Plasma metabolic markers for early diagnosis or prediction of gestational diabetes and their applications

Abstract

This invention belongs to the field of diagnostic testing technology. Addressing the problem that existing gestational diabetes screening methods may miss the optimal time for early intervention, leading to the failure to promptly detect and manage some potentially hyperglycemic pregnant women, increasing the risk of adverse maternal and fetal outcomes, this invention proposes a plasma metabolic biomarker for the early diagnosis or prediction of gestational diabetes and its application. This plasma metabolic biomarker is at least one of L-lysine, propargylglycine, N6-acetyl-L-lysine, cyclic leucine, and glycine-deoxycholic acid. The five plasma metabolic biomarkers provided by this invention can be applied to the early diagnosis or prediction of gestational diabetes, and are of great significance for the prevention or treatment of gestational diabetes.

Description

Technical Field

[0001] This invention relates to the field of diagnostic testing technology, specifically to a plasma metabolic marker for the early diagnosis or prediction of 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 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. GDM is diagnosed if the blood glucose value reaches any of these criteria at any time point. However, an FPG ≥ 5.1 mmol / L alone in early pregnancy is not sufficient for diagnosis and requires follow-up.

[0005] However, recent studies have found a significant association between gestational diabetes mellitus diagnosed in early pregnancy and adverse pregnancy outcomes. For example, early-onset gestational diabetes can 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 some potentially hyperglycemic pregnant women not being detected and managed in time, increasing the risk of adverse maternal and fetal outcomes. Therefore, it is urgent to explore biomarkers or optimal combinations of tests for earlier diagnosis or prediction of individual gestational diabetes risk, and to develop effective and inexpensive methods for early diagnosis and screening. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a plasma metabolic marker for the early diagnosis or prediction of gestational diabetes and its application. This invention can improve the early diagnosis rate of gestational diabetes, improve the prognosis of the disease, and has clinical application and promotion value.

[0007] The first objective of this invention is to provide a plasma metabolic marker for the early diagnosis or prediction of gestational diabetes mellitus, wherein the plasma metabolic marker is at least one of L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid.

[0008] Preferably, plasma metabolic markers are L-lysine, propargyl glycine, N6-acetyl-L-lysine, cycloleucine, and glycine deoxycholic acid.

[0009] Preferably, plasma metabolic markers are: L-lysine, N6-acetyl-L-lysine, cycloleucine, and glycine-deoxycholic acid; or, L-Lysine, propargylglycine, cycloleucine, and glycine-deoxycholic acid; or, L-lysine, glycine, N6-acetyl-L-lysine, and cyclic leucine.

[0010] Preferably, plasma metabolic markers are: Propylglycine, N6-acetyl-L-lysine, and cycloleucine; or, L-lysine, N6-acetyl-L-lysine, and glycine-deoxycholic acid; or, Glycine deoxycholic acid, L-lysine, and cyclic leucine.

[0011] Preferably, plasma metabolic markers are: L-lysine and glycine-deoxycholic acid; or, N6-acetyl-L-lysine and glycine-deoxycholic acid; or, Propylglycine and cyclic leucine.

[0012] Preferably, the plasma metabolic marker is propionyl glycine.

[0013] A second objective of this invention is to provide the application of the above-mentioned plasma metabolic markers for early diagnosis or prediction of gestational diabetes mellitus in the preparation of reagents for early diagnosis or prediction of gestational diabetes mellitus.

[0014] A third objective of this invention is to provide the application of the above-mentioned plasma metabolic markers for early diagnosis or prediction of gestational diabetes mellitus in the preparation of a kit for early diagnosis or prediction of gestational diabetes mellitus.

[0015] A fourth objective of this invention is to provide a kit for the early diagnosis or prediction of gestational diabetes mellitus, comprising the aforementioned plasma metabolic markers for the early diagnosis or prediction of gestational diabetes mellitus.

[0016] Preferably, the kit also includes a solvent and an internal standard.

[0017] Preferably, the solvent is methanol and 50% acetonitrile aqueous solution, and the internal standard is L-phenylalanine.

