Application of advanced pregnancy urine metabolism marker of gestational diabetes mellitus and kit

Abstract

The invention discloses application of a late pregnancy urine metabolism marker of gestational diabetes mellitus and a kit, and provides application of the late pregnancy urine metabolism marker of gestational diabetes mellitus in preparation of related drugs for prevention and / or treatment of gestational diabetes mellitus. According to the invention, three urine metabolism markers in late pregnancy, which are obviously associated with the occurrence risk of gestational diabetes mellitus, are obtained through screening, the expression of 17alpha-hydroxyprogesterone and alcadipine is up-regulated, the expression of 7-methyluric acid is down-regulated, and the three urine metabolism markers can be used as detection targets for early prediction of gestational diabetes mellitus, and can be used for early prediction of gestational diabetes mellitus. Or the compound can be used as a target for research and development of related drugs for preventing and treating gestational diabetes mellitus.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to the application and reagent kit of urinary metabolic markers in late pregnancy for gestational diabetes mellitus. Background Technology

[0002] Gestational diabetes mellitus (GDM) is a metabolic disorder characterized by elevated blood glucose levels that first occurs or is discovered during pregnancy. It is the most common complication of pregnancy. Statistics show that, due to differences in diagnostic criteria and screening methods, the global incidence of GDM ranged from 1% to 28% in 2012, with China's rate reaching as high as 15.6%. GDM has significant short- and long-term impacts on maternal and fetal health, such as increasing the risk of birth defects, miscarriage, premature birth, preeclampsia, macrosomia, and cesarean section. Pregnant women with GDM have a nearly 10 times higher risk of developing type 2 diabetes mellitus (T2DM) postpartum compared to women with normal blood glucose levels during pregnancy. Their offspring also have a significantly higher risk of developing obesity, autism, and attention deficit hyperactivity disorder (ADHD) later in life compared to women without GDM. However, effective predictive biomarkers for GDM are currently lacking. Actively exploring predictive indicators for GDM is of great significance for the prevention and treatment of this disease and for reducing adverse pregnancy outcomes.

[0003] It's important to note that doctors often categorize gestational diabetes and diabetes complicated by pregnancy as simply elevated blood sugar during pregnancy. However, there are several differences between the two. Pre-pregnancy diabetes primarily involves abnormal immune function, impaired pancreatic cell function, and insulin resistance, while gestational diabetes mainly results from the body's inability to compensate for increased insulin resistance during pregnancy, leading to elevated blood sugar. Secondly, pre-pregnancy diabetes often has a longer course, greater blood sugar fluctuations, and is difficult to control during pregnancy, while gestational diabetes usually occurs during pregnancy, has a shorter course, and is relatively easier to control. Furthermore, pre-pregnancy diabetes is often associated with damage to vital organs, such as chronic kidney disease, hypertension, and retinopathy, while gestational diabetes causes relatively less damage to the mother's organs. Finally, pre-pregnancy diabetes has a greater impact on the fetus, with a higher probability of birth defects or malformations, and a relatively higher risk of adverse pregnancy outcomes such as premature birth, shoulder dystocia, and cesarean section. In conclusion, compared to pre-pregnancy diabetes, the prevention and control of gestational diabetes is more significant for improving pregnancy outcomes. In other words, if gestational diabetes can be predicted early and preventive measures are taken for high-risk groups, adverse maternal and fetal pregnancy outcomes caused by the disease will be greatly reduced.

[0004] Changes in metabolite levels are the ultimate outcome of the development and progression of many diseases, and these changes often occur before the appearance of clinical symptoms. In recent years, metabolomics testing has played a crucial role in the prediction, prognostic analysis, and efficacy evaluation of various diseases. The samples used for testing include various types such as blood, urine, and tissue samples. Among these, analyzing metabolic changes in urine samples has advantages such as being non-invasive, having a simple collection process, abundant metabolites, and high patient compliance, and has been widely used in the early diagnosis, prevention, and personalized treatment of various diseases in recent years. Studies have found significant differences in the expression of certain urinary metabolites between early and mid-pregnancy, and these differentially expressed metabolites may serve as good indicators of maternal and fetal health.

[0005] Actively exploring urinary metabolic biomarkers for gestational diabetes mellitus is of great significance for early prediction and diagnosis of gestational diabetes mellitus, reducing maternal and fetal pregnancy risks, and minimizing adverse pregnancy outcomes. Summary of the Invention

[0006] The purpose of this invention is to overcome the deficiency of existing technologies in the lack of effective predictive biomarkers for gestational diabetes.

[0007] To achieve the above objectives, the present invention provides the application of late-pregnancy urinary metabolic markers of gestational diabetes mellitus in the preparation of drugs related to the prevention and / or treatment of gestational diabetes mellitus, wherein the metabolic markers comprise any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

[0008] This invention provides the application of urinary metabolic markers for gestational diabetes mellitus in late pregnancy in the preparation of a kit, wherein the metabolic markers include any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid, and the kit is used to predict the risk of gestational diabetes mellitus in late pregnancy.

[0009] Optionally, compared to pregnant women with normal blood glucose levels, pregnant women with gestational diabetes mellitus in late pregnancy showed upregulated expression of the metabolic markers 17α-hydroxyprogesterone and / or acalidinov.

[0010] Optionally, compared to pregnant women with normal blood sugar, the expression of the metabolic marker 7-methyluric acid was downregulated in pregnant women with gestational diabetes mellitus in late pregnancy.

[0011] The present invention also provides a kit for detecting metabolic biomarkers of gestational diabetes mellitus in pregnant women in late pregnancy. The kit includes a detection reagent for detecting the content of the metabolic biomarkers in a test sample. The metabolic biomarkers include any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

[0012] Optionally, the test sample is a urine sample.

[0013] Optionally, the test sample is a urine sample from a pregnant woman at 28 to 39 weeks of gestation.

[0014] Optionally, the test samples may also include pregnant women with normal blood sugar and pregnant women with pre-existing diabetes.

[0015] Optionally, the kit may also contain standards for 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

[0016] Compared to the prior art, the beneficial effects of the present invention include at least the following: This invention screened three urinary metabolic biomarkers in late pregnancy that are significantly associated with the risk of gestational diabetes mellitus. Among them, the expression of the metabolic biomarkers 17α-hydroxyprogesterone and acalidinic acid is upregulated, while the expression of 7-methyluric acid is downregulated. These three urinary metabolic biomarkers can be used as detection targets for the early prediction of gestational diabetes mellitus, or as targets for the development of drugs related to the prevention and treatment of gestational diabetes mellitus. Attached Figure Description

[0017] Figure 1 The above are the score plot and permutation distribution plot of the orthogonal partial least squares discriminant analysis of the present invention.

[0018] Figure 2 This invention presents a heatmap of the top ten upregulated and downregulated differentially expressed metabolites in the urine of gestational diabetes mellitus (GDM) and pre-pregnancy diabetes mellitus (PGDM). Specifically, A represents a heatmap of the top ten upregulated and downregulated differentially expressed metabolites in urine between the GDM and normoglycemic groups, and B represents a heatmap of the top ten upregulated and downregulated differentially expressed metabolites in urine between the pre-pregnancy diabetes and normoglycemic groups.

[0019] Figure 3 This is a comparative analysis diagram of differential signaling pathways between the gestational diabetes mellitus (GDM) group, the pre-gestational diabetes mellitus (PGDM) group, and the normal control group according to the present invention; wherein, A represents the difference in metabolic pathways between the gestational diabetes mellitus (GDM) group and the normal control group (NC), and B represents the difference in metabolic pathways between the pre-gestational diabetes mellitus (PGDM) group and the normal control group (NC).

[0020] Figure 4 Venn diagram of 46 differential metabolites of GDM that are different from both NC and GDM as identified in this invention.

[0021] Figure 5 This is an ROC curve of urinary metabolic biomarkers with potential predictive value for gestational diabetes mellitus (GDM) according to the present invention; wherein, A is 17α-hydroxyprogesterone, B is acalcidin, and C is 7-methyluric acid. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Case recruitment (Subjects Enrollment) This invention collected urine samples from singleton pregnancies between March 1, 2025 and September 31, 2025, at Shanghai First People's Hospital during prenatal checkups. The study included 31 cases of gestational diabetes mellitus, 30 cases of pre-gestational diabetes mellitus (PGDM), and 34 cases of normal blood glucose levels. Each pregnant woman's medical history was collected through a standardized electronic medical record system.

[0024] The diagnostic criteria for pre-gestational diabetes mellitus (PDM) and gestational diabetes mellitus (GDM) are based on the 2014 IAPSG and the Chinese guidelines for gestational hyperglycemia. A 75g oral glucose tolerance test (OGTT) is performed between 24 and 28 weeks of gestation. A diagnosis of GDM is made if any one of the following three criteria is met: fasting blood glucose exceeding 5.1 mmol / L; 1 hour post-glucose intake blood glucose exceeding 10.0 mmol / L; or 2 hours post-glucose intake blood glucose exceeding 8.5 mmol / L. Pre-gestational diabetes mellitus is diagnosed if fasting blood glucose ≥7.0 mmol / L, glycated hemoglobin ≥6.5%, or random blood glucose ≥11.1 mmol / L, accompanied by typical hyperglycemic symptoms or a hyperglycemic crisis. The normal control group consists of individuals with normal blood glucose levels who had a normal 75g OGTT result during mid-pregnancy.

[0025] Pregnant women who meet any of the following criteria will not be included in this study: (1) either spouse is not Chinese; (2) twin or multiple pregnancy; (3) those who have not undergone systematic and standardized prenatal check-ups; (4) those who have not been diagnosed with diabetes before pregnancy and have not undergone OGTT testing during pregnancy; (5) pregnant women who do not wish to participate in this study.

[0026] Sample collection Collect 1 ml of random urine from pregnant women, centrifuge at 1000 rpm for 5 minutes at 4°C. After filtering for microorganisms using a biofilter, aliquot 600 μl into EP tubes and store at -80°C. Take the sample after thawing in an ice-water bath according to the ratio, add water to 100 μL, add 400 μL of extraction buffer (methanol:acetonitrile = 1:1 (V / V) containing isotope-labeled internal standard mixture), vortex mix for 30 s. Sonicate for 10 min (ice-water bath), and incubate at -40°C for 1 h. Centrifuge the sample at 12000 rpm (centrifugal force 13800 (×g), radius 8.6 cm) for 15 min at 4°C. Collect the supernatant in a sample vial for analysis. Mix equal volumes of supernatant from all samples to form a QC sample for analysis. This invention utilizes a Vanquish (Thermo Fisher Scientific) ultra-high performance liquid chromatograph (UHPLC) with a Waters ACQUITY UPLC BEH Amide (2.1 mm × 100 mm, 1.7 μm) column for chromatographic separation of target compounds. Phase A of the HPLC was aqueous, containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia; Phase B was acetonitrile. The sample pan temperature was 4°C, and the injection volume was 2 μL. For non-polar metabolites, this project uses a Vanquish (Thermo Fisher Scientific) UHPLC with a Phenomenex Kinetex C18 (2.1 mm × 100 mm, 2.6 μm) column for chromatographic separation of target compounds. Phase A of the HPLC was aqueous, containing 0.01% acetic acid; Phase B was isopropanol:acetonitrile (1:1, v / v). The sample pan temperature was 4°C, and the injection volume was 2 μL. The Orbitrap Exploris 120 mass spectrometer can perform primary and secondary mass spectrometry data acquisition under the control of software (Xcalibur, version 4.4, Thermo). Detailed parameters are as follows: Sheath gas flow rate: 50 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 320℃, Full ms resolution: 60000, MS / MS resolution: 15000, Collision energy: SNCE 20 / 30 / 40, Spray voltage: 3.8 kV (positive) or -3.4 kV (negative). The Orbitrap Exploris 120 mass spectrometer can perform primary and secondary mass spectrometry data acquisition under the control of software (Xcalibur, version 4.4, Thermo).Detailed parameters are as follows: Sheath gasflow rate: 50 Arb, Aux gasflow rate: 15 Arb, Capillary temperature: 320℃, Full ms resolution: 60000, MS / MS resolution: 15000, Collision energy: SNCE 20 / 30 / 40, Spray voltage: 3.8 kV (positive) or -3.4 kV (negative).

[0027] Experimental results 1. Demographic characteristics and biological indicators of the cases Basic information and biological indicators of the enrolled pregnant women were collected through the electronic medical record system, and the results are summarized in Table 1. Statistical analysis showed that there were statistically significant differences in age, fasting insulin, and glycated hemoglobin among the three groups of pregnant women, while there were no statistically significant differences in BMI, fasting blood glucose, triglycerides, and total cholesterol.

[0028] Table 1. Comparison of biological characteristics and detection indicators among sample groups. # indicates heteroscedasticity, and Welch's ANOVA test is used; pairwise comparisons between groups are performed using the Games-Howell method. If homogeneity of variance is satisfied, standard ANOVA test is used, and Bonferroni correction is used for pairwise comparisons between groups. p-values: p1: NC group vs. GDM group; p2: NC group vs. PGDM group; p3: GDM group vs. PGDM group.

[0029] 2. Metabolomics analysis like Figure 1 As shown, the metabolic profiles of pregnant women in the GDM group, PGDM group, and NC group (normal control group) were well separated from each other. The explanation rate between the GDM group and the NC group was 91.3%, and the prediction rate was 51.4%; the explanation rate between the PGDM group and the NC group was 93.3%, and the prediction rate was 51.1%; the explanation rate between the PGDM group and the GDM group was 85.7%, and the prediction rate was 31.5%.

[0030] 3. Differences in metabolites among groups There are 279 differentially regulated metabolites specific to gestational diabetes mellitus (GDM), of which 102 are upregulated and 177 are downregulated. There are 382 differentially regulated metabolites specific to the PGDM and NC groups, of which 124 are upregulated and 258 are downregulated. The top ten upregulated and top ten downregulated urinary metabolites between the gestational diabetes mellitus (GDM) and normoglycemic groups, arranged in ascending order of p-value, are shown in Table 2. Figure 2 As shown in Table 3, the top ten urinary metabolites with the highest upregulation and the top ten with the lowest downregulation in the pre-pregnancy diabetes mellitus (PGDM) group and the normoglycemic group, arranged from smallest to largest p-value, are shown in Table 3. Figure 2 As shown.

[0031] Table 2. Top ten differentially expressed metabolites (upregulated and downregulated) between the gestational diabetes mellitus (GDM) group and the normoglycemic group. Table 3. Top 10 differentially expressed metabolites (upregulated and downregulated) between the pre-pregnancy diabetes mellitus (PGDM) group and the normoglycemic group. 4. Comparison of metabolite enrichment pathways specific to GDM and PGDM like Figure 3 As shown, the enrichment pathways of 279 urinary differential metabolites specific to GDM and NC, and 382 urinary differential metabolites specific to PGDM and NC were analyzed. The results showed that five common signaling pathways were found among the enrichment pathways of GDM-specific differential metabolites: metabolic pathways, steroid hormone biosynthesis pathways, nicotinate and nicotinamide metabolism pathways, alanine, aspartate and glutamate metabolism pathways, and pantothenic acid and coenzyme A biosynthesis pathways. However, except for the steroid hormone biosynthesis signaling pathway, which was downregulated in both GDM and PGDM, the other four signaling pathways showed opposite metabolic trends in the PGDM-specific differential metabolites: in GDM, these four signaling pathways were upregulated; while in PGDM, they were downregulated. Furthermore, in the PGDM-specific urinary metabolite enrichment pathways, the sphingomyelin metabolism pathway showed a significant upregulated trend.

[0032] Note: (1) Nicotinate and nicotinamide metabolism. Core function: Generate NAD+ / NADP+ coenzymes, which participate in redox reactions and energy metabolism. It is significantly upregulated in the GDM group and downregulated in the PGDM group.

[0033] (2) Alanine, aspartate, and glutamate metabolism. Alanine: generates pyruvate through transamination, participating in gluconeogenesis or the TCA cycle; Aspartate: acts as an intermediate in the urea cycle, participating in ammonia detoxification; Glutamate: deaminates to α-ketoglutarate via glutamate dehydrogenase, connecting amino acids and energy metabolism. Alanine was significantly upregulated in the GDM group and downregulated in the PGDM group.

[0034] (3) Pantothenate and CoA biosynthesis. Pantothenate (vitamin B5) is a precursor of coenzyme A (CoA) and participates in fatty acid metabolism and acetylation. CoA, as an acyl carrier, plays a key role in the TCA cycle and ketone body metabolism. It is significantly upregulated in the GDM group and downregulated in the PGDM group.

[0035] (4) Steroid hormone biosynthesis: Steroid hormone metabolism. Core pathway: Cholesterol is catalyzed by cytochrome P450 enzymes (CYP11A1, CYP17A1, etc.) to produce pregnenolone → progesterone → androstenedione → testosterone / estradiol, etc. It is significantly downregulated in the GDM group and upregulated in the PGDM group.

[0036] (5) Sphingolipid metabolism. Sphingolipid metabolism can affect diabetes metabolism through the following mechanisms: 1. Insulin resistance: Ceramide, as a core molecule of sphingolipid metabolism, blocks the insulin signaling pathway by inhibiting the phosphorylation of insulin receptor substrates (IRS), leading to impaired glucose uptake in peripheral tissues; 2. β-cell dysfunction: Imbalance in sphingolipid metabolism (such as ceramide accumulation) directly damages the mitochondrial function of pancreatic β-cells, reducing insulin secretion capacity. Clinically, this has some applications: Plasma ceramide (C16:0, C24:1, etc.) levels are significantly positively correlated with the risk of developing type 2 diabetes and can be used as an early predictive indicator; specific sphingolipid subtypes (such as long-chain sphingomyelin) are related to the degree of β-cell dysfunction, which helps in the identification of diabetes subtypes. It can also be used for treatment: for example, inhibiting ceramide synthase (such as CerS2) can improve β-cell function. This pathway only showed an upregulation trend in the comparison between PGDM and NC groups. There was no significant difference in the comparison between GDM and NC.

[0037] 5. This invention addresses 46 differentially expressed metabolites of GDM that are neither identical to NC nor to GDM. Figure 4 Analysis of Table 4 showed that 28 differentially expressed urinary metabolites were upregulated and 18 were downregulated. Figure 5 As shown in Figures A, B, and C, three substances with an area under the ROC curve greater than 0.7 may have good predictive value for GDM. Listed in descending order of ROC curve area: 17alpha-Hydroxyprogesterone, 5'-Phosphoribosyl-5-amino-4-imidazolecarboxamide, and 7-Methyluric acid. In patients with gestational diabetes mellitus (GDM), the expression of 17alpha-hydroxyprogesterone in urine is upregulated, with an area under the receiver operating characteristic (AUC) of 0.77 (95% confidence interval: 0.65–0.833). Similarly, the expression of acalcidin in urine is upregulated in patients with GDM, with an AUC of 0.73 (95% confidence interval: 0.608–0.855). 7-methyluric acid expression was downregulated in the urine of patients with gestational diabetes mellitus (GDM), with an area under the receiver operating characteristic (AUC) of 0.71 (95% confidence interval 0.587–0.84).

[0038] Table 4. 46 Differential Metabolites Trend symbols: ↓ indicates that the expression level of this metabolite was significantly downregulated in the comparison group, and ↑ indicates that it was significantly upregulated.

[0039] Groups: GDM represents gestational diabetes mellitus, NC represents normal control, and PGDM represents pre-gestational diabetes mellitus.

[0040] This invention, through analysis of urinary metabolites from 95 pregnant women, revealed significant differences in urinary metabolites between women with gestational diabetes, those with pre-existing diabetes, and women with normal blood glucose levels. Furthermore, significant differences were found in the metabolic enrichment pathways between gestational diabetes and pre-existing diabetes. These differences regulate signaling pathways involved in important biological activities such as cellular energy metabolism, the tricarboxylic acid cycle, and lipid metabolism. Therefore, gestational diabetes and pre-existing diabetes exhibit differences in energy metabolism processes involving glucose, lipids, and amino acids.

[0041] Among the 46 specific urinary metabolites that differentiate GDM from PGDM and normoglycemic groups, 17alpha-hydroxyprogesterone, 5'-Phosphoribosyl-5-amino-4-imidazolecarboxamide (Acaldine), and 7-Methyluric acid showed good predictive value. 17alpha-hydroxyprogesterone is a steroid hormone secreted by the adrenal glands and gonads, primarily involved in the synthesis of cortisol and sex hormones. 17alpha-hydroxyprogesterone caproate is very similar to 17alpha-hydroxyprogesterone in structure and biological activity. 17alpha-hydroxyprogesterone is an endogenous substance, making it inconvenient for scientific research. Hydroxyprogesterone caproate is an exogenous substance and is often used as an exogenous form of 17alpha-hydroxyprogesterone in research. A study on the effects of 17α-Hydroxyprogesterone Caproate on gestational blood glucose showed that pregnant women who used 17α-Hydroxyprogesterone Caproate weekly to prevent recurrent preterm birth had a significantly higher probability of elevated blood glucose and developing gestational diabetes than the unexposed group. This coincides with the findings of this invention regarding elevated urinary 17α-hydroxyprogesterone levels in pregnant women with gestational diabetes.

[0042] AMPK is a multifunctional protease crucial for maintaining glucose homeostasis. Activation of AMPK is significant for reversing metabolic abnormalities associated with type 2 diabetes. Acalidicin, with the chemical structure 5-Aminoimidazole-4-carboxamide ribonucleo-tide, is an endogenous metabolite in humans and an agonist of the AMPK signaling pathway. Studies have shown that acalidicin may exhibit different effects on apoptosis in INS-1E cells under different culture conditions. This invention can further incorporate blood concentrations of acalidicin to further predict its role in GDM.

[0043] 7-Methyluric acid is a purine metabolite. Purine metabolites in plasma may serve as markers for predicting gestational diabetes. 7-Methyluric acid in urine has some predictive value for diabetic nephropathy. These findings are similar to those of this invention.

[0044] Based on the above, the present invention provides a kit for detecting metabolic biomarkers of gestational diabetes mellitus in pregnant women in late pregnancy. The kit includes a detection reagent for detecting the content of the metabolic biomarkers in the test sample. The metabolic biomarkers include any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid. The kit also contains standards of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

[0045] In summary, this invention provides the application of late-pregnancy urinary metabolic biomarkers for gestational diabetes mellitus (GDM) in the preparation of drugs related to the prevention and / or treatment of GDM. This invention screened out three late-pregnancy urinary metabolic biomarkers that are significantly associated with the risk of developing GDM, of which 17α-hydroxyprogesterone and acalidinovic expression are upregulated, and 7-methyluric acid expression is downregulated. These three urinary metabolic biomarkers can be used as detection targets for the early prediction of GDM, or as targets for the development of drugs related to the prevention and treatment of GDM.

[0046] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. The application of urinary metabolic markers of gestational diabetes mellitus in late pregnancy in the preparation of drugs related to the prevention and / or treatment of gestational diabetes mellitus, characterized in that, The metabolic markers include any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

2. The application of urinary metabolic markers for gestational diabetes mellitus in late pregnancy in the preparation of a reagent kit, characterized in that, The metabolic markers include any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid, and the kit is used to predict the risk of gestational diabetes in pregnant women during late pregnancy.

3. The application as described in claim 2, characterized in that, Compared to pregnant women with normal blood sugar, pregnant women with gestational diabetes mellitus in late pregnancy have upregulated expression of the metabolic markers 17α-hydroxyprogesterone and / or acalidinov.

4. The application as described in claim 2, characterized in that, Compared to pregnant women with normal blood sugar, pregnant women with gestational diabetes in late pregnancy have downregulated expression of the metabolic marker 7-methyluric acid.

5. A kit for detecting metabolic markers of gestational diabetes mellitus in pregnant women during late pregnancy, characterized in that, The kit includes a detection reagent for detecting the content of the metabolic biomarker in the test sample, wherein the metabolic biomarker comprises any one or more of 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

6. The reagent kit as described in claim 5, characterized in that, The test sample was a urine sample.

7. The kit according to claim 6, characterized in that, The test samples were urine samples from pregnant women between 28 and 39 weeks of gestation.

8. The reagent kit as described in claim 6, characterized in that, The test samples also included pregnant women with normal blood sugar and pregnant women with pre-existing diabetes.

9. The reagent kit as described in claim 5, characterized in that, The kit also contains standards for 17α-hydroxyprogesterone, acalcidin, and 7-methyluric acid.

Images