A biomarker for predicting or diagnosing gestational diabetes mellitus and use thereof

Abstract

The application belongs to the technical field of screening of diagnostic markers for gestational diabetes, and particularly relates to a biomarker for predicting or diagnosing gestational diabetes and application thereof. The application discloses a biomarker for predicting or diagnosing gestational diabetes, wherein the biomarker is selected from one or more of hypoxanthine, cortisol, dihydrostercorol and cycloleucine, and more preferably comprises hypoxanthine, cortisol, dihydrostercorol, (3R, 4R)-D-erythrulose, creatine, cycloleucine and L-hydroxyproline. The seven biomarkers disclosed in the application can be used alone or in combination to predict or diagnose gestational diabetes in the early stage of pregnancy through plasma detection, effectively improve the early diagnosis rate of gestational diabetes, improve the prognosis of the disease, and have clinical use and promotion value.

Description

Technical Field

[0001] This application belongs to the field of gestational diabetes diagnosis and testing technology, specifically relating to a biomarker for predicting or diagnosing gestational diabetes and its application. Background Technology

[0002] Metabolomics is a discipline that performs qualitative and quantitative analysis of small molecule metabolites with a relative molecular weight of less than 1000 in the body. Metabolomics analysis can reflect the physiological and pathological conditions of the body and distinguish differences between individuals. With the development of mass spectrometry technology, liquid chromatography-mass spectrometry (LC-MS) has become a core tool for the discovery of metabolic biomarkers due to its high sensitivity and broad spectral coverage.

[0003] Gestational diabetes mellitus (GDM) is a pregnancy complication. Currently, clinical diagnosis is mainly based on testing the pregnant woman's blood sugar during the second trimester. Since gestational diabetes can lead to fetal malformations and, in severe cases, endanger the health of both mother and child, the earlier gestational diabetes is diagnosed, the better for the patient.

[0004] Currently, the diagnostic criteria for gestational diabetes mellitus (GDM) vary across different countries and regions. In my country, the oral glucose tolerance test (OGTT) of 75 grams, performed between 24 and 28 weeks of gestation, is used as the diagnostic method for GDM. The diagnostic criteria are: 5.1 mmol / L ≤ fasting plasma glucose (FPG) < 7.0 mmol / L, OGTT 1-hour blood glucose ≥ 10.0 mmol / L, and 8.5 mmol / L ≤ OGTT 2-hour blood glucose < 11.1 mmol / L. A blood glucose level meeting any of these criteria at any time point is sufficient for a diagnosis of GDM. However, an FPG ≥ 5.1 mmol / L alone in early pregnancy is not sufficient for a diagnosis and requires follow-up. Recent studies have found a significant correlation between gestational diabetes mellitus diagnosed in early pregnancy and adverse pregnancy outcomes. For example, early-onset gestational diabetes mellitus may occur before 20 weeks of gestation. If screening is only conducted between 24 and 28 weeks, the optimal window for early intervention may be missed, leading to the failure to detect and manage some potentially hyperglycemic pregnant women in a timely manner, increasing the risk of adverse maternal and fetal outcomes.

[0005] Therefore, it is urgent to explore biomarkers or optimal combinations of tests for earlier prediction or diagnosis of gestational diabetes risk in individuals, and to create effective and inexpensive methods for early diagnosis and screening. Summary of the Invention

[0006] To overcome the aforementioned technical problems, this application discloses a biomarker for predicting or diagnosing gestational diabetes mellitus. This biomarker can predict or diagnose gestational diabetes mellitus through plasma testing in early pregnancy, effectively improving the early diagnosis rate of gestational diabetes mellitus, improving disease prognosis, and has clinical application and promotion value.

[0007] In a first aspect, this application provides a biomarker for predicting or diagnosing gestational diabetes, said biomarker being selected from one or more of hypoxanthine, cortisol, dihydrotachysterol, and cyclic leucine.

[0008] Furthermore, the biomarkers also include one or more of (3R,4R)-D-erythrolide, creatine, and L-hydrogenated orotic acid.

