Serum marker FSTL3 for early pregnancy prediction of gestational diabetes mellitus and application of serum marker FSTL3

Abstract

The invention belongs to the technical field of biomedical detection, and particularly relates to a serum marker FSTL3 for early pregnancy prediction of gestational diabetes and application of the serum marker FSTL3. According to the present invention, the FSTL3 is adopted as the GDM serum marker for the first time, the traditional clinical risk factors are combined to develop the GDM early prediction and diagnosis kit, the stable, efficient, credible and economic GDM serum marker early diagnosis standard process is established, the disease detection efficiency is improved, the verification is performed in more clinical samples, and the GDM early prediction and diagnosis kit can be used for early diagnosis of the GDM serum marker. According to the invention, the blank in the field of early diagnosis of the disease in China is filled, the detection and diagnosis technology of the disease is improved to the international leading level, and the increasing clinical requirements are met.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a serum biomarker FSTL3 for early pregnancy prediction of gestational diabetes and its application. Background Technology

[0002] Gestational diabetes mellitus (GDM) refers to varying degrees of glucose metabolism abnormalities that first occur during pregnancy, with gestational hyperglycemia as the main clinical manifestation. The hyperglycemic state in GDM has a significant impact on the metabolic homeostasis and health of both the mother and offspring. GDM not only increases the risk of adverse outcomes in pregnant women, such as preeclampsia, premature birth, cesarean section, polyhydramnios, postpartum hemorrhage, and infection, but also significantly increases the risk of diabetes in subsequent pregnancies, with a postpartum risk of developing type 2 diabetes mellitus (T2DM) and cardiovascular disease that is more than 10 times higher than in women with normal blood sugar levels during pregnancy. Simultaneously, GDM has a profound impact on the health of the fetus and newborn. GDM is a significant factor in inducing fetal respiratory distress syndrome, jaundice, hypocalcemia, hypoglycemia, and polycythemia vera. GDM can induce macrosomia, which can lead to shoulder dystocia and hypoxic-ischemic encephalopathy in newborns. Furthermore, the intrauterine environment the fetus is exposed to during the perinatal period acts as a metabolic imprint, which is a significant contributing factor to various diseases after birth, especially metabolic diseases. Offspring of mothers with GDM have a significantly increased risk of developing obesity and type 2 diabetes mellitus (T2DM) compared to offspring of mothers without GDM, with a risk 7-20 times higher. Therefore, the gradually increasing incidence of GDM seriously affects the health of both mothers and infants.

[0003] Current clinical diagnostic criteria for GDM are based on the multicenter prospective clinical study (HAPO) on the relationship between hyperglycemia and adverse pregnancy outcomes. Diagnosis is performed via an oral glucose tolerance test (OGTT) at 24–28 weeks of gestation. Diagnosis at this time, coupled with proactive intervention, improves maternal and infant outcomes. However, recent research data indicates that even strict glycemic control cannot completely eliminate postpartum metabolic risks in GDM mothers and the risk of obesity and type 2 diabetes mellitus (T2DM) in offspring, suggesting that current diagnostic and intervention timelines may be too late. There is an urgent clinical need to develop biomarkers that can predict the occurrence and adverse outcomes of GDM in early pregnancy. Although the International Association for Diabetes and Pregnancy Study (IADPSG) recommends early pregnancy fasting blood glucose (FPG) for diagnosing GDM, further research has found that early pregnancy fasting blood glucose cannot diagnose GDM, but only serves as a predictor. Therefore, in-depth research into the differential expression of biomolecules in early pregnancy in GDM patients and their impact on GDM progression, and the identification of new early predictive biomarkers for GDM, is of great significance. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes for the first time to use FSLT3 as a serum biomarker for predicting gestational diabetes in early pregnancy, and combines it with traditional clinical risk factors for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in early pregnancy, successfully achieving significantly better predictive results.

[0005] As used in this article, FSLT3 refers to follistatin-related protein 3 (FSTL3), which is known to be closely related to glucose metabolism homeostasis, pancreatic β-cell proliferation, and insulin secretion regulation.

[0006] In a first aspect, the present invention provides the use of tools for detecting FSLT3 expression levels and tools for assessing conventional clinical risk factors in subjects in the preparation of kits or systems for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy.

[0007] Furthermore, the traditional clinical risk factors include one or more of age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI.

[0008] Furthermore, the FSLT3 expression level refers to the serum FSLT3 expression level of subjects at 14-16 weeks of gestation.

[0009] Furthermore, tools for detecting FSLT3 expression levels include reagents for detecting FSLT3 protein levels in serum.

[0010] Furthermore, reagents for detecting FSLT3 protein levels in serum include those for detecting protein levels using parallel reaction monitoring (PRM) technology.