[0018] A fifth objective of this invention is to provide a method for screening plasma metabolic markers for the early diagnosis or prediction of gestational diabetes mellitus, comprising the following steps: Samples were collected from a healthy control group and a group of patients with gestational diabetes. LC-MS was used to detect samples from healthy control groups and gestational diabetes groups, and discriminant analysis was performed to obtain multiple candidate differentially expressed metabolites. Receiver operating characteristic (ROC) curve analysis was performed on multiple candidate differentially expressed metabolites and their combinations to identify plasma metabolic biomarkers for early diagnosis or prediction of gestational diabetes.

[0019] Preferably, the LC-MS detection conditions are as follows: Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm*100mm; 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. The flow rate is 0.4 mL / min. The elution gradient program is as follows: At 0 min, the volume ratio of phase A to phase B was 95:5; At 11.0 min, the volume ratio of phase A to phase B was 10:90; At 12.0 min, the volume ratio of phase A to phase B was 10:90; At 12.1 min, the volume ratio of phase A to phase B was 95:5; At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0020] Compared with the prior art, the advantages of this invention are as follows: This invention, by comparing the differences in plasma metabolomes between healthy pregnant women and those with gestational diabetes, screened five plasma metabolic biomarkers for the early diagnosis or prediction of gestational diabetes. The area under the ROC curve (AUC) for a single plasma metabolic biomarker was greater than 0.6, ranging from 0.634 to 0.839. The performance of combinations of multiple plasma metabolic biomarkers was significantly better than that of single biomarkers, with AUC values ​​ranging from 0.853 to 0.987. Using the five plasma metabolic biomarkers of this invention in combination for detection and diagnosis can greatly improve the early diagnosis rate, while also offering advantages in clinical accessibility and cost-effectiveness. Attached Figure Description

[0021] Figure 1 This is an OPLS-DA statistical chart of the metabolites according to Example 1 of the present invention; Figure 2 Volcano plots of differentially metabolized metabolites in the healthy and disease groups; Figure 3 Violin plot of metabolite content obtained from screening in the healthy group and the disease group; Figure 4 ROC curves for the five metabolite combinations provided in this invention; Figure 5 ROC curves for different combinations of metabolites provided by this invention. Detailed Implementation

[0022] The following will be described in conjunction with embodiments of the present invention. Figures 1 to 5 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] Experimental methods in the following examples, unless otherwise specified, are generally performed using methods known in the art. Unless otherwise specified, all experimental materials used in the following examples were purchased from conventional biochemical reagent stores.

[0024] Key experimental reagents are shown in Table 1 below: Table 1 Experimental Reagents Key instrument information is shown in Table 2 below: Table 2 Information on Experimental Instruments This invention provides a plasma metabolic marker for the early diagnosis or prediction of gestational diabetes mellitus, wherein the plasma metabolic marker is at least one of L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid.

[0025] Specifically, the plasma metabolic markers are L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid.

[0026] Specifically, plasma metabolic markers are: L-lysine, N6-acetyl-L-lysine, cycloleucine, and glycine-deoxycholic acid; or, L-Lysine, propargylglycine, cycloleucine, and glycine-deoxycholic acid; or, L-lysine, glycine, N6-acetyl-L-lysine, and cyclic leucine.

[0027] Specifically, plasma metabolic markers are: Propylglycine, N6-acetyl-L-lysine, and cycloleucine; or, L-lysine, N6-acetyl-L-lysine, and glycine-deoxycholic acid; or, Glycine deoxycholic acid, L-lysine, and cyclic leucine.

[0028] Specifically, plasma metabolic markers are: L-lysine and glycine-deoxycholic acid; or, N6-acetyl-L-lysine and glycine-deoxycholic acid; or, Propylglycine and cyclic leucine.

[0029] Specifically, the plasma metabolic marker is propargyl glycine.