[0009] Furthermore, the biomarkers include hypoxanthine, cortisol, dihydrotachysterol, cyclic leucine, and L-hydrogenated orotic acid.

[0010] Furthermore, the biomarkers include hypoxanthine, dihydrotachysterol, (3R,4R)-D-erythrolide, creatine, cyclic leucine, and L-hydrogenated orotic acid.

[0011] Furthermore, the biomarkers include hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythrolide, creatine, cyclic leucine, and L-hydrogenated orotic acid.

[0012] Secondly, this application also provides a product for predicting or diagnosing gestational diabetes, characterized in that the kit includes detection reagents for the biomarkers described in the first aspect.

[0013] Furthermore, the detection reagent is a reagent used to detect the content of the biomarker in the subject sample.

[0014] In some embodiments, the content of the biomarker can be obtained by any existing known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, gas chromatography-mass spectrometry (GC-MS), chromatographic mass spectrometry (CC-MS), liquid chromatography-tandem mass spectrometry (LC-MS-MS), nuclear magnetic resonance spectroscopy (NMR), immunochromatographic test strips, immunoreaction chips, capillary electrophoresis, infrared spectroscopy, etc., as long as it can be used to detect the content of biomarkers in the sample.

[0015] Furthermore, the kit also includes a dissolving reagent, an extraction reagent, and an internal standard, wherein the internal standard is L-phenylalanine.

[0016] Thirdly, this application also provides the use of the detection reagents for the biomarkers described in the first aspect in the preparation of products for predicting or diagnosing gestational diabetes.

[0017] Furthermore, the detection reagent is a reagent used to detect the content of the biomarker in the subject sample.

[0018] In some embodiments, the content of the biomarker can be obtained by any existing known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, gas chromatography-mass spectrometry (GC-MS), chromatographic mass spectrometry (CC-MS), liquid chromatography-tandem mass spectrometry (LC-MS-MS), nuclear magnetic resonance spectroscopy (NMR), immunochromatographic test strips, immunoreaction chips, capillary electrophoresis, infrared spectroscopy, etc., as long as it can be used to detect the content of biomarkers in the sample.

[0019] Furthermore, the sample is blood, plasma, or serum.

[0020] Furthermore, the product may be a chip, reagent, test strip, drug, formulation, reagent kit, or high-throughput screening platform.

[0021] Fourthly, this application also provides a system for predicting or diagnosing gestational diabetes mellitus. The system includes a data analysis module, which constructs a sample dataset based on the relative abundance of the biomarkers described in the first aspect, divides the sample dataset into a test set and a training set, and constructs and trains the model for predicting or diagnosing gestational diabetes mellitus using machine learning methods.

[0022] Furthermore, an early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, cycloleucine, and cortisol. The score was calculated as follows: Score = 0.00979 - 0.61212 × cycloleucine - 0.47657 × hypoxanthine - 0.29983 × cortisol - 0.4059 × dihydrotachysterol + 0.23193 × L-hydroorotic acid. The critical value of the model was 0.493. The relative abundance of the corresponding compounds in the serum markers was input into the model. When the score was ≤ 0.493, the probability of predicting or diagnosing early gestational diabetes mellitus was low; when the score was > 0.493, the probability of predicting or diagnosing early gestational diabetes mellitus was high.

[0023] Furthermore, an early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythroside lactone, cyclic leucine, and creatine. The score was calculated as follows: Score = 0.01387 - 0.61979 × cyclic leucine - 0.44462 × hypoxanthine - 0.46101 × dihydrotachysterol - 0.179 × (3R,4R)-D-erythroside lactone - 0.35281 × creatine + 0.18452 × L-hydroorotic acid. The critical value of the model was 0.449. The relative abundance of the corresponding compounds in the serum was input into the model. When the score was ≤ 0.449, the probability of predicting or diagnosing early gestational diabetes mellitus was low; when the score was > 0.449, the probability of predicting or diagnosing early gestational diabetes mellitus was high.