[0011] Furthermore, when FSLT3 expression levels are below the reference level and conventional clinical risk factors are above the reference level, subjects are at risk of developing gestational diabetes and / or adverse pregnancy outcomes.

[0012] Furthermore, the reference serum expression level of FSLT3 was 8.681 ng / mL.

[0013] Furthermore, the reference level for traditional clinical risk factors is the level of healthy subjects (such as pregnant women) or subjects without gestational diabetes (such as pregnant women).

[0014] In a second aspect, the present invention provides tools for detecting the expression levels of FSLT3, FN1 and FBLN1, and tools for assessing conventional clinical risk factors in subjects, for use in the preparation of kits or systems for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy.

[0015] Furthermore, the expression levels of FSLT3, FN1, and FBLN1 are the serum expression levels of FSLT3, FN1, and FBLN1 in subjects at 14-16 weeks of gestation.

[0016] Furthermore, tools for detecting the expression levels of FSLT3, FN1, and FBLN1 include reagents for detecting the protein levels of FSLT3, FN1, and FBLN1 in serum.

[0017] Furthermore, reagents for detecting FSLT3, FN1, and FBLN1 protein levels in serum include reagents for detecting protein levels using parallel reaction monitoring (PRM) technology.

[0018] In a third aspect, the present invention provides a kit for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy, characterized in that it includes tools for detecting FSLT3 expression levels and tools for assessing conventional clinical risk factors in subjects.

[0019] Furthermore, the kit also includes tools for detecting the expression levels of FN1 and FBLN1.

[0020] Furthermore, tools for detecting FSLT3, FN1, or FBLN1 expression levels include reagents for detecting FSLT3, FN1, or FBLN1 protein levels in serum.

[0021] Furthermore, reagents for detecting FSLT3, FN1, or FBLN1 protein levels in serum include reagents for detecting protein levels using parallel reaction monitoring (PRM) technology.

[0022] Furthermore, the detection of protein levels using parallel reaction monitoring (PRM) technology includes the following steps: blood sample preparation, protein decomposition, internal standard preparation, external standard curve, and target protein detection.

[0023] Furthermore, reagents used for detecting protein levels via parallel reaction monitoring (PRM) technology include the BCA kit and High-Select kit. TM High-abundance protein removal resin, urea, DL-dithiothreitol, iodoacetamide, ammonium bicarbonate, trifluoroacetic acid, mass spectrometry grade acetonitrile, PBS buffer (tablets), trypsin, formic acid.

[0024] In a fourth aspect, the present invention provides a system for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in a subject during early pregnancy, the system comprising:

[0025] Data collection module: Acquires risk factors related to gestational diabetes mellitus of subjects, including FSTL3 level, age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI;

[0026] Data analysis module: Nomogram was constructed using FSTL3 level, age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI as variables;

[0027] Outcome prediction module: Based on the prediction cutoff value of the Nomogram, predict the risk of gestational diabetes and / or adverse pregnancy outcomes in the subjects.

[0028] Furthermore, the FSLT3 level refers to the serum FSLT3 expression level of the subject at 14-16 weeks of gestation.

[0029] Furthermore, the FSLT3 level was detected using reagents for detecting protein levels via parallel reaction monitoring (PRM) technology.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] This invention targets the detection of serum FSTL3 levels in early pregnancy for GDM, providing an early prediction method for the risk and adverse outcomes of GDM. This invention is the first to use FSTL3 as a serum biomarker for GDM, combining it with traditional clinical risk factors to develop an early prediction diagnostic kit for GDM. It establishes a stable, efficient, reliable, and economical standard procedure for the early diagnosis of GDM using serum biomarkers, improving the detection efficiency of this disease. The results have been validated in a wider range of clinical samples. This invention fills a gap in the field of early diagnosis of this disease in China, elevating the detection and diagnostic technology of this disease to an internationally leading level and meeting the growing clinical needs.

[0032] Furthermore, this invention also found that the combination of FSLT3, FN1, and FBNL1 can further improve the diagnostic rate of GDM. Attached Figure Description

[0033] Figure 1 This illustrates the principle of PRM analysis;

[0034] Figure 2 The FN1 standard curve is shown;

[0035] Figure 3 The FBLN1 standard curve is shown;

[0036] Figure 4 The FSTL3 standard curve is shown;

[0037] Figure 5The expression level of serum FSTL3 in early pregnancy was detected by ELISA in NGT population (n=320) and GDM patients (n=80) (A), and the correlation between FSTL3 in early pregnancy and fasting blood glucose, 1-hour blood glucose and 2-hour blood glucose at OGGT in mid-pregnancy was shown (B, C, D).

