A biomarker for predicting gestational diabetes and its application

Abstract

This invention relates to a kit for predicting the risk of gestational diabetes mellitus in subjects and its application. The kit includes reagents for detecting the levels of biomarkers in biological samples, wherein the biomarkers are selected from one or more of trimethylamine oxide and its precursor compounds and phenylacetylglutamine. The biomarker detection method of this invention for predicting gestational diabetes mellitus requires a small serum volume and only a single test, reducing the waiting time for pregnant women. It is anticipated that the clinical use of the biomarkers of this invention as detection targets can reduce the adverse effects of late-onset gestational diabetes mellitus on both the mother and newborn.

Description

Technical Field

[0001] This invention relates to the field of biological detection, specifically to a biomarker for predicting gestational diabetes and its application. Background Technology

[0002] Gestational diabetes mellitus (GDM) refers to impaired glucose tolerance first detected during pregnancy and is one of the most common pregnancy complications. GDM can lead to adverse pregnancy outcomes such as macrosomia, neonatal hypoglycemia, and neonatal hyperinsulinemia, while also significantly increasing the risks of cesarean section and postpartum hemorrhage. Traditional risk factors for GDM include obesity, advanced maternal age, family history of hypertension and diabetes, and unhealthy lifestyle and dietary habits. However, despite controlling these traditional risk factors, the incidence of GDM continues to rise annually. Therefore, there is an urgent clinical need to find new biomarkers for GDM.

[0003] Currently, the diagnostic methods for gestational diabetes refer to the standards set by the International Association for the Study of Diabetes and Pregnancy. The 75g oral glucose tolerance test requires testing fasting blood glucose, 1-hour blood glucose, and 2-hour blood glucose, which is quite cumbersome and requires three blood draws. The entire diagnostic method requires a large amount of blood and is frequent, with a long waiting time, which can easily cause dissatisfaction among pregnant women and their families. In addition, this method is not very useful for predicting gestational diabetes.

[0004] The human gut microbiota is a vast community of microbes that plays a crucial role in the host's health and disease. Imbalances and alterations in the gut microbiota are associated with a variety of diseases. Previous studies have repeatedly reported significant differences in the gut microbiota between women with gestational diabetes mellitus (GDM) and healthy controls. Furthermore, the gut microbiota can interact with the host, synthesizing various metabolites that affect human health. For example, short-chain fatty acids and bile acids have been shown to improve glucose tolerance and insulin sensitivity, and regulate metabolism and inflammation. Therefore, the gut microbiota and its metabolites hold the potential to help explore the pathogenesis and pathophysiological processes of gestational diabetes mellitus.

[0005] Dietary precursors choline, betaine, and L-carnitine are converted into trimethylamine (TMA) through the action of the gut microbiota. TMA is then oxidized in the liver by flavin monooxygenase 3 to form a small organic compound, trimethylamine N-oxide (TMAO). TMAO has been shown to be a potential risk factor for obesity, renal insufficiency, cardiovascular disease, cancer, and other diseases.

[0006] The essential amino acid phenylalanine can be metabolized by gut microbes into phenylacetic acid, which is then further metabolized in the liver into phenylacetylglutamine (PAGln). PAGln has been reported as a potential biomarker for various cardiovascular diseases, including heart failure, atrial fibrillation, and ischemic stroke.

[0007] Currently, there are no reports on using trimethylamine oxide and phenylacetylglutamine as biomarkers for predicting gestational diabetes. Summary of the Invention

[0008] To solve the above-mentioned technical problems, the present invention includes the following aspects:

[0009] A first aspect of the present invention provides the use of a reagent for detecting the level of a biomarker in a biological sample in the preparation of a kit for predicting the risk of a subject having gestational diabetes, wherein the biomarker is selected from one or more of trimethylamine oxide, trimethylamine oxide precursor compounds, and phenylacetylglutamine.

[0010] Preferably, the biomarker is trimethylamine oxide, a trimethylamine oxide precursor compound, or phenylacetylglutamine.

[0011] Preferably, the biomarker is a composition of trimethylamine oxide and a trimethylamine oxide precursor compound.

[0012] Preferably, the trimethylamine oxide precursor compound is selected from one or more of choline, betaine, and L-carnitine.

[0013] Preferably, the trimethylamine oxide precursor compound is L-carnitine.

