Screening and Application of Maternal Blood Lipid Biomarkers during Pregnancy with Fetal Growth Restriction

Abstract

The present invention relates to the technical field of lipidomics and human health, and specifically relates to the screening and application of maternal blood lipid biomarkers during pregnancy for fetal growth restriction. The lipid biomarkers are selected from one or any combination of DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG 16:0 / 16:0 / 18:0, and TG 16:0 / 16:0 / 18:1. The lipid biomarkers of the present invention can accurately diagnose the onset of fetal growth restriction and provide a specific and non-invasive diagnostic method for fetal growth restriction.

Description

Technical Field

[0001] The present invention relates to the field of lipidomics and human health technology, and in particular to the screening and application of maternal blood lipid biomarkers for fetal growth restriction during pregnancy. Background Art

[0002] Fetal growth restriction (FGR) is an important cause of perinatal morbidity and mortality, and may also lead to long-term adverse outcomes, including cognitive impairment in adolescence and adult diseases, such as increased risk of obesity, type 2 diabetes, cardiovascular disease, stroke and other diseases. It is particularly important to scientifically prevent fetal growth restriction, conduct early screening, diagnosis and intrauterine monitoring of fetal growth restriction, and terminate pregnancy at the right time. However, there is currently no effective treatment for fetal growth restriction, and the lack of timely and reliable screening methods is the primary problem currently faced. The correct detection rate of fetal growth restriction is also less than 50%. Therefore, there is an urgent need to find a new biomarker with high confidence to predict fetal growth restriction.

[0003] In recent years, the academic community has reported a series of biomarkers that may be used to predict fetal growth restriction, such as β-HCG, PAPP-A protein markers, small molecule RNA, amino acids, etc. However, although many biomarkers with potential predictive value have been found, the prediction of fetal growth restriction still faces great challenges; for β-HCG and PAPP-A protein markers, they are greatly affected by factors during pregnancy, and the correlation between the expression level in the middle and late pregnancy and fetal growth restriction is still controversial; for small molecule RNA, amino acids, etc., the limitations of detection technology and platform lead to poor reproducibility and high cost of detection technology. Due to the existence of the above problems, their clinical application may be limited.

[0004] Lipidomics is a large-scale detection of thousands of lipids in the body and their dynamic changes in physiological metabolism and pathological states. It is of great significance for in-depth research on various lipids in biological membrane structure, energy conversion, signal transduction, etc. Lipids play an indispensable and important role in both normal physiological and abnormal pathological processes of the human body. Lipidomics detection technology has great advantages in clinical disease diagnosis, tumor detection and treatment, and prediction of disease risk.

[0005] In view of this, discovering lipid markers that can effectively diagnose fetal growth restriction is a technical problem to be solved by the present invention. Summary of the invention

[0006] The purpose of the present invention is to provide screening and application of maternal blood lipid biomarkers for fetal growth restriction during pregnancy, and to provide a new approach for clinical diagnosis of fetal growth restriction.

[0007] The present invention adopts the following technical solution:

[0008] Use of a lipid biomarker detection reagent in the preparation of a fetal growth restriction diagnostic product, wherein the lipid biomarker is selected from one of DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG16:0 / 16:0 / 18:0 and TG 16:0 / 16:0 / 18:1 or any combination thereof.

[0009] The present invention obtains six lipid compounds with significantly different contents in the serum of healthy pregnant women and FGR pregnant women based on lipidomics screening, and the six lipid compounds respectively have excellent performance in diagnosing fetal growth restriction, among which, without adjustment, the AUC of DG 14:0 / 18:2 is 0.796, the AUC of DG 16:0 / 20:4 is 0.803, the AUC of TG 14:0 / 16:0 / 18:1 is 0.811, the AUC of TG 14:0 / 16:0 / 18:2 is 0.818, the AUC of TG 16:0 / 16:0 / 18:0 is 0.742, and the AUC of TG 16:0 / 16:0 / 18:1 is 0.803, indicating that the six lipid compounds are effective biomarkers for diagnosing fetal growth restriction, and the diagnostic effect of the six lipid compounds is still robust when used in combination.