[0038] Figure 6 The ROC curves (AUC = 0.773) for predicting GDM occurrence using basal metabolic indicators (pre-pregnancy BMI, fasting blood glucose in early pregnancy) are shown in Figure 1 (AUC = 0.773) (A), and the ROC curves (AUC = 0.824) for predicting GDM occurrence using FSTL3 combined with basal metabolic indicators (pre-pregnancy BMI, fasting blood glucose in early pregnancy) are shown in Figure 2 (B).

[0039] Figure 7 The study demonstrates the use of parallel reaction monitoring (PRM) technology to detect serum FSTL3, FN1, and FBNL1 protein levels in early pregnancy in a validation cohort of NGT patients and GDM patients.

[0040] Figure 8 ROC curve analysis of serum FSTL3, FN1, FBNL1 combined with basal metabolic indicators (pre-pregnancy BMI, fasting blood glucose in early pregnancy) in the validation cohort is shown to predict the occurrence of GDM.

[0041] Figure 9 The nomogram (A), correction curve (B), and DCA curve (C) for predicting the occurrence of GDM based on FSTL3 combined with traditional clinical risk factors are shown.

[0042] Figure 10 A schematic diagram of a serum marker kit for early diagnosis of GDM based on PRM technology is shown. Detailed Implementation

[0043] In view of the current state of technology for early prediction of GDM occurrence and development, this invention proposes an early prediction method for the risk and adverse outcomes of GDM. This method measures the serum FSTL3 level in pregnant women during early pregnancy. This method can predict the occurrence of GDM and adverse outcomes at an early stage, and allows for early individualized intervention for high-risk groups of GDM.

[0044] To achieve the above objectives, the present invention adopts the following technical solution:

[0045] Step (1): Pregnant women are followed up at the hospital between 14 and 16 weeks of gestation. After fasting overnight for 10 hours, peripheral blood and serum samples are collected the next morning. Clinical questionnaire information is also collected at the same time.

[0046] Step (2): A complete blood count was performed on a whole blood sample from early pregnancy using a SYSMEX-X12100 hematology analyzer (Kobe, Japan).

[0047] Step (3): Biochemical indicators of serum samples from early pregnancy were detected using a Cobas 8000 Automatic Biochemical Analyzer (Roche, Basel, Switzerland). The remaining serum samples were stored at -80℃ for long-term testing of FSTL3 concentration.

[0048] Step (4): Pregnant women were followed up again at 24-28 weeks of gestation. After fasting for 10 hours overnight, a 75g-OGTT test was performed. According to the diagnostic criteria for GDM: fasting blood glucose (FBG) ≥ 5.1 mmol / L, 1h BG ≥ 10.0 mmol / L or 2h BG ≥ 8.5 mmol / L, the population was further divided into NGT and GDM groups. Clinical data from the second trimester were also collected.

[0049] Step (5): Collect clinical data and delivery outcome information of pregnant women who delivered in our hospital's obstetrics department during late pregnancy.

[0050] Step (6): Development of parallel reaction monitoring (PRM) methodology to standardize the detection of protein marker levels in the serum of NGT and GDM populations in early pregnancy.

[0051] The specific steps of step (6) are as follows:

[0052] 6.1) Detection Principle

[0053] PRM is an ion monitoring technique based on high-resolution, high-precision mass spectrometry. Its principle is similar to Selected Reaction Monitoring (SRM) / Multiple Reaction Monitoring (MRM), but it is more convenient for the absolute quantitative analysis of proteins and peptides. This method is best suited for the quantitative detection of multiple proteins in complex samples. PRM is based on the representative quadrupole high-resolution mass spectrometry platform of Q-Orbitrap. Unlike SRM, which completes only one transition at a time, PRM performs a full scan of each transition using precursor ions, i.e., it monitors all fragments of the precursor ions in parallel. (See attached image) Figure 1 As shown, PRM first uses a quadrupole (Q1) to select precursor ions, with a selection window typically m / z ≤ 2; then, the precursor ions are fragmented in a collision cell (Q2); finally, an orbitrap replaces Q3 to scan all product ions with high resolution and high accuracy. Therefore, PRM technology not only has the target quantitative analysis capability of SRM / MRM, but also qualitative analysis capability.

[0054] Advantages of the PRM method:

[0055] a. The quality accuracy can reach the ppm level, which can better eliminate background interference and false positives than SRM / MRM, and effectively improve the detection limit and sensitivity in complex backgrounds;

[0056] b. Full scan of product ions eliminates the need for ion pair selection and fragment energy optimization, making it easier to establish a determination method;

[0057] c. Wider linear range: increased by 5-6 orders of magnitude.

[0058] 6.2) Skyline Data Analysis

[0059] Skyline is an open-source software for targeted proteomics and metabolomics data analysis that supports various workflows, including selected reaction monitoring (SRM), multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and data-independent acquisition (DIA). Therefore, it can efficiently and rapidly acquire large amounts of m / z and RT values ​​of peptides related to proteins of interest for method editing, and accurately integrate fragment peak areas of precursor ions for quantitative analysis.