[0024] Furthermore, an early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, cortisol, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythroside lactone, cyclic leucine, and creatine. The score was calculated as follows: Score = 0.04744 × hypoxanthine - 0.40976 × cortisol - 1.25761 × dihydrotachysterol - 0.27981 × (3R,4R)-D-erythroside lactone - 1.70999 × cyclic leucine - 0.83457 × creatine + 0.36254 × L-hydroorotic acid. When the score was ≤0.396, the probability of predicting or diagnosing early gestational diabetes mellitus was low; when the score was >0.396, the probability of predicting or diagnosing early gestational diabetes mellitus was high.

[0025] Furthermore, the system also includes a data storage module, a data input interface, and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0026] Fifthly, this application also provides a method for screening the biomarkers described in the first aspect, the screening method comprising the following steps:

[0027] 1) Samples were collected from the healthy control group and the gestational diabetes patient group, respectively;

[0028] 2) LC-MS was used to detect samples from healthy control groups and gestational diabetes patients, and candidate differentially expressed metabolites were obtained through discriminant analysis;

[0029] 3) Receiver operating characteristic (ROC) analysis was performed on the differential metabolites and their combinations to identify metabolic biomarkers for early prediction or diagnosis of gestational diabetes.

[0030] Furthermore, the conditions for the LC-MS detection are as follows:

[0031] Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm×100mm;

[0032] Mobile phase: Phase A is an aqueous solution containing 0.04% acetic acid, and Phase B is an acetonitrile solution containing 0.04% acetic acid; flow rate: 0.4 mL / min.

[0033] The elution gradient program is as follows:

[0034] At 0 min, the volume ratio of phase A to phase B was 95:5;

[0035] At 11.0 min, the volume ratio of phase A to phase B was 10:90;

[0036] At 12.0 min, the volume ratio of phase A to phase B was 10:90;

[0037] At 12.1 min, the volume ratio of phase A to phase B was 95:5;

[0038] At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0039] Sixthly, this application also provides a method for predicting or diagnosing gestational diabetes, the method comprising the following steps:

[0040] 1) Obtain detection data of biomarkers in biological samples of subjects, wherein the biomarkers include the biomarkers described in the first aspect of this application;

[0041] 2) The detection data are processed using the prediction or diagnosis model for gestational diabetes obtained in the fourth aspect of this application to output the prediction or diagnosis results for gestational diabetes.

[0042] Compared with existing technologies, this application compares the differences in plasma metabolomes between healthy pregnant women and pregnant women with gestational diabetes mellitus, and screens out seven metabolic biomarkers for the early diagnosis of gestational diabetes mellitus. The area under the ROC curve (AUC) of a single metabolic biomarker is greater than 0.6, ranging from 0.643 to 0.839. The performance of combinations of multiple metabolic biomarkers is significantly better than that of single metabolic biomarkers, with AUC values ​​ranging from 0.722 to 0.971. Using the plasma metabolic biomarkers of this invention for single or combined detection and diagnosis can greatly improve the early diagnosis rate of gestational diabetes mellitus, while also offering advantages in clinical accessibility and cost-effectiveness. Attached Figure Description

[0043] The accompanying drawings, which are provided to further illustrate this application and form part of this application, do not constitute an undue limitation thereof.

[0044] Figure 1 The OPLS-DA statistic is based on the metabolite from Example 1.

[0045] Figure 2 Volcano plot of differential metabolites in the healthy group and the disease group.

[0046] Figure 3 Box plots showing the content of metabolites screened in the healthy group and the disease group.

[0047] Figure 4 The ROC curves for the training and testing sets of the five different metabolite combinations provided in Example 2 are shown.

[0048] Figure 5 The ROC curves for the training and testing sets of the six metabolite combinations provided in Example 2 are shown.

[0049] Figure 6 The ROC curves for the training and testing sets of the seven metabolite combinations provided in Example 2 are shown. Detailed Implementation

[0050] The present application will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the present application.

[0051] The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the specific embodiments of the invention without inventive effort are within the protection scope of the invention.

