First-Trimester Serum Targeted Metabolomics for Eicosanoids Reveals Predictive Potential and Preventive Targets for Severe Preeclampsia: A Nested Prospective Cohort Study

First-Trimester Serum Targeted Metabolomics for Eicosanoids Reveals Predictive Potential and Preventive Targets for Severe Preeclampsia: A Nested Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article First-Trimester Serum Targeted Metabolomics for Eicosanoids Reveals Predictive Potential and Preventive Targets for Severe Preeclampsia: A Nested Prospective Cohort Study Yongqiang Ma, Linjie Li, Yiwen Fang, Wei Cai, Jingbo Yang, Liuyang Zhang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4132010/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The metabolic profiles of eicosanoids before the clinical onset of preeclampsia remain incompletely understood. This study aimed to use a targeted metabolomic approach to identify eicosanoid metabolites in first-trimester blood samples and assess their potential to predict severe preeclampsia. Methods : We carried out a nested case-control study focusing on eicosanoid metabolites within a prospective cohort of 5,809 pregnant women. The study analyzed 45 participants who subsequently developed severe preeclampsia and 41 controls with uncomplicated pregnancies. Metabolomic data were examined, and the predictive performance of these metabolites was evaluated using receiver operating characteristic (ROC) curves. Results : Among 40 eicosanoids metabolites quantified, the levels of 10 metabolites differed statistically between groups. Further analysis revealed an increased activation of cyclooxygenase (COX) and 12/15-lipoxygenase (LOX) pathways, alongside a compromised cytochrome P450 (CYP450) pathway, as the underlying mechanisms in the altered eicosanoid metabolomics preceding the clinical onset of severe preeclampsia. Notably, ratios of metabolites indicating a shift from heightened (COX and 12/15-LOX) to compromised (CYP450) pathways demonstrated clinically relevant predictive potential: the performance of the Fetal Medicine Foundation first-trimester preeclampsia screening algorithms (area under curve [AUC] = 0.77, 95% confidence interval [CI]: 0.67 to 0.87) was significantly improved by incorporating these ratios, with the highest increment achieved by the 14-hydroxy-docosahexaenoic acid/19,20-epoxydocosapentaenoic acid ratio (AUC = 0.87, 95% CI: 0.80 to 0.94; ΔAUC = 0.10, 95% CI: 0.03 to 0.18, P = 0.008). Conclusions : Our findings revealed novel prediction models for severe preeclampsia based on first-trimester eicosanoid metabolomics, and provide mechanistic evidence supporting early aspirin use for COX pathway inhibition and suggest that rebalancing the 12/15-LOX and CYP450 pathways may be a potential strategy for preventing severe preeclampsia. Trial registration : Chinese Clinical Trial Registry Identifier: ChiCTR-EOC-15007644 Preeclampsia Prediction Model Eicosanoids Polyunsaturated Fatty Acid Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Preeclampsia, a pregnancy-related hypertensive disorder with consequent multisystem involvement, contributes to 46,000 maternal deaths and 500,000 fetal and neonatal deaths annually.[ 1 ] The pathological features of preeclampsia involves placental insufficiency, releasing soluble factors into the circulation, triggering maternal inflammatory responses, and causing endothelial dysfunction.[ 2 ] In early-onset, preterm, severe preeclampsia, the severity of placental insufficiency is heightened, elevating the risks of complications for both mothers and neonates during pregnancy and postpartum.[ 3 , 4 ] The precise etiology and mechanisms underlying preeclampsia remain unclear and the only cure is the delivery of the placenta. Early and accurate prediction is crucial for optimal management.[ 5 ] Low-dose aspirin prophylaxis, while capable of reducing the risk of preterm preeclampsia by approximately 50% in high-risk women, should be initiated before 16 weeks of gestation.[ 5 , 6 ] Therefore, identifying pregnant women at high risk during the first trimester is clinically significant, providing a therapeutic time window for intervention. Eicosanoids, a category of C20 fatty acids, encompass arachidonic acid (AA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), along with other derivatives. These compounds derive from ω-3 and ω-6 polyunsaturated fatty acids (PUFAs) through three major pathways: cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP450).[ 7 , 8 ] Eicosanoids play a crucial role in maintaining cardiovascular homeostasis,[ 8 ] and their significance in the pathophysiology of pregnancy, particularly in the development of preeclampsia, has been increasingly recognized.[ 9 ] Most of the reports,[ 9 ] including our previous work,[ 10 ] have shown the reduced maternal levels of ω-3 PUFAs as well as a deficiency of pro-resolving or anti-inflammatory eicosanoids in preeclamptic patients. Some studies have investigated changes of eicosanoids or their metabolites before the onset of preeclampsia,[ 11 – 15 ] suggesting the potential predictive value of eicosanoids for preeclampsia. These studies have predominantly centered on specific metabolites like prostacyclin I 2 (PGI 2 ) and thromboxane A 2 (TXA 2 ),[ 11 ] or adopted non-targeted approaches,[ 12 – 15 ] which has limited their ability to precisely uncover the role of eicosanoids metabolism in the development of preeclampsia prior to the onset of clinical symptoms. In contrast, targeted metabolomics methods are typically utilized when research has progressed to the stage of working with an identified target or pathway, offering more precise quantitative estimations with higher sensitivity and specificity.[ 16 ] In this study, based on a large prospective pregnancy cohort, we used a nested case-control design and applied targeted metabolomic approach to quantify circulating eicosanoids in blood samples collected during early pregnancy. The objective was to identify candidate eicosanoid metabolites and assess their predictive potential, aiming for first-trimester prediction of the subsequent development of severe preeclampsia in a hypothesis-driven manner. METHODS Study Design and Participants The Pre-Cryst (Prevention of Future Cardiovascular Risk in Hypertensive Disorders of Pregnancy Cohort) Study is a prospective cohort study conducted in Tianjin, China, and registered at Chinese Clinical Trial Registry (ChiCTR-EOC-15007644). The overall goal of this study was to identify potential risk factors for hypertensive disorders in pregnancy (HDP) and postpartum cardiometabolic dysfunction. Here, we report findings from one of the nested studies within the cohort study. Between November 1, 2016 to May 30, 2018, we consecutively enrolled all pregnant women in their first trimester who were registered in 19 community health service centers in Tianjin. The inclusion criteria involved women at weeks of 11 to 13 of pregnancy, permanent residents in Tianjin (capable of long-term follow-up), without severe clinical illnesses. Patients who did not want to be followed up, had communication difficulties and mental disorders, and were planning to terminate their pregnancy were excluded. At the time of the first pregnancy examination, a baseline questionnaire was administered and a first-trimester blood sample was collected. The