Lipid biomarker for predicting preeclampsia and application thereof
Through extensive targeted lipidomics screening and UPLC-TQMS detection, combined with the LIPID MAPS database, preeclampsia-related lipid biomarkers were screened out, solving the problems of insufficient detection range and accuracy in existing technologies, and achieving efficient lipid metabolite prediction and clinical support.
Patent Information
- Application Number
- CN202511335970.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-18
- Publication Date
- 2025-10-24
AI Technical Summary
Existing technologies fail to effectively utilize broadly targeted lipidomics to screen serum lipid metabolites associated with the pathogenesis of preeclampsia, resulting in problems such as wide detection range but low accuracy or limited detection but high sensitivity.
A broadly targeted lipidomics approach was used to detect serum samples from pregnant women in late pregnancy by UPLC-TQMS. Combined with the LIPID MAPS lipid database, differential lipid metabolites were screened out, and a multivariate logistic model was used to predict preeclampsia and identify lipid biomarkers.
It achieved high-coverage, high-throughput and high-sensitivity lipid metabolite detection, improved the detection rate, comprehensively explored the association between serum lipid metabolites in late pregnancy and preeclampsia, and provided support for clinical research.
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Figure CN120831442A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of molecular biology, in particular to a lipid biomarker for predicting preeclampsia. BACKGROUND
[0002] Preeclampsia (PE) is one of the common complications during pregnancy, which seriously endangers the health of mother and fetus. PE is defined as the occurrence of systolic blood pressure ≥ 140 mmHg and / or diastolic blood pressure ≥ 90 mmHg after 20 weeks of gestation, with or without proteinuria, but with important organ and system damage.
[0003] For a long time, PE has been considered as a placental disease, and a large number of studies have been carried out around the placenta. Some studies believe that PE is a syndrome disease with multiple factors, mechanisms and pathways, which shares similar risk factors with cardiovascular diseases, including obesity, diabetes, chronic kidney disease, chronic hypertension, congenital heart disease, etc.; maternal cardiovascular dysfunction may occur before placental dysfunction, and placental dysfunction may only be a link in the pathogenesis of PE, and placental vascular remodeling has little to do with the repeated ischemia-reperfusion in placental tissue. It is currently believed that the occurrence of early-onset and preterm PE may be related to poor early development of placenta, while the placenta of late-onset and term PE is usually normal, and its pathogenesis may be related to syncytiotrophoblast stress. In addition, studies have shown that changes in maternal intestinal and vaginal microbial flora, exposure to toxic substances in the environment are all related to the occurrence of PE.
[0004] The pathogenesis of PE has been studied for a long time, and various theories have been formed, including "genetic imprinting theory", "immune imbalance theory", "placental ischemia theory", "endothelial dysfunction and oxidative stress theory", etc. The "two-stage model" proposed by Redman et al. in 2009 is currently a more recognized theory. This theory believes that the clinical symptoms are not obvious in the early stage of pregnancy, while the ischemia and hypoxia lead to dysfunction of trophoblasts, which leads to insufficient remodeling of uterine spiral arteries, resulting in abnormal development of placental function. The continued pregnancy of the placenta leads to the release of a large number of placental-derived adverse factors, and the mother has excessive inflammatory response and vascular endothelial damage, which eventually leads to a series of clinical symptoms. In 2014, Redman proposed the "six-stage model": (1) from fertilization to embryo implantation, the mother has immune intolerance to the paternal genes of the embryo; (2) uterine spiral artery remodeling period, which is the key period of placenta formation, and vascular remodeling disorder enters the third stage; (3) placental stress period, due to poor placenta formation leading to stress response; (4) placental factor release period, placental-derived adverse factors enter the maternal blood circulation; (5) clinical symptoms appear, such as high blood pressure and other clinical symptoms; (6) disease aggravation period, placental perfusion continues to decrease and forms thrombosis and infarction. This theory further refines the "two-stage model", but is still based on the pathological and physiological changes of placental trophoblasts, and a large number of late-onset PE cases have not found spiral artery remodeling and placental perfusion abnormalities, so it still cannot explain the pathogenesis of all types of PE.
[0005] Lipidomics is an important branch of metabolomics. Lipidomics technology uses the principles of analytical chemistry to analyze the lipid components in biological samples with high throughput. This technology has shown great power in analyzing small sample sizes of lipids, especially in capturing small changes in lipid molecules in the maternal circulation system, and is a powerful tool for revealing the fine mechanisms of biological processes.