[0010] Furthermore, the detection reagent includes a reagent for lipid extraction and protein precipitation, and the reagent for lipid extraction and protein precipitation is a mixture of butanol, methanol and ammonium formate. The mixture is prepared by mixing equal volumes of butanol and methanol and then adding ammonium formate, and the concentration of ammonium formate in the mixture is 0.8-1mM.

[0011] Furthermore, the detection reagent includes an internal standard mixture, which consists of PC 14:0, PE 17:0, CE16:0 (d7), COH (d7), BMP 14:0, SM 12:0, PG 14:0, TG 16:0 / 18:0 / 16:0 (d5).

[0012] Further, the fetal growth restriction diagnostic product is matched with a detection device to detect lipid biomarkers

[0013] Furthermore, the detection device includes a liquid chromatography-tandem mass spectrometer, and the detection reagent obtains the content of each lipid in the lipid biomarker by liquid chromatography-tandem mass spectrometry.

[0014] Furthermore, the sample tested was the blood of the pregnant women.

[0015] Based on the same inventive concept, the present invention also provides the use of the lipid biomarker in the preparation of a medicament for treating and / or preventing fetal growth restriction, wherein the lipid biomarker is selected from at least one of DG 14:0 / 18:2, DG 16:0 / 20:4, TG14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG 16:0 / 16:0 / 18:0, and TG 16:0 / 16:0 / 18:1.

[0016] Furthermore, the medicament is a lipid-regulating drug or a nutritional supplement.

[0017] Based on the same inventive concept, the present invention also provides a screening method for the lipid biomarker, wherein the lipid biomarker refers to the above-mentioned lipid biomarker.

[0018] Furthermore, the screening method includes:

[0019] Sample pretreatment: for the extraction of lipid compounds in blood samples;

[0020] Sample lipidomics detection: for the detection and quantification of lipid compounds;

[0021] Data quality control: for the quality control of lipidomics data, determining the quantification limit of each lipid compound and the coefficient of variation within and between batches;

[0022] Quantitative data pretreatment: for filtering lipidomics quantitative data and filling in blank values, and normalizing and standardizing the concentrations of each lipid compound;

[0023] Differential lipid molecule screening and analysis: screening is carried out by methods such as two-independent-sample t-test, orthogonal partial least squares discriminant analysis, and false discovery rate correction to obtain lipids with concentration differences between the fetal growth restriction group and the non-fetal growth restriction group and lipids associated with the risk of disease occurrence; the prediction efficacy of the prediction model is evaluated by the area under the curve in the receiver operating characteristic curve to screen for early pregnancy blood lipid biomarkers for fetal growth restriction; the lipid biomarker is one or any combination of DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG 16:0 / 16:0 / 18:0, and TG 16:0 / 16:0 / 18:1.

[0024] Furthermore, the screening method also includes lipid biomarker verification: evaluating the association between the lipid biomarker and the risk of fetal growth restriction by multivariate-adjusted logistic regression to improve the robustness of the screened biomarker for predicting growth restriction.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] The present invention characterizes the fetal growth restriction markers through lipidomics characteristics, and screens out lipid biomarkers for monitoring the degree of fetal growth restriction. The obtained lipid biomarkers have high value for the early prediction of fetal growth restriction and the identification of high-risk pregnant women.

[0027] The lipid biomarkers screened by the present invention have good biological stability and representativeness, and their biological activities can be guaranteed even after long-term storage, which can ensure the reliability of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 They are the determination results of lipid profiles of humans and animals.

[0029] Among them, A is a design diagram of a case-control study from two different geographical locations.

[0030] B is a diagram of a FGR mouse model caused by prednisone PDN exposure.

[0031] C is a diagram of a FGR mouse model caused by cadmium chloride exposure.

[0032] D is a diagram of a FGR mouse model caused by high-fat HFD exposure.