Tianjin Maternal and Child Health Care system was used to collect the laboratory test results of prenatal examination and each visit. The follow-up period was from enrollment at 11 + 0 weeks of gestation to 42 days after delivery. Written consent was obtained from all participants. The research protocol was approved by the Ethics Committee of Characteristic Medical Center of the CPAPF (PJHEC-2015-A1). It was conducted in accordance with the principles of the Declaration of Helsinki. Participants were not compensated for their participation in the study. Nested Case–Control Approach We required participants to have a singleton pregnancy with severe preeclampsia and without HDP who completed follow-up. The overall study included 5809 pregnant women. Of whom 208 were excluded (42 terminated their pregnancies before 20 weeks of gestation, 22 dropped out of the study, 23 were lost to follow-up, 96 had twin or multiple pregnancies, and 25 had missing samples). Among 5601 participants with singleton pregnancies, 5326 had undiagnosed HDP. Preeclampsia occurred in 134 pregnant women, including 87 cases of severe preeclampsia and 47 cases of non-severe preeclampsia. The diagnosis of HDP was made according to the 2015 Chinese Society of Obstetrics and Gynecology practice guideline,[ 17 ] which was generally in accordance with the guideline from American College of Obstetricians and Gynecologists.[ 18 ] Preeclampsia was defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg after 20 weeks of pregnancy, with proteinuria (24-hour urine protein ≥ 300 mg, or protein/creatinine ratio of ≥ 0.3, or dipstick test ≥ 2+), or in the absence of proteinuria, with the new onset of any of the following conditions: thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, or new-onset headache. Severe preeclampsia was defined by at least one of the following criteria: SBP ≥ 160 mmHg or DBP ≥ 110 mmHg, with two consecutive BP measurements taken at least 4 hours apart, accompanied by the new onset of clinical conditions mentioned above and/or visual disturbances. Among all participants who completed follow-up, 37 of 87 patients with severe preeclampsia had insufficient blood samples obtained at the first perinatal visit for metabolomics analysis. Employing a nested case-control design based on the prospective cohort, after propensity-score matching on the basis of age, gestational weeks, booking body mass index (BMI) and baseline medical history, the remaining 50 severe preeclamptic patients who had first-visit blood samples were successfully matched in a 1:1 ratio with participants without HDP and pregnancy complications, and these two groups form the basis for the current report. 5 cases in severe preeclampsia group and 9 cases in the control group were excluded due to inadequate sample quality, resulting in 41 controls and 45 severe preeclampsia for analysis (Additional file 1: Fig. S1 ). Sample Preparation and Quantification of Eicosanoids During the first prenatal visit (average gestational age of 12 weeks), fasting peripheral blood samples were collected for routine blood tests and biochemical analysis. Ethylenediaminetetraacetic acid-anticoagulated blood was centrifuged at 1000g for 10 minutes at 4℃, and the obtained serum samples were stored at -80℃. Detection and quantification of eicosanoids followed our previous publication[ 10 ]. Samples were pre-treated using solid phase extraction. The sample was then loaded onto a well-equilibrated solid phase extraction column (Waters Oasis HLB 60 mg). After washing and drying under vacuum, the samples were eluted and dried with nitrogen. The samples were re-dissolved before metabolomic analysis. The targeted metabolomic analysis was performed by UPLC-MS/MS, utilizing a BEH C18 UPLC column (1.7 µm, 50 mm × 2.1 mm) for the separation of eicosanoids. The flow rate of the mobile phase was 0.25 mL/min, with a sample injection volume of 10 µL. Eicosanoids were detected in multiple reaction monitoring scanning mode using a 5500 QTRAP hybrid triple quadrupole-linear ion trap mass spectrometer (AB Sciex, Foster City, CA), equipped with a TurboIonSpray electrospray ionization source. The instrument was operated using Analyst 1.5.1 software. The ion source parameters were set as follows: CUR = 40 psi, GS1 = 30 psi, GS2 = 30 psi, IS = − 4,500 V, CAD = MEDIUM, TEMP = 500 ℃. MultiQuant 3.0 was utilized for peak discovery, integration, and concentration calculation. Statistical Analysis Baseline characteristics were reported as means (± standard deviation) for normally distributed data and medians with 25th and 75th percentiles for skewed data. Categorical variables were presented as percentages. Fisher's exact test was used to compare differences between groups for binomial data, while the Mann-Whitney U test and unpaired t-test were used for group comparisons of continuous variables with skewed and normal distributions, respectively. Sample normalization by median, log transformation, and mean centering were first performed on the metabolomic data. Then we used a volcano plot to intuitively select significant features, and levels of metabolites were considered significant at P 1.2 or < 0.8. Significant features were additionally visualized by boxplots. As a further visualization method, a heatmap with an agglomerative hierarchical cluster was chosen. Partial least squares discriminant analysis was performed to examine whether different metabolite components could be used to differentiate severe preeclampsia. The variable importance in projection (VIP) was measured to assess importance. In addition, we analyzed the correlation between metabolites with a correlation heatmap and linear regression. The eXtreme Gradient Boosting (XGBoost) model was used to evaluate the significance of metabolites and the ratios of upregulated to downregulated entities. This approach aimed to identify the most optimal metabolites for predicting severe preeclampsia. After the candidate metabolite screening, two predictive models were developed using logistic regression: (1) a clinical variable model and (2) a clinical variable + metabolites model. Diagnostic performance was measured and compared by the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value. The additive value was further evaluated by net reclassification improvement and integrated discrimination improvement. Calibration was used to measure the coordination between predicted risk and observed risk, which was evaluated using the calibration plots. For missing values, we imputed the missing data using k-nearest neighbors based on similar features method. Metabolomic data analysis was conducted through the MetaboAnalyst 5.0 metabolomics analysis platform ( https://www.metaboanalyst.ca ). Other statistical analyses were performed using STATA 16.0 (StataCorp, College Station, TX) and R (version 4.2.3). Statistical significance was determined at a P value < 0.05 using a two-tailed test. RESULTS Clinical Characteristics Table 1 compares relevant maternal characteristics between 45 participants who subsequently developed severe preeclampsia and 41 propensity score- matched participants who had no obvious maternal and neonatal complications during pregnancy. There were no statistically significant differences between the two groups in terms of age, BMI, maternal history of chronic hypertension