[0060] 6.3) Experimental Procedure

[0061] 6.3.1) Instruments and Equipment (Table 1)

[0062] Table 1 Instruments and Equipment

[0063] Instrument Name factory Q Exactive mass spectrometer ThermoFisher benchtop centrifuge Eppendorf benchtop centrifugal concentrator ThermoScientific Thermostatic mixer ThermoScientific Multifunctional ELISA reader SpectraMaxI3X Vortex instrument SciLogex Electronic balance Ohaus

[0064] 6.3.2) Experimental reagents (Table 2)

[0065] Table 2 Experimental Reagents

[0066]

[0067]

[0068] 6.3.3) Blood Sample Preparation

[0069] ① Removal of high-abundance albumin and antibody components from plasma

[0070] Plasma samples from the same group were pooled into one sample (FN for pregnant women with NGT; FG for patients with GDM).

[0071] Remove high-abundance proteins from the centrifuge column and equilibrate to room temperature.

[0072] Remove the column cap and add 5 μL of sample (6 x 5 μL FN + 6 x 5 μL FG) directly into the resin slurry in the column.

[0073] Cover the column and invert it several times until the resin is completely homogeneous in the solution.

[0074] At room temperature, gently incubate the mixture end-to-end within the column for 30 minutes. Ensure the sample is mixed with the resin during incubation.

[0075] After incubation, unscrew the bottom cap and replace the top cap. Place the mini-column into a 1.5 mL centrifuge tube and centrifuge at 1,000 × g for 2 minutes. After centrifugation, collect approximately 260 μL of liquid from each tube.

[0076] Discard the resin centrifuge column and store the collection tube at 4°C for use in subsequent experiments.

[0077] ② BCA protein detection

[0078] Reagent A: 0.1M sodium hydroxide solution containing diquinoline acid (BCA), sodium carbonate, sodium tartrate, and sodium bicarbonate.

[0079] Reagent B: 4% (w / v) copper sulfate pentahydrate (II).

[0080] Protein Standard (BSA) Solution: BSA is stored at a concentration of 1 mg / mL, dissolved in 0.15 M sodium chloride, and 0.05% sodium azide is used as a preservative.

[0081] a. Preparation of BCA working reagent (WR)

[0082] The BCA working reagent is made by mixing 50 parts of reagent A and 1 part of reagent B.

[0083] Mix the BCA working reagent until it turns light green (Table 3).

[0084] The BCA working reagent (a mixture of reagent A and reagent B) can be used stably for one day.

[0085] Table 3 Volume of working reagent for BCA preparation

[0086]

[0087] b. Preparation of BSA standard solution

[0088] The BSA stock solution is 2 mg / mL. For the dilution steps of the BSA series standard solutions, refer to Table 4 and dilute with water (200, 400, 600, 800, 1,000 μg / mL).

[0089] c. Measurement

[0090] Use a pipette to transfer 25 μL of BSA series standard solution, blank control solution, and sample solution into each well of a 96-well plate. Prepare one replicate. Add 200 μL of working reagent to each well, mix gently for 30 seconds, and then cap the plate.

[0091] Incubate the sample at 37°C for 30 minutes.

[0092] Cool the plate to room temperature.

[0093] The absorbance of the solution was measured at a wavelength of 562 nm using a multi-functional microplate reader. A standard curve was constructed based on the concentration of the BSA protein standard or the protein content in the BSA protein standard.

[0094] The protein concentration was determined by comparing the absorbance of the unknown sample (FN / FG) with a standard curve plotted using BSA protein standards.

[0095] Table 4 Examples of Standard Sample Preparation for Testing

[0096]

[0097] 6.3.4) Protein decomposition

[0098] ①Sample vacuum concentration

[0099] Transfer approximately 200 μg of FG / FN sample from step 6.3.3)① to 1.5 mL centrifuge tubes and concentrate them at room temperature for 2.5 hours using a centrifuge concentrator.

[0100] Dissolve in 100 μL of 8M urea (weigh 14.42 g of urea and dissolve in 30 mL of ultrapure water).

[0101] ②DTT restoration

[0102] Add 5.26 μL of 100 mM DTT to each sample and incubate at 56 °C for 30 minutes. The final concentration of DTT should be 5 mM.

[0103] 10mM ammonium bicarbonate (ABC) solution: Weigh 31.63mg of ABC and dissolve it in 40mL of ultrapure water.

[0104] 100mM DTT solution: Weigh 15.425mg DTT and dissolve it in 1mL of 10mM ABC solution.