[0033] E is a diagram of the number of lipid molecules with differences in maternal blood of FGR in cohort CI.

[0034] F is a diagram of the number of lipid molecules with differences in maternal blood of FGR in cohort CII.

[0035] G is a diagram of the number of lipid molecules with differences in maternal blood of FGR in the PDN model.

[0036] H is a diagram of the number of lipid molecules with differences in maternal blood of FGR in the Cd model.

[0037] I is a diagram of the number of lipid molecules with differences in maternal blood of FGR in the HFD model.

[0038] J is a diagram of the number of lipid molecules with differences in maternal blood of FGR in the 3MA model.

[0039] K is a diagram of the number of lipid molecules with differences in maternal blood of FGR in the Rap model.

[0040] L is a heat map showing lipid molecules with differences in maternal blood in different cohorts.

[0041] Figure 2 For analyzing the longitudinal changes of lipid molecules in different pregnancy stages.

[0042] Among them, A is a diagram of the collection of samples at different pregnancy stages.

[0043] Figure B shows the PCA analysis diagram of lipid molecules at different times.

[0044] Figure C shows the diagram of differential lipid changes in the first trimester.

[0045] Figure D shows the diagram of differential lipid changes in the second trimester.

[0046] Figure E shows the diagram of differential lipid changes in the third trimester.

[0047] Figure F shows the heatmap of lipid changes of various types at different times.

[0048] Figure G shows the line chart of total lipid changes at different times.

[0049] Figure H shows the line chart of triglyceride changes at different times.

[0050] Figure I shows the line chart of diglyceride changes at different times.

[0051] Figure J shows the line chart of cholesteryl ester changes at different times.

[0052] Figure 3 This is a diagram for exploring the potential of lipid molecules as FGR markers.

[0053] Among them, Figure A shows the diagram for screening lipid molecules with high predictive ability (AUC>0.800). Figure B shows the AUC verification diagram of lipid molecule DG 14:0 / 18:2 in cohort C-II.

[0054] Figure C shows the AUC verification diagram of lipid molecule DG 16:0 / 20:4 in cohort C-II.

[0055] Figure D shows the AUC verification diagram of lipid molecule TG 14:0 / 16:0 / 18:1 in cohort C-II.

[0056] Figure E shows the AUC verification diagram of lipid molecule TG 14:0 / 16:0 / 18:2 in cohort C-II.

[0057] Figure F shows the AUC verification diagram of lipid molecule TG 16:0 / 16:0 / 18:0 in cohort C-II.

[0058] Figure G shows the AUC verification diagram of lipid molecule TG 16:0 / 16:0 / 18:1 in cohort C-II.

[0059] Figure H shows the combined prediction AUC diagram of 6 lipid molecules with high predictive ability in cohort C-II.

[0060] Figure I shows the combined prediction AUC diagram of 6 lipid molecules with high predictive ability in cohort C-III.

[0061] J is a risk assessment chart of 6 lipid molecules with high predictive ability in cohort C-II.

[0062] K is a risk assessment chart of 6 lipid molecules with high predictive ability in cohort C-III.

[0063] The red line represents Model 1, and the blue line represents Model 2. Model 1: unadjusted model; Model 2: adjusted according to maternal age, pre-pregnancy BMI, parity, pregnancy, gestational diabetes mellitus (GDM), gestational hypertension (GH), and fetal sex. Abbreviations: AUC, area under the curve; ROC, receiver operating characteristic curve. Specific implementation manner

[0064] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments, but it should not be construed as a limitation of the present invention. Unless otherwise specified, the technical means used in the following embodiments are conventional means well-known to those skilled in the art. The materials, reagents, etc. used in the following embodiments can be obtained from commercial sources unless otherwise specified.

