CN114034781A - Biomarker, method and early warning model for predicting gestational diabetes in early pregnancy - Google Patents

Abstract

The invention discloses a biomarker for predicting gestational diabetes in early pregnancy, which comprises the following components: one or more of 30 biomarkers such as eicosapentaenoic acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal); the biomarker undergoes a fold change in content difference between a pregnant female and a healthy pregnant female at an early stage of pregnancy. The invention also provides a method for predicting gestational diabetes mellitus in the early stage of pregnancy and an early warning model, so that the occurrence of gestational diabetes mellitus can be predicted more individually and accurately in the early stage of pregnancy by only using a small amount of blood samples.

Description

Biomarker, method and early warning model for predicting gestational diabetes in early pregnancy

Technical Field

The invention relates to a method for predicting gestational diabetes mellitus in early pregnancy. More particularly, the present invention relates to a biomarker, a method and an early warning model for predicting gestational diabetes mellitus in an early pregnancy period.

Background

Gestational Diabetes Mellitus (GDM) refers to the abnormal sugar metabolism that occurs or is found to varying degrees for the first time in gestational period. With the late childbearing age and the change of life style, the incidence rate of GDM has a remarkable rising trend and tends to be younger and developed, which becomes a global pregnancy health problem. GDM increases the risk of various complications of the mother and the offspring, such as abortion, pregnancy hypertension, giant children, congenital defects, malformation, perinatal death, etc., and the risk of type 2 diabetes (T2 DM) of women with GDM history and the risk of endocrine metabolism disorder, obesity, cardiovascular disease, etc. of the offspring are obviously increased.

Epidemiological studies have found that there are many risk factors for GDM, such as elderly pregnant women, ethnicity, family history of type 2 diabetes, and eating and exercise habits. Metabolic abnormalities in GDM patients include mainly increased insulin resistance and loss of beta cell function, which may have been present before pregnancy, but have not been discovered and appreciated. Due to the influence of placenta secretion hormone, the metabolic adaptability in pregnancy increases insulin resistance, insulin beta cells are required to secrete a large amount of insulin, the pancreatic gland compensation function is reduced, so that the insulin secretion is insufficient, the effect of inhibiting endogenous glucose is reduced, and the glucose uptake of skeletal muscle and fat is weakened, thereby causing hyperglycemia. Maternal hyperglycemia transported across the placenta also leads to fetal hyperinsulinemia and, through metabolic reprogramming, obesity of the fetus, even metabolic dysfunction in adulthood.

At present, GDM is screened in the late pregnancy (24-28 weeks), and diet guidance and medicine (such as insulin) intervention are carried out on pregnant women diagnosed with GDM so as to reduce the occurrence of maternal complications and fetal poor pregnancy outcome. However, the intervention time is short when the pregnancy is near the middle and late pregnancy, the detection method is time-consuming and labor-consuming, the sugar load of potential GDM pregnant women is increased, the intervention chance at the early pregnancy is lost, and the risk is high. The early gestation is a key period of fetal growth and development, and a fetus exposed in an intrauterine adverse environment has a high mortality rate, so that the risk of GDM is found in advance, intervention and treatment are given, the outcome of a mother and a baby is improved, and the early gestation is of great clinical significance. At present, specific biomarkers for GDM screening in early pregnancy with high accuracy and good specificity are still lacked. The GDM early biomarker is explored and screened, the generation mechanism of the GDM is further explained, and the GDM early biomarker is expected to become a key target for preventing and treating GDM, so that the maternal and offspring fate is improved, and the GDM early biomarker has important clinical significance.

Disclosure of Invention

An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.

Still another object of the present invention is to provide a biomarker for predicting gestational diabetes in early pregnancy, which is closely related to the occurrence and development of gestational diabetes, can be used as an early warning marker for predicting gestational diabetes in early pregnancy, and can be used for predicting gestational diabetes more individually and accurately in early pregnancy.