[0105] ③ Iodoacetamide alkylation

[0106] Cool the FG and FN solutions in step ② to room temperature, add 5.54 μL of 220 mM IAA to each solution, and incubate in the dark at room temperature for 30 minutes. The final concentration of IAA should be 11 mM.

[0107] 25mM ammonium bicarbonate (ABC) solution: Weigh 79mg of ABC and dissolve it in 40mL of ultrapure water.

[0108] 220mM iodoacetamide (IAA) solution: Weigh 20.4mg IAA and dissolve it in 500μL ABC solution (IAA should be stored in the dark).

[0109] ④ Enzyme digestion

[0110] Add seven times the volume of 25mM ABC (775.6μL) to the sample in step ③, and control the final concentration of urea below 1M.

[0111] Add 4 μL of 1 mg / mL Promega trypsin (protein:enzyme mass ratio = 50:1) to FG and FN, and incubate overnight for 15 hours.

[0112] Fifteen hours later, 9 μL of 10% TFA was added to stop the enzyme digestion. The final concentration of TFA was 1%.

[0113] Incubate on ice for 10 minutes, centrifuge at 5,000×g for 2 minutes, and collect the supernatant.

[0114] ⑤ Sample desalination

[0115] a. Desalination and concentration

[0116] Place the spin column on the waste liquid collection tube, add 200 μL of acetonitrile, centrifuge at 5,000 × g for 1 minute at room temperature, and discard the waste liquid in the collection tube.

[0117] Add 200 μL of 0.1% TFA solution, centrifuge at 5,000 × g for 1 minute at room temperature, and discard the waste liquid in the collection tube.

[0118] 0.1% TFA solution: Mix 1 μL of TFA with 999 μL of ultrapure water.

[0119] b. Sample loading

[0120] Transfer the sample from step ④ to a spin column, centrifuge at 5,000×g for 2 minutes, and discard the waste liquid in the collection tube.

[0121] Add 300 μL of 0.1% TFA, centrifuge at 5,000 × g for 1 minute, and discard the waste liquid in the collection tube.

[0122] c. Washing (twice)

[0123] Replace the sample collection tube, add 200 μL of 60% acetonitrile solution, and centrifuge at 5,000 × g for 1 minute.

[0124] 60% acetonitrile solution: Mix 480 μL of acetonitrile with 320 μL of ultrapure water.

[0125] d. Drying and concentration

[0126] The sample was dried in a centrifugal concentrator at 30°C for 2.5 hours (to remove acetonitrile).

[0127] Before analysis, dilute each sample (40 μL) with a 5% acetonitrile + 0.1% formic acid solution containing an internal standard (0.04 μg / μL).

[0128] 6.3.5) Preparation of internal standard

[0129] ① Screening of internal standard peptides

[0130] The internal standard method is used to calibrate protein concentrations in blood samples.

[0131] From the previous FG and FN sample mass spectrometry PRM results, the best-performing peptide in each protein was selected as an internal standard by comparing peak shape and peak intensity.

[0132] Ordering isotope internal standards from GJ Biochemical:

[0133] Four amino acids were extended at both ends to correct the efficiency of enzyme digestion.

[0134] The double labeling of 13C and 15N is used to label lysine (K) and arginine (R).

[0135] The molecular weight of the internal isotope standard is about 5 Da greater than that of the external isotope standard.

[0136] Preliminary experimental results showed no statistically significant difference between removing high-abundance proteins from the standard peptides (p = 0.924), so this step was omitted.

[0137] ② Protein breakdown

[0138] Equilibrate the stock solutions of the three internal standard peptides (1 μg / μL) to room temperature, and mix 100 μL of each solution.

[0139] Referring to step 6.3.4), for the internal standard mixed sample, vacuum concentration, DTT reduction, iodoacetamide alkylation, and enzyme digestion are performed.

[0140] Since the amount of protein in the internal standard mixture is 3 / 2 of the amount of protein in the blood sample in step 6.3.3)①, the amount of solvent used is also 3 / 2 of the volume in step 6.3.3)①.

[0141] ③ Sample desalination

[0142] Referring to step 6.3.4), the internal standard pool is desalted and concentrated using the same method.

[0143] After the internal standard was dried, a 5% acetonitrile + 0.1% formic acid solution was added to dilute the internal standard mixture to 0.012 μg / μL.

[0144] At this point, the concentration of a single isotope internal standard polypeptide in the diluent is 0.04 μg / μL.

[0145] This diluent is used to dilute external standard samples and blood samples (FG / FN) before they are used in the laboratory.

[0146] The diluents containing internal standards are shown in Table 5.

[0147] Table 5. Isotopic internal standard peptides and their concentrations in the dilution solution.

[0148]

[0149] 6.3.6) Setting External Standard Curve

[0150] ① External standard peptide screening

[0151] External standard method is used to create standard curves to quantify protein concentrations in blood samples.