[0065] Example 1

[0066] I. Experimental method

[0067] 1. Population cohort experimental protocol

[0068] (1) Establishment and maintenance of the population cohort

[0069] In the Hefei Parent-Child Birth Cohort, pregnant women who gave birth in the Department of Obstetrics and Gynecology of Chaohu Hospital Affiliated to Anhui Medical University, the First Affiliated Hospital of Anhui Medical University, and the Second Affiliated Hospital of Anhui Medical University were used as research subjects. Inclusion criteria: The research subjects were singleton pregnancies and lived in Hefei during pregnancy; Exclusion criteria: Both husband and wife had received assisted reproductive technology, had severe chronic diseases or allergic diseases, used dietary supplements during the investigation, and refused to answer questions during the investigation. The research protocol was approved by the Medical Research Ethics Committee of Anhui Medical University (approval number: 83241213), and all recruited research subjects provided written informed consent.

[0070] Collect the baseline data, questionnaire information, and clinical visit records of the research subjects to ensure the accuracy of the characteristic information. Exact pregnancy time and pregnancy period - activity patterns of pregnant women; disease history, family history; history of exposure to harmful factors during pregnancy: medication use during pregnancy, chemical exposure, smoking, alcohol consumption, eating habits, psychology, social support, etc.; delivery method: pregnancy complications and comorbidities, various inflammatory and infectious diseases, intrapartum complications; birth outcomes: live birth, stillbirth, fetal death, birth defects; record the gestational age, number of fetuses, gender, body length, birth weight, chest circumference, and head circumference of the neonates; clinical biochemical test and examination data throughout the pregnancy. More than 2000 pairs of pregnant women with singleton pregnancies and their offspring have been recruited in the Hefei Parent-Child Cohort; collect peripheral blood of pregnant women at 3 different pregnancy trimesters (early, middle, and late), placenta, and umbilical cord blood samples during delivery, aliquot them, and place them in an -80°C refrigerator for storage in an orderly manner; all samples are maintained and managed by a dedicated person.

[0071] (2) The sample size estimation of the cohort population referred to the study on the association between parental environmental heavy metal exposure and the metabolome of offspring. There were a total of 670 pairs in this study. Considering the factor of missing data, the actual number of subjects included in the survey: >2000 pairs.

[0072] (3) Sample collection Add 50 μL of serum from the aliquot to 8 μL of internal standard mixture (containing 400 pmol PC14:0, PE 17:0, CE16:0(d7), COH(d7), BMP 14:0, SM 12:0, PG 14:0, and 50 pmol TG 16:0 / 18:0 / 16:0(d5)), and continue to mix with 125 μL of a mixture of butanol:methanol (1:1, v / v) and 1 mM ammonium formate for lipid extraction and protein precipitation. The sample mixture is first vortexed thoroughly for 1 minute, then sonicated in ice water for 1 hour, and finally centrifuged at 14000 g for 10 minutes at 20°C. Take the supernatant for detection and analysis.

[0073] (4) Association analysis between maternal blood lipid levels and fetal growth restriction and biomarker screening Detect the blood lipid levels such as triglyceride, total cholesterol, low-density lipoprotein, high-density lipoprotein, and lipoprotein in the serum by a biochemical analyzer; determine the lipid profiles in the maternal blood, placenta, and umbilical cord blood of the birth cohort by LC-MS / MS, and draw the lipid map of the maternal blood. Use the logistic regression model to analyze the association strength between the levels of various lipid molecules in the maternal blood and fetal growth restriction. Use the ROC curve adjusted for population characteristics to explore the predictive ability of lipid molecules in the late pregnancy for fetal growth restriction; compare the selected specific lipid molecules with classical fetal growth restriction markers to explore their superiority.

[0074] 2. Animal experiments

[0075] (1) Animal source and ethical certification

[0076] C57BL / 6 mice (male mice at 8 weeks old: 28 - 32 g, female mice at 8 weeks old: 18 - 22 g) were all purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. All mice were caged and mated after 1 week of adaptive feeding. All animal experiment procedures were carried out in accordance with the animal experiment operation principles and standard operating procedures formulated by the Animal Experiment Ethics Committee of Anhui Medical University. For details, see the animal experiment ethics report (No. LLSC20210463).