[0052] In the embodiments, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the embodiments of the present invention, unless specifically specified, the technical means used are all conventional means well known to those skilled in the art.

[0053] Example 1

[0054] This embodiment provides a method for screening plasma metabolic markers for early gestational diabetes mellitus, including the following steps.

[0055] S1. Sample collection

[0056] With the consent of the patients, this study collected peripheral venous blood plasma samples from 42 healthy pregnant women (healthy control group) and 42 pregnant women with gestational diabetes mellitus (disease group) at 6-13 weeks of gestation from the Clinical Medical Research Center. All samples were taken from individuals with no history of other malignant tumors, other major systemic diseases, or chronic diseases requiring long-term medication. Age, weight, height, and BMI were matched among the groups.

[0057] Blood samples were collected in the early morning on an empty stomach. All plasma samples were centrifuged and stored at -80°C. Samples were thawed before each study for subsequent analysis.

[0058] S2, broadly targeted plasma metabolomics analysis

[0059] (1) Sample pretreatment

[0060] Remove the samples collected in step S1 from the -80 °C freezer and thaw them on ice until no ice remains (all subsequent operations must be performed on ice). After thawing, vortex for 10 seconds to mix, and add 50 µL of the sample to the corresponding numbered centrifuge tube. Add 300 µL of pure methanol internal standard extraction buffer (containing 100 ppm L-phenylalanine internal standard). Vortex for 5 min, let stand for 24 h, and then centrifuge at 12000 r / min and 4 °C for 10 min. Collect 270 µL of the supernatant and concentrate for 24 h. Add 100 µL of the reconstitution solution (composed of acetonitrile and water in a 1:1 volume ratio) for LC-MS / MS analysis. Take 20 µL of each sample and mix them to form a quality control sample (QC), which is collected every 15 samples.

[0061] (2) Detection of metabolites in samples

[0062] The liquid chromatography conditions were determined as follows: column: Waters ACQUITY UPLC HSS T3 C18 1.8 µm, 2.1 mm × 100 mm; column temperature: 40 °C; injection volume: 2 µL.

[0063] Mobile phases: Phase A was an aqueous solution containing 0.04% acetic acid, and Phase B was an acetonitrile solution containing 0.04% acetic acid. The elution gradient program was as follows: 0 min, volume ratio of Phase A to Phase B was 95:5; 11.0 min, volume ratio of Phase A to Phase B was 10:90; 12.0 min, volume ratio of Phase A to Phase B was 10:90; 12.1 min, volume ratio of Phase A to Phase B was 95:5; 14.0 min, volume ratio of Phase A to Phase B was 95:5, flow rate was 0.4 mL / min.

[0064] The mass spectrometry conditions were determined as follows: electrospray ionization (ESI) temperature 500℃, mass spectrometry voltage 5500 V (positive) or -4500 V (negative), ion source gas I (GS I) 55 psi, gas II (GS II) 60 psi, curtain gas (CUR) 25 psi, and collision-activated dissociation (CAD) parameter set to high.

[0065] In the triple quadrupole (Qtrap), each ion pair is detected by MRM mode scanning based on optimized declustering potential (DP) and collision energy (CE).

[0066] Samples were analyzed under defined liquid chromatography and mass spectrometry conditions: 20% of samples from the healthy control group and 20% of the gestational diabetes mellitus group were randomly selected. Metabolomics methods combining enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) with multiple reaction monitoring (MRM) acquisition mode were employed, and a gestational diabetes mellitus plasma metabolite database was constructed by integrating a local standard database. The collected plasma samples were analyzed using liquid chromatography-mass spectrometry and the constructed gestational diabetes mellitus plasma metabolite database to obtain raw mass spectrometry data for each plasma sample.