[0006] Compared with traditional clinical lipid detection, lipidomics not only can obtain the measurement results of TG and other conventional indicators, but also can accurately identify and quantitatively analyze complex biological active lipids such as sphingomyelin (SM), ceramide (Cer), and phosphatidylcholine (PC), providing a more comprehensive reflection of lipid metabolism changes.
[0007] Previous studies collected maternal plasma in early, middle and late pregnancy, and found that the levels of Cer 14:0, SM 16:0 and SM 18:0 in the plasma of PE patients in early pregnancy were significantly lower than those in the control group. In addition, a study analyzed maternal serum in late pregnancy and found that 10 lipids such as PC, cholesterol ester, Cer were significantly associated with the onset of severe PE. However, most previous studies used non-targeted lipidomics detection technology or targeted lipidomics technology to detect single category of lipid metabolites. Non-targeted lipidomics has wide coverage and can detect a large number of lipid metabolites in the sample, but it is relatively quantitative and less accurate. Compared with non-targeted lipidomics detection technology, targeted lipidomics has higher sensitivity and better quantitative accuracy, but it can only detect a limited number of lipid metabolites. As a new type of lipidomics detection technology, extensive targeted lipidomics combines the wide range of non-targeted lipidomics and the accuracy of quantitative lipidomics, and has the characteristics of wide coverage, high throughput, high sensitivity, qualitative and quantitative accuracy, etc.
[0008] At present, there is no related report on screening serum lipid metabolites related to the onset of PE based on extensive targeted lipidomics. SUMMARY
[0009] To solve the above technical problems, the present application includes the following aspects: The first aspect of the present application provides a method for screening lipid biomarkers for predicting preeclampsia based on extensive targeted lipidomics, which comprises the following steps: (1) Sample pretreatment: take the serum sample of pregnant women in late pregnancy, add organic solvent, mix and centrifuge, mix the supernatant with organic solvent, and reserve; (2) Targeted lipidomics detection: the sample treated in step (1) is subjected to targeted lipidomics detection by UPLC-TQMS; (3) Qualitative and quantitative analysis of lipid markers: the raw data generated by UPLC-TQMS in step (2) is processed by software, the processed data is compared with LIPID MAPS lipid database, the specific structure of the lipid marker is determined, and the content of the lipid molecule is calculated by isotope internal standard; (4) Screening of differential lipid markers: two-sample t-test is used to analyze the lipid metabolites that show significant differences in PE group and control group, and the differential lipid metabolites are screened into a multifactor logistic model to obtain lipid biomarkers for predicting preeclampsia.
[0010] Preferably, the serum sample of pregnant women in late pregnancy comprises serum sample of preeclampsia pregnant women in late pregnancy (PE group) and serum sample of normal pregnant women in late pregnancy (control group).
[0011] Preferably, the organic solvent in step (1) is a methanol solution containing ammonium acetate. More preferably, the organic solvent in step (1) is a methanol solution containing 5 mM ammonium acetate.
[0012] Preferably, the pretreatment process of step (1) is as follows: 10 μL of the serum sample of the pregnant woman in the third trimester of pregnancy is taken and added to 300 μL of a methanol solution containing 5 mM ammonium acetate, vortexed and centrifuged, 20 μL of the supernatant is transferred and mixed with 80 μL of a methanol solution containing 5 mM ammonium acetate, and is ready for use.
[0013] Preferably, the UPLC chromatographic conditions in step (2) are as follows: the chromatographic column is an ACQUITY UPLC® BEH C18 1.7 µM analytical column (2.1 x 100 mm), the column temperature is 40°C, the flow rate is 0.3 mL / min, the injection volume is 2 µL, the mobile phase A is a mixed solution of acetonitrile and water with a volume ratio of 6:4 containing 5 mM ammonium carbamate and 0.1% formic acid, the mobile phase B is a mixed solution of isopropanol and acetonitrile with a volume ratio of 9:1 containing 5 mM ammonium carbamate and 0.1% formic acid, and the gradient elution conditions are as follows:
[0014] Preferably, the TQMS mass spectrometry conditions in step (2) are as follows: the capillary voltage is 3 Kv (ESI+), the ion source temperature is 150°C, the desolvation temperature is 550°C, the desolvation gas flow rate is 1000 L / h, and the collision gas flow rate is 0.13 L / h.
[0015] Preferably, the lipid biomarker selected in step (4) is selected from one or more of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), CE 16:1.