[0077] (3) Animal experiment protocol:

[0078] To verify the effect of maternal lipid levels on fetal growth restriction, 60 pregnant mice were randomly divided into 6 groups, namely the PDN control group (5% CMC - Na, PDN solvent), prednisone group (PDN, 2.5 mg / kg), Cd control group (reverse osmosis (RO) water, Cd solvent), cadmium group (CdCl2, Cd, 2 mg / kg), HFD control group (normal diet), and high - fat group (60 kcal%, HFD), with 10 mice in each group. The PDN group was given intragastric administration throughout pregnancy from gestational day 0 (GD0) to GD16. The Cd group was given a single intraperitoneal injection of CdCl2 on GD8. The HFD group was fed a high - fat diet from 1 week before pregnancy to GD18. All pregnant mice were sacrificed on GD16 or GD18, and maternal blood, placenta, and fetal blood were collected. Lipid metabolism detection: Serum triglyceride / total cholesterol / low - density lipoprotein / high - density lipoprotein / lipoprotein and other lipid metabolism - related levels were detected by a biochemical analyzer; the sample was dissolved in methanol, and the placental lipid profile expression level was detected by LC - MS / MS; the whole - genome expression level of the placenta and related lipid metabolism signaling pathways were analyzed by transcriptomics.

[0079] II. Experimental materials

[0080] The lipid autophagy activator Rap and inhibitor 3MA were purchased from Sigma. Biochemical reagents such as absolute ethanol and methanol were planned to be purchased from Sinopharm Chemical Reagent Co., Ltd.

[0081] III. Statistical analysis

[0082] 1. Data entry

[0083] In this study, the Epidata data entry software was used to record and organize the information of pregnant women and fetuses. Two professional data recorders performed double - entry, and another investigator verified the data. Finally, a characteristic data set of pregnant women and fetuses was formed for subsequent statistical analysis.

[0084] 2. Comparison between groups

[0085] In this study, SPSS 23.0 was used for between-group comparisons among samples. For data that were normally distributed or approximately normally distributed after logarithmic transformation, parametric tests were used. The t-test was used for comparisons between two groups, and analysis of variance was used for comparisons among multiple groups, and post hoc tests were performed based on the Bonferroni method. Non-parametric tests were used for data that were not normally distributed. The study provided the FDR values after multiple comparisons. In human and animal experiments, when the P value or FDR value was less than 0.05, the difference was considered statistically significant.

[0086] 3. Lipid profiling

[0087] Lipid profiling was performed using Metaboanalyst 6.0, and logarithmic transformation was performed on the quantitative lipid data matrix. In univariate statistics, the t-test was used, and multiple comparisons were performed based on the benjaminihochberg method, and the FDR value was provided. In multivariate statistics, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to provide the variance inflation factor (VIP); the screening of differential lipid molecules included the following two conditions simultaneously: ① FDR < 0.05 in univariate statistics; ② VIP value > 1 in multivariate statistics. Binary logistic regression analysis was performed using SPSS 23.0 to evaluate the association between lipid molecules and the risk of FGR.

[0088] 4. Biomarker screening and diagnostic ability verification

[0089] In the screening of biomarkers, we provided two models, namely, a crude model (only including two variables: lipid molecules and fetal growth restriction) and an adjusted model (in addition to the variables in the crude model, covariates such as the age, parity, pre-pregnancy BMI, chronic diseases, fetal sex, and gestational age of pregnant women were also adjusted). The sample pretreatment module was used for the extraction of lipid compounds from blood samples; the lipidomics detection module was used for the detection and quantification of lipid compounds; the data quality control module was used for the quality control of lipidomics data. After determining the quantification limit of each analyte and the coefficient of variation within and between batches, the quantitative data pretreatment module was used for the filtration of lipidomics quantitative data and the filling of blank values, and the concentrations of individual lipid molecules were normalized and standardized. Screening was performed by methods such as two independent sample t-tests, orthogonal partial least squares discriminant analysis, and false discovery rate correction to obtain lipids with concentration differences between the fetal growth restriction group and the non-fetal growth restriction group and lipids significantly associated with the risk of disease occurrence. The area under the ROC-AUC curve was used to judge the predictive ability of specific lipid molecules or classical indicators for FGR. Finally, the lipid biomarker verification module was used to further verify the obtained biomarkers, and the association between lipid biomarkers and the risk of fetal growth restriction was evaluated by multivariate-adjusted logistic regression to improve the robustness of the screened biomarkers for predicting growth restriction, and then specific lipid molecules with high predictive ability were screened out.

[0090] IV. Experimental Results

[0091] 1. The increase in the types of lipids in maternal serum at birth is positively correlated with fetal growth restriction

[0092] To evaluate the association between the blood lipid profile and fetal growth restriction, as Figure 1 shown in A, we measured and quantified the blood lipid profiles in maternal serum samples of the normal (n = 85)-fetal growth restriction (n = 55) groups in discovery cohort I (C-I) using HPLC-MS / MS. As Figure 1 shown in E, we found 65 upregulated lipids and 17 downregulated lipids in the fetal growth restriction group. Among them, as Figure 1 shown in L, the levels of DGs and TGs in the mothers of the fetal growth restriction group were significantly higher compared with those in the non-fetal growth restriction group. These results indicate that the increase in the levels of DG and TG in the mother is related to fetal growth restriction.

[0093] Next, we used three different mouse models to consolidate this observation, as Figure 1 shown in B, C, and D. These three models were FGR models induced by prednisone (PDN), cadmium chloride (Cd), or high-fat diet (HFD). The circulating lipids of female mice were measured by lipidomics technology. As Figure 1As shown in G and H, 191, 192, and 169 lipids were detected in the sera of pregnant mice treated with PDN, Cd, and HFD, respectively. Consistent with the results of the population study, as Figure 1 shown in I, J, and K, elevated levels of DGs and TGs were also observed in three fetal growth restriction mouse models. In summary, the increase in the types of lipids in maternal serum is positively correlated with fetal growth restriction.

[0094] To verify these results, as Figure 1 shown in A, we analyzed the lipid profiles of sera from 222 pregnant women (normal = 208, fetal growth restriction = 14) from an independent cohort II (C-II) using the same method as the discovery cohort C-I. As expected, as Figure 1 shown in F, there were also differences in DGs and TGs in the sera of mothers in the independent cohort II (C-II), and the number of elevated lipid species in the independent cohort II (C-II) was 52. In summary, the levels of DG and TG species in the sera of mothers in the fetal growth restriction group were significantly elevated.

[0095] 2. Changes in differential lipid molecules in maternal serum during different pregnancy trimesters

[0096] As Figure 2 shown in A, to explore the changes in blood lipid species during different pregnancy trimesters, we selected 154 pregnant women from cohort III (C-III) and collected their sera at different pregnancy trimesters. First, as Figure 2 shown in B, the lipidomic characteristics in sera at different gestational stages showed different clusters. To understand the longitudinal changes in serum lipidome characteristics, we used the paired t-test for comparative analysis of three periods, as Figure 2 shown in C, D, and E, mid-pregnancy compared with early pregnancy, late pregnancy compared with mid-pregnancy, and late pregnancy compared with early pregnancy. Stratified analysis showed that in the comparative analysis of the three periods, compared with the normal group, some lipids in the fetal growth restriction group gradually increased with the progress of pregnancy. Notably, the increase in DGs and TGs was the most obvious. In addition, from the trend of lipid changes in early pregnancy, mid-pregnancy, and late pregnancy, the total concentration of lipid species showed a continuous upward trend, as Figure 2 shown in F and G. Among the lipids, DGs and TGs were most consistent with the overall changes in lipids, as Figure 2 shown in H, I, and J. These data indicate that the changes in DGs and TGs are more obvious in fetal growth restriction.

[0097] 3. Screening and validation of lipid biomarkers for diagnosing fetal growth restriction in maternal serum

[0098] To screen lipid biomarkers for the diagnosis of fetal growth restriction, the differential lipid molecules with ROC-AUC > 0.800 in C-III were intersected with C-I and C-II, and the results are as follows Figure 3 As shown in A-G of Figure 3 , a total of 6 lipid molecules were screened: DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG 16:0 / 16:0 / 18:0, TG 16:0 / 16:0 / 18:1. Further, ROC-AUC analysis was performed on the combined diagnostic ability of these 6 lipid molecules, and the results showed that the combination of the above 6 lipid molecules still had good diagnostic ability in C-II and C-III ( Figure 3 H and 3I). At the same time, the risk of fetal growth restriction was evaluated for the above 6 lipid molecules, and it was found that all 6 lipid molecules could increase the risk of fetal growth restriction to varying degrees, among which DG 14:0 / 18:2 and DG 16:0 / 20:4 were the most significant ( Figure 3 J and 3K). The above indicates that these 6 lipid biomarkers can be used for the diagnosis of fetal growth restriction, with high specificity and sensitivity, and have broad clinical application value.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0100] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. Use of lipid biomarkers in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The lipid biomarkers consisted of DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG16:0 / 16:0 / 18:0 and TG 16:0 / 16:0 / 18:

1.

2. Use of the lipid biomarker according to claim 1 in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The diagnostic product includes a detection reagent, which includes a reagent for lipid extraction and protein precipitation. The reagent for lipid extraction and protein precipitation is a mixture of butanol, methanol and ammonium formate. The mixture is prepared by mixing equal volumes of butanol and methanol and then adding ammonium formate, and the concentration of ammonium formate in the mixture is 0.8~1mM.

3. Use of the lipid biomarker according to claim 2 in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The detection reagent includes an internal standard mixture, which consists of PC14:0, PE17:0, CE16:0-d7, COH-d7, BMP14:0, SM12:0, PG14:0, and TG16:0 / 18:0 / 16:0-d5.

4. Use of the lipid biomarker according to claim 3 in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The fetal growth restriction diagnostic product is matched with a detection device to detect the lipid biomarker.

5. Use of the lipid biomarker according to claim 4 in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The detection device comprises a liquid chromatography-tandem mass spectrometer, and the detection reagent is used to obtain the content of the lipid biomarker through liquid chromatography-tandem mass spectrometry.

6. Use of the lipid biomarker according to claim 5 in the preparation of a diagnostic product for fetal growth restriction, characterized in that, The samples tested are blood from pregnant women.

7. A method for screening lipid biomarkers, characterized in that, The screening method comprises: Sample pretreatment: used for the extraction of lipid compounds in blood samples; Sample lipidomics testing: used for the detection and quantification of lipid compounds; Data quality control: used for quality control of lipidomics data to determine the limit of quantification of each lipid compound and the coefficient of variation within and between batches; Quantitative data pre-processing: used for filtering lipidomics quantitative data and filling blank values, and normalizing and standardizing the concentration of each lipid compound; Screening and analysis of differential lipid molecules: used for screening through two independent sample t-test, orthogonal partial least squares discriminant analysis and false discovery rate correction methods to obtain lipids with different concentrations between the fetal growth restriction group and the non-fetal growth restriction group and lipids associated with the risk of disease; the area under the curve in the receiver operating characteristic curve was used to evaluate the predictive efficacy of the prediction model, and the early pregnancy blood lipid biomarkers of fetal growth restriction were screened; the lipid biomarkers consisted of DG 14:0 / 18:2, DG 16:0 / 20:4, TG 14:0 / 16:0 / 18:1, TG 14:0 / 16:0 / 18:2, TG16:0 / 16:0 / 18:0, TG 16:0 / 16:0 / 18:

1.

8. The screening method of the lipid biomarker according to claim 7, characterized in that, The screening method also includes lipid biomarker validation: evaluating the association between lipid biomarkers and the risk of fetal growth restriction by multivariate adjusted logistic regression to evaluate the robustness of the screened lipid biomarkers for predicting growth restriction.

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