[0067] (3) Preprocessing and integration of peak area in the spectrum

[0068] Based on a database of plasma-specific metabolites in gestational diabetes mellitus, mass spectrometry was used for qualitative and quantitative analysis of metabolites in the samples. Liquid chromatography (LC) can separate metabolites of different molecular weights. A triple quadrupole multiple reaction monitoring (MRM) mode was used to screen for characteristic ions of each substance, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The sample mass spectrometry file was opened using MultiQuant software. The raw mass spectrometry data was preprocessed and corrected according to the mass-to-charge ratio and retention time. Peak integration and correction were performed. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Peaks with a S / N > 5 and a retention time shift not exceeding 0.2 min were retained. The relative content information of metabolites was obtained by calculating the peak area based on the mass spectrometry peak intensity. Finally, all integrated peak area data were exported and saved for further statistical analysis.

[0069] (4) Experimental quality control

[0070] By overlaying and analyzing the total ion chromatograms of mass spectrometry analysis of different QC samples, the repeatability of metabolite extraction and detection, i.e., technical repeatability, can be determined. The high stability of the instrument provides crucial assurance for data repeatability and reliability. The coefficient of variation (CV) is the ratio of the standard deviation to the mean of the original data, reflecting the degree of data dispersion. Using the empirical cumulative distribution function (ECDF), the frequency of CV values ​​for substances with values ​​less than the reference value can be analyzed. A higher proportion of substances with lower CV values ​​in the QC samples indicates more stable experimental data: a proportion of substances with CV values ​​less than 0.5 exceeding 85% indicates relatively stable experimental data; a proportion of substances with CV values ​​less than 0.3 exceeding 75% indicates very stable experimental data. Simultaneously monitoring the change in the CV value of the L-phenylalanine internal standard during detection, a change of less than 20% in the internal standard CV value indicates good instrument stability during detection.

[0071] (5) Data processing and analysis

[0072] All peak area integral data from the sample tests were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis. Figure 1 By establishing an orthogonal partial least squares discriminant analysis (OPLS-DA) model, we identified metabolites (VIP>1.0) that significantly contributed to the difference between the disease group and the healthy control group. Figure 2 The size of the midpoint was labeled as the VIP value, and metabolites with a VIP > 1.0 were screened. Then, a T-test was performed, with a P-value < 0.05 as the statistical significance criterion. Finally, metabolites with a VIP > 1.0 and a P-value < 0.05 were selected, which may be potential metabolic biomarkers for gestational diabetes mellitus diagnosis.

[0073] The above analysis screened potential metabolic biomarkers for gestational diabetes mellitus. Based on their retention times, primary and secondary mass spectrometry data, the molecular weight and molecular formula of the biomarkers were inferred and compared with spectral information in a metabolite spectral database for qualitative identification. Finally, by purchasing standards and comparing their molecular weight, chromatographic retention times, and corresponding multi-stage MS fragmentation spectra, the structure of the metabolic biomarkers was verified.

[0074] Seven differentially expressed metabolites that can be used to diagnose and differentiate early gestational diabetes mellitus were screened using binary logistic regression stepwise method: hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythrolide, creatine, cyclic leucine, and L-hydrogenated orotic acid. Specific information on the metabolites is shown in Tables 1 and 2 below.

[0075] Table 1. Seven plasma metabolic markers used for early diagnosis or screening of gestational diabetes.

[0076]

[0077] Table 2. Differences in metabolites between the gestational diabetes mellitus group and the healthy control group.

[0078]

[0079] From the above table 2 and Figure 3 It can be seen that, compared with the healthy control group, the content of L-hydrogenated orotic acid was increased in the gestational diabetes patient group, while the content of metabolites such as hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythrolide, creatine and cycloleucine decreased simultaneously.

[0080] Example 2: Construction of a model for predicting or diagnosing gestational diabetes mellitus

[0081] 1. Models constructed using combinations of different markers

[0082] In this embodiment, a model constructed using the seven biomarkers obtained in Example 1—hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythrolide, creatine, cyclic leucine, and L-hydrogenated orotic acid—was studied.

[0083] While a single biomarker can predict the likelihood of a subject having early gestational diabetes, generally, combining multiple biomarkers results in higher accuracy in differentiation or prediction. However, a single biomarker that is more accurate in predicting early gestational diabetes does not necessarily have a greater effect in combination with one or more other biomarkers. Furthermore, a higher number of biomarkers does not necessarily lead to higher predictive accuracy (AUC value) for the combined combination; therefore, extensive validation experiments are still needed.