To achieve these objects and other advantages in accordance with the present invention, there is provided a biomarker for predicting gestational diabetes early in pregnancy, wherein the biomarker is selected from the group consisting of: one or more of eicosapentaenoic acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal); wherein the biomarker is altered in fold difference in content between a pregnant female and a healthy pregnant female at an early stage of pregnancy.

Preferably, the biomarker further comprises: aminoimidazole nucleotide (aminoimidazole), glycerophosphatidylcholine (hepatic metabolism) (glycophosphorylline), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine-1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), phenylacetic acid (phenylacetic acid), 3 α,7 α,12 α -trihydroxy-5 β -cholate (3 α,7 α,12 α -trihydroxy-5 β -cholate), 4 a-hydroxytetrahydropterin (4a-hydroxytetrahydrobiopterin), 4-guanidobutyrate (4-guanidobutyrate), phenylacetaldehyde (phenylacetaldehyde linolenic acid), leukotriene D4 (leukin D4), dopamine (dopamine), choline (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), phenylacetic acid (4-hydroxy-phenylacetate), phenylacetaldehyde (phenylindole-5-hydroxy-pyruvate) (4-methoxyindole-5-phenylacetate (4-hydroxy-5 β -phenylacetate), phenylindole-5-hydroxy-dihydroindole-pyruvate (4-hydroxy-dihydroindole-5 β -dihydroindole acetate), and a-hydroxy-dihydroindole, N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; one or more of O2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40: 7; wherein the biomarker is altered in fold difference in content between a pregnant female and a healthy pregnant female at an early stage of pregnancy.

Preferably, the change in the fold difference in content is such that the fold difference in content of the biomarker is all up-regulated compared to a healthy pregnant female.

The present invention provides a method for predicting gestational diabetes in an early pregnancy period based on the biomarker, which comprises: measuring the fold difference in the content of at least one biomarker in a biological sample obtained from a pregnant female, to predict the risk of gestational diabetes of the pregnant female, wherein the biomarker is selected from the group consisting of: eicosapentaenoic acid fatty acid (EPA), glucose (glucose), 4-aminobutanal (4-aminobutanal),: aminoimidazole nucleotide (aminoimidazole), glycerophosphatidylcholine (hepatic metabolism) (glycophosphorylline), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine-1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), phenylacetic acid (phenylacetic acid), 3 α,7 α,12 α -trihydroxy-5 β -cholate (3 α,7 α,12 α -trihydroxy-5 β -cholate), 4 a-hydroxytetrahydropterin (4a-hydroxytetrahydrobiopterin), 4-guanidobutyrate (4-guanidobutyrate), phenylacetaldehyde (phenylacetaldehyde linolenic acid), leukotriene D4 (leukin D4), dopamine (dopamine), choline (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), phenylacetic acid (4-hydroxy-phenylacetate), phenylacetaldehyde (phenylindole-5-hydroxy-pyruvate) (4-methoxyindole-5-phenylacetate (4-hydroxy-5 β -phenylacetate), phenylindole-5-hydroxy-dihydroindole-pyruvate (4-hydroxy-dihydroindole-5 β -dihydroindole acetate), and a-hydroxy-dihydroindole, N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; o2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40: 7.

Preferably, the measurement of the fold increase in the level difference of the at least one biomarker in the biological sample from which the pregnant female is obtained is an indication that the pregnant female is at risk for gestational diabetes.

Preferably, the biological sample is serum.

Preferably, the measuring comprises: analyzing the biological sample by using a combined ultra-high liquid phase-high resolution mass spectrometry, and introducing original data obtained by the combined ultra-high liquid phase-high resolution mass spectrometry into analysis software for peak identification, peak alignment and isotope removal to obtain a two-dimensional data matrix containing information such as mass-to-charge ratio, retention time, peak area and the like; and (3) carrying out variable statistical analysis on the mass spectrum data, carrying out primary identification on the screened polar metabolites by utilizing metabonomics analysis software, and carrying out primary identification on the non-polar metabolites by comparing the non-polar metabolites with a lipid database.