[0030] Example 1: Screening of plasma metabolic biomarkers This invention provides a method for screening plasma metabolic biomarkers for early diagnosis or prediction of gestational diabetes, comprising the following steps: S1. Sample collection This invention, after obtaining patient consent, collected peripheral venous blood plasma samples from 42 healthy pregnant women (healthy control group) and 42 pregnant women with gestational diabetes mellitus (gestational diabetes group) at 6-13 weeks of gestation from a clinical medical research center. All samples were collected 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. Blood was collected in the morning on an empty stomach. All plasma samples were centrifuged and stored at -80°C. Samples were thawed and analyzed separately before the study.

[0031] S2, broadly targeted plasma metabolomics analysis (1) Sample pretreatment 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.

[0032] (2) Detection of metabolites in samples The liquid chromatography conditions were determined as follows: Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8 µm, 2.1 mm * 100 mm; column temperature: 40℃; injection volume: 2 µL.

[0033] 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.

[0034] The elution gradient program is as follows: At 0 min, the volume ratio of phase A to phase B was 95:5; At 11.0 min, the volume ratio of phase A to phase B was 10:90; At 12.0 min, the volume ratio of phase A to phase B was 10:90; At 12.1 min, the volume ratio of phase A to phase B was 95:5; At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0035] The flow rate was 0.4 mL / min.

[0036] The mass spectrometry conditions are determined as follows: The electrospray ionization (ESI) source temperature was 500℃, the mass spectrometry voltage was 5500V (positive) or -4500V (negative), the ion source gas I (GS I) was 55psi, the gas II (GS II) was 60psi, the curtain gas (CUR) was 25psi, and the collision-activated dissociation (CAD) parameter was set to high.

[0037] In the triple quadrupole (Qtrap), each ion pair is detected by MRM mode scanning based on optimized declustering potential (DP) and collision energy (CE).

[0038] The samples were analyzed and detected according to the determined liquid chromatography and mass spectrometry conditions: 20% of the samples from the healthy control group and the gestational diabetes group were randomly selected and mixed into one sample. Metabolomics methods using enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) combined with multiple reaction monitoring acquisition mode were used, and a gestational diabetes plasma metabolite database was constructed by integrating a local standard database.

[0039] Eighty-four collected plasma samples were randomly divided into a screening set and a validation set. The screening set included plasma samples from 29 healthy pregnant women (healthy control group) and 29 pregnant women with gestational diabetes mellitus (gestational diabetes group), while the validation set included plasma samples from 13 healthy pregnant women (healthy control group) and 13 pregnant women with gestational diabetes mellitus (gestational diabetes group). The plasma samples in the screening set were analyzed using liquid chromatography-mass spectrometry (LC-MS) and a constructed gestational diabetes mellitus plasma metabolite database to obtain raw mass spectrometry data for each plasma sample.

[0040] (3) Preprocessing and integration of peak area in the spectrum Based on a gestational diabetes mellitus plasma metabolite database, mass spectrometry was used to perform qualitative and quantitative analysis of metabolites in the samples. Liquid chromatography was used to separate metabolites of different molecular weights. A triple quadrupole multiple reaction monitoring (MRM) mode was employed to screen for characteristic ions of each substance, and the signal intensity (CPS) of the characteristic ions was obtained in the detector.

[0041] Open the sample mass spectrometry file using MultiQuant software. Preprocess and correct the raw mass spectrometry data based on the mass-to-charge ratio and retention time. Perform peak integration and correction. The peak area of ​​each chromatographic peak represents the relative content of the corresponding substance. Set S / N>5 and retain peaks with retention time shifts not exceeding 0.2 min. The relative content of metabolites is obtained by calculating the peak area based on the mass spectrometry peak intensity. Finally, the integrated data of all chromatographic peak areas are exported and saved for further statistical analysis.

[0042] (4) Experimental quality control By overlaying and analyzing the total ion chromatograms of mass spectrometry analysis of different quality control (QC) samples, the reproducibility of metabolite extraction and detection, i.e., technical reproducibility, can be determined. The high stability of the instrument provides an important guarantee for the reproducibility and reliability of the data. The CV value, or coefficient of variation, is the ratio of the standard deviation of the original data to the mean of the original data, reflecting the degree of data dispersion.