[0084] The study dataset consisted of peripheral venous blood plasma samples from 84 healthy pregnant women (healthy control group) and 84 pregnant women with gestational diabetes mellitus (disease group) during weeks 6-13 of gestation. All samples were from individuals with no history of other malignant tumors, other major systemic diseases, or chronic diseases requiring long-term medication. Age, weight, height, and BMI were matched among the groups. The 168 plasma samples were randomly divided into a training set and a test set. The training set included plasma samples from 42 healthy pregnant women (healthy control group) and 42 pregnant women with gestational diabetes mellitus (disease group), while the test set also included plasma samples from these two groups.

[0085] In the training set, a joint predictive model for multiple biomarkers was constructed using a combination of machine learning methods. The area under the receiver operating characteristic (ROC) curve (AUC) was estimated using predicted probability values ​​with 95% confidence intervals (CI) to evaluate the discriminative power of the multivariate predictive model. Furthermore, the ROC values ​​for individual biomarkers and different combinations were constructed and compared. Statistical analysis was performed using R3.6.1, and a p-value less than 0.05 was considered statistically significant.

[0086] Table 3 Comparison of the areas under the ROC curves of models constructed with different combinations of biomarkers in the training group

[0087]

[0088] Table 4. AUC values ​​of any combination of metabolites for the prediction or diagnosis of gestational diabetes.

[0089]

[0090] As shown in Table 3, the four differentially metabolites, namely hypoxanthine, dihydrotachysterol, cycloleucine, and cortisol, are individually effective in the early diagnosis of gestational diabetes mellitus, with an area under the ROC curve (AUC) greater than 0.7, which is clinically significant.

[0091] At the same time, by Figure 4-6 The results show that some of the selected combinations of metabolic biomarkers and their models have good diagnostic performance. The statistical results are as follows.

[0092] A diagnostic model for early gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, cyclic leucine, and cortisol. The combined AUC of 0.945, sensitivity 0.954, and specificity 0.851 for early prediction or diagnosis of gestational diabetes mellitus.

[0093] A diagnostic model for early gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythrolide, cyclic leucine, and creatine. The combined AUC of 0.951, sensitivity 0.919, and specificity 0.885 for early prediction or diagnosis of gestational diabetes mellitus.

[0094] Depend on Figure 5 The results show that when these seven differentially metabolites are used in combination for early prediction or diagnosis of gestational diabetes, the AUC is further improved. The AUC of the seven combined in the diagnosis of early gestational diabetes reaches 0.971, with a sensitivity of 0.965 and a specificity of 0.862.

[0095] 2. Optimization of model parameters

[0096] Selecting combinations of biomarkers to construct predictive or diagnostic models for gestational diabetes.

[0097] The model is a generalized linear model, and its equation is: .

[0098] Where Y is the score, i represents the i-th biomarker, and m represents the number of biomarkers. Xi This represents the detection value (relative abundance value) of the i-th biomarker. Ki Let represent the coefficient of the i-th biomarker, and b be a constant.

[0099] The complete model equations are as follows.

[0100] An early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, cycloleucine, and cortisol. The score was calculated as follows: Score = 0.00979 - 0.61212 × cycloleucine - 0.47657 × hypoxanthine - 0.29983 × cortisol - 0.4059 × dihydrotachysterol + 0.23193 × L-hydroorotic acid. The critical value of the model was 0.493. The relative abundance of the corresponding compounds in the serum was input into the model. When the score was ≤ 0.493, the probability of predicting or diagnosing early gestational diabetes mellitus was low; when the score was > 0.493, the probability of predicting or diagnosing early gestational diabetes mellitus was high.