[0014] Preferably, the trimethylamine oxide precursor compound is a composition of choline, betaine, and L-carnitine.

[0015] Preferably, the biomarker is a combination of choline and L-carnitine.

[0016] Preferably, the biological sample is blood.

[0017] Preferably, the subject is a woman with a gestational age of ≤28 weeks, and the biomarker is trimethylamine oxide or L-carnitine.

[0018] Preferably, the subject is a woman with a gestational age of ≤28 weeks, and the biomarker is a combination of trimethylamine oxide and L-carnitine.

[0019] Preferably, the subject is a woman with a gestational age greater than 28 weeks, and the biomarker is trimethylamine oxide, choline, betaine, or L-carnitine.

[0020] Preferably, the subject is a woman with a gestational age of >28 weeks, and the biomarker is a combination of trimethylamine oxide, choline, betaine and L-carnitine.

[0021] A second aspect of the invention provides a kit for predicting the risk of a subject having gestational diabetes, the kit comprising reagents for detecting the level of a biomarker in a biological sample, the biomarker being selected from one or more of trimethylamine oxide, trimethylamine oxide precursor compounds, and phenylacetylglutamine.

[0022] Preferably, the biomarker is trimethylamine oxide, a trimethylamine oxide precursor compound, or phenylacetylglutamine.

[0023] Preferably, the biomarker is a composition of trimethylamine oxide and a trimethylamine oxide precursor compound.

[0024] Preferably, the trimethylamine oxide precursor compound is selected from one or more of choline, betaine, and L-carnitine.

[0025] Preferably, the trimethylamine oxide precursor compound is L-carnitine.

[0026] Preferably, the trimethylamine oxide precursor compound is a composition of choline, betaine, and L-carnitine.

[0027] Preferably, the biomarker is a combination of choline and L-carnitine.

[0028] Preferably, the biological sample is blood.

[0029] Preferably, the subject is a woman with a gestational age of ≤28 weeks, and the biomarker is trimethylamine oxide or L-carnitine.

[0030] Preferably, the subject is a woman with a gestational age of ≤28 weeks, and the biomarker is a combination of trimethylamine oxide and L-carnitine.

[0031] Preferably, the subject is a woman with a gestational age greater than 28 weeks, and the biomarker is trimethylamine oxide, choline, betaine, or L-carnitine.

[0032] Preferably, the subject is a woman with a gestational age of >28 weeks, and the biomarker is a combination of trimethylamine oxide, choline, betaine and L-carnitine.

[0033] A third aspect of the present invention provides a biomarker composition for predicting the risk of a subject having gestational diabetes, said biomarker composition being selected from two or more of trimethylamine oxide, trimethylamine oxide precursor compounds, and phenylacetylglutamine.

[0034] Preferably, the biomarker composition is trimethylamine oxide and a trimethylamine oxide precursor compound.

[0035] Preferably, the trimethylamine oxide precursor compound is selected from one or more of choline, betaine, and L-carnitine.

[0036] Preferably, the biomarker composition is a combination of trimethylamine oxide, choline, betaine, and L-carnitine.

[0037] Preferably, the biomarker composition is a combination of trimethylamine oxide and L-carnitine.

[0038] Preferably, the subject is a woman with a gestational age of ≤28 weeks.

[0039] Preferably, the subject is a woman with a gestational age greater than 28 weeks.

[0040] A fourth aspect of the present invention provides the use of a biomarker composition in predicting the risk of a subject having gestational diabetes, said biomarker composition being selected from two or more of trimethylamine oxide, trimethylamine oxide precursor compounds, and phenylacetylglutamine.

[0041] Preferably, the biomarker composition is trimethylamine oxide and a trimethylamine oxide precursor compound.

[0042] Preferably, the trimethylamine oxide precursor compound is selected from one or more of choline, betaine, and L-carnitine.

[0043] Preferably, the biomarker composition is a combination of trimethylamine oxide, choline, betaine, and L-carnitine.

[0044] Preferably, the biomarker composition is a combination of trimethylamine oxide and L-carnitine.

[0045] Preferably, the subject is a woman with a gestational age of ≤28 weeks.

[0046] Preferably, the subject is a woman with a gestational age greater than 28 weeks.