[0016] Preferably, the lipid biomarkers screened in step (4) are a combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), and CE 16:1.
[0017] More preferably, the lipid biomarkers screened in step (4) are a combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1).
[0018] A second aspect of the application provides a lipid biomarker for predicting pre-eclampsia, the lipid biomarker selected from one or more of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), CE 16:1.
[0019] TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), and CE 16:1.
[0020] More preferably, the lipid biomarkers are in combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1).
[0021] A third aspect of the application provides the use of a lipid biomarker selected from one or more of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), CE 16:1 in the prediction of pre-eclampsia.
[0022] TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), and CE 16:1.
[0023] More preferably, the lipid biomarkers are in combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1).
[0024] Preferably, the use is for non-diagnostic and non-therapeutic purposes.
[0025] The fourth aspect of the application provides use of a reagent for detecting a lipid biomarker selected from one or more of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), CE 16:1 in the manufacture of a kit for predicting pre-eclampsia.
[0026] TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, DAG (18:1 / 18:1), TAG (54:5 / 20:4), TAG (54:4 / 20:4), PC 36:1, PEA 32:1, DAG (16:0 / 18:1), TAG (50:2 / 16:1), TAG (56:5 / 16:0), TAG (51:2 / 17:1), PEA 34:1, TAG (56:8 / 22:5), TAG (52:2 / 20:1), CE 23:4, CE 26:6, CE 17:1, DAG (18:2 / 22:4), Cer (d18:1 / 20:0), TAG (49:2 / 17:1), PC 32:0, Gb3 (d18:1 / 18:0), CE 19:1, TAG (50:2 / 18:1), TAG (52:3 / 20:2), CE 17:0, TAG (52:5 / 20:4), PC 40:4, PI 38:1, PEA 36:1, DAG (18:0 / 18:1), TAG (56:6 / 18:1), TAG (50:1 / 16:0), and CE 16:1.
[0027] More preferably, the lipid biomarkers are a combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1).
[0028] Technical effects of the present application: 1、The application adopts a wide-targeting lipidomics to screen a lipid biomarker for predicting preeclampsia, and the wide-targeting lipidomics, as a new type of lipidomics detection technology, combines the wide range of non-targeting lipidomics and the accuracy of targeting lipidomics in quantification, has the characteristics of wide coverage, high throughput, high sensitivity, qualitative and quantitative accuracy, etc., more lipid metabolites are detected in the actual sample, the fragmentation mode of all sub-class substances is optimized, and finally the detection rate is improved.
[0029] 2、The application comprehensively discusses the correlation between serum lipid metabolites in the late pregnancy and the risk of PE, determines lipid metabolite markers such as DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3 and DAG (18:1 / 18:1), and is expected to provide clinical research support for clinical exploration of the pathogenesis of preeclampsia. BRIEF DESCRIPTION OF DRAWINGS
[0030] Figure 1 is a lithograph of principal component analysis of lipid metabolites; Figure 2 is the category and constituent ratio of the lipid metabolites; Figure 3 is the correlation strength OR value of different categories of lipids and PE occurrence risk; Figure 4 is a volcano plot of the difference of lipid metabolites between the PE group and the control group; Figure 5 is the pathway analysis result of the serum differential lipid metabolites of the PE case group and the control group; Figure 6 is the receiver operating characteristic curve of the lipid biomarker of the application for predicting preeclampsia. DETAILED DESCRIPTION
[0031] The application will be further described below in combination with examples, but the implementation mode of the application is not limited thereto. The experimental methods used in the following examples are conventional methods unless otherwise specified.
[0032] Test Example 1, screening and confirmation of a lipid biomarker for predicting preeclampsia 1、Test method 1.1、Research object This study was a nested case-control study, and the subjects were pregnant women who received antenatal care and gave birth at the First People's Hospital of Taicang, Suzhou from May 2021 to March 2024. Inclusion criteria included: (1) age 18-45 years; (2) singleton pregnancy; (3) informed consent and good communication skills. Exclusion criteria included: (1) miscarriage, stillbirth, or stillbirth due to reasons other than PE; (2) patients with other serious diseases.