[0043] The Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of CV values ​​for substances with values ​​less than the reference value. A higher proportion of substances with lower CV values ​​in the QC sample 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, the change in the CV value of the L-phenylalanine internal standard during the detection process is monitored. A change in the internal standard CV value of less than 20% indicates good instrument stability during the detection process.

[0044] (5) Data processing and analysis The peak area integral data of all samples were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis.

[0045] like Figure 1 As shown, an orthogonal partial least squares (OPLS-DA) discriminant model was established to identify metabolites (VIP>1.0) that contribute significantly between the disease group and the healthy control group. Figure 2 The size of the midpoint was marked 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 identified, totaling 383 significantly different metabolites. Specifically, 216 metabolites were upregulated, and 167 were downregulated. These significantly different metabolites may be potential early diagnostic or predictive biomarkers for gestational diabetes mellitus.

[0046] The above analysis screened potential plasma metabolic biomarkers for the diagnosis or prediction of gestational diabetes. Based on their retention time and 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 time, and corresponding multi-stage MS fragmentation spectra, the structure of the metabolic biomarkers was verified.

[0047] In this embodiment, five differential plasma metabolic markers that can diagnose and differentiate early gestational diabetes were screened using a binary logistic regression forward stepwise method: L-lysine, propargyl glycine, N6-acetyl-L-lysine, cycloleucine, and glycine deoxycholic acid. Specific information on plasma metabolic markers is shown in Tables 3 and 4 below.

[0048] Table 3. Five plasma metabolic markers used for early diagnosis or prediction of gestational diabetes mellitus. Table 4. Differences in plasma metabolic markers between the gestational diabetes mellitus group and the healthy control group. Based on Table 4 above and Figure 3 The results showed that, compared with the healthy control group, the levels of metabolites such as propargyl glycine and glycine deoxycholic acid were increased in the gestational diabetes group, while the levels of metabolites such as L-lysine, N6-acetyl-L-lysine and cycloleucine were decreased.

[0049] The diagnostic performance of plasma metabolic markers for gestational diabetes mellitus was analyzed using receiver operating characteristic (ROC) curves from plasma samples in the validation set. The AUC and OR values ​​for single and combined use of 2–4 plasma metabolic markers in the diagnosis and prediction of gestational diabetes mellitus are shown in Tables 5 and 6 below.

[0050] Table 5. AUC and OR values ​​of a single plasma metabolic marker for the diagnosis of gestational diabetes mellitus

[0051] Table 6. AUC values ​​of multiple plasma metabolic markers used in combination for the diagnosis of gestational diabetes mellitus The statistical results in Table 5 show that cycloleucine, glycodeoxycholic acid, and propargylglycine, as individual differentially expressed plasma metabolic markers, are highly effective for the early diagnosis of gestational diabetes mellitus, with area under the ROC curve (AUC) all greater than 0.7, indicating clinical significance. The results in Table 6 show that when plasma metabolic markers are used in combination for the early diagnosis and prediction of gestational diabetes mellitus, the AUC further increases.

[0052] pass Figure 4The results show that when these five differentially expressed plasma metabolic markers are used for the early diagnosis or prediction of gestational diabetes, the AUC is further improved, and the AUC of the combined diagnosis of early gestational diabetes by the five plasma metabolic markers reaches 0.987.

[0053] pass Figure 5 The results showed that different combinations of plasma metabolic markers had good performance in early diagnosis and prediction of gestational diabetes mellitus (GDM). In particular, when using propargyl glycine and cycloleucine to construct an early diagnostic model for GDM, the combined AUC value of these two metabolic markers for early diagnosis and prediction of GDM reached 0.897. When using L-lysine, glycodeoxycholic acid, and cycloleucine to construct a diagnostic model for GDM, the combined AUC value of these three metabolic markers for early diagnosis or prediction of GDM reached 0.941. When using glycodeoxycholic acid, L-lysine, propargyl glycine, and cycloleucine to construct an early diagnostic model for GDM, the combined AUC value of these four metabolic markers for early diagnosis and prediction of GDM reached 0.983.