The invention further provides an early pregnancy diabetes early warning model, wherein the input variable of the model is the content difference multiple of the biomarker for predicting early pregnancy diabetes.

Preferably, the early gestational diabetes mellitus early warning model is constructed by the following steps:

measuring the content of the biomarker in a biological sample of a pregnant female;

secondly, carrying out ratio analysis on the biomarkers to obtain content difference multiples of the biomarkers;

and step three, incorporating one or more biomarkers into a logistic regression model for analysis, and combining all the biomarkers by utilizing binary logistic regression to establish an early pregnancy gestational period diabetes early warning model.

Preferably, in the third step, three biomarkers of eicosapentaenoic acid fatty acid (EPA), glucose (glucose) and 4-aminobutyraldehyde (4-aminobutanal) are selected to construct an early pregnancy gestational period diabetes mellitus warning model.

The invention at least comprises the following beneficial effects: the biomarker for predicting gestational diabetes in the early pregnancy period is obtained by carrying out non-targeted metabonomics analysis on early pregnancy serum, and 30 biomarkers comprising 4-aminobutanal (4-aminobutanal) are found and determined to be used for predicting the occurrence of gestational diabetes, so that the biomarker has stable and reliable effects. The biomarker is further included in a logistic regression model for analysis, and the compounds are combined by using binary logistic regression, so that the early gestational period diabetes early warning model established by using 3 biomarkers of eicosapentaenoic acid (EPA), glucose and 4-aminobutyraldehyde has higher accuracy, wherein the AUC is 0.81, and the 95% confidence interval is 0.73-0.89. The biomarker, the method and the early pregnancy diabetes early warning model realize the diagnosis and the prediction of the occurrence and the development of late pregnancy diabetes by using a small amount of blood samples. The method and the model can be used for predicting the occurrence of the diabetes mellitus in the late pregnancy period more individually and accurately in the early pregnancy period so as to intervene the disease condition as early as possible, improve the pregnancy outcome and the fetal prognosis of a patient, and reduce the medical cost and the social burden caused by the poor outcome of the diabetes mellitus in the pregnancy period.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.

Drawings

FIG. 1 shows the average of (A) VIP values and (B-C) changes for 30 biomarkers of the invention;

FIG. 2 is a graph of the OPLS-DA model score of serum samples from gestational diabetes group and healthy control group according to the present invention;

FIG. 3 shows ROC curves of an early gestational diabetes warning model constructed based on 3 biomarkers of eicosapentaenoic acid (EPA), glucose and 4-aminobutyraldehyde;

FIG. 4 is a scatter plot of the mean values of 3 biomarkers of eicosapentaenoic acid (EPA), glucose and 4-aminobutyraldehyde in accordance with the present invention.

Detailed Description

The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.

It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.

The invention provides a biomarker for predicting gestational diabetes mellitus in an early pregnancy period, wherein the biomarker is eicosapentaenoic acid fatty acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal); wherein the biomarker is upregulated by a fold difference in content between the pregnant female and a healthy pregnant female at an early stage of pregnancy.

In one embodiment, the biomarker further comprises: aminoimidazole nucleotide (aminoimidazole), glycerophosphatidylcholine (hepatic metabolism) (glycophosphorylline), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine-1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), phenylacetic acid (phenylacetic acid), 3 α,7 α,12 α -trihydroxy-5 β -cholate (3 α,7 α,12 α -trihydroxy-5 β -cholate), 4 a-hydroxytetrahydropterin (4a-hydroxytetrahydrobiopterin), 4-guanidobutyrate (4-guanidobutyrate), phenylacetaldehyde (phenylacetaldehyde linolenic acid), leukotriene D4 (leukin D4), dopamine (dopamine), choline (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), phenylacetic acid (4-hydroxy-phenylacetate), phenylacetaldehyde (phenylindole-5-hydroxy-pyruvate) (4-methoxyindole-5-phenylacetate (4-hydroxy-5 β -phenylacetate), phenylindole-5-hydroxy-dihydroindole-pyruvate (4-hydroxy-dihydroindole-5 β -dihydroindole acetate), and a-hydroxy-dihydroindole, N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; one or more of O2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40: 7; wherein the biomarker is changed with the content difference multiple up-regulated between the pregnant female body and the healthy pregnant female body in the early pregnancy.