[0152] From the previous FG and FN sample mass spectrometry PRM results, two peptides with better performance in each protein were selected as external standards by comparing peak shape and peak intensity.

[0153] Ordering externally sourced peptides from Jier Biochemical:

[0154] Four amino acids were extended at both ends to correct the efficiency of enzyme digestion.

[0155] Preliminary experimental results showed no statistically significant difference between removing high-abundance proteins from the standard peptides (p = 0.924), so this step was omitted.

[0156] 6.3.7) Protein decomposition

[0157] The stock solutions of the six external standard peptides were equilibrated (1 μg / μL) to room temperature, and a certain amount was taken and mixed. The mixed standard preparation is shown in Table 6.

[0158] Table 6. Isotopic internal standard peptides and their concentrations in the dilution solution.

[0159]

[0160] Referring to step 6.3.4), a total of 100 μL of external standard mixed sample was taken and subjected to vacuum concentration, DTT reduction, iodoacetamide alkylation, and enzyme digestion.

[0161] Since the amount of protein in the external standard mixture is half the amount of protein in the blood sample in step 6.3.3)①, the amount of solvent used is also half the volume in step 6.3.3)①.

[0162] 6.3.8) Sample desalination

[0163] Refer to step 6.3.4), and desalt and concentrate the external standard mixture using the same method.

[0164] 6.3.9) Serial dilution of external standard

[0165] After drying the external standard mixture, add a 5% acetonitrile + 0.1% formic acid solution containing the internal standard (0.04 μg / μL) as a solvent and dilute to 0.5 μg / μL as STD1.

[0166] The external standard was serially diluted as shown in Table 7.

[0167] Table 7. Serial dilution of external standard medley

[0168]

[0169]

[0170] 6.3.10) Experimental Data and Results Analysis

[0171] ①Mass spectrometry conditions

[0172] Target protein detection was performed using a Q Exactive mass spectrometer. The primary MS resolution was set to 17500, and the automatic gain control was set to 3e6. The mass spectrometry scan was set to a full scan mass-charge ratio (m / z) range of 400-1600. The MS / MS resolution was set to 35000, the automatic gain control was set to 1e5, and the maximum ion accumulation time was set to 100 ms.

[0173] 6.3.11) Export and process data

[0174] A blood sample was injected twice using a Q Exactive mass spectrometer, once for each of the FG and FN samples, with three replicates for each of the eight external standard gradients. The source file (.RAW) obtained from the mass spectrometer was imported into Skyline, and the peak areas of each daughter ion of the target peptide VDVIPVNLPGEHGQR from sp|P02751|FN1, the target peptide TGYYFDGISR from sp|P23142|FBLN1, and the target peptide INLLGFLGLVHCLPCK from sp|O95633|FSTL3, as well as the peak areas of their isotopic internal standards, were obtained.

[0175] 6.4) The standard curve for quantifying sp|P02751|FN1 in blood samples is shown in the attached figure. Figure 2 As shown.

[0176] The quantitative analysis of FN1 protein in blood samples was consistent with the results of proteomics analysis of early placental tissue. The concentration of FN1 protein in blood was significantly lower in patients with GDM and pregnant women with NGT (Table 8).

[0177] Table 8. Quantitative analysis of FN1 protein in blood samples

[0178]

[0179] 6.5) The standard curve for quantifying sp|P23142|FBLN1 in blood samples is shown in the attached figure. Figure 3 As shown.

[0180] The quantitative analysis of FBLN1 protein in blood samples was consistent with the results of proteomics analysis of early placental tissue. The concentration of FBLN1 protein in blood was significantly lower in patients with GDM and pregnant women with NGT (Table 9).

[0181] Table 9. Quantitative analysis of FBLN1 protein in blood samples

[0182]

[0183] 6.6) The standard curve for quantifying sp|O95633|FSTL3 in blood samples is shown in the attached figure. Figure 4 As shown.

[0184] The quantitative analysis of FSTL3 protein in blood samples was consistent with the results of proteomics analysis of early placental tissue. The concentration of FSTL3 protein in blood was slightly lower in patients with GDM and pregnant women with NGT (Table 10).

[0185] Table 10 Quantitative analysis of FSTL3 protein in blood samples

[0186]

[0187] Step 7): Statistical analysis of the clinical data characteristics of the subjects was performed using SPSS 24.0 software. All continuous data underwent normality testing, and normally distributed data were expressed as mean ± standard deviation. Two-sample t-tests were used for comparisons between groups. Non-normally distributed data were expressed as median (interquartile range), and non-parametric Mann-Whitney U tests were used for comparisons between groups. Count data were expressed as the number of positive cases or percentage. Chi-square tests were used for comparisons of categorical variables. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve was calculated to determine the best predictor for diagnosing GDM. Restricted spline regression analysis was used in R (version R 4.2.0) to analyze the non-linear relationship between early pregnancy FSTL3 concentration and GDM incidence. Combined analysis of early pregnancy FSTL3 concentration and clinical baseline data was used to predict the risk of GDM and adverse pregnancy outcomes. P < 0.05 was considered statistically significant.