and type 2 diabetes, parity, gravidity, and conception method. Notably, compared to the control group, the BP levels in early pregnancy was significantly elevated in patients with severe preeclampsia ( P < 0.001). The mean gestational age at delivery was 37.3 weeks and 39.1 weeks for severe preeclampsia and controls, respectively. Table 1 Clinical characteristics of participants at baseline (gestational age of 11 to 13 weeks) Total Controls sPE P value n = 86 n = 41 n = 45 Age, year 31.7 (3.3) 32.1 (3.4) 31.4 (3.2) 0.33 SBP, mmHg 110 (14.8) 107.0 (9.03) 110 (17.8) 0.004 DBP, mmHg 69.5 (10.6) 64.6 (5.57) 70.0 (12.3) < 0.001 MBP, mmHg 83.3 (11.5) 78.7 (5.8) 87.5 (13.7) 1), % 37 (43%) 21 (51%) 16 (36%) 0.19 Parity (> 1), % 21 (24%) 11 (27%) 10 (22%) 0.80 Type 2 Diabetes, % 5 (6%) 1 (2%) 4 (9%) 0.36 Chronic hypertension, % 5 (6%) 1 (2%) 4 (9%) 0.36 In vitro fertilization, % 2 (2%) 1 (2%) 1 (2%) 1.00 Waistline, cm 85.8 (13.2) 85.7 (14.0) 85.9 (12.7) 0.94 Hipline, cm 98.5 (12.7) 97.2 (13.6) 99.7 (11.8) 0.38 Red blood cells, 10 12 /L 4.3 (0.4) 4.3 (0.5) 4.4 (0.4) 0.30 Hemoglobin, g/L 132.6 (9.8) 132.1 (9.4) 133.2 (10.1) 0.62 Platelets, 10 9 /L 229.6 (63.7) 215.0 (61.2) 242.9 (63.8) 0.042 Fasting glucose, mmol/L 5.4 (2.9) 5.8 (4.1) 5.1 (1.3) 0.30 Alanine transaminase, µmol/L 22.2 (21.0) 22.3 (16.2) 22.1 (24.8) 0.96 Aspartate aminotransferase, µmol/L 19.6 (13.9) 22.3 (17.6) 19.2 (13.7) 0.69 Serum urea nitrogen, mmol/L 3.7 (3.3) 3.3 (0.6) 4.0 (4.6) 0.34 Serum creatinine, µmol/L 55.0 (11.2) 57.2 (11.3) 53.1 (10.9) 0.089 sPE: severe preeclampsia; SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure; BMI: body mass index First-Trimester Metabolic Features of Serum Eicosanoids in Participants Who Later Developed Severe Preeclampsia Versus the Uncomplicated Controls A total of 40 eicosanoids metabolites were identified and quantified (Fig. 1 A). There was no statistical difference in the levels of AA, DHA and EPA between groups. To visualize the quantitative clustering relationship of multiple metabolites across samples, a heatmap was presented for eicosanoid metabolomics data (Fig. 1 A). Utilizing hierarchical cluster analysis on the top 20 metabolites exhibiting the greatest variability, the samples from both controls and severe preeclampsia were grouped into two clusters, albeit with some degree of overlap (Fig. 1 B). We then calculated the VIP scores, and Fig. 1 C displays the top 15 metabolites contributing most to the classification. It is noteworthy that metabolites with VIP > 1.0 primarily represent COX and LOX metabolites of AA. Figure 2 A illustrates the metabolic pathways of products derived from AA, DHA, and EPA.[ 19 , 20 ] The levels of 10 metabolites, with an increase in 6 and a decrease in 4, differed statistically between groups (Fig. 2 B). Among the 6 increased metabolites, two (TXB 2 and PGE 2 ) are AA-COX-derived, two (12-HETE and 15 HETE) are AA-12/15-LOX-derived, one (14-HDoHE) is DHA-12/15-LOX-derived, and one (11-HETE) is AA-derived auto-oxidation product. Of the 4 decreased metabolites, two (16,17-EDP and 19,20-EDP) are DHA-CYP450-derived, one (5,6-DHET) is AA-CYP450-derived, and one (5-HEPE) is EPA-5 LOX-derived. These consistent findings indicate an increased activation of COX and 12/15-LOX pathways, alongside a compromised CYP450 pathway, underlying the disturbed circulating eicosanoid metabolism in first trimester before the clinical onset of severe preeclampsia. We next assessed correlations between eicosanoid metabolites, presenting the correlation coefficient matrix in Additional file 1: Fig. S2 . As anticipated, metabolites within the same major pathways exhibited positive correlations. For instance, there were positive correlations observed in AA-COX/LOX-derived products (TXB2, PGE2, 12-HETE and 15-HETE) and in DHA-CYP450-derived products (16,17-EDP and 19,20-EDP). Conversely, metabolites from pathways competing for the same substrate displayed negative correlations. For example, AA-CYP450-derived product (5,6-DHET) exhibited negative correlations with AA-COX-derived products (TXB 2 , PGE 2 , 12-HETE, 15-HETE), and DHA-12/15-LOX-derived product (14-HDoHE) showed negative correlations with DHA-CYP450-derived products (16,17-EDP, 19,20-EDP). Notably, DHA and EPA, as well as DHA-CYP450/auto-oxidation-derived metabolites (16,17-EDP, 19,20-EDP, 4-HDoHE, 8-HDoHE, 16-HDoHE and 20-HDoHE), exhibited negative correlations with AA-COX-derived products (TXB 2 , PGE 2 , 12-HETE, 15-HETE). Construction and Performance of Prediction Models for Severe Preeclampsia We then conducted a univariate logistic regression analysis to examine the association between 10 metabolites that statistically differed and the risk of severe preeclampsia. As depicted in Table 2 , six metabolites (14-HDoHE, 12-HETE, TXB 2 , PGE 2 , 11-HETE and 15-HETE) exhibited statistically significant relationships with severe preeclampsia, with an AUC ranging from 0.65 to 0.71. To further investigate the predictive potential associated with these metabolites, we analyzed the ratios of upregulated to downregulated metabolites. This analysis takes into consideration the inherent imbalances within the key pathways of eicosanoid metabolism. These ratios included: AA-COX-derived/AA-CYP450-derived metabolites (TXB2/5,6-DHET and PGE2/5,6-DHET), DHA-12/15-LOX-derived/DHA-CYP450-derived metabolites (14-HDoHE/16,17-EDP and 14-HDoHE/19,20-EDP), as well as other combinations. The XGBoost model revealed that the 14-HDoHE/19,20-EDP ratio achieved the highest score in feature importance ( Additional file 1: Fig. S3; the 14-HDoHE/16,17-EDP ratio was omitted due to its substantial collinearity with the 14-HDoHE/19,20-EDP ratio). Table 2 Logistic regression analyses and AUC for potential biomarkers for PE Biomarker OR (95% CI) * P value AUC (95%CI) 14-HDoHE 0.45 (0.27–0.75) 0.002 0.71 (0.60–0.82) 12-HETE 1.97 (1.23–3.18) 0.005 0.69 (0.57–0.80) TXB 2 2.01 (1.22–3.29) 0.006 0.69 (0.58–0.80) PGE 2 1.90 (1.17–3.08) 0.01 0.68 (0.56–0.79) 11-HETE 1.88 (1.16–3.05) 0.01 0.65 (0.53–0.76) 15-HETE 1.88 (1.16–3.05) 0.01 0.66 (0.54–0.77) 16,17-EDP 0.82 (0.54–1.27) 0.38 0.55 (0.43–0.68) 19,20-EDP 0.79 (0.51–1.23) 0.30 0.60 (0.47–0.72) 5,6-DHET 0.78 (0.51–1.21) 0.27 0.56 (0.43–0.68) 5-HEPE 0.94 (0.61–1.44) 0.77 0.53 (0.40–0.65) * The unit is 1 SD after the normalization transformation. AUC: area under the curve; CI: confidence interval; OR: odds ratio Given the well-established status and clinical validation of the Fetal Medicine Foundation (FMF) preeclampsia risk calculator ( www.fetalmedicine.com ) as a model relying on clinical variables for predicting preeclampsia, we chose to utilize it as the base model. The clinical variables integrated into the FMF model, adapted to our cohort's information availability, included age, BMI, conception method, medical history, obstetric history, and biophysical measurements (average arterial pressure), which yielded an AUC of 0.77 (95% CI: 0.67 to 0.87). We further investigated whether the incorporation of a single eicosanoid metabolite ratio could augment predictive value, ensuring the preservation of model parsimony. The AUC for the single metabolite ratio-based model ranged from 0.66 (15-HETE/5-HEPE) to 0.75 (14-HDoHE/16,17 EDP and 14-HDoHE/19,20 EDP), as shown in Additional file 1: Tables S1 to S6 . The incorporation of ratios into the FMF model significantly