[0054] Example 2: Application of plasma metabolic markers This invention provides a kit for the early diagnosis or prediction of gestational diabetes, comprising: (1) The plasma metabolic markers provided in Example 1 are: L-lysine, propargyl glycine, N6-acetyl-L-lysine, cycloleucine and glycine deoxycholic acid, which are individually packaged or mixed and packaged.

[0055] (2) Solvent: Pure methanol and a 50% acetonitrile aqueous solution were used for sample extraction.

[0056] A 50% aqueous solution of acetonitrile can be used as a solvent to dissolve standards.

[0057] (3) Internal standard: L-phenylalanine This invention also provides a screening method for the early diagnosis or prediction of gestational diabetes using the above-mentioned kit, comprising the following steps: S1, collect plasma samples, preprocess them, and obtain test solutions.

[0058] S2, the test solution was analyzed by LC-MS to obtain information on the changes in the content of L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine and glycine deoxycholic acid.

[0059] S3. Based on the changes in the levels of the aforementioned plasma metabolic markers, determine whether the patient belongs to early gestational diabetes mellitus. The specific determination method is as follows: Based on the changes in the levels of the above metabolic markers, scores are calculated using the following model: 1. The biomarker combination consists of L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid; The calculation model is as follows: Score = 0.00034942 - 0.0043896 * L-lysine + 0.01348415 * propargyl glycine - 0.00982619 * N6-acetyl-L-lysine - 0.01600362 * cycloleucine + 0.01028519 * glycine deoxycholic acid.

[0060] Specifically, the relative abundance of the corresponding compounds of the markers in the serum is input into the above model, and the critical value of the model is 0.501; when the score is ≤0.501, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.501, the probability of being diagnosed with early gestational diabetes is high.

[0061] 2. The biomarker combination consists of L-lysine, propargyl glycine, N6-acetyl-L-lysine, and cyclic leucine; The calculation model is as follows: Score = -0.06164511 * L-lysine + 0.14057521 * propargyl glycine - 0.08772994 * N6-acetyl-L-lysine - 0.14222312 * cycloleucine + 0.00002421.

[0062] Specifically, the relative abundance of the corresponding compounds of the markers in the serum is input into the above model. The critical value of the model is 0.496. When the score is ≤0.496, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.496, the probability of being diagnosed with early gestational diabetes is high.

[0063] 3. The biomarker combination consists of L-lysine, N6-acetyl-L-lysine, cyclic leucine, and glycine-deoxycholic acid; The calculation model is as follows: Score = 0.10822267 * L-lysine - 2.42133522 * N6-acetyl-L-lysine - 4.72249068 * cycloleucine + 3.45389488 * glycine-deoxycholic acid + 0.25849673.

[0064] The critical value of the model is 0.354. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.354, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.354, the probability of being diagnosed with early gestational diabetes is high.

[0065] 4. The biomarker combination consists of L-lysine, propargyl glycine, cyclic leucine, and glycine deoxycholic acid; The calculation model is as follows: Score = -0.69891182 * L-lysine + 1.14283645 * propargyl glycine - 1.49787778 * cycloleucine + 1.17317245 * glycine deoxycholic acid - 0.16228254.

[0066] The critical value of the model is 0.524. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.524, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.524, the probability of being diagnosed with early gestational diabetes is high.

[0067] 5. The biomarker combination consists of propargyl glycine, N6-acetyl-L-lysine, and cyclic leucine; The calculation model is as follows: Score = 0.01349917 * Pyral glycine - 0.00984758 * N6-acetyl-L-lysine - 0.01600777 * Cyclic leucine + 0.00086528.

[0068] The critical value of the model is 0.500. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.500, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.500, the probability of being diagnosed with early gestational diabetes is high.

[0069] 6. The biomarker combination is L-lysine, N6-acetyl-L-lysine, and glycine-deoxycholic acid; The calculation model is as follows: Score = 0.49374285 * L-lysine - 1.26329417 * N6-acetyl-L-lysine + 1.51286996 * glycine-deoxycholic acid - 0.17814245.