[0033] Diagnostic criteria for PE include new-onset hypertension (systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg) occurring after 20 weeks of gestation combined with any of the following: urine protein ≥0.3 g / 24 hours or a urine protein-to-creatinine ratio ≥0.3 or spot urine protein ≥ (+); damage to vital organs such as the heart, liver, lungs, and kidneys; abnormal changes in the central nervous system and hematologic system; and placental-fetal involvement. The control group included pregnant women who had a successful delivery and did not experience gestational hypertension, gestational diabetes, preterm birth, or intrauterine growth restriction. PE cases and healthy controls were matched by age and gestational age at blood sampling. This study was approved by the Ethics Committee of Taicang First People's Hospital, and all participants provided written informed consent.
[0034] 1.2 Clinical Data and Sample Collection Basic information about the study subjects was collected via a questionnaire after the pregnant women signed informed consent. This information included age, height, pre-pregnancy weight, education level, smoking history, maternal and childbearing history, personal medical history (chronic hypertension, diabetes), and family medical history. Blood samples were collected by professional medical personnel. Fasting blood was collected from the pregnant women and centrifuged at 3200 rpm for 5 minutes. The serum was then separated and aliquoted into cryovials using a sterile pipette or pipette and stored at -80°C until subsequent analysis. Information on the occurrence of pregnancy complications (such as gestational hypertension and gestational diabetes) and pregnancy outcomes was extracted from electronic medical records.
[0035] 1.3 Lipidomics detection Ultra-performance liquid chromatography-triple quadrupole mass spectrometry (UPLC-TQMS) was used to perform targeted lipidomics analysis on serum samples from pregnant women in late pregnancy.
[0036] Mass spectrometry grade acetonitrile, isopropanol and chromatography grade ammonium acetate were purchased from Thermo-Fisher Scientific (Fair Lawn, NJ, USA). Ultrapure water was prepared by a Millipore Reference ultrapure water system (Billerica, MA, USA) equipped with a 0.22 micron filter head for liquid chromatography-mass spectrometry.
[0037] Firstly, samples were thawed in ice bath, then 10 μL serum sample was pipetted into 96-well plate, 300 μL methanol solution containing 5 mM ammonium acetate was added, vortex mixed for 20 min, then the 96-well plate was centrifuged at 4000 g for 20 min, 20 μL supernatant was transferred to a new 96-well plate and mixed with 80 μL methanol solution containing 5 mM ammonium acetate for subsequent analysis.
[0038] Targeted lipidomics was performed by UPLC-TQMS, and the detection parameters of the instrument are shown in Table 1.
[0039] Table 1 UPLC-TQMS detection conditions Reagent blank samples were used to evaluate the reagent interference in the sample preparation process, and the real samples were treated at the same time. The quality control sample was a mixture of all measured samples, which roughly represented the biological average of the entire sample set. In order to eliminate the errors caused by the order in the analysis process, the samples to be tested were randomly sampled according to the group information, and the quality control samples and blank samples were detected in the whole sample.
[0040] 1.4, Statistical analysis (1) Analysis of baseline data Numerical variables were described by mean and standard deviation, and categorical variables were described by N (%). For numerical variables, t-test was used, and for categorical variables, chi-square test or Fisher's exact probability method was used to compare the differences between PE group and control group.
[0041] (2) Lipidomics data preprocessing A total of 750 lipid metabolites were detected in this experiment, and the original data generated by UPLC-TQMS were processed by MassLynx software. Peak extraction, integration and quantification were performed for each lipid, and after filtering, 615 effective data of lipid metabolites were obtained. The structure and name of the lipid metabolite were determined by comparing the LIPID MAPS lipid database. Since the lipidomics data is mostly skewed, the concentration value of each lipid is log-transformed to ensure normality before subsequent analysis. The relative content of different lipid molecules in the organism was calculated by isotopic internal standard.
[0042] (3) Screening of differential lipid metabolites Two-sample t-test was used to analyze the lipid metabolites that showed significant difference between PE and control group, and then these different lipid metabolites were further screened by multivariate logistic model to calculate odds ratio (OR) and 95% CI. The adjusted covariates included age, gestational age at blood sampling, education, parity, pre-pregnancy BMI, smoking status, history of chronic hypertension and family history of hypertension.
[0043] In multiple testing, it is usually necessary to correct the P value to control the false positive rate caused by multiple testing. However, there is a strong biological correlation among lipid metabolites, and multiple lipid levels on the same metabolic pathway often increase or decrease at the same time, which seems to be multiple testing, but in fact may only reflect 1-2 independent biological signals. At this time, using the conventional method to correct the P value is too conservative and increases the risk of type II error. In order to overcome this drawback, in this test example, principal component analysis was first performed on the lipid metabolites to reduce the multidimensionality of metabolomics data and determine the number of independent tests, and the results showed that the first two principal components explained most of the variance (see Figure 1 ). Therefore, in the screening of different lipid metabolites, a two-sided P <0.025 (0.05 / 2) was considered statistically significant.