improved its performance, resulting in an enhanced AUC ranging from 0.81 to 0.87. Specifically, there was a significant improvement in AUC from 0.77 (the FMF model alone) to 0.87 by integrating the 14-HDoHE/19,20 EDP ratio (Fig. 3 ; ΔAUC = 0.10, 95% CI: 0.03 to 0.18, P = 0.008). Notably, the addition of the 14-HDoHE/19,20 EDP ratio not only improved the net reclassification and integrated discrimination improvement but also enhanced the overall model fit and calibration (Fig. 3 and Additional file 1: Fig. S4 ). DISCUSSION Based on a large prospective cohort, by using a nested case-control design, this study focused on the circulating eicosanoid metabolites in blood samples collected in pregnant women during the first trimester who later developed severe preeclampsia. Through targeted metabolic profiling, we observed distinctive features in women who later experienced severe preeclampsia, as compared with the non-complicated controls. Our analysis revealed an increased activation of COX and 12/15-LOX pathways, alongside a compromised CYP450 pathway, as the underlying mechanisms in the altered eicosanoid metabolomics preceding the clinical onset of severe preeclampsia. Furthermore, the metabolite ratios representing the metabolic shift from heightened pathways (COX and 12/15-LOX) to the compromised pathway (CYP450) enzymes not only exhibited clinically accepted discrimination in univariable models but also provided additive value over the established FMF first-trimester prediction algorithms. Therefore, based on the principles of parsimony and clinical interpretability, this study uncovered novel prediction models for severe preeclampsia, emphasizing eicosanoid metabolic profiling. Additionally, our findings provide mechanistic evidence supporting early aspirin use for COX pathway inhibition and suggest that rebalancing the 12/15-LOX and CYP450 pathways may be a potential strategy for preventing severe preeclampsia. Towards the end of the first trimester (10 to 12 weeks), the intrauterine environment undergoes substantial changes, as featured by the initiation of maternal arterial circulation and the shift towards hemotrophic nutrition.[ 21 ] During this critical transition period, the abrupt rise in oxygen level elicits the production of reactive oxygen species and inflammatory response. With crucial roles in pro- and anti-inflammatory processes, eicosanoids are gaining attention for their potential implications in pregnancy-associated disorders.[ 9 , 22 ] Previous efforts have been made to explore the feasibility of using early trimester circulating markers measured during this critical period to predict subsequent risk of preeclampsia.[ 11 – 15 ] These studies utilized either non-targeted metabolomic approaches,[ 12 – 14 ] multi-omics approaches,[ 15 ] or concentrated on specific eicosanoid metabolites,[ 11 ] thereby precluding the presentation of an overview of eicosanoids metabolomics. To the best our knowledge, the present study represents the first effort to explore the predictive capacity of eicosanoids in early pregnancy for subsequent preeclampsia prediction. This is achieved through the utilization of targeted eicosanoid metabolomic approaches in a hypothesis-driven manner, leveraging data from a large perspective cohort. Our finding that the AA-COX pathway becomes activated (elevated TXB 2 , a stable metabolite of TXA 2 ) in the first trimester among pregnant women who later experience severe preeclampsia offers support for initiating low-dose aspirin in early pregnancy for high-risk populations. This observation, combined with previous report relied on the longitudinal measurements of urinary TXA 2 /PGI 2 levels,[ 11 ] our earlier work on pregnant women with established preeclamptic symptoms,[ 10 ] and recent findings from a multi-omics study,[ 15 ] fortifies the evidence indicating that AA-COX pathway activation is unique to early pregnancy. This collective evidence provides a mechanistic explanation for the ineffectiveness of delayed initiation of aspirin beyond this crucial transition period (before 16 weeks of gestation) for preventing preeclampsia.[ 23 ] As the results of AA-COX pathway activation, PGE 2 , another product derived from the AA-COX pathway, exhibits increased levels before the onset of severe preeclampsia. While this observation may appear contradictory to previous findings in studies relying on measurements from preeclamptic patients,[ 24 , 25 ] it could probably represent a compensatory mechanism aimed at mitigating the vasoconstriction and platelet hyper-aggregability induced by elevated TXA 2 in the early pregnancy. We observed the consistent elevation of circulating levels of 12-HETE and 15-HETE preceding the onset of severe preeclampsia symptoms, suggesting the activation of the AA-12/15-LOX pathway in the first trimester. Furthermore, we found that the level of 11-HETE, a metabolite originating from AA auto-oxidation (occasionally proposed in literature as a COX side-product, serving as an indicator of COX activity),[ 26 ] displayed an increase in pregnant women who subsequently developed severe preeclampsia. HETEs, particularly 12-HETE and 15-HETE, play a pivotal role in inflammation, oxidative stress, and endothelial dysfunction, thereby influencing key processes involved in the pathogenesis of cardiovascular disease.[ 27 ] Alterations in certain HETEs among preeclamptic patients have been reported in previous studies, including elevated levels of 12-HETE and 15-HETE in placental tissue,[ 28 ] increased human umbilical arterial 15-HETE,[ 29 ] and elevated serum levels of 15-HETE in preeclamptic patients compared to those with normotension.[ 30 ] Noteworthy is the fact that both the pharmacological inhibition of 12/15-LOX in angiotensin II-infused ovariectomized female mice[ 31 ] and the genetic deletion of macrophage 12/15-LOX in high-salt-fed mice[ 32 ] ameliorate hypertensive responses. This suggests that the enhanced activity of the AA-12/15-LOX pathway is implicated in the pathogenesis of hypertension. Therefore, our observations in early pregnancy preceding preeclampsia onset, other group’s findings in preeclamptic patients, and findings in hypertension animal models, collectively imply that the sustained activation of the AA-12/15-LOX pathway precedes and accompanies the development of preeclampsia and is linked to hypertension in non-pregnant state. Moreover, the increased production of 14-HDoHE, a metabolite of DHA via 12/15-LOX, serves as additional evidence for the activation of the 12/15-LOX pathway. We observed decreased CYP450 pathway activation in women later developed severe preeclampsia, as evident by the decreased levels of 5,6-DHET (a stable metabolite form of 5,6-EET) and the decreased levels of 16,17-EDP of 19,20-EDP, the products of AA-CYP450 and DHA-CYP450 pathways, respectively. Our findings seem contradictory to a previous report, which showed higher levels of 5,6-(EET and DHET) in the circulation of women with preeclampsia starting from the first three months of pregnancy,[ 33 ] as well as the elevated CYP2J2 isoform in the preeclamptic placenta. As the products of AA-CYP450 pathway, EETs are generally thought to mediate vasodilation and anti-inflammatory effect, therefore confers protection against hypertension. CYP2J2 exhibited notable regioselectivity in metabolizing ω-3 PUFAs, specifically, demonstrating exclusivity in converting DHA to 19,20-EDP. Thus, the consistent reduction in products from AA-CYP450 and DHA-CYP450 provides robust evidence supporting the hypothesis that CYP450 is less likely to be activated during early pregnancy in women who later developed severe preeclampsia. Moreover, a recent work showed that increased EETs in preeclamptic placenta and umbilical cord (~ 33.8 weeks of gestation) are likely due to reduced soluble hydrolase (metabolizing EETs to DHETs), not the increased expression of CYP2J2.