In one embodiment, the invention also provides a method for predicting gestational diabetes in early pregnancy based on the biomarker, which specifically comprises the following steps:

s1, sample pretreatment: a blood sample of a pregnant female body was collected and centrifuged at 3000rpm and 4 ℃ for 10 minutes, 50. mu.L of serum was transferred to a 10mL glass centrifuge tube, 1.5mL of MeOH/MTBE (1:1, v: v) was added thereto, 3200g was obtained after vortexing at 2500rpm for 5 minutes, centrifuged at 4 ℃ for 10 minutes, the supernatant was transferred to a new glass tube, 1.5mL of MTBE and 0.6mL of H2O were added thereto, 3200g was obtained after vortexing at 2500rpm for 5 minutes, centrifuged at 4 ℃ for 10 minutes, and the liquids were separated into layers. Blowing the lower layer water phase by nitrogen, adding 50 mu L acetonitrile and water (2:98, v: v) for redissolution; the upper organic phase was dried with nitrogen and redissolved in 200. mu.L MeOH: CHCl3(1:1, v: v), filtered through 96-well plates (Agilent Technologies, USA) and assayed. All serum samples are mixed by 10 mu L respectively to obtain a quality control sample (QC) sample, and the pretreatment method is the same as that of clinical serum samples. In the injection sequence, a QC sample prepared by mixing all samples to be tested in equal amount is inserted between every 10 samples. Wherein the pregnant female comprises: gestational diabetes group and healthy pregnant woman control group. All the volunteers in the group were pregnant women in the Han nationality of China, and prenatal archives were established before recruitment. Inclusion and exclusion criteria were as follows:

1.1 gestational diabetes group selection criteria: the age is 20-45 years, GDM occurs in the late pregnancy, and no other diseases occur.

1.2 the selection standard of a healthy pregnant woman control group is as follows: age 20-45 years old; no other diseases.

1.3 exclusion criteria were as follows: (1) a non-single tire; (2) fasting blood glucose is more than or equal to 6.1mmol/L or HbAlc is more than 6.5 percent, or diabetes is diagnosed before pregnancy; (3) non-Han nationality; (4) those with a prior history of autoimmune disease or who are using glucocorticoids; (5) patients with hyperthyroidism or hypothyroidism are identified; (6) c-reactive protein (CRP) >10 mg/L; (7) patients with liver and kidney insufficiency; (8) there was a history of thalassemia.

1.4 Subjects were women from the early pregnancy facility in the area of cisternal care, and were followed to birth, data collected and clinical findings were judged by the obstetrician.

1.5 informed consent: the purpose and significance of the scientific research are introduced to the patients in detail, the patients voluntarily sign informed consent, the privacy of the patients is guaranteed, a case information base is established, the demographic data of the patients are recorded in detail, questionnaires are carried out, and the patients are numbered.

S2, LC-MS analysis: the polar and non-polar extracts were chromatographed using an Agilent 1200 high performance liquid chromatograph and a Waters ACQUITY UPLC HSS T3 column (2.1X 100mm,1.8 μm) at a column temperature of 35 ℃ and a flow rate of 250 μ l/min. Polar moiety: mobile phase A H2O (0.1% formic acid); acetonitrile, and the elution procedure is as follows: 0min, 2% B; 9min, 60% B; 18min, 60% B; 20-22min, 100% B; 22.2-24min, 2% B. The non-polar moiety: phase A was an aqueous solution containing 0.1% formic acid and 0.2mmol/L ammonium acetate, and phase B was acetonitrile and isopropanol (1:1, v: v) containing 0.1% formic acid and 0.2mmol/L ammonium acetate. The elution program was 0min, 35% B; 1min, 50% B; 3min, 70% B; 10min, 90% B; 15-28min, 100% B; 28.2-35min, 35% B. Mass spectrometry was performed using a high resolution mass spectrometer (Q active, Thermo Fisher Scientific, USA) with the following detailed parameters: positive and negative ion mode, full scan (m/z 67-1000); the spray voltage (+/-) was 4.5 kV; the capillary temperature was 450 ℃ and the sheath gas flow rate (+/-) was 40 au. The reagents used in the sample pretreatment and LC-MS analysis processes comprise: acetonitrile and methanol (chromatographically pure) were purchased from Fisher Scientific, formic acid (chromatographically pure) was purchased from Merck, methyl tert-butyl ether (MTBE) and chloroform (analytically pure) were purchased from Nanjing chemical reagents GmbH, and the experimental water was Wahaha purified water (Hangzhou Waha Kagaku Co., Ltd.). Other chemical reagents are analytically pure or more pure.