[0101] An early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythroside lactone, cyclic leucine, and creatine. The score was calculated as follows: Score = 0.01387 - 0.61979 × cyclic leucine - 0.44462 × hypoxanthine - 0.46101 × dihydrotachysterol - 0.179 × (3R,4R)-D-erythroside lactone - 0.35281 × creatine + 0.18452 × L-hydroorotic acid. The critical value of the model was 0.449. The relative abundance of the corresponding compounds in the serum was input into the model. When the score was ≤ 0.449, the probability of predicting or diagnosing early gestational diabetes mellitus was low; when the score was > 0.449, the probability of predicting or diagnosing early gestational diabetes mellitus was high.

[0102] An early prediction or diagnostic model for gestational diabetes mellitus was constructed using hypoxanthine, cortisol, dihydrotachysterol, L-hydrogenated orotic acid, (3R,4R)-D-erythroside lactone, cyclic leucine, and creatine. The score was calculated as follows: Score = 0.04744 - 0.78674 × hypoxanthine - 0.40976 × cortisol - 1.25761 × dihydrotachysterol - 0.27981 × (3R,4R)-D-erythroside lactone - 1.70999 × cyclic leucine - 0.83457 × creatine + 0.36254 × L-hydrogenated orotic acid, the critical value of the model is 0.396, and the relative abundance of the corresponding compounds of the marker in the serum is input into the model: when the score is ≤0.396, the probability of predicting or diagnosing early gestational diabetes is low, and when the score is >0.396, the probability of predicting or diagnosing early gestational diabetes is high.

[0103] Example 3: Construction of a model for predicting or diagnosing gestational diabetes mellitus

[0104] The constructed model was validated on the test set of Example 2, and ROC curves were plotted, such as... Figure 4-6 As shown,

[0105] When using five biomarkers for joint detection, the predictive model achieved an AUC of 0.911, sensitivity of 0.846, and specificity of 0.846 in the test group. With six biomarkers, the AUC was 0.928, sensitivity was 0.862, and specificity was 0.923. With seven biomarkers, the AUC was 0.967, sensitivity was 0.942, and specificity was 0.851. This demonstrates that the sensitivity and specificity of using five, six, or seven biomarkers in combination are consistent with the results in the training set, exhibiting good predictive performance and accuracy. Furthermore, the combination of seven biomarkers demonstrates the best diagnostic efficacy.

[0106] Example 4

[0107] This embodiment provides a kit for early prediction or diagnosis of gestational diabetes, the kit comprising:

[0108] 1) Standards of metabolic markers: hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythritol lactone, creatine, cyclic leucine and L-hydrogenated orotic acid, individually packaged or mixed in packaging;

[0109] 2) Solvents: Pure methanol and 50% acetonitrile aqueous solution, used for sample extraction; 50% acetonitrile aqueous solution can be used as a solvent for dissolving standards;

[0110] 3) Internal standard: L-phenylalanine.

[0111] Example 5

[0112] The screening method using the test kit for early prediction or diagnosis of gestational diabetes in Example 4 includes the following steps.

[0113] S1, collect plasma samples, preprocess them, and obtain test solutions.

[0114] S2, the test solution was analyzed by LC-MS to obtain information on the relative abundance of hypoxanthine, cortisol, dihydrotachysterol, (3R,4R)-D-erythritol lactone, creatine, cyclic leucine and L-hydrogenated orotic acid.

[0115] S3, based on the above information on the changes in the content (relative abundance) of metabolic markers, calculate the score according to the model in Example 2;

[0116] Using a combined diagnostic method involving hypoxanthine, dihydrotachysterol, L-hydroorotic acid, cyclic leucine, and cortisol, a score ≤ 0.493 indicates a low probability of predicting or diagnosing early gestational diabetes mellitus, while a score > 0.493 indicates a high probability.

[0117] Using a combined diagnostic method involving hypoxanthine, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythrolide, cyclic leucine, and creatine, a score ≤ 0.449 indicates a low probability of predicting or diagnosing early gestational diabetes mellitus (EGDM), while a score > 0.449 indicates a high probability.

[0118] Using a combined assay of hypoxanthine, cortisol, dihydrotachysterol, L-hydroorotic acid, (3R,4R)-D-erythrolide, cyclic leucine, and creatine, a score ≤ 0.396 indicated a low probability of being diagnosed with early gestational diabetes mellitus, while a score > 0.396 indicated a high probability of being diagnosed with early gestational diabetes mellitus.

[0119] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A biomarker for predicting or diagnosing gestational diabetes mellitus, characterized by, The biomarker is any one of, 1) a combination comprising hypoxanthine, cortisol, dihydrostercols, cycloleucine and L-hydromorphone; or, 2) a combination comprising hypoxanthine, dihydrostercols, (3R,4R)-D-erythrulose lactone, creatine, cycloleucine and L-hydromorphone; or, 3) a combination comprising hypoxanthine, cortisol, dihydrostercols, (3R,4R)-D-erythrulose lactone, creatine, cycloleucine and L-hydromorphone.

2. A product for predicting or diagnosing gestational diabetes, characterized by, The product comprises the biomarker of claim 1, and the product is a chip, a reagent, a test paper, a drug, a preparation, a kit or a high-throughput screening platform.

3. Use of a detection reagent for a biomarker according to claim 1 for the manufacture of a product for predicting or diagnosing gestational diabetes, characterized in that, The product is a chip, a reagent, a test paper, a drug, a preparation, a kit or a high-throughput screening platform.

4. Use according to claim 3, characterized in that, The detection reagent is a reagent for detecting the content of the biomarker in a sample of a subject.

5. Use according to claim 4, characterized in that, The sample is blood, plasma or serum.

6. A system for predicting or diagnosing gestational diabetes, characterized by, The system comprises a data analysis module, which constructs a sample data set based on the detected content of the biomarker of claim 1, divides the sample data set into a training set and a test set, and constructs and trains the model for predicting or diagnosing gestational diabetes mellitus by a machine learning method, 1) the model for predicting or diagnosing gestational diabetes mellitus constructed by using hypoxanthine, dihydrostercols, L-hydromorphone, cycloleucine and cortisol is score = 0.00979-0.61212 x cycloleucine-0.47657 x hypoxanthine-0.29983 x cortisol-0.4059 x dihydrostercols+0.23193 x L-hydromorphone, the critical value of the model is 0.493, and when the relative abundance of the corresponding compound of the marker in serum is input into the model, the possibility of predicting or diagnosing an early gestational diabetes mellitus patient is low when the score is ≤0.493, and the possibility of predicting or diagnosing an early gestational diabetes mellitus patient is high when the score is >0.493; 2) the model for predicting or diagnosing gestational diabetes mellitus constructed by using hypoxanthine, dihydrostercols, L-hydromorphone, (3R,4R)-D-erythrulose lactone, cycloleucine and creatine is score = 0.01387-0.61979 x cycloleucine-0.44462 x hypoxanthine-0.46101 x dihydrostercols-0.179 x (3R,4R)-D-erythrulose lactone-0.35281 x creatine+0.18452 x L-hydromorphone, the critical value of the model is 0.449, and when the relative abundance of the corresponding compound of the marker in serum is input into the model, the possibility of predicting or diagnosing an early gestational diabetes mellitus patient is low when the score is ≤0.449, and the possibility of predicting or diagnosing an early gestational diabetes mellitus patient is high when the score is >0.449; 3) the model for predicting or diagnosing gestational diabetes mellitus is constructed by using hypoxanthine, cortisol, dihydrofusidol, L-hydromorphone, (3R,4R)-D-erythrulose lactone, cycloleucine and creatine, score = 0.04744 x hypoxanthine - 0.40976 x cortisol - 1.25761 x dihydrofusidol - 0.27981 x (3R,4R)-D-erythrulose lactone - 1.70999 x cycloleucine - 0.83457 x creatine + 0.36254 x L-hydromorphone, the critical value of the model is 0.396, the relative abundance of the corresponding compounds of the markers in serum is input into the model, when the score is ≤0.396, it is predicted or diagnosed that the possibility of early gestational diabetes mellitus patient is low, when the score is >0.396, it is predicted or diagnosed that the possibility of early gestational diabetes mellitus patient is high.

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