materials and Reagents

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

[0044] Formic acid, ammonium formate, methanol, acetonitrile (chromatographic grade, Merck, Germany); purified water was prepared using a Nanopure purification system (Barnstead, USA).

[0045] 2.2 Plasma Sample Pretreatment

[0046] Test sample: Take 200 μL of plasma sample and add 1 mL of methanol-acetonitrile mixture (1:1, V / V) for protein precipitation. Centrifuge at 12,000 rpm for 5 minutes at 4 °C using an Eppendorf 5920R centrifuge (Hamburg, Germany). Transfer the supernatant to a 1.5 mL centrifuge tube, dry it at 40 °C using a Labconco CentriVap vacuum desiccator (Missouri, USA), and then reconstitute it with 100 μL of 5% methanol.

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

[0048] 2.3 Detection using liquid chromatography-mass spectrometry

[0049] Chromatographic conditions:

[0050] Chromatographic columns: 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℃; flow rate: 0.4 mL / min; injection volume: 5 μL; mobile phase A was an aqueous solution of 0.1% (v / v) formic acid and 5 mM ammonium formate, mobile phase B was ACN, column temperature: 40℃, injection volume: 5.00 μL, flow rate: 0.4 mL / min, gradient elution for 22.5 min.

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

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

[0053] 3. Analysis Results

[0054] Raw mass spectrometry data were exported after total ion normalization using Waters' Progenesis QI software. Support vector regression was used to correct the test sample data with QC sample data to reduce batch effects (7). The normalized mass spectrometry data are detailed in the appendix to this document. Mass spectrometry features (retention time-m / z pairs) with missing values ​​>20% in the test samples were removed, and the missing values ​​of the remaining features were filled with 1 / 5 of the minimum value of that feature across all samples. The data after the above processing were used for subsequent analysis.

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

[0056] Further filter the data according to 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 detection rate (FDR) < 0.05, fold change > 2, and variable projection importance (VIP) > 1 in orthogonal partial least squares discriminant analysis (OPLS-DA). Selected mass spectrometry features were identified using Progenesis QI software based on the Human Metabolome Database (HMDB version 5.0) by matching m / z or molecular weight, isotopic similarity, and fragmentation patterns. The mass tolerance for both precursor and product ions was set to 50 ppm. When a single mass spectrometry feature matched multiple metabolites, the one with the highest score was selected; features that did not match any metabolites in HMDB were not further investigated.

[0059] Normality tests were performed on each dataset before statistical analysis. Based on the data distribution characteristics, Student's t-test or Mann-Whitney test was used for inter-group comparisons, and Fisher's exact test was used for comparisons of categorical variables. Pearson, Spearman, or Kendall's Tau-b correlation tests were selected to assess the association between indicators based on the dataset characteristics. A p-value <0.05 was considered statistically significant. Logistic regression was used to screen predictors and calculate combined predictors to distinguish between the two groups. The diagnostic efficacy of the predictors was evaluated using receiver operating characteristic (ROC) curves. Inter-group 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). Based on the above steps, two plasma metabolites were identified: fructose-glycine and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

[0060] like Figure 1 As shown, the original abundances (area under the curve of the liquid chromatography-mass spectrometry signal) of fructose glycine (cutoff value 5600) and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) (cutoff value 54000) detected in the GDM and CON groups, 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) indicate that the original abundances of fructose glycine and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) are significantly different between the healthy group and the gestational diabetes mellitus group.

[0061] Figure 2 The study showed that in the modeling group, fructose glycine and LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) were effective markers for identifying patients with gestational diabetes mellitus.

[0062] Example 2

[0063] The subjects of this study were patients with gestational diabetes and healthy volunteers in the validation group.

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

[0065] The ROC curve of the model using two biomarkers and a joint predictor model ( Figure 3 Calculate the area under the curve, i.e., the AUC area. According to... Figure 3 The results showed that both biomarkers [fructoglycine 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) of each individual biomarker exceeded 0.9, while the AUC of the combined predictive factor constructed based on both biomarkers reached or exceeded 0.98 in both datasets. The results predicting a high risk of gestational diabetes mellitus met the following criteria: a signal intensity of fructose-glycine (AUC) greater than 5600; a signal intensity of LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) greater than 54000; and a calculated value of the combined predictive factor greater than 350000.

[0066] It should be noted that the specific embodiments described above are exemplary, and those skilled in the art can devise various solutions inspired by the disclosure of this invention. These solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents.

Claims

1. The application of a combination of diagnostic biomarkers for gestational diabetes mellitus in the preparation of reagents for the detection of gestational diabetes mellitus, characterized in that, The diagnostic markers for gestational diabetes mellitus include fructose glycine and lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0).

2. The application according to claim 1, characterized in that, The diagnostic markers for gestational diabetes mellitus are derived from one or more of plasma, serum, and whole blood.

3. A diagnostic device for gestational diabetes mellitus, characterized in that, include: The information acquisition module is used to acquire the abundance of diagnostic markers in the subject samples, wherein the diagnostic markers include fructose glycine and lysophosphatidylethanolamine LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0). And an information processing module for providing predictions of gestational diabetes based on the abundance of the diagnostic biomarkers.

4. The gestational diabetes diagnostic device according to claim 3, characterized in that, When it appears The signal intensity of fructose-glycine, 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 greater than 54000; and / or When the calculated value of the joint predictor factor is higher than 350,000, the information processing module provides an assessment result predicting a high risk of gestational diabetes mellitus. The calculation formula is as follows: The calculated value of the joint predictor is equal to the signal strength of fructose-glycine. Signal strength of 2.708 + LysoPE (20:5(5Z,8Z,11Z,14Z,17Z) / 0:0) 6.059-9.088。

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