S3, data processing: the raw data obtained by LC-MS analysis of each group of samples was imported into prognesis QI (Waters, Milford, MA, USA) software for peak identification, peak alignment and isotope removal to obtain a two-dimensional data matrix containing information such as mass-to-charge ratio, retention time, peak area, etc. The MS data are imported into SIMCA-P software, and 30 biomarkers are obtained by performing partial least squares discriminant analysis (OPLS-DA) modeling analysis and interclass differential metabolite screening (VIP >1 and P < 0.05).

S4, analyzing content difference multiple (ratio) of 30 biomarkers, wherein the content difference multiple of 30 different metabolites in gestational diabetes patients is up-regulated as shown in figure 1, wherein the 30 biomarkers are eicosapentaenoic acid (EPA), aminoimidazole nucleotide (aminoimidazole), 4-aminobutyraldehyde (4-aminobutyrate), glycerophosphatidylcholine (glycophosphatidylcholine), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine 1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), glucose (glucose), phenylacetic acid (phenylacetic acid), 3 alpha, 7 alpha, 12 alpha-5 beta-trihydroxy-5 beta-cholesterol ester (3 alpha, 7 alpha, 12 alpha-trihydroxy-5 beta-cholesterol ester), and 4-tetrahydrobiopterin (tetrahydrofolate-4-hydroxy-4-tetrahydrobiopterin) respectively, 4-guanadinobutanoate, phenylacetaldehyde (phenacylaldehyde), leukotriene D4(leukotriene D4), dopamine (dopamine), choline (choline), 4-hydroxyphenylacetic acid (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), linolenic acid (dihomo-gamma-linolenate), 5-methoxyindoleacetic acid (5-methoxyindolacetate), N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; o2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40:7, and the content fold difference of 30 biomarkers between the gestational diabetes group and the healthy pregnant woman control group is measured, and the detailed information of the result is shown in Table 1. As can be seen from Table 1, the fold difference between the levels of 30 biomarkers is up-regulated, and thus, when the fold difference between the levels of one or more of the biomarkers is up-regulated in the serum of a pregnant female, the pregnant female can be predicted to have the risk of developing gestational diabetes.

TABLE 1 comparison of biomarkers described in gestational diabetes group with control group of healthy pregnant women

In order to monitor the stability of an LC-MS analysis system during sample detection and ensure that the obtained data is real and reliable, the method carries out Principal Component Analysis (PCA) on all Quality Control (QC) sample data, and the result shows that 93% of peak area variation of the Quality Control (QC) sample is controlled within 2SD, thereby indicating that the analysis method has good stability. The model was further constructed using orthogonal partial least squares discriminant analysis (OPLS-DA). As shown in fig. 2, certain clustering and grouping is shown between the two groups in the positive and negative ion detection mode. In FIG. 2, (A) and (B) are polar metabolites in positive and negative ion detection modes, respectively; (C) and (D) non-polar metabolites in positive and negative ion detection modes, respectively.

In one embodiment, the invention also provides an early pregnancy gestational diabetes mellitus early warning model, and the model comprises the following component steps:

measuring the content of the biomarker in a biological sample, namely serum, of a pregnant female;

secondly, carrying out ratio analysis on the biomarkers to obtain content difference multiples of the biomarkers;

step three, selecting three biomarkers to construct an early pregnancy diabetes mellitus early warning model, wherein the three biomarkers are as follows: eicosapentaenoic acid fatty acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal).

The third step is specifically as follows: the three biomarkers were included in a logistic regression model for analysis and combined by binary logistic regression, as shown in fig. 3, the early gestational diabetes early warning model established by using 3 biomarkers of eicosapentaenoic acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal) had higher accuracy, AUC was 0.81 (95% confidence interval of 0.73-0.89), as shown in fig. 4, the three biomarkers had significant changes in early gestational diabetes group metabolism. In the figure, B is glucose, C is eicosapentaenoic acid and D is 4-aminobutyraldehyde. Thus, the present invention enables the prediction of the risk of diabetes in late gestation pregnancy by using a small amount of maternal serum (50 μ L).

According to the invention, through the research of Chinese pregnant woman queue, 30 biomarkers related to gestational diabetes are discovered through serum non-targeted metabonomics in the early pregnancy and analysis research on the cross section in the early pregnancy, and a gestational diabetes early warning model is constructed by using one or more biomarkers, so that the diagnosis and the prediction of the occurrence and development of gestational diabetes by applying a small amount of blood samples in the early pregnancy are realized clinically. The early gestational period diabetes early warning model provided by the invention enables the biomarkers to be brought into a logistic regression model for analysis, and combines the biomarkers by using binary logistic regression, so that the early gestational period diabetes early warning model established by using 3 biomarkers of eicosapentaenoic acid (EPA), glucose and 4-aminobutyraldehyde has high accuracy (AUC is 0.81, and 95% confidence interval is 0.73-0.89). The method and the biomarker can be used for predicting the occurrence of diabetes mellitus in late pregnancy and gestation period more individually and accurately in early pregnancy so as to intervene the state of an illness as early as possible, improve pregnancy outcome and fetal prognosis of a patient and reduce medical cost and social burden caused by poor outcome of the diabetes mellitus in the pregnancy.

While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (10)

1. Biomarker for predicting gestational diabetes in the early pregnancy, characterized in that it is selected from: one or more of eicosapentaenoic acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal); wherein the biomarker is altered in fold difference in content between a pregnant female and a healthy pregnant female at an early stage of pregnancy.

2. The biomarker for predicting gestational diabetes in an early pregnancy period according to claim 1, further comprising: aminoimidazole nucleotide (aminoimidazole), glycerophosphatidylcholine (hepatic metabolism) (glycophosphorylline), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine-1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), phenylacetic acid (phenylacetic acid), 3 α,7 α,12 α -trihydroxy-5 β -cholate (3 α,7 α,12 α -trihydroxy-5 β -cholate), 4 a-hydroxytetrahydropterin (4a-hydroxytetrahydrobiopterin), 4-guanidobutyrate (4-guanidobutyrate), phenylacetaldehyde (phenylacetaldehyde linolenic acid), leukotriene D4 (leukin D4), dopamine (dopamine), choline (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), phenylacetic acid (4-hydroxy-phenylacetate), phenylacetaldehyde (phenylindole-5-hydroxy-pyruvate) (4-methoxyindole-5-phenylacetate (4-hydroxy-5 β -phenylacetate), phenylindole-5-hydroxy-dihydroindole-pyruvate (4-hydroxy-dihydroindole-5 β -dihydroindole acetate), and a-hydroxy-dihydroindole, N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; one or more of O2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40: 7; wherein the biomarker is altered in fold difference in content between a pregnant female and a healthy pregnant female at an early stage of pregnancy.

3. The biomarker for predicting gestational diabetes mellitus early in pregnancy according to any one of claims 1 or 2, wherein the change in fold difference in content is such that the fold difference in content of the biomarker is all up-regulated compared to a healthy pregnant female.

4. A method for predicting gestational diabetes mellitus at an early stage of pregnancy based on the biomarker of claim 3, comprising: measuring the fold difference in the content of at least one biomarker in a biological sample obtained from a pregnant female, to predict the risk of gestational diabetes of the pregnant female, wherein the biomarker is selected from the group consisting of: eicosapentaenoic acid fatty acid (EPA), glucose (glucose), 4-aminobutanal (4-aminobutanal),: aminoimidazole nucleotide (aminoimidazole), glycerophosphatidylcholine (hepatic metabolism) (glycophosphorylline), L-alanine (L-alanine), sphingosine-1-phosphate (sphingosine-1-phosphate), phenylpyruvate (phenylpyruvate), tyramine (tyramine), phenylacetic acid (phenylacetic acid), 3 α,7 α,12 α -trihydroxy-5 β -cholate (3 α,7 α,12 α -trihydroxy-5 β -cholate), 4 a-hydroxytetrahydropterin (4a-hydroxytetrahydrobiopterin), 4-guanidobutyrate (4-guanidobutyrate), phenylacetaldehyde (phenylacetaldehyde linolenic acid), leukotriene D4 (leukin D4), dopamine (dopamine), choline (4-hydroxyphenylacetate), 4-hydroxyphenylpyruvate (4-hydroxyphenylpyruvate), phenylacetic acid (4-hydroxy-phenylacetate), phenylacetaldehyde (phenylindole-5-hydroxy-pyruvate) (4-methoxyindole-5-phenylacetate (4-hydroxy-5 β -phenylacetate), phenylindole-5-hydroxy-dihydroindole-pyruvate (4-hydroxy-dihydroindole-5 β -dihydroindole acetate), and a-hydroxy-dihydroindole, N1-methyl-2/4 pyridine-5-carboxamide (N1-methyl-2/4-pyridone-5-carboxamide), fatty acid FA 10:1, sphingolipid ST24: 1; o4, ST 32: 2; o5 and IPC 36: 0; o2, lysophosphatidylcholine LPC 20:5, phosphatidylcholine PC 34:0, phosphatidylethanolamine PE O-38:7 and phosphatidylserine PS O-40: 7.

5. The method of claim 4, wherein measuring an upregulation in fold difference in the level of at least one biomarker in the biological sample from which the pregnant female is obtained indicates a risk of gestational diabetes in the pregnant female.

6. The method of claim 4, wherein the biological sample is serum.

7. The method of claim 5, wherein the measuring comprises: analyzing the biological sample by using a combined ultra-high liquid phase-high resolution mass spectrometry, and introducing original data obtained by the combined ultra-high liquid phase-high resolution mass spectrometry into analysis software for peak identification, peak alignment and isotope removal to obtain a two-dimensional data matrix containing information such as mass-to-charge ratio, retention time, peak area and the like; and (3) carrying out variable statistical analysis on the mass spectrum data, carrying out primary identification on the screened polar metabolites by utilizing metabonomics analysis software, and carrying out primary identification on the non-polar metabolites by comparing the non-polar metabolites with a lipid database.

8. An early pregnancy diabetes mellitus early warning model, characterized in that, the input variables of the model are the content difference multiple of the biomarker for predicting early pregnancy diabetes mellitus according to any one of claims 1 to 2.

9. The early pregnancy diabetes mellitus early warning model of claim 8, which is constructed by the following steps:

measuring the content of the biomarker in a biological sample of a pregnant female;

secondly, carrying out ratio analysis on the biomarkers to obtain content difference multiples of the biomarkers;

and step three, incorporating one or more biomarkers into a logistic regression model for analysis, and combining all the biomarkers by utilizing binary logistic regression to establish an early pregnancy gestational period diabetes early warning model.

10. The early gestational diabetes mellitus early warning model of claim 9, wherein in the third step, three biomarkers of eicosapentaenoic acid fatty acid (EPA), glucose (glucose) and 4-aminobutanal (4-aminobutanal) are selected to construct the early gestational diabetes mellitus early warning model.

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