[0044] (4) Lipid pathway enrichment analysis MetaboAnalyst6.0 website was used to perform pathway enrichment analysis on the different lipid metabolites screened, P <0.05 or pathway impact value >0.2, it was considered that the lipid metabolic pathway was highly associated with PE.
[0045] In this test example, R (version 4.2.3) was used for statistical analysis. Except for the cases specified, a two-sided P <0.05 was considered statistically significant.
[0046] 2. Test results 2.1. Basic characteristics of the study subjects In this test example, a total of 112 pregnant women with singleton pregnancy were included, including 39 in the PE group and 73 in the control group. The average gestational age was 30.06 ± 4.78 years, and the average gestational age at blood sampling was 31.54 ± 4.39 weeks, with no significant difference between the case group and the control group. Compared with the control group, the pregnant women in the PE group had lower education level, were more likely to be multiparous, and were more likely to have a history of chronic hypertension. No significant difference was observed in pre-pregnancy BMI, smoking status, history of diabetes and family history of hypertension between the PE group and the control group (see Table 2).
[0047] Table 2. Baseline characteristics of study subjects 2.2. Lipid classes and subclasses In this test example, 615 lipid metabolites were detected, including four major categories of lipids: glycerophospholipids, sphingolipids, glycerolipids and sterols. Among them, glycerophospholipids include monoacylglycerophosphocholines (LPC), PC, 1-alkenyl, 2-acylglycerophosphocholines (PC_P), 1-alkyl, 2-acylglycerophosphocholines (PC_O), monoacylglycerophosphoethanolamines (LPE), diacylglycerophosphoethanolamines (PEA), 1-alkenyl, 2-acylglycerophosphoethanolamines (PE_P), 1-alkyl, 2-acylglycerophosphoethanolamines (PE_O), monoacylglycerophosphoglycerols (LPG), diacylglycerophosphoglycerols (DPA), 1-alkyl, 2-acylglycerophosphoglycerols (PG_P), 1-alkyl, 2-acylglycerophosphoglycerols (PG_O), monoacylglycerophosphoethanolamines (LPE), diacylglycerophosphoethanolamines (PEA), 1-alkenyl, 2-acylglycerophosphoethanolamines (PE_P), 1-alkyl, 2-acylglycerophosphoethanolamines (PE_O), monoacylglycerophosphoglycerols (LPG), diacylglycerophosphoglycerols (DPA), 1-alkyl, 2-acylglycerophosphoglycerols (PG_P), 1-alkyl, 2-acylglycerophosphoglycerols (PG_O), monoacylglycerophosphoethanolamines (LPE), diacylglycerophosphoethanolamines (PEA), 1-alkenyl, 2-acylglycerophosphoethanolamines (PE_P), 1-alkyl, 2-acylglycerophosphoethanolamines (PE_O), monoacylglycerophosphoglycerols (LPG), diacylglycerophosphoglycerols (DPA), 1-alkyl, 2-acylglycerophosphoglycerols (PG_P), 1-alkyl, 2-acylglycerophosphoglycerols (PG_O), monoacylglycerophosphoethanolamines (LPE), diacylglycerophosphoethanolamines (PEA), 1-alkenyl, 2-acylglycerophosphoethanolamines (PE_P), 1-alkyl, 2-acylglycerophosphoethanolamines (PE_O), monoacylglycerophosphoglycerols (LPG), diacylglycer2-acylglycerophosphoethanolamines, PE_O), monoacylglycerophosphoserines (Monoacylglycerophosphoserines, LPS), diacylglycerophosphoserines (Diacylglycerophosphoserines, PS), diacylglycerophosphoinositols (Diacylglycerophosphoinositols, PI), monoacylglycerophosphoglycerols (Monoacylglycerophosphoglycerols, LPG), diacylglycerophosphoglycerols (Diacylglycerophosphoglycerols, PG), monoacylglycerophosphomonoradylglycerols (Monoacylglycerophosphomonoradylglycerols, BMP), diacylglycerophosphates (Diacylglycerophosphates, PA), and cardiolipins (Cardiolipins, CL), sphingolipids including Cer, ceramide phosphoethanolamines (Ceramide Phosphoethanolamines, CerPE), ceramide phosphoinositols (Ceramide Phosphoinositols, CerPI), hexosylceramides (Hexosylceramides, HexCer), lactosylceramides (Lactosylceramides, LacCer), globotriaosylceramides (Globotriaosylceramides, Gb3), SM, and gangliosides (Gangliosides, GM), glycerolipids including diacylglycerols (Diacylglycerols, DAG) and triacylglycerols (Triacylglycerols, TAG), and sterol esters (Cholesterol Esters, CE). The main lipid classes and their composition are shown in Table 1. Figure 2