[0047] Preferably, the application is for non-therapeutic and non-diagnostic purposes.

[0048] The technical effects of this invention are as follows:

[0049] This invention requires only 20 μl of serum for the detection of biomarkers to predict gestational diabetes mellitus, significantly less than the amount required for a 75g oral glucose tolerance test. Furthermore, only a single test is needed, eliminating the need for a second test after 2 hours, thus reducing waiting time for pregnant women. The biomarkers of this invention provide new insights and scientific evidence for exploring the mechanisms by which gestational diabetes mellitus leads to adverse pregnancy outcomes. They can reduce the adverse effects of late-onset gestational diabetes on both the mother and newborn, and have broad clinical application prospects. Attached Figure Description

[0050] Figure 1 This is a comparison of serum levels of gut microbiota metabolites between the case group and the control group.

[0051] Figure 2 This is a comparison of the area under the ROC curve for four different models. Detailed Implementation

[0052] Experimental Example 1: A Study on the Association Between Gut Microbiota Metabolites and Gestational Diabetes Mellitus

[0053] 1. Test Methods

[0054] 1.1 Research Subjects

[0055] This study included pregnant women who underwent an oral glucose tolerance test (OGTT) at the First People's Hospital of Taicang City between June 2, 2021 and July 23, 2022, and were diagnosed with GDM according to the IADPSG criteria. The control group was randomly selected from pregnant women who had never had GDM, matched by age (±5 years) and gestational age at blood collection (±3 weeks) at a ratio of 1:1. Inclusion criteria for participants included: (1) age ≥20 years; (2) singleton pregnancy; (3) no pre-diagnosis of diabetes; and (4) willingness to participate in this study and signing an informed consent form. Exclusion criteria were: (1) patients with autoimmune diseases, thyroid diseases, heart diseases, liver or kidney diseases; (2) patients with tumors; and (3) patients with hematological diseases and other diseases affecting glucose and lipid metabolism. 201 participants were included in each of the case group and the control group in this study.

[0056] 1.2 Data Collection

[0057] 1.2.1 General Data Collection

[0058] Participants' basic information was collected during the oral glucose tolerance test (OGTT) for fasting blood glucose (FBG). Under the guidance of medical professionals, participants completed a standardized questionnaire, which included information such as maternal age, height, pre-pregnancy weight, gestational age, smoking and alcohol consumption history, family history (diabetes, hyperlipidemia, hypertension, and coronary heart disease), personal medical history, and recent medication history (hypoglycemic, antihypertensive, and lipid-lowering drugs). Information on pregnancy complications was retrieved from medical records after delivery, and basic infant information (birth weight, gestational age at delivery, delivery method, and Apgar score) was extracted from medical records or birth certificates.

[0059] 1.2.2 Blood Collection and Processing

[0060] (1) Blood collection process: Select a suitable collection site, disinfect the collection site with a sterile cotton ball, and puncture the blood vessel along the direction of the blood vessel. After successful puncture, medical staff connect the blood vessel to the blood collection tube, open the tube, and collect blood. After collection, remove the needle and apply pressure to the collection site with a sterile cotton ball or bandage to avoid bleeding or infection.

[0061] (2) Centrifugation of specimens: Place the blood sample in a centrifuge in a symmetrical manner, centrifuge for 5 minutes at a speed of 3200 r / min, and after centrifugation, take out the blood sample and use a sterile pipette or tube to collect the separated serum into a 2 mL cryopreservation tube.

[0062] (3) Specimen preservation: The collected serum was placed in a storage cryopreservation box and stored in a refrigerator at -80°C. After the serum was frozen solid, it was placed in a dry ice box and transported to Suzhou Nomi Metabolism Co., Ltd. for liquid chromatography-mass spectrometry (LC-MS) analysis.

[0063] 1.3 Determination of TMAO and its precursors

[0064] 1.3.1 Sample Pretreatment

[0065] Weigh appropriate amounts of choline, betaine, TMAO, and L-carnitine standards, and measure appropriate amounts of each mother liquor to prepare a mixed standard. Dilute each standard to the appropriate concentration with 1% formic acid-acetonitrile to prepare a working standard solution. Accurately transfer an appropriate amount of sample into a 2 mL centrifuge tube, accurately add 10 μL of internal standard solution, and then add 750 μL of 1% formic acid-acetonitrile solution; vortex for 30 s, centrifuge at 12000 rpm at 4℃ for 5 min, and take 500 μL of the supernatant and filter it through a 0.22 μm membrane. Add the filtrate to the test bottle.

[0066] 1.3.2 LC-MS Detection Method

[0067] Chromatographic conditions: using A BEHHILIC column (2.1 × 100 mm, 1.7 μm, Waters Corporation, USA) was used. The injection volume was 5 μL, and the column temperature was 40 °C. The mobile phases were A: 10 mM ammonium formate aqueous solution (containing 0.1% formic acid) and B: acetonitrile. The gradient elution conditions were: 0–1 min, 80% B; 1–2 min, 80–70% B; 2–2.5 min, 70% B; 2.5–3 min, 70–50% B; 3–3.5 min, 50% B; 3.5–4 min, 0–80% B; 4–6 min, 80% B, with a flow rate of 0.4 mL / min.

[0068] Mass spectrometry conditions: Electrospray ionization (ESI) source, positive ion ionization mode. Ion source temperature 500℃, ion source voltage 5500V, collision gas 6psi, curtain gas 30psi, nebulizer gas and auxiliary gas both 50psi, multiple reaction monitoring was used for scanning.

[0069] 1.4. PAGln Determination

[0070] 1.4.1 Sample Pretreatment

[0071] Weigh an appropriate amount of one type of PAGln standard and prepare a single-standard stock solution with 100% methanol. Measure appropriate amounts of each stock solution to prepare a mixed standard, and dilute each standard to the appropriate concentration with 100% methanol to prepare a working standard solution. Accurately weigh an appropriate amount of sample into a 2mL EP tube, accurately add 200μL of methanol solution, vortex for 1 min, repeat twice, centrifuge at 12000rpm at 4℃ for 5 min, collect the supernatant, filter through a 0.22μm membrane, and add it to the test bottle.

[0072] 1.4.2 LC-MS Detection Method

[0073] Chromatographic conditions: A Synergi FUSION-RP column (Column 50 × 2 mm) was used, with an injection volume of 5 μL, a column temperature of 40℃, mobile phase A: 5 mM ammonium formate - 0.1% formic acid - water, mobile phase B: acetonitrile, and a flow rate of 0.40 mL / min; gradient elution conditions were: 0–1.00 min, 5% B; 1.00–4.5 min, 45% B; 5.00–6.5 min, 95% B; 7–8 min, 5% B.

[0074] Mass spectrometry conditions: Electrospray ionization (ESI) source, negative ion ionization mode, ion source temperature 500℃, ion source voltage 4500V, collision gas 6psi, curtain gas 30psi, nebulizer gas and auxiliary gas both 50psi, multiple reaction monitoring was used for scanning.

[0075] 1.5 Statistical Analysis

[0076] 1.5.1 Baseline Data Analysis

[0077] For continuous variables that follow a normal distribution, the mean ± standard deviation (SD) is used. For numerical variables that are skewed, the median (M) and interquartile range (IQR) are used. For categorical variables, rates (%) are used. Paired t-tests and chi-square tests are used for comparisons between groups.

[0078] 1.5.2 Correlation Analysis of TMAO and its Precursors, PAGln, and GDM

[0079] Medcalc software was used to analyze the cutoff values ​​for TMAO and its precursors, as well as PAGln, in diagnosing GDM, with the highest Youden index as the selection criterion. These gut microbiota metabolites were then classified as binary variables, and a conditional logistic model was used to calculate the odds ratio (OR) and 95% confidence interval (CI) to assess the relationship between TMAO and its precursors, PAGln levels, and the risk of GDM. Two models were created for this study: a univariate model without adjustment; and a multivariate model adjusted for age, pre-pregnancy BMI, medical history (hypertension and hyperlipidemia), family history (diabetes, hypertension, family history of coronary heart disease), smoking history, alcohol consumption history, parity, gestational age, and education level. Furthermore, due to the significant changes in gut microbiota during pregnancy and the recommended diagnostic time for GDM being 24-28 weeks of gestation, subgroup analysis was performed using 28 weeks of gestation as the cutoff point based on the gestational age at blood collection. Finally, the area under the curve (AUC) is calculated using the receiver operating characteristic (ROC) curve, including the following models:

[0080] Model 1: Traditional factors (including traditional factors such as maternal age, gestational age at blood collection, and pre-pregnancy BMI);

[0081] Model 2: Traditional factors plus TMAO and its precursor levels, which are divided into two categories;

[0082] Model 3: Traditional factors plus the binary variable PAGln level;

[0083] Model 4: In addition to the traditional factors, binary variables TMAO and its precursors, and PAGln levels were added.

[0084] The AUC of the four models were compared to determine whether there were statistical differences, thereby analyzing the diagnostic capabilities of TMAO, its precursors, and PAGln for GDM.

[0085] Statistical correlation analysis was performed using the statistical analysis software SAS (version 9.4, SAS Institute, Cary, NC, USA), R Studio (version 4.0.3), and Medcalc (version 19.7.4, MedCalc Software Ltd, Ostend, Belgium). A two-sided P < 0.05 was considered statistically significant.

[0086] 2. Test Results

[0087] 2.1 Baseline characteristics of the research subjects

[0088] This study included 201 patients with gestational diabetes mellitus (GDM) and 201 controls. The mean age of the GDM group was 30.04 ± 4.00 years, and the mean gestational age at blood collection was 29.75 ± 3.13 weeks, both of which were not significantly different from the control group. However, there were statistically significant differences between the two groups in terms of weight, pre-pregnancy BMI, alcohol consumption, family history of diabetes and hyperlipidemia, and gestational age at delivery. In addition, the GDM group had a higher incidence of small for gestational age and macrosomia than the control group, and also had a lower Apgar score.

[0089] 2.2 The relationship between TMAO and its precursors, PAGln and GDM

[0090] This study detected four serum TMAO and its precursors: TMAO, choline, betaine, and L-carnitine. The median serum TMAO level in the GDM group was 0.32 μmol / L, while that in the control group was 0.38 μmol / L. The median serum choline level in the GDM group was 30.43 μmol / L, while that in the control group was 29.96 μmol / L. The levels of betaine and L-carnitine in the GDM group were lower than those in the control group, but the differences were not statistically significant (P > 0.05). Furthermore, the median serum PAGln level was 0.28 μmol / L in both the GDM and control groups, with no statistically significant difference between the groups. Figure 1 (Table 1).

[0091] Table 1 Comparison of gut microbiota metabolite levels between GDM case group and control group

[0092] The variable, M(IQR) Total population Comparison GDM p-value TMAO, μmol / L 0.36(0.14,1.75) 0.38(0.13,1.70) 0.32(0.14,1.76) 0.922 Choline, μmol / L 30.12(23.32,38.75) 29.96(25.01,35.83) 30.43(21.78,42.27) 0.755 Betaine, μmol / L 12.78(10.16,16.90) 12.89(10.23,16.90) 12.73(9.91,16.88) 0.790 L-carnitine, μmol / L 16.55(14.05,21.53) 16.64(14.27,22.34) 16.44(13.86,20.92) 0.255 PAGln, μmol / L 0.28(0.15,0.45) 0.28(0.15,0.43) 0.28(0.15,0.47) 0.716

[0093] As shown in Table 2, the cutoff values ​​for TMAO, choline, betaine, L-carnitine, and PAGln were 1.399 μmol / L, 37.280 μmol / L, 10.935 μmol / L, 23.879 μmol / L, and 0.446 μmol / L, respectively. In univariate analysis, when choline levels were >37.280 μmol / L, the risk of GDM was 1.80 times that of ≤37.280 μmol / L (95% CI = 1.18, 2.76). After adjusting for age, preconception BMI, medical history (hypertension and hyperlipidemia), family history (diabetes, hypertension, family history of coronary heart disease), smoking history, alcohol consumption history, parity, gestational age, and education level, the high choline group (>37.280 μmol / L) remained positively correlated with GDM, with an OR of 1.83 (95% CI = 1.05, 3.19). Conversely, high serum L-carnitine concentrations (>23.879 μmol / L) were associated with a reduced risk of GDM in both univariate analysis [OR = 0.50, 95% CI = (0.29, 0.87)] and multivariate analysis [OR = 0.30, 95% CI = (0.14, 0.64)], indicating a protective factor against GDM. However, TMAO, betaine, and PAGln showed no statistical significance in either the univariate or multivariate models.

[0094] Table 2. Results of conditional logistic regression analysis on the relationship between gut microbiota metabolites and GDM.

[0095]

[0096] Note: *P < 0.05 compared with the control group.

[0097] 2.3 Subgroup analysis of TMAO and its precursors, PAGln and GDM

[0098] Subgroup analysis was conducted with 28 weeks of gestation as the cutoff point for blood collection. When the gestational age at blood collection was ≤28 weeks, the number of patients with GDM and the control group were 144 and 134, respectively; while when the gestational age was >28 weeks, the number of patients with GDM was 57 and the number of patients in the control group was 67. Serum levels of TMAO and L-carnitine differed between the GDM and control groups in both subgroups (P < 0.05, Table 3). Notably, before 28 weeks of gestation, serum levels of TMAO and L-carnitine in the GDM group were significantly higher than those in the control group, while after 28 weeks of gestation, the concentrations in the control group were higher than those in the GDM group. Furthermore, after 28 weeks of gestation, the control group had relatively higher betaine levels compared to the GDM group, and the difference between the groups was statistically significant (P < 0.05).

[0099] Table 3. Subgroup comparison of gut microbiota metabolite levels between GDM case group and control group

[0100]

[0101]

[0102] Note: *P < 0.05 compared with the control group.

[0103] Logistic regression analysis was also performed in both subgroups in this study. In univariate analysis, TMAO levels were associated with the risk of GDM regardless of blood collection time (OR = 1.30, 95% CI = 1.04, 1.63). Elevated L-carnitine levels after 28 weeks of gestation were associated with a decreased risk of GDM (OR = 0.83, 95% CI = 0.76, 0.90), while serum choline levels were positively associated with the risk of GDM (OR = 1.04, 95% CI = 1.00, 1.07). The results changed after adjusting for age, preconception BMI, medical history, family history, smoking history, alcohol consumption history, parity, gestational age, and education level. Before 28 weeks of gestation, TMAO levels were no longer associated with GDM; however, after 28 weeks of gestation, elevated TMAO (OR = 0.77, 95% CI = 0.60, 0.98) and betaine (OR = 0.89, 95% CI = 0.81, 0.98) levels were associated with a reduced risk of GDM. Meanwhile, elevated L-carnitine levels after 28 weeks of gestation still had a protective effect against GDM (OR = 0.81, 95% CI = 0.73, 0.90), while serum choline levels were still associated with an increased risk of GDM (OR = 1.06, 95% CI = 1.01, 1.11, Table 4).

[0104] Table 4 Logistic regression analysis of gut microbiota metabolite subgroups

[0105]

[0106] Note: *P < 0.05 compared with the control group.

[0107] 2.4 Diagnostic value of TMAO and its precursors, PAGln, for GDM

[0108] Figure 2The ROC curves for the four models are shown. Model 1, established using traditional factors such as maternal age, pre-pregnancy BMI, parity, gestational age at blood collection, and family medical history, had an AUC of 0.704. Model 2, established by adding TMAO and its precursor levels as binary variables to the traditional factors, had an AUC of 0.743, showing a statistically significant difference from Model 1 (P = 0.026). Model 3, established by adding the binary variable PAGln and traditional risk factors, had an AUC of 0.710, showing no significant difference from the traditional Model 1 (P > 0.05). Model 4, established by adding the binary variables TMAO and its precursors, and PAGln levels to the traditional factors, had an AUC of 0.745, showing a statistically significant difference from Model 1 (P = 0.023). In conclusion, the inclusion of TMAO and its precursors significantly improves the diagnostic ability for GDM. The above experimental results indicate that TMAO and its precursors can serve as auxiliary biomarkers for GDM monitoring.

[0109] Although specific embodiments of the invention have been described, those skilled in the art will recognize that various changes and modifications can be made to the invention without departing from its scope or spirit. Therefore, the invention is intended to cover all such changes and modifications falling within the scope of the appended claims and their equivalents.

Claims

1. The use of a reagent for detecting the level of a biomarker in a biological sample in the preparation of a kit, characterized in that, The kit is used to predict the risk of gestational diabetes in subjects. The biomarker is a combination of trimethylamine oxide, choline, betaine and L-carnitine. The subjects are women with a gestational age of >28 weeks, and the biological sample is blood.

Images