CN112730692A - Biomarkers and methods for predicting premature rupture of membranes - Google Patents
Biomarkers and methods for predicting premature rupture of membranes Download PDFInfo
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Abstract
The invention discloses a biomarker and a method for predicting premature rupture of membranes, wherein the biomarker is selected from the following components: one or more of 4-methylpentanal (Isohexanal), Sphingolipid (SP) metabolites, Glyceride (GL) metabolites and Glycerophospholipid (GP) like metabolites; wherein the biomarker undergoes a fold change in content difference between a pregnant female at risk of premature rupture of the fetal membrane and a healthy pregnant female. The biomarker and the prediction method for predicting the premature rupture of the fetal membranes have important significance for early prediction of the premature rupture of the fetal membranes before parturition and establishment of corresponding intervention measures.
Description
Technical Field
The present invention relates to a marker for predicting premature rupture of membranes. More particularly, the present invention relates to a biomarker and method for predicting premature rupture of membranes.
Background
Premature rupture of membranes (PROM) refers to spontaneous rupture of membranes occurring more than 24 hours before parturition, one of the common complications in the perinatal period, with the incidence rate of 2.7% -7.0%, which can cause intrauterine infection, premature birth, premature rupture of placenta, fetal distress and the like. The long-time rupture of the fetal membranes is closely related to the maternal and child complications and death caused by operation risks, seriously harms the life health of pregnant and lying-in women and fetuses, and therefore, has important significance for early prediction of premature rupture of the fetal membranes before delivery and establishment of corresponding intervention measures.
The cause and specific mechanism of premature rupture of the fetal membrane are not completely clear, but inflammation and apoptosis are known to play a crucial role in the process. At present, no method for accurately predicting and diagnosing premature rupture of membranes exists clinically. The traditional diagnosis standard is that medical staff observes whether the vaginal vault hydrops exists or not according to patient complaints, the pH value of vaginal fluid and a vaginal speculum, or the diagnosis is carried out by means of obstetrical ultrasonic and the like. The amniotic fluid index (less than or equal to 7cm) is used as a standard for diagnosing premature rupture of the fetal membranes, the sensitivity is only 30.0%, the specificity is 91.8%, and the accuracy is 62.0%. Especially, when the fetal membranes are ruptured, amniotic fluid is very likely to be polluted by cervical mucus, blood or vaginal secretion, so that the accuracy of the traditional detection method is reduced, and false positive or false negative diagnosis is caused, thereby delaying treatment or triggering more obstetrical intervention measures. Therefore, it is important for the health of mother and infant to accurately and timely predict and find the premature rupture of the fetal membranes.
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 premature rupture of membranes, which is closely related to the occurrence and development of premature rupture of membranes before delivery, can be used as an early warning marker for predicting the occurrence of premature rupture of membranes, and can be used for more individually and accurately predicting the occurrence of premature rupture of membranes before delivery in the early stage of pregnancy.
To achieve these objects and other advantages in accordance with the present invention, there is provided a biomarker for predicting premature rupture of a membrane, wherein the biomarker is selected from the group consisting of: one or more of 4-methylpentanal (Isohexanal), Sphingolipid (SP) metabolites, Glyceride (GL) metabolites and Glycerophospholipid (GP) like metabolites; wherein the biomarker undergoes a fold change in content difference between a pregnant female at risk of premature rupture of the fetal membrane and a healthy pregnant female.
Preferably, the Sphingolipid (SP) metabolites include Sphingosine (Sphingosine) and ceramide (Cer 40: 0; O2), the glyceride (Glycerolipids, GL) metabolites include triacylglycerol (TG65: 8); the glycerophospholipid-like (GP) metabolites include: the biological marker comprises one or more of glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1) and glycerophospholipid ethanolamine (PE-NMe36:0), wherein the biological marker is changed in content difference multiple between a pregnant female body with the risk of premature rupture of a fetal membrane and a healthy pregnant female body.
Preferably, the change in the fold difference in content comprises: the content difference multiple of 4-methyl valeraldehyde (Isohexanal) is up-regulated; the content of Sphingosine (Sphingosine) as Sphingolipid (SP) metabolite is reduced by multiple times, and the content of ceramide (Cer 40: 0; O2) is increased by multiple times; content difference fold up of triglyceride (Glycerolipids, GL) metabolite triacylglycerol (TG65: 8); the content of Glycerophospholipid (GP) like compounds has a fold difference of 6 down-regulation and 3 up-regulation. The down-regulation is: glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4) and phosphatidylcholine (PC O-40: 6); the up-regulation is as follows: phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1) and glycerophospholipid ethanolamine (PE-NMe36:0)
The invention also provides a method for predicting premature rupture of membranes based on the biomarker, which comprises the following steps: measuring the fold difference in the amount of at least one biomarker in a biological sample obtained from a pregnant female, wherein the biomarker is selected from the group consisting of 4-methylpentanal (Isohexanal), to predict the risk of premature rupture of the fetal membrane of the pregnant female; sphingosine (Sphingosine) and ceramide (Cer 40: 0; O2), Sphingolipid (SP) metabolites; the triglyceride (GL) metabolite triacylglycerol (TG65: 8); glycerophosphatidylethanolamine (PE O-38:2), a metabolite of Glycerophospholipids (GP), Glycerophospholipids ethanolamine (PE O-38:5), Glycerophospholipids ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1), and Glycerophospholipids ethanolamine (PE-NMe36: 0).
Preferably, the measured fold difference in the amount of the at least one biomarker in the biological sample from which the pregnant female is obtained is up-or down-regulated, indicating that the pregnant female is at risk of premature rupture of the fetal membranes.
Preferably, the fold difference of the content of the polar metabolite 4-methylpentanal (Isohexanal) in the biological sample from which the pregnant female is obtained is measured to be up-regulated, or/and the fold difference of the content of the Sphingolipid (SP) metabolite Sphingosine (Sphingosine) is measured to be down-regulated, or/and the fold difference of the content of ceramide (Cer 40: 0; O2) is measured to be up-regulated, or/and the fold difference of the content of the triglyceride (Glycerolipids, GL) metabolite triacylglycerol TG65:8 is measured to be up-regulated, or/and the fold difference of the content of glycerophospholipid ethanolamine (PE O-38:2) is measured to be down-regulated, or/and the content of glycerophospholipid ethanolamine (PE O-38:5) is measured to be down-regulated, or/and the fold difference of glycerophospholipid ethanolamine (PE 38:1) is measured to be down-regulated, or/and the content difference multiple of phosphatidylcholine (PC O-38:4) is reduced, or/and the content difference multiple of phosphatidylcholine (PC O-40:6) is reduced, or/and the content difference multiple of phosphatidylglycerol (PG O-40:3) is increased, or/and the content difference multiple of phosphatidylcholine (PC O-32:1) is increased, or/and the content difference multiple of glycerophospholipid ethanolamine (PE-NMe36:0) is increased, indicating that the pregnant female body has the risk of premature rupture of the fetal membranes.
Preferably, the biological sample used in the measurement is serum.
Preferably, the measuring comprises: analyzing the biological sample using ultra-high liquid phase-high resolution mass spectrometry.
Preferably, the measuring further comprises: introducing original data obtained by the combined analysis of the ultra-high liquid phase and the high resolution mass spectrum 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 at least comprises the following beneficial effects:
the invention is based on the non-targeted metabonomics and lipidomics analysis of high-resolution mass spectra, utilizes a metabonomic big data analysis strategy labeled by high-flux metabolites, finds 13 biomarkers which can accurately distinguish a premature rupture of fetal membranes group and a healthy pregnant woman control group, is used for predicting the premature rupture of fetal membranes, and provides a reference basis for the early prediction of clinical premature rupture of fetal membranes and the establishment of a targeted intervention scheme.
The method for predicting the premature rupture of the fetal membrane based on the biomarker detects a serum sample of a subject by using a liquid phase-high resolution mass spectrometry combined technology, compares the difference of the serum metabolism of a pregnant woman (n 41) with the premature rupture of the fetal membrane before delivery and a healthy control pregnant woman (n 106) in the early pregnancy, and obtains 13 early pregnancy serum biomarkers for predicting the premature rupture of the fetal membrane. Wherein, when the difference multiple of 13 biomarkers is up-regulated by 4-methyl valeraldehyde (Isohexanal); sphingosine (Sphingosine), a Sphingolipid (SP) metabolite, is down-regulated, ceramide (Cer 40: 0; O2) is up-regulated; triacylglycerol TG65:8, a metabolite of glycerides (Glycerolipids, GL), is upregulated; glycerol Phospholipid (GP) like compounds 9, 6 of which were down-regulated and 3 were up-regulated. The method is based on the non-targeted metabonomics and lipidomics analysis of high-resolution mass spectra, and utilizes the established high-throughput metabolite labeled metabonomic group big data analysis strategy to find 13 different metabolites which can distinguish a premature rupture of a fetal membrane group from a healthy pregnant woman control group.
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 is a score plot of Principal Component Analysis (PCA) performed on all quality control sample data according to the present invention;
FIG. 2 is a graph of OPLS-DA model scores for serum samples from a premature rupture of membranes and a healthy control group according to the present invention;
FIG. 3 is a bar graph showing the fold difference of metabolites in the premature rupture of membranes of the present invention compared with a control group of healthy pregnant women.
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 present invention provides a biomarker for predicting premature rupture of membranes, comprising: 4-methylpentanal (Isohexanal), Sphingosine (sphingolipids, SP) metabolites and ceramide (Cer 40: 0; O2), triacylglycerol (TG65:8) metabolites of glycerides (Glycerolipids, GL), Glycerophospholipids-like (GP) metabolites Glycerophospholipids ethanolamine (PE O-38:2), Glycerophospholipids ethanolamine (PE O-38:5), Glycerophospholipids ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1) and Glycerophospholipids ethanolamine (PE-NMe36:0), for a total of 13. The metabolites are closely related to the occurrence and development of premature rupture of the fetal membrane before delivery, can be used as an early warning marker for predicting the occurrence of premature rupture of the fetal membrane, and currently, absolute indexes for predicting and diagnosing premature rupture of the fetal membrane are not available clinically.
The etiology of premature rupture of the fetal membrane is complex, and the premature rupture of the fetal membrane is generally considered to be related to various pathological changes such as the structure of the fetal membrane, genital tract infection, increased pressure of a amniotic cavity, inflammatory reaction, apoptosis and the like. Premature rupture of the membrane is associated with oxidative stress, during which unstable peroxygen intermediates are decomposed into reactive aldehydes, including 4-methylpentanal. Previous studies found that rats released 4-methylpentanal and hexanal during stress experiments, both of which activated anxiety-related circuits (especially the telebed nucleus) and triggered a series of anxiety responses. Lipids play an important role in cellular activities, maintaining cellular structure, providing energy, and participating in signaling. Lipid metabolism is associated with the regulation of signaling pathways such as cell growth, proliferation, differentiation, apoptosis, and inflammation. Premature rupture of membranes is probably related to apoptosis, p53 can induce the expression of apoptosis gene Bax and inhibit the expression of anti-apoptosis gene B cell lymphoma 2(Bcl-2), thereby accelerating apoptosis, the expression level of p53 and the caspase content of patients with premature rupture of membranes are higher than those with intact membranes, and the two have obvious correlation. Sphingosine is phosphorylated to Sphingosine 1-phosphate (S1P) under the action of Sphingosine kinase (SphK), and S1P can promote cell survival and proliferation; it has been reported that S1P could block the apoptosis of oocytes caused by ceramide by down-regulating the expression of p53 gene. In addition, lipid metabolism can alter membrane composition and permeability leading to the development of many diseases. According to literature reports, sphingolipids and glycerophospholipids regulate pathways directly on the cytotoxic response to maintain cellular homeostasis. Sphingomyelin-mediated cellular level toxicity is mitochondrial and endoplasmic reticulum stress and apoptosis, sphingolipids also mediate glycerophospholipid homeostasis and related signaling pathways, and the above membrane lipid disorders are closely related to cellular inflammatory responses. The metabolites are closely related to oxidative stress, inflammatory reaction and apoptosis, and the biological significance of the biomarkers proves the close relationship with premature rupture of membranes.
In one embodiment, the metabolite in the premature rupture of membranes group is up-regulated in fold difference from the healthy pregnant woman control group by the metabolite 4-methylpentanal (Isohexanal); sphingosine (Sphingosine), a Sphingolipid (SP) metabolite, is down-regulated, ceramide (Cer 40: 0; O2) is up-regulated; triacylglycerol TG65:8, a metabolite of glycerides (Glycerolipids, GL), is upregulated; glycerophospholipid (GP) like compounds 9, 6 of which were downregulated and 3 were upregulated; wherein, the down-regulation is as follows: glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4) and phosphatidylcholine (PC O-40: 6); the up-regulation is as follows: phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1), and glycerophospholipid ethanolamine (PE-NMe36: 0).
The invention also provides a method for predicting premature rupture of membranes based on the biomarker, which comprises the following steps: measuring the fold difference in the amount of at least one biomarker in a biological sample obtained from a pregnant female, wherein the biomarker is selected from the group consisting of 4-methylpentanal (Isohexanal), to predict the risk of premature rupture of the fetal membrane of the pregnant female; sphingosine (Sphingosine) and ceramide (Cer 40: 0; O2), Sphingolipid (SP) metabolites; the triglyceride (GL) metabolite triacylglycerol (TG65: 8); glycerophosphatidylethanolamine (PE O-38:2), a metabolite of Glycerophospholipids (GP), Glycerophospholipids ethanolamine (PE O-38:5), Glycerophospholipids ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1), and Glycerophospholipids ethanolamine (PE-NMe36: 0).
In one embodiment, measuring the fold up or down difference in the amount of at least one biomarker in a biological sample from which a pregnant female is obtained indicates that the pregnant female is at risk of premature rupture of the fetal membrane. Wherein, when the polar metabolite 4-methyl valeraldehyde (Isohexanal) is up-regulated; or/and Sphingosine (Sphingosine) as a Sphingolipid (SP) metabolite is down-regulated, or/and ceramide (Cer 40: 0; O2) is up-regulated; or/and triacylglycerol (GL) TG65:8, a metabolite of glycerides; or/and 9 Glycerophospholipid (GP) like compounds, 6 of which are down-regulated and 3 of which are up-regulated; wherein, the down-regulation is as follows: glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4) and phosphatidylcholine (PC O-40: 6); the up-regulation is as follows: phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1), and glycerophospholipid ethanolamine (PE-NMe36: 0).
In one embodiment, the biological sample used in the measurement is serum.
In one embodiment, the measuring comprises: analyzing the biological sample using ultra-high liquid phase-high resolution mass spectrometry.
In one embodiment, the measuring further comprises: introducing original data obtained by the ultrahigh liquid phase-high resolution mass spectrometry into analysis software such as Progenetics 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 and the like; and (3) carrying out variable statistical analysis on the mass spectrum data, for example, introducing MS data into SIMCA-P software, carrying out partial least squares discrimination analysis (OPLS-DA) modeling analysis and interclass differential metabolite screening (VIP >1 and P <0.05), carrying out primary identification on the screened polar metabolites by utilizing a metabonomic analysis platform such as https:// www.metaboanalyst.ca/according to accurate mass numbers, and carrying out primary identification on the nonpolar metabolites by comparing the nonpolar metabolites with a lipid database such as http:// www.lipidmaps.org.
Examples
The reagents acetonitrile and methanol (chromatographically pure) used in the method of the invention are purchased from Fisher Scientific company, formic acid (chromatographically pure) is purchased from Merck company, methyl tert-butyl ether (MTBE) and chloroform (analytically pure) are purchased from Nanjing chemical reagent GmbH, and the experimental water is Wahaha purified water (Hangzhou Wahaha Co., Ltd.). Other chemical reagents are analytically pure or more pure.
The method for predicting the premature rupture of the fetal membrane based on the biomarker specifically comprises the following steps:
1. and establishing a standard procedure for predicting the implementation of the fetal membrane premature rupture metabonomics clinical research. All the volunteers in the group were pregnant women in the Han nationality of China, and prenatal archives were established before recruitment.
1.1 the selection standard of the premature rupture of membranes: the age is 20-45 years old, premature rupture of fetal membranes occurs, and no other diseases occur.
1.2 healthy control group inclusion criteria: 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 followed up to production, and data were collected and clinical findings were judged by obstetricians.
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.
In this example, 147 Chinese pregnant women (20-41 years old) were enrolled, 106 healthy women, 41 with premature rupture of membranes and no other diseases, demographic characteristics and biochemical indicators are shown in Table 1, and there was no significant difference in gestational age, enrolled BMI, gestational days, hemoglobin, C-reactive protein, homocysteine and leukocyte values in both groups (P > 0.05).
TABLE 1.147 demographic characteristics and biochemical indexes of Chinese pregnant women
2. Sample pretreatment
Clinical serum samples: the above venous blood at the early stage of pregnancy (5-14 weeks) of a pregnant woman was treated to obtain a serum sample. The serum samples obtained were collected and subjected to extraction procedures within 72 hours. Transferring 50 μ L of serum into a 10mL glass centrifuge tube, adding 1.5mL MeOH/MTBE (1:1, v: v), vortexing at 2500rpm for 5min, centrifuging at 3200rpm at 4 ℃ for 10min, and collecting supernatant; the supernatant was transferred to a new glass tube, 1.5mL of MTBE and 0.6mL of H2O were added, and after vortexing at 2500rpm for 5min, the mixture was centrifuged at 3200rpm at 4 ℃ for 10min, and the liquids were separated. 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.
Quality Control (QC) samples: all clinical serum samples are mixed by 10 mu L respectively to obtain Quality Control (QC) samples, and the pretreatment method is the same as that of the clinical serum samples.
In the injection sequence, a Quality Control (QC) sample prepared by mixing all clinical serum samples to be tested in equal amount is inserted between every 10 clinical serum samples.
3. Instruments and parameters
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 is water (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 water 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 Q Orbitrap mass analyzer (Q active, Thermo Fisher Scientific, USA) mass spectrometer with the detailed parameters set as follows: positive and negative ion mode, full scan (m/z 67-1000); the spray voltage (+/-) was 4.5 kV; the capillary temperature is 450 ℃; the sheath gas flow rate (+/-) was 40 au.
4. Data processing
The original data obtained by LC-MS analysis of all serum samples were 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 introduced into SIMCA-P software, partial least squares discrimination analysis (OPLS-DA) modeling analysis and interclass differential metabolite screening (VIP >1 and P <0.05) are carried out, the polar metabolites obtained through screening are preliminarily identified by https:// www.metaboanalyst.ca/according to the accurate mass number, and the nonpolar metabolites are preliminarily identified by comparing with a lipid database (http:// www.lipidmaps.org).
5. Metabonomics data quality assessment
5.1 to monitor the stability of the LC-MS analysis system during the sample detection and ensure that the obtained data is true and reliable, Principal Component Analysis (PCA) is performed on all Quality Control (QC) sample data, and the projection results of the Quality Control (QC) sample on the first principal component and the second principal component are shown in fig. 1. In fig. 1, (a) and (B) are first and second principal component analyses of positive ion-detected polar metabolites, (C) and (D) are first and second principal component analyses of negative ion-detected polar metabolites, (E) and (F) are first and second principal component analyses of positive ion-detected nonpolar metabolites, and (G) and (H) are first and second principal component analyses of negative ion-detected polar metabolites. As can be seen from FIG. 1, under the positive and negative ion detection mode, the peak area variation of 93% of Quality Control (QC) samples is controlled within 2SD, indicating that the stability of the analysis method is good.
5.2 FIG. 2 shows the orthogonal partial least squares discriminant analysis (OPLS-DA) model score plots for serum samples from the premature rupture of membranes and healthy controls. Wherein (A) and (B) are polar metabolites under positive ion and negative ion detection modes, respectively; (C) and (D) non-polar metabolites in positive and negative ion detection modes, respectively. As shown in fig. 2, certain clustering and grouping is shown between the two groups in the positive and negative ion detection mode.
6. Differential metabolite and ratio analysis
6.1 comparing the premature rupture of membranes group (n is 41) with the healthy pregnant woman control group (n is 106), and carrying out serum metabolite analysis on the premature rupture of membranes patients by utilizing a metabonomics big data processing method to obtain that the 13 biomarkers have differences in fold difference change (P <0.05 and VIP >1), and detailed information is shown in Table 2.
TABLE 2 differential metabolites obtained by comparing the premature rupture of membranes group with the control group of healthy pregnant women
6.2 ratio analysis of the above differential metabolites was performed, and the change in the premature rupture of membranes group is shown in FIG. 3. Fig. 3 shows fold difference of 13 biomarkers, i.e. differential metabolites, in the premature rupture of membranes group compared to a control group of healthy pregnant women; wherein, compared with a control group, the fold change >1 of the premature rupture group of the fetal membranes is 4-methyl valeraldehyde (Isohexanal), ceramide (Cer 40: 0; O2), triacylglycerol (TG65:8), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1) and glycerophospholipid ethanolamine (PE-NMe36:0), and the fold change < 1 is: sphingosine (Sphingosine), glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), and phosphatidylcholine (PC O-40: 6). It can be seen that 4-methylpentanal (Isohexanal) is up-regulated in the premature rupture of membranes group; sphingosine (Sphingosine), a Sphingolipid (SP) metabolite, is down-regulated, ceramide (Cer 40: 0; O2) is up-regulated; triacylglycerol (TG65:8), a metabolite of glycerides (Glycerolipids, GL), is upregulated; glycerol Phospholipid (GP) like compounds 9, 6 of which were down-regulated and 3 were up-regulated.
Therefore, when the content difference multiple of one or more biomarkers in the 13 biomarkers is detected and obtained to be changed, the risk of premature rupture of the fetal membranes is increased, and the premature rupture of the fetal membranes can be predicted. The method is stable and reliable, and provides a reference basis for early prediction of clinical premature rupture of membranes and establishment of a targeted intervention scheme.
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 (9)
1. Biomarker for predicting premature rupture of membranes, characterized in that said biomarker is selected from: one or more of 4-methylpentanal (Isohexanal), Sphingolipid (SP) metabolites, Glyceride (GL) metabolites and Glycerophospholipid (GP) like metabolites; wherein the biomarker undergoes a fold change in content difference between a pregnant female at risk of premature rupture of the fetal membrane and a healthy pregnant female.
2. The biomarker for predicting premature rupture of membranes according to claim 1, wherein the Sphingolipid (SP) metabolites comprise Sphingosine (Sphingosine) and ceramide (Cer 40: 0; O2), the glyceride (Glycerolipids, GL) metabolites comprise triacylglycerol (TG65: 8); the glycerophospholipid-like (GP) metabolites include: the biological marker comprises one or more of glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1) and glycerophospholipid ethanolamine (PE-NMe36:0), wherein the biological marker is changed in content difference multiple between a pregnant female body with the risk of premature rupture of a fetal membrane and a healthy pregnant female body.
3. The biomarker for predicting premature rupture of membranes according to claim 2, wherein the change in fold difference in content comprises: the content difference multiple of 4-methyl valeraldehyde (Isohexanal) is up-regulated; the content of Sphingosine (Sphingosine) as Sphingolipid (SP) metabolite is reduced by multiple times, and the content of ceramide (Cer 40: 0; O2) is increased by multiple times; content difference fold up of triglyceride (Glycerolipids, GL) metabolite triacylglycerol (TG65: 8); the content of Glycerophospholipid (GP) like compounds has a fold difference of 6 down-regulation and 3 up-regulation.
4. A method for predicting premature rupture of membranes based on biomarkers according to any of claims 1 to 3, comprising: measuring the fold difference in the amount of at least one biomarker in a biological sample obtained from a pregnant female, wherein the biomarker is selected from the group consisting of 4-methylpentanal (Isohexanal), to predict the risk of premature rupture of the fetal membrane of the pregnant female; sphingosine (Sphingosine) and ceramide (Cer 40: 0; O2), Sphingolipid (SP) metabolites; the triglyceride (GL) metabolite triacylglycerol (TG65: 8); glycerol Phospholipid (GP) like metabolites glycerophospholipid ethanolamine (PE O-38:2), glycerophospholipid ethanolamine (PE O-38:5), glycerophospholipid ethanolamine (PE 38:1), phosphatidylcholine (PC O-36:3), phosphatidylcholine (PC O-38:4), phosphatidylcholine (PC O-40:6), phosphatidylglycerol (PG O-40:3), phosphatidylcholine (PC O-32:1), and glycerophospholipid ethanolamine (PE-NMe36: 0).
5. The method of claim 4, wherein measuring the fold up or down difference in the level of at least one biomarker in the biological sample from which the pregnant female is obtained indicates the presence of a risk of premature rupture of the membrane in the pregnant female.
6. The method according to claim 5, wherein the amount of the metabolite 4-methylpentanal (Isohexanal) is measured to be up-regulated by a multiple of the difference in the content of the metabolite 4-methylpentanal (Sphingosine) or/and the amount of the metabolite Sphingosine (Sphingosine) of sphingolipids (SPhingolipids, SP) or/and the amount of ceramide (Cer 40: 0; O2) or/and the amount of the metabolite triacylglycerol TG65:8 of glycerides (Glycerolipids, GL) or/and the amount of glycerophospholipid ethanolamine (PE O-38:2) or/and the amount of glycerophospholipid ethanolamine (PE O-38:5) or/and the amount of glycerophospholipid ethanolamine (PE 38:1) or/and the content of phosphatidylcholine (O-36: 3) in the biological sample from which the pregnant female is obtained, or/and the content difference multiple of phosphatidylcholine (PC O-38:4) is reduced, or/and the content difference multiple of phosphatidylcholine (PC O-40:6) is reduced, or/and the content difference multiple of phosphatidylglycerol (PG O-40:3) is increased, or/and the content difference multiple of phosphatidylcholine (PC O-32:1) is increased, or/and the content difference multiple of glycerophospholipid ethanolamine (PE-NMe36:0) is increased, indicating that the pregnant female body has the risk of premature rupture of the fetal membranes.
7. The method of claim 4, wherein the biological sample used in the measurement is serum.
8. The method of claim 7, wherein the measuring comprises: analyzing the biological sample using ultra-high liquid phase-high resolution mass spectrometry.
9. The method of claim 8, wherein the measuring further comprises: introducing original data obtained by the combined analysis of the ultra-high liquid phase and the high resolution mass spectrum 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.
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