[ 34 ] Accordingly, EETs increase patients with established symptoms of preeclampsia may exert compensatory activity toward the vasoconstrictor, antiangiogenic activities.[ 34 ] The exact mechanisms underlying the altered eicosanoid metabolomics remain unexplored. Recently, ferroptosis, a form of cell death dependent on iron and mediated by lipid peroxidation, has gained prominence in its role as a crucial pathological process connecting to preeclampsia.[ 21 ] The 12/15-LOX as a class of non-heme iron-containing enzymes, play an important role in promoting ferroptosis by facilitating the peroxidation of PUFA-phosphatidylethanolamines complex in ferroptotic process.[ 35 ] In our recent genome-wide association study (GWAS) conducted on the present cohort, we pinpointed three potential single-nucleotide polymorphisms (SNPs) associated with preeclampsia risk ( HSF2 , GJA1 , and TRIM36 ).[ 36 ] Notably, none of these SNPs has been validated in a recent preeclampsia GWAS involving the Finnish and Estonian populations.[ 37 ] An intriguing observation is that TRIM36 (tripartite motif-containing 36), encoding a microtubule-associated E3 ligase, was also found to be implicated in ferroptosis.[ 38 ] This evidence contributes to the growing body of support, indicating the involvement of ferroptosis in the pathogenesis of preeclampsia. Our findings indicating increased activities in the AA-COX and AA/DHA-12/15-LOX pathways preceding the clinical onset of severe preeclampsia not only advocate for the early implementation of COX inhibition (such as low-dose aspirin) but also propose the potential strategies of rebalancing of the metabolic shift by modulating heightened AA/DHA-12/15-LOX activity. This modulation could involve interventions like supplementing with ω-3 PUFAs, recognized for their competition with AA for the same metabolic enzymes and their role in nutritional control of ferroptosis.[ 39 ] Another strategy could be the utilization of medications known for their safety during pregnancy, which exhibit inhibitory effects on AA/DHA metabolism, with a specific focus on targeting 12/15-LOX. This study's robustness is evident in its incorporation into a sizable prospective cohort, where blood samples were collected preceding disease onset. Furthermore, the development of clinically interpretable models was achieved through a hypothesis-driven methodology. We acknowledged the following limitations. First, our findings are only based on a Chinese population. It is crucial to validate these findings in external cohorts and diverse ethnic populations. Second, our analysis relies on circulating metabolomics, and while we have noted an imbalance in the activities of three key eicosanoid metabolism pathways, the specific enzymes responsible for these metabolic shifts still need to be elucidated. Third, despite our primary focus on the early prediction of severe preeclampsia, unraveling the therapeutic possibilities linked to changes in circulating eicosanoid metabolomics necessitates a clearer understanding of the dynamic fluctuations in key enzyme activities and metabolite levels. Conclusion In a prospective cohort, by using a targeted metabolomic approach, this study revealed the predictive capacities of first-trimester circulating eicosanoid metabolite alterations for severe preeclampsia, with added value over established models. The identified metabolic pathways, characterized by heightened activation of COX and 12/15-LOX pathways and compromised CYP450 pathway, provide novel preventive targets for severe preeclampsia during early pregnancy. External validation in diverse ethnic group is warranted. Abbreviations sPE: severe preeclampsia HDP: hypertensive disorders in pregnancy AA: arachidonic acid DHA: docosahexaenoic acid EPA: eicosapentaenoic acid PUFA: polyunsaturated fatty acid COX: cyclooxygenase CYP450: Cytochrome P450 LOX: lipoxygenase TXB 2 : thromboxane B 2 PGE 2 : prostaglandin E 2 CYP2J2: CYP subfamily 2J polypeptide 2 HETE: hydroxyeicosatetraenoic acid HEPE: hydroxyeicosapentaenoic acid HDoHE: hydroxydocosahexaenoic acid EDP: epoxydocosapentaenoic acid DHET: dihydroxyeicosatrienoic acid EET: epoxyeicosatrienoic acid FMF: Fetal Medicine Foundation BMI: body mass index SBP: systolic blood pressure DBP: diastolic blood pressure MBP: mean blood pressure FC: fold change VIP: variable importance in projection AUC: area under the curve CI: confidence interval OR: odds ratio PPV: positive predictive value NPV: negative predictive value XGBoost: extreme gradient boosting GWAS: genome-wide association study SNPs: single-nucleotide polymorphisms Declarations Acknowledgements We are deeply grateful to all who provided their support and insights for this study. Author Contributions Conceptualization: Y.M, X.Z; Methodology: Y.M, L.L, Y. F, J.Y; Data collection: L.Z, X.N, S.C; Funding acquisition: X.Z, Q.Y, H.C; Supervision: Y.Y, C.H, H.C; Writing: Y.M, L.L, X.Z; Review and editing: All author. Funding This work was supported by National Natural Science Foundation of China (82321001, 72274133), Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-069C) and the Double First-Class Project of Tianjin Medical University (SYL001-303078100822). Data availability The data, analytic methods, and study materials will be made available for onsite audits by third parties for the purposes of reproducing the results or replicating the procedure. Ethics Approval and Consent to Participate Written consent was obtained from all participants. The research protocol was approved by the Ethics Committee of Characteristic Medical Center of the CPAPF (PJHEC-2015-A1). Consent for publication Not applicable. 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Genome-wide association study reveals hsf2, gja1 and trim36 as susceptibility genes for preeclampsia: A community-based population study in tianjin, china. Hypertens Pregnancy . 2023;42:2256863 Tyrmi JS, Kaartokallio T, Lokki AI, Jaaskelainen T, Kortelainen E, Ruotsalainen S, et al. Genetic risk factors associated with preeclampsia and hypertensive disorders of pregnancy. JAMA Cardiol . 2023;8:674-683 Liu X, Yan C, Chang C, Meng F, Shen W, Wang S, et al. Foxa2 suppression by trim36 exerts anti-tumor role in colorectal cancer via inducing nrf2/gpx4-regulated ferroptosis. Adv Sci (Weinh) . 2023;10:e2304521 Mishima E, Conrad M. Nutritional and metabolic control of ferroptosis. Annu Rev Nutr . 2022;42:275-309 Supplementary Files Additionalfile1JTM.docx GraphicalAbstract.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4132010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287631298,"identity":"52939a2a-0c1e-4b74-8f0f-a23b349b8f22","order_by":0,"name":"Yongqiang 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1","display":"","copyAsset":false,"role":"figure","size":2926983,"visible":true,"origin":"","legend":"\u003cp\u003eFirst-trimester serum targeted metabolomic analysis of eicosanoids in participants who later developed severe preeclampsia and in the uncomplicated controls. \u003cstrong\u003eA\u003c/strong\u003e The cluster analysis of 40 eicosanoid metabolites. The red and blue colors represent increased and decreased abundance, respectively, while the color intensity reflects the corresponding metabolite levels. \u003cstrong\u003eB\u003c/strong\u003ePrincipal component analysis scatter plot comparing the severe preeclampsia and the uncomplicated controls. This scatter plot displays the segregation of two distinct groups: uncomplicated controls (red circles) and severe preeclampsia (green circles). \u003cstrong\u003eC\u003c/strong\u003e VIP scores for selected metabolites in a component analysis. The color gradient on the right represents the expression level of each metabolite in two conditions: the uncomplicated controls (black) and severe preeclampsia (purple to red). VIP: variable importance in projection; sPE: severe preeclampsia\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/abf02e1d74af102a7b2f1493.jpg"},{"id":54370091,"identity":"de64e8ca-fbee-4e80-994c-b3a40b63b12f","added_by":"auto","created_at":"2024-04-09 13:06:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4175797,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in eicosanoid metabolites in participants who later developed severe preeclampsia and in the uncomplicated controls. \u003cstrong\u003eA \u003c/strong\u003eMetabolic pathway map highlighting the arachidonic acid (AA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) metabolism, with color-coded indications of upregulated (red) and downregulated (green) metabolites in severe preeclampsia compared to the uncomplicated controls. \u003cstrong\u003eB\u003c/strong\u003e Volcano plot representing the statistical significance (−log(\u003cem\u003eP\u003c/em\u003e-value)) and magnitude of change (log(FC)) in metabolite levels between severe preeclampsia and the uncomplicated controls. Upregulated metabolites are highlighted in red, downregulated in green. \u003cstrong\u003eC\u003c/strong\u003e to \u003cstrong\u003eG\u003c/strong\u003e Box plots detailing the serum concentrations of 10 significantly differed eicosanoid metabolites. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 ** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01 *** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. AA: arachidonic acid; DHA: docosahexaenoic acid; EPA: eicosapentaenoic acid; COX: cyclooxygenase; CYP450: Cytochrome P450; LOX: lipoxygenase; FC: fold change; HETE: hydroxyeicosatetraenoic acid; HEPE: hydroxyeicosapentaenoic acid; HDoHE: hydroxydocosahexaenoic acid; EDP: epoxydocosapentaenoic acid; oxo-ETE: ketoeicosatetraenoic acid; diHETE: dihydroxyeicosatetraenoic acid; DHET: dihydroxyeicosatrienoic acid; EET: epoxyeicosatrienoic acid\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/b666574046ff4de86a57da9d.jpg"},{"id":54370092,"identity":"f67be6b8-4efb-4c7e-84c9-44246ae6d719","added_by":"auto","created_at":"2024-04-09 13:06:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466706,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the FMF preeclampsia prediction model and the model incorporating the 14-HDoHE/19,20-EDP Ratio. AUC: area under the curve; IDI: integrated discrimination improvement; NRI: net reclassification improvement; FMF: Fetal Medicine Foundation\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/a558370b0c5424537e2be680.jpg"},{"id":55694525,"identity":"888ef446-a71a-4f3f-b2ba-a5be51ad4263","added_by":"auto","created_at":"2024-05-02 00:45:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1056337,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/a636a0fa-aafd-4b8b-a7af-8068ea4303e7.pdf"},{"id":54370094,"identity":"2c055c88-6c29-4f3e-b297-a78d5b2761f8","added_by":"auto","created_at":"2024-04-09 13:06:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2252754,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1JTM.docx","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/a26632c7955be2c9a1bd0f1d.docx"},{"id":54370095,"identity":"1fdf7446-ce4e-470c-b89d-5f679d12a073","added_by":"auto","created_at":"2024-04-09 13:06:22","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2996720,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-4132010/v1/7b3f5c79638ba5800132218f.tif"}],"financialInterests":"","formattedTitle":"First-Trimester Serum Targeted Metabolomics for Eicosanoids Reveals Predictive Potential and Preventive Targets for Severe Preeclampsia: A Nested Prospective Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePreeclampsia, a pregnancy-related hypertensive disorder with consequent multisystem involvement, contributes to 46,000 maternal deaths and 500,000 fetal and neonatal deaths annually.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] The pathological features of preeclampsia involves placental insufficiency, releasing soluble factors into the circulation, triggering maternal inflammatory responses, and causing endothelial dysfunction.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In early-onset, preterm, severe preeclampsia, the severity of placental insufficiency is heightened, elevating the risks of complications for both mothers and neonates during pregnancy and postpartum.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The precise etiology and mechanisms underlying preeclampsia remain unclear and the only cure is the delivery of the placenta. Early and accurate prediction is crucial for optimal management.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Low-dose aspirin prophylaxis, while capable of reducing the risk of preterm preeclampsia by approximately 50% in high-risk women, should be initiated before 16 weeks of gestation.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Therefore, identifying pregnant women at high risk during the first trimester is clinically significant, providing a therapeutic time window for intervention.\u003c/p\u003e \u003cp\u003eEicosanoids, a category of C20 fatty acids, encompass arachidonic acid (AA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), along with other derivatives. These compounds derive from ω-3 and ω-6 polyunsaturated fatty acids (PUFAs) through three major pathways: cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP450).[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Eicosanoids play a crucial role in maintaining cardiovascular homeostasis,[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and their significance in the pathophysiology of pregnancy, particularly in the development of preeclampsia, has been increasingly recognized.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Most of the reports,[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] including our previous work,[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] have shown the reduced maternal levels of ω-3 PUFAs as well as a deficiency of pro-resolving or anti-inflammatory eicosanoids in preeclamptic patients. Some studies have investigated changes of eicosanoids or their metabolites before the onset of preeclampsia,[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] suggesting the potential predictive value of eicosanoids for preeclampsia. These studies have predominantly centered on specific metabolites like prostacyclin I\u003csub\u003e2\u003c/sub\u003e (PGI\u003csub\u003e2\u003c/sub\u003e) and thromboxane A\u003csub\u003e2\u003c/sub\u003e (TXA\u003csub\u003e2\u003c/sub\u003e),[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] or adopted non-targeted approaches,[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] which has limited their ability to precisely uncover the role of eicosanoids metabolism in the development of preeclampsia prior to the onset of clinical symptoms.\u003c/p\u003e \u003cp\u003eIn contrast, targeted metabolomics methods are typically utilized when research has progressed to the stage of working with an identified target or pathway, offering more precise quantitative estimations with higher sensitivity and specificity.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] In this study, based on a large prospective pregnancy cohort, we used a nested case-control design and applied targeted metabolomic approach to quantify circulating eicosanoids in blood samples collected during early pregnancy. The objective was to identify candidate eicosanoid metabolites and assess their predictive potential, aiming for first-trimester prediction of the subsequent development of severe preeclampsia in a hypothesis-driven manner.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThe Pre-Cryst (Prevention of Future Cardiovascular Risk in Hypertensive Disorders of Pregnancy Cohort) Study is a prospective cohort study conducted in Tianjin, China, and registered at Chinese Clinical Trial Registry (ChiCTR-EOC-15007644). The overall goal of this study was to identify potential risk factors for hypertensive disorders in pregnancy (HDP) and postpartum cardiometabolic dysfunction. Here, we report findings from one of the nested studies within the cohort study.\u003c/p\u003e \u003cp\u003eBetween November 1, 2016 to May 30, 2018, we consecutively enrolled all pregnant women in their first trimester who were registered in 19 community health service centers in Tianjin. The inclusion criteria involved women at weeks of 11 to 13 of pregnancy, permanent residents in Tianjin (capable of long-term follow-up), without severe clinical illnesses. Patients who did not want to be followed up, had communication difficulties and mental disorders, and were planning to terminate their pregnancy were excluded.\u003c/p\u003e \u003cp\u003eAt the time of the first pregnancy examination, a baseline questionnaire was administered and a first-trimester blood sample was collected. The Tianjin Maternal and Child Health Care system was used to collect the laboratory test results of prenatal examination and each visit. The follow-up period was from enrollment at 11\u0026thinsp;+\u0026thinsp;0 weeks of gestation to 42 days after delivery. Written consent was obtained from all participants. The research protocol was approved by the Ethics Committee of Characteristic Medical Center of the CPAPF (PJHEC-2015-A1). It was conducted in accordance with the principles of the Declaration of Helsinki. Participants were not compensated for their participation in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eNested Case\u0026ndash;Control Approach\u003c/h2\u003e \u003cp\u003eWe required participants to have a singleton pregnancy with severe preeclampsia and without HDP who completed follow-up. The overall study included 5809 pregnant women. Of whom 208 were excluded (42 terminated their pregnancies before 20 weeks of gestation, 22 dropped out of the study, 23 were lost to follow-up, 96 had twin or multiple pregnancies, and 25 had missing samples). Among 5601 participants with singleton pregnancies, 5326 had undiagnosed HDP. Preeclampsia occurred in 134 pregnant women, including 87 cases of severe preeclampsia and 47 cases of non-severe preeclampsia.\u003c/p\u003e \u003cp\u003eThe diagnosis of HDP was made according to the 2015 Chinese Society of Obstetrics and Gynecology practice guideline,[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] which was generally in accordance with the guideline from American College of Obstetricians and Gynecologists.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Preeclampsia was defined as systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg after 20 weeks of pregnancy, with proteinuria (24-hour urine protein\u0026thinsp;\u0026ge;\u0026thinsp;300 mg, or protein/creatinine ratio of \u0026ge;\u0026thinsp;0.3, or dipstick test\u0026thinsp;\u0026ge;\u0026thinsp;2+), or in the absence of proteinuria, with the new onset of any of the following conditions: thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, or new-onset headache. Severe preeclampsia was defined by at least one of the following criteria: SBP\u0026thinsp;\u0026ge;\u0026thinsp;160 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;110 mmHg, with two consecutive BP measurements taken at least 4 hours apart, accompanied by the new onset of clinical conditions mentioned above and/or visual disturbances.\u003c/p\u003e \u003cp\u003eAmong all participants who completed follow-up, 37 of 87 patients with severe preeclampsia had insufficient blood samples obtained at the first perinatal visit for metabolomics analysis. Employing a nested case-control design based on the prospective cohort, after propensity-score matching on the basis of age, gestational weeks, booking body mass index (BMI) and baseline medical history, the remaining 50 severe preeclamptic patients who had first-visit blood samples were successfully matched in a 1:1 ratio with participants without HDP and pregnancy complications, and these two groups form the basis for the current report. 5 cases in severe preeclampsia group and 9 cases in the control group were excluded due to inadequate sample quality, resulting in 41 controls and 45 severe preeclampsia for analysis (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample Preparation and Quantification of Eicosanoids\u003c/h2\u003e \u003cp\u003eDuring the first prenatal visit (average gestational age of 12 weeks), fasting peripheral blood samples were collected for routine blood tests and biochemical analysis. Ethylenediaminetetraacetic acid-anticoagulated blood was centrifuged at 1000g for 10 minutes at 4℃, and the obtained serum samples were stored at -80℃. Detection and quantification of eicosanoids followed our previous publication[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Samples were pre-treated using solid phase extraction. The sample was then loaded onto a well-equilibrated solid phase extraction column (Waters Oasis HLB 60 mg). After washing and drying under vacuum, the samples were eluted and dried with nitrogen. The samples were re-dissolved before metabolomic analysis. The targeted metabolomic analysis was performed by UPLC-MS/MS, utilizing a BEH C18 UPLC column (1.7 \u0026micro;m, 50 mm \u0026times; 2.1 mm) for the separation of eicosanoids. The flow rate of the mobile phase was 0.25 mL/min, with a sample injection volume of 10 \u0026micro;L. Eicosanoids were detected in multiple reaction monitoring scanning mode using a 5500 QTRAP hybrid triple quadrupole-linear ion trap mass spectrometer (AB Sciex, Foster City, CA), equipped with a TurboIonSpray electrospray ionization source. The instrument was operated using Analyst 1.5.1 software. The ion source parameters were set as follows: CUR\u0026thinsp;=\u0026thinsp;40 psi, GS1\u0026thinsp;=\u0026thinsp;30 psi, GS2\u0026thinsp;=\u0026thinsp;30 psi, IS\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4,500 V, CAD\u0026thinsp;=\u0026thinsp;MEDIUM, TEMP\u0026thinsp;=\u0026thinsp;500 ℃. MultiQuant 3.0 was utilized for peak discovery, integration, and concentration calculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were reported as means (\u0026plusmn;\u0026thinsp;standard deviation) for normally distributed data and medians with 25th and 75th percentiles for skewed data. Categorical variables were presented as percentages. Fisher's exact test was used to compare differences between groups for binomial data, while the Mann-Whitney U test and unpaired t-test were used for group comparisons of continuous variables with skewed and normal distributions, respectively.\u003c/p\u003e \u003cp\u003eSample normalization by median, log transformation, and mean centering were first performed on the metabolomic data. Then we used a volcano plot to intuitively select significant features, and levels of metabolites were considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change in level\u0026thinsp;\u0026gt;\u0026thinsp;1.2 or \u0026lt;\u0026thinsp;0.8. Significant features were additionally visualized by boxplots. As a further visualization method, a heatmap with an agglomerative hierarchical cluster was chosen. Partial least squares discriminant analysis was performed to examine whether different metabolite components could be used to differentiate severe preeclampsia. The variable importance in projection (VIP) was measured to assess importance. In addition, we analyzed the correlation between metabolites with a correlation heatmap and linear regression.\u003c/p\u003e \u003cp\u003eThe eXtreme Gradient Boosting (XGBoost) model was used to evaluate the significance of metabolites and the ratios of upregulated to downregulated entities. This approach aimed to identify the most optimal metabolites for predicting severe preeclampsia. After the candidate metabolite screening, two predictive models were developed using logistic regression: (1) a clinical variable model and (2) a clinical variable\u0026thinsp;+\u0026thinsp;metabolites model. Diagnostic performance was measured and compared by the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value and negative predictive value. The additive value was further evaluated by net reclassification improvement and integrated discrimination improvement. Calibration was used to measure the coordination between predicted risk and observed risk, which was evaluated using the calibration plots. For missing values, we imputed the missing data using k-nearest neighbors based on similar features method.\u003c/p\u003e \u003cp\u003eMetabolomic data analysis was conducted through the MetaboAnalyst 5.0 metabolomics analysis platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Other statistical analyses were performed using STATA 16.0 (StataCorp, College Station, TX) and R (version 4.2.3). Statistical significance was determined at a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using a two-tailed test.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical Characteristics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e compares relevant maternal characteristics between 45 participants who subsequently developed severe preeclampsia and 41 propensity score- matched participants who had no obvious maternal and neonatal complications during pregnancy. There were no statistically significant differences between the two groups in terms of age, BMI, maternal history of chronic hypertension and type 2 diabetes, parity, gravidity, and conception method. Notably, compared to the control group, the BP levels in early pregnancy was significantly elevated in patients with severe preeclampsia (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean gestational age at delivery was 37.3 weeks and 39.1 weeks for severe preeclampsia and controls, respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical characteristics of participants at baseline (gestational age of 11 to 13 weeks)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esPE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;86\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.1 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.4 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.0 (9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.5 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.6 (5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.0 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.3 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.7 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.5 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.7 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGravidity (\u0026gt;\u0026thinsp;1), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity (\u0026gt;\u0026thinsp;1), %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType 2 Diabetes, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic hypertension, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn vitro fertilization, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaistline, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.8 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.7 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.9 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHipline, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.5 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.2 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.7 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed blood cells, 10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.6 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.1 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133.2 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelets, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229.6 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215.0 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242.9 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting glucose, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine transaminase, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartate aminotransferase, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.6 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum urea nitrogen, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum creatinine, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.0 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.2 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.1 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003esPE: severe preeclampsia; SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure; BMI: body mass index\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFirst-Trimester Metabolic Features of Serum Eicosanoids in Participants Who Later Developed Severe Preeclampsia Versus the Uncomplicated Controls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA total of 40 eicosanoids metabolites were identified and quantified (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). There was no statistical difference in the levels of AA, DHA and EPA