[0188] The present invention will be further illustrated below with reference to specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise specified, the reagents, methods, and equipment used in the present invention are conventional reagents, methods, and equipment in this technical field.

[0189] Example

[0190] 1. We collected clinical and biochemical data from 80 women with GDM and 320 pregnant women with NGT during prenatal follow-up. We also collected peripheral blood whole blood and serum samples from the study population at 14-16 weeks of gestation.

[0191] 2. Complete blood count (CBC) of whole blood samples from early pregnancy was performed using a SYSMEX-X12100 hematology analyzer (Kobe, Japan).

[0192] 3. Biochemical parameters of serum samples from early pregnancy were detected using a Cobas 8000 Automatic Biochemical Analyzer (Roche, Basel, Switzerland). Remaining serum samples were stored at -80℃ for long-term testing of FSTL3 concentration.

[0193] 4. Pregnant women should return for follow-up between 24 and 28 weeks of gestation. After a 10-hour overnight fast, a 75g-OGTT test should be performed. Based on the diagnostic criteria for GDM: fasting blood glucose (FBG) ≥ 5.1 mmol / L, 1hBG ≥ 10.0 mmol / L or 2hBG ≥ 8.5 mmol / L, the population should be further divided into non-glucose tolerance (NGT) and GDM groups. Clinical data from the second trimester should also be collected.

[0194] 5. Collect clinical data and delivery outcome information of pregnant women who delivered in our hospital's obstetrics department during late pregnancy.

[0195] 6. Using commercially available ELISA kits, the expression level of serum FSTL3 in early pregnancy was uniformly detected in 320 NGT patients and 80 GDM patients.

[0196] 7. Parallel reaction monitoring (PRM) technology was used to uniformly detect the levels of protein markers FN1, FBLN1, and FSTL3 in the serum of 320 NGT patients and 80 GDM patients in early pregnancy.

[0197] 8. SPSS 24.0 statistical software was used to perform statistical analysis on the clinical data characteristics. All continuous data underwent normality testing, and normally distributed data were expressed as mean ± standard deviation. Two-sample t-tests were used for comparisons between groups. Non-normally distributed data were expressed as median (interquartile range), and non-parametric Mann-Whitney U tests were used for comparisons between groups. Count data were expressed as the number of positive cases or percentage. Chi-square tests were used for comparisons of categorical variables. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve was calculated to determine the best predictor for the diagnosis of GDM. Restricted spline regression analysis was used in R (version R 4.2.0) to analyze the non-linear relationship between early pregnancy FSTL3 concentration and GDM incidence. Combined analysis of early pregnancy FSTL3 concentration and clinical baseline data was used to predict the risk of GDM. P < 0.05 was considered statistically significant.

[0198] (1) Analysis of baseline clinical data of all subjects showed that, compared with normal pregnant women, GDM patients had significantly higher white blood cell count, neutrophil count, hemoglobin level, liver function indicators, serum uric acid, and triglyceride levels in early pregnancy, while total cholesterol and low-density lipoprotein cholesterol were significantly lower in GDM patients (P<0.05). In mid-pregnancy, fasting blood glucose, OGTT-1h blood glucose, OGTT-2h blood glucose, glycated hemoglobin, and HOMA-IR levels were significantly increased, while HOMA-β was significantly decreased (P<0.001), as shown in Table 11. Furthermore, compared with NGT patients, GDM patients had a significantly increased incidence of adverse pregnancy outcomes, including premature delivery, cesarean section rate, macrosomia, LGA incidence, and neonatal adverse outcomes, as shown in Table 12.

[0199] Table 11 Clinical baseline characteristics of all subjects

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[0203] Table 12 Pregnancy outcomes for all subjects (including maternal outcomes, delivery outcomes, and infant outcomes)

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[0205]

[0206] (2) The expression level of FSTL3 in maternal serum during early pregnancy was detected in all subjects (NGT = 320 cases, GDM = 80 cases) in the GDM cohort during early pregnancy using a commercially available ELISA kit (R&D Systems, catalog number DFLRG0). The results showed that the serum FSTL3 level in GDM patients during early pregnancy was significantly lower than that in the NGT group (P<0.001), and the maternal serum FSTL3 level during early pregnancy was significantly negatively correlated with maternal fasting blood glucose, 1-hour blood glucose, and 2-hour blood glucose at OGGT in mid-pregnancy. The results are shown in the attached figure. Figure 5 As shown in the attached figure, serum FSTL3 combined with basal metabolic indicators (pre-pregnancy BMI, fasting blood glucose in early pregnancy) has good diagnostic value for predicting the occurrence of GDM in early pregnancy, and is significantly better than independent basal metabolic indicators (AUC = 0.824 vs. AUC = 0.773). Figure 6 As shown.