[0048] 3.3, Association of each lipid class with PE The association strength indicator OR values between each lipid class and PE risk after adjusting for age, blood draw gestational age, education, parity, pre-pregnancy BMI, smoking status, chronic hypertension history, and family history of hypertension are shown in Table 2. Figure 3
[0049] 3.4, Screening of lipid differential metabolites The results of two-sample t-test showed that the levels of 112 lipid metabolites were significantly different between PE and control groups (p < 0.05, Table 3). P of which 109 were up-regulated and 3 were down-regulated in PE pregnant women (see Table 3). Figure 4 The down-regulated lipids were PE_P (18:0 / 22:6), PC_O 36:1 and LPC 17:0, and the most significant up-regulated lipids were PC 40:5, TAG (56:5 / 22:4), DAG (18:1 / 20:4), PEA 40:5 and DAG (16:1 / 18:1) and so on.
[0050] Then, the 112 lipid metabolites with significant differences in t-test were put into the single factor and multi-factor logistic regression model one by one. After adjusting for age, gestational age at blood sampling, education, parity, early pregnancy BMI, smoking status, history of chronic hypertension and family history of hypertension, it was still observed that 79 lipids were significantly associated with PE (P < 0.025) (see Table 3). P
[0051] Table 3 Logistic regression analysis of different lipid metabolites and PE risk in screening set Table 3 (continued) Table 3 (continued) Table 3 (continued) Table 3 (continued) Table 3 (continued) * Lipid metabolite name according to the standard name of LIPID MAPS lipid database Then, the 79 different lipid metabolites significantly associated with PE (see Table 3, lipid names in bold) obtained above were put into the multi-factor logistic regression model in the validation set (another 68 pregnant women who underwent prenatal examination in the First People's Hospital of Taicang City, Suzhou City from May 2021 to March 2024 were included, of which 24 were in the PE group and 44 were in the control group). The results showed that the levels of 47 different lipid metabolites were significantly different between the PE group and the control group (P < 0.025) (see Table 4, lipid names in bold). P
[0052] Table 4 Logistic regression analysis of different lipid metabolites and PE risk in validation set Table 4 Table 4 Table 4 Table 4 The combination of the top 16 lipid metabolites (see the lipid names in bold italics in Table 4) with P < 0.01 in Table 4 was selected, and the receiver operating characteristic curve (ROC) was used to analyze its diagnostic performance for preeclampsia. The results showed that the combination of the above 16 lipid biomarkers had a strong ability to predict preeclampsia, with an area under the ROC curve (AUC) value of 0.873 (see Figure 6 ), which has good clinical diagnostic significance.
[0053] Pathway enrichment analysis like Figure 5 As shown in Table 5, this study conducted pathway enrichment analysis on 79 differential lipid metabolites and found that the differential lipid metabolites were mainly concentrated in the glycerophospholipid metabolism pathway ( P <0.05) and sphingolipid metabolism pathway (pathway impact value>0.2).
[0054] Table 5 Pathway enrichment analysis Although specific embodiments of the present invention have been described, it will be appreciated by those skilled in the art that various changes and modifications may be made to the present invention without departing from the scope or spirit of the present invention. Therefore, the present invention is intended to cover all such changes and modifications that fall within the scope of the appended claims and their equivalents.
Claims
1. Use of a reagent for detecting a lipid biomarker in the manufacture of a kit for predicting preeclampsia, characterized in that, The lipid biomarkers are a combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1). The lipid biomarkers are a combination of DAG (16:1 / 18:1), TAG (54:6 / 22:5), PC 40:5, PEA 38:5, PEA 40:5, PC 42:4, DAG (18:1 / 20:4), TAG (52:2 / 16:1), PC 42:6, LPE 22:5, TAG (52:3 / 16:1), PC 38:5, TAG (56:5 / 22:4), DAG (16:0 / 20:4), CE 22:3, and DAG (18:1 / 18:1). The lipid biomarkers are a combination of DAG (16:1 / 18: