Screening and application of pregnancy maternal blood lipid biomarker with fetal growth limitation

Abstract

The invention relates to the technical field of lipidomics and human health, in particular to screening and application of a pregnancy maternal lipid biomarker with fetal growth limitation, and the lipid biomarker is 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 biomarker provided by the invention can accurately diagnose the pathogenesis of the fetal growth restriction, and provides a specific and noninvasive diagnosis means for the 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 when used in combination is still robust.

[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 drug 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 drug is a lipid-regulating drug or a nutritional supplement.

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

[0018] Furthermore, the screening method comprises:

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

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

[0021] 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;

[0022] Quantitative data pre-processing: used for filtering lipidomics quantitative data and filling blank values, and normalizing and standardizing the concentration of each lipid compound;

[0023] Differential lipid molecule screening and analysis: 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 predictive efficacy of the prediction model is evaluated by the area under the curve of the receiver operating characteristic curve, and the early pregnancy blood lipid biomarkers of fetal growth restriction are screened; the lipid biomarkers are 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, TG 16:0 / 16:0 / 18:0, TG 16:0 / 16:0 / 18:1 or any combination thereof.

[0024] Furthermore, 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 improve the robustness of the screened biomarkers for predicting growth restriction.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

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

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

[0028] Figure 1 Results of lipid profile determination for humans and animals.

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

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

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

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

[0033] E is a graph of lipid molecule numbers in the maternal blood of FGRs in cohort CI.

[0034] F is a graph showing the number of lipid molecules in the maternal blood of FGRs in cohort CII.

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

[0036] H is the lipid molecule number diagram of the difference in FGR maternal blood in the Cd model.

[0037] I is a graph showing the number of lipid molecules in the maternal blood of FGR patients in the HFD model.

[0038] J is a graph showing the number of lipid molecules in the maternal blood of FGR in the 3MA model.

[0039] K is the number of lipid molecules in the FGR maternal blood difference in the Rap model.

[0040] L is a heat map showing the lipid molecules in maternal blood that differ among different cohorts.

[0041] Figure 2 To analyze the longitudinal changes of lipid molecules at different stages of pregnancy.

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

[0043] B is the PCA analysis of lipid molecules at different periods.

[0044] C is a graph showing the differential lipid changes during the first trimester.

[0045] D is a graph showing the differential lipid changes during the second trimester.

[0046] E is a graph showing the changes in differential lipids during the third trimester.

[0047] F is a heat map of the changes in various lipids during different periods.

[0048] G is a line graph showing the changes in total lipids during different periods.

[0049] H is a line graph showing changes in triglycerides during different periods.

[0050] I is a line graph showing the changes in diacylglycerol during different periods.

[0051] J is a line graph showing the changes in cholesterol esters during different periods.

[0052] Figure 3 Figure 1. To explore the potential of lipid molecules as FGR markers.

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

[0054] C is the AUC validation graph of lipid molecule DG 16:0 / 20:4 in cohort C-II.

[0055] D is the AUC validation graph of lipid molecule TG 14:0 / 16:0 / 18:1 in cohort C-II.

[0056] E is the AUC validation graph of lipid molecule TG 14:0 / 16:0 / 18:2 in cohort C-II.

[0057] F is the AUC validation graph of lipid molecule TG 16:0 / 16:0 / 18:0 in cohort C-II.

[0058] G is the AUC validation graph of lipid molecule TG 16:0 / 16:0 / 18:1 in cohort C-II.

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

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

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

[0062] K is the risk assessment diagram 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 for maternal age, pre-pregnancy BMI, parity, gestation, gestational diabetes mellitus (GDM), gestational hypertension (GH), and fetal sex. Abbreviations: AUC, area under the curve; ROC, receiver operating characteristic curve. DETAILED DESCRIPTION

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

[0065] Implementation 1

[0066] 1. Experimental Methods

[0067] 1. Population cohort experimental plan

[0068] (1) Establishment and maintenance of crowd queues

[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 selected as research subjects. Inclusion criteria: the research subjects were singleton pregnant and lived in Hefei during pregnancy; exclusion criteria: both spouses had received assisted reproductive technology, had serious chronic diseases or allergic diseases, used dietary supplements during the investigation, and refused to answer questions in 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] Baseline data, questionnaire information and clinical records of the subjects were collected to ensure that the characteristic information was accurate. The exact time of pregnancy and pregnancy time-activity pattern of pregnant women; medical history, family history; exposure history of harmful factors during pregnancy: medication use during pregnancy, chemical exposure, smoking, drinking, eating habits, psychology, social support, etc.; mode of delivery: pregnancy complications and complications, various inflammatory and infectious diseases, and complications during delivery; birth outcomes: live birth, stillbirth, stillbirth, birth defects; newborn records of gestational age, number of fetuses, gender, height, birth weight, chest circumference and head circumference; clinical biochemical tests and examination data throughout pregnancy. The Hefei parent-child cohort has recruited more than 2,000 pairs of pregnant women with singleton pregnancies and their offspring; peripheral blood samples of pregnant women in early, middle and late pregnancy, and placenta and umbilical cord blood samples during delivery were collected, and the samples were placed in a -80℃ refrigerator in an orderly manner after packaging; all samples were maintained and managed by dedicated personnel.

[0071] (2) The sample size of the cohort population was estimated based on a study on the association between parental environmental heavy metal exposure and the metabolome of offspring. This study included 670 pairs of subjects. Taking into account missing data, the actual number of subjects included in the survey was >2000 pairs.

[0072] (3) Sample collection: 50 μl of serum aliquots were added to 8 μl of an internal standard mixture (containing 400 pmo1 PC14:0, PE 17:0, CE16:0 (d7), COH (d7), BMP 14:0, SM 12:0, PG 14:0 and 50 pmo1 TG 16:0 / 18:0 / 16:0 (d5)), and then mixed 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 was first thoroughly vortexed for 1 min, then placed in ice water for sonication for 1 h, and finally centrifuged at 14,000 g for 10 min at 20°C. The supernatant was collected for detection and analysis.

[0073] (4) Analysis of the association between maternal blood lipid levels and fetal growth restriction and screening of biomarkers. Serum triglycerides, total cholesterol, low-density lipoprotein, high-density lipoprotein, lipoprotein and other blood lipid levels were detected by biochemical analyzer; lipid profiles in maternal blood, placenta and umbilical cord blood of pregnant women in the birth cohort were determined by LC-MS / MS, and the lipid profile of maternal blood was drawn. The logistic regression model was used to analyze the strength of the association between the levels of various lipid molecules in maternal blood and fetal growth restriction. The ROC curve after adjusting the population characteristics was used to explore the predictive ability of lipid molecules in the late pregnancy for fetal growth restriction; the specific lipid molecules screened were compared with the classic markers of fetal growth restriction to explore their superiority.

[0074] 2. Animal experiments

[0075] (1) Animal origin and ethical certification

[0076] C57BL / 6 mice (8-week-old male mice: 28-32 g, 8-week-old female mice: 18-22 g) were purchased from Beijing Weitong Lihua Experimental Animal Technology Co., Ltd. All mice were co-bred after 1 week of adaptive feeding. All animal experimental treatments were in accordance with the animal experimental operation principles and standard operating procedures formulated by the Experimental Animal Ethics Committee of Anhui Medical University. For details, see the Animal Experiment Ethics Report (No. LLSC20210463).

[0077] (3) Animal experiment plan:

[0078] To verify the effect of maternal blood lipid levels on fetal growth restriction, 60 pregnant mice were randomly divided into 6 groups, namely, 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 (ordinary diet) and high-fat group (60 kcal%, HFD), with 10 mice in each group. The PDN group was given oral administration from gestational day 0 (GD0) to GD16 throughout pregnancy, the Cd group was given a single intraperitoneal injection of CdCl2 on GD8, and the HFD group was fed with a high-fat diet from 1 week before pregnancy to GD18. All pregnant mice were killed at 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 biochemical analyzer; methanol was used as the solvent for the sample, and the expression level of placental lipid spectrum was detected by LC-MS / MS; transcriptomics was used to analyze the expression level of the whole genome of the placenta and related lipid metabolism signaling pathways.

[0079] 2. Experimental Materials

[0080] The lipophagy activator Rap and inhibitor 3MA were purchased from Sigma; anhydrous ethanol, methanol and other biochemical reagents were purchased from Sinopharm Chemical Reagent Co., Ltd.

[0081] 3. Statistical Analysis

[0082] 1. Data Entry

[0083] This study used Epidata data entry software to record and organize information about pregnant women and fetuses. Two professional data recorders performed double entry, and another investigator verified the data, ultimately forming a characteristic data set of pregnant women and fetuses for subsequent statistical analysis.

[0084] 2. Comparison between groups

[0085] This study used SPSS23.0 to compare samples between groups. Parametric tests were used for data with normal distribution or approximately normal distribution after logarithmic transformation. The t test was used for comparison between two groups. Analysis of variance was used for comparison between multiple groups, and post hoc tests were performed based on the Bonferroni method. Non-parametric tests were used for data with non-normal distribution. The study provided FDR values ​​after multiple comparisons. In human and animal experiments, differences were considered statistically significant when the P value or FDR value was less than 0.05.

[0086] 3. Lipid profile analysis

[0087] Metaboanalyst 6.0 was used for lipid profile analysis, and the quantitative lipid data matrix was logarithmically transformed. 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: ① FDR < 0.05 in univariate statistics; ② VIP value > 1 in multivariate statistics. SPSS23.0 was used for binary logistic regression analysis to evaluate the association between lipid molecules and the risk of FGR.

[0088] 4. Biomarker screening and diagnostic capability verification

[0089] In the screening of biomarkers, we provide two models, namely the crude model (containing only two variables, lipid molecules and fetal growth restriction) and the adjusted model (in addition to the variables in the crude model, covariates such as the age of the pregnant woman, parity, pre-pregnancy BMI, chronic diseases, fetal gender, gestational age, etc. are also adjusted). The sample pre-processing module is used to extract lipid compounds from blood samples; the lipidomics detection module is used for the detection and quantification of lipid compounds; the data quality control module is used for the quality control of lipidomics data. After determining the quantitative limit of each analyte and the coefficient of variation within and between batches, the quantitative data pre-processing module is used to filter the lipidomics quantitative data and fill in blank values, and the concentration of each lipid molecule is normalized. The samples were screened by two independent sample t-test, orthogonal partial least squares discriminant analysis and false discovery rate correction to obtain lipids with different concentrations between the fetal growth restriction group and the non-fetal growth restriction group and lipids significantly associated with the risk of disease. The area under the ROC-AUC curve was used to determine the predictive ability of specific lipid molecules or classic indicators for FGR. Finally, the lipid marker validation module was used to further validate the biomarkers obtained. 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 to screen out specific lipid molecules with higher predictive ability.

[0090] IV. Experimental Results

[0091] 1. Elevated lipid species in maternal serum at birth are positively correlated with fetal growth restriction

[0092] To evaluate the association between blood lipid profile and fetal growth restriction, e.g. Figure 1 As shown in Figure A, we used HPLC-MS / MS to measure and quantify the lipid profiles in maternal serum samples from the normal (n=85) and fetal growth restriction (n=55) groups in the discovery cohort I (CI). Figure 1 As shown in Figure E, we found 65 up-regulated lipids and 17 down-regulated lipids in the fetal growth restriction group. Figure 1 As shown in Figure 1, the levels of DGs and TGs in the mothers of the fetal growth restriction group were significantly higher than those in the non-fetal growth restriction group. These results indicate that the increase in the levels of DG and TG in the mothers is associated with fetal growth restriction.

[0093] Next, we used three different mouse models to consolidate this observation, e.g. Figure 1 As shown in Figures B, C, and D, these three models are FGR models induced by prednisone (PDN), cadmium chloride (Cd), or high-fat diet (HFD). The circulating lipids of the mother mice were measured by lipidomics technology. Figure 1As shown in Figures G and H, 191, 192, and 169 lipids were detected in the sera of PDN, Cd, and HFD-treated mothers, respectively. Figure 1 As shown in Figures I, J, and K, elevated levels of DG and TG were also observed in the three fetal growth restriction mouse models. In summary, elevated lipid species in maternal serum are positively correlated with fetal growth restriction.

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

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

[0096] like Figure 2 As shown in A, in order to explore the changes in blood lipid types at different stages of pregnancy, we selected 154 pregnant women from cohort III (C-III) and collected their serum at different stages of pregnancy. Figure 2 As shown in Figure B, the lipidomic characteristics in serum at different stages of pregnancy showed different groups. To understand the longitudinal changes in serum lipidomic characteristics, we used the paired t-test to perform comparative analysis over the three periods. Figure 2 As shown in C, D, and E, the second trimester was higher than the first trimester, the third trimester was higher than the second trimester, and the third trimester was higher than the first trimester. 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. It is worth noting that the increase in DGs and TGs was the most obvious. In addition, from the perspective of lipid change trends in the early, second, and third trimesters, the total concentration of lipid types showed a continuous upward trend, such as Figure 2 Among lipids, DGs and TGs were most consistent with the overall changes in lipids, as shown in F and G. Figure 2 H, I, and J. These data indicate that changes in DGs and TGs are more pronounced in fetal growth restriction.

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

[0098] In order to screen lipid biomarkers for diagnosing fetal growth restriction, the differential lipid molecules with ROC-AUC>0.800 in C-III were intersected with CI and C-II. The results are shown in Figure 3 As shown in AG, 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. The combined diagnostic ability of these 6 lipid molecules was further analyzed by ROC-AUC. The results showed that the above 6 lipid molecules still had good diagnostic ability in C-II and C-III after combination ( Figure 3 H and 3I). At the same time, the risk of fetal growth restriction caused by the above six lipid molecules was evaluated and it was found that all six 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 shows that these six lipid biomarkers can be used to diagnose fetal growth restriction with high specificity and sensitivity and have wide clinical application value.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The use of a lipid biomarker detection reagent in the preparation of a fetal growth restriction diagnostic product, characterized in that: 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, TG 16:0 / 16:0 / 18:0 and TG 16:0 / 16:0 / 18:1 or any combination thereof.

2. The use of the lipid biomarker detection reagent according to claim 1 in the preparation of a fetal growth restriction diagnosis product, characterized in that: The detection reagent includes a reagent for lipid extraction and protein precipitation, which 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. The use of the lipid biomarker detection reagent according to claim 2 in the preparation of a fetal growth restriction diagnosis product, characterized in that: The detection reagent includes an internal standard mixture, which is composed 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. The use of the lipid biomarker detection reagent according to claim 3 in the preparation of a fetal growth restriction diagnosis product, 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 detection reagent according to claim 4 in the preparation of a fetal growth restriction diagnostic product, 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 detection reagent according to claim 5 in the preparation of a fetal growth restriction diagnostic product, characterized in that: The samples tested are blood from pregnant women.

7. Use of lipid biomarkers in the preparation of drugs for treating and / or preventing fetal growth restriction, characterized in that: The lipid biomarker is at least 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, TG 16:0 / 16:0 / 18:0 and TG 16:0 / 16:0 / 18:1 according to claim 1.

8. Use of the lipid biomarker according to claim 7 in the preparation of a drug for treating and / or preventing fetal growth restriction, characterized in that: The medicine is a lipid-regulating medicine or a nutritional supplement.

9. A method for screening lipid biomarkers, characterized in that: The lipid biomarker refers to the lipid biomarker described in claim 1; 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; Differential lipid molecule screening and analysis: 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 predictive efficacy of the prediction model is evaluated by the area under the curve of the receiver operating characteristic curve, and the early pregnancy blood lipid biomarkers of fetal growth restriction are screened; the lipid biomarkers are 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, TG 16:0 / 16:0 / 18:1 or any combination thereof.

10. The method for screening lipid biomarkers according to claim 9, 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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