[0207] (3) Establishment and performance evaluation of a GDM early diagnostic kit based on parallel reaction monitoring (PRM) technology: Researchers tested serum samples from all subjects (320 NGT group and 80 GDM group) in early pregnancy from a previously constructed large-sample prospective cohort of GDM patients to optimize efficacy and stability. Simultaneously, the kit's operating procedures, experimental reagents, and materials were optimized, determining the kit's operating steps, component composition, and dosage. The detection methods and procedures were standardized, further determining various parameters and indicators of the kit's reaction system. The results showed that serum FSTL3, FN1, and FBNL1 protein levels in early pregnancy were significantly lower in GDM patients than in the NGT group (P<0.05). The results are shown in the attached figure. Figure 7 As shown in the attached figure. Furthermore, serum FSTL3 combined with basal metabolic indicators (pre-pregnancy BMI, fasting blood glucose in early pregnancy) in early pregnancy has good diagnostic value for predicting GDM. The diagnostic cutoff value for FSTL3 is 8.681 ng / mL, and it is significantly superior to FN1, FBNL1 combined with basal metabolic indicators (AUC = 0.805 vs. AUC = 0.787 and AUC = 0.786). Figure 8 As shown in the attached figure. Based on the risk factors for GDM identified in previous studies, FSTL3, age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI were used as variables to construct a nomogram for predicting the occurrence of GDM. The calibration curve and DCA curve showed that the nomogram had good discriminative power. Figure 9 As shown.

[0208] (4) Based on the above, the researchers constructed a kit for three serum biomarkers for early diagnosis of GDM, namely FSTL3, FN1, and FBNL1 proteins, based on PRM technology. The results are shown in the attached figure. Figure 10As shown, complementary bioinformatics analysis software was also developed. Large-sample validation showed that the kit's precision coefficient of variation (CV) was less than 15%, and the kit's accuracy, including detection errors across different batches, methods, and sample sources, was less than 1%.

[0209] It should be noted that while the preferred embodiments of the present invention are given in the specification and accompanying drawings, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are not intended to impose additional limitations on the content of the present invention; their purpose is to provide a more thorough and comprehensive understanding of the disclosure of the present invention. Furthermore, the above-described technical features can be combined with each other to form various embodiments not listed above, all of which are considered to be within the scope of the present invention specification. Moreover, those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. The use of tools for detecting FSLT3 expression levels and tools for assessing conventional clinical risk factors in subjects in the preparation of kits or systems for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy.

2. The use according to claim 1, characterized in that, The traditional clinical risk factors include one or more of age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI.

3. The use according to claim 1, characterized in that, The FSLT3 expression level refers to the serum FSLT3 expression level of subjects at 14-16 weeks of gestation.

4. The use according to claim 3, characterized in that, Tools for detecting FSLT3 expression levels include reagents for detecting FSLT3 protein levels in serum.

5. The use according to claim 4, characterized in that, Reagents used to detect FSLT3 protein levels in serum include those used to detect protein levels via parallel reaction monitoring (PRM) technology.

6. The use according to claim 1, characterized in that, When FSLT3 expression levels are below the reference level and conventional clinical risk factors are above the reference level, subjects are at risk of developing gestational diabetes and / or adverse pregnancy outcomes.

7. The use according to claim 6, characterized in that, The reference serum expression level of FSLT3 is 8.681 ng / mL.

8. A kit for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy, characterized in that, This includes tools for detecting FSLT3 expression levels and tools for assessing high levels of conventional clinical risk factors in subjects.

9. A system for predicting the risk of gestational diabetes and / or adverse pregnancy outcomes in subjects during early pregnancy, characterized in that, The system includes: Data collection module: Acquires risk factors related to gestational diabetes mellitus of subjects, including FSTL3 level, age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI; Data analysis module: Nomogram was constructed using FSTL3 level, age, neutrophils, hemoglobin, fasting blood glucose in early pregnancy, and pre-pregnancy BMI as variables; Outcome prediction module: Based on the prediction cutoff value of the Nomogram, predict the risk of gestational diabetes and / or adverse pregnancy outcomes in the subjects.

10. The system according to claim 9, characterized in that, The FSLT3 level refers to the serum FSLT3 expression level of the subject at 14-16 weeks of gestation. The FSLT3 level was detected using reagents for detecting protein levels via parallel reaction monitoring (PRM) technology.

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