[0070] The critical value of the model is 0.540. The relative abundance of the corresponding compounds of the marker in the serum is input into the model. When the score is ≤0.540, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.540, the probability of being diagnosed with early gestational diabetes is high.

[0071] 7. The biomarker combination is L-lysine, cyclic leucine, and glycine-deoxycholic acid; The calculation model is as follows: Score = -0.00441233 * L-lysine - 0.01608045 * cycloleucine + 0.01032251 * glycine deoxycholic acid + 0.00067139.

[0072] The critical value of the model is 0.501. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.501, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.501, the probability of being diagnosed with early gestational diabetes is high.

[0073] 8. The biomarker combination is L-lysine and glycine-deoxycholic acid; The calculation model is as follows: Score = -0.30560931 * L-lysine + 1.03761741 * glycine deoxycholic acid - 0.10807248.

[0074] The critical value of the model is 0.582. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.582, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.582, the probability of being diagnosed with early gestational diabetes is high.

[0075] 9. The biomarker combination is N6-acetyl-L-lysine and glycine-deoxycholic acid; The calculation model is as follows: Score = -0.99691587 * N6-acetyl-L-lysine + 1.14357803 * glycine-deoxycholic acid - 0.0759557.

[0076] The critical value of the model is 0.637. The relative abundance of the corresponding compounds of the marker in the serum is input into the model. When the score is ≤0.637, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.637, the probability of being diagnosed with early gestational diabetes is high.

[0077] 10. The biomarker combination is propionyl glycine and cyclic leucine; The calculation model is as follows: Score = 1.04198304 * Pyral glycine - 1.86105909 * Cyclic leucine + 0.08352264.

[0078] The critical value of the model is 0.674. The relative abundance of the corresponding compounds of the markers in the serum is input into the model. When the score is ≤0.674, the probability of being diagnosed with early gestational diabetes is low, and when the score is >0.674, the probability of being diagnosed with early gestational diabetes is high.

[0079] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus, characterized in that, Plasma metabolic markers include at least one of L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid.

2. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to claim 1, characterized in that, The plasma metabolic markers are L-lysine, propargyl glycine, N6-acetyl-L-lysine, cyclic leucine, and glycine deoxycholic acid.

3. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to claim 1, characterized in that, Plasma metabolic markers are: L-lysine, N6-acetyl-L-lysine, cycloleucine, and glycine-deoxycholic acid; or, L-Lysine, propargylglycine, cycloleucine, and glycine-deoxycholic acid; or, L-lysine, glycine, N6-acetyl-L-lysine, and cyclic leucine.

4. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to claim 1, characterized in that, Plasma metabolic markers are: Propylglycine, N6-acetyl-L-lysine, and cycloleucine; or, L-lysine, N6-acetyl-L-lysine, and glycine-deoxycholic acid; or, Glycine deoxycholic acid, L-lysine, and cyclic leucine.

5. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to claim 1, characterized in that, Plasma metabolic markers are: L-lysine and glycine-deoxycholic acid; or, N6-acetyl-L-lysine and glycine-deoxycholic acid; or, Propylglycine and cyclic leucine.

6. A plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to claim 1, characterized in that, The plasma metabolic marker is propionyl glycine.

7. The use of a plasma metabolic marker for early diagnosis or prediction of gestational diabetes mellitus according to any one of claims 1-6 in the preparation of a reagent for early diagnosis or prediction of gestational diabetes mellitus.

8. A kit for the early diagnosis or prediction of gestational diabetes, characterized in that, Including the plasma metabolic markers for early diagnosis or prediction of gestational diabetes as described in any one of claims 1-6.

9. A kit for early diagnosis or prediction of gestational diabetes according to claim 8, characterized in that, It also includes solvents and internal standards.

10. A kit for early diagnosis or prediction of gestational diabetes according to claim 9, characterized in that, The solvents were methanol and 50% acetonitrile aqueous solution, and the internal standard was L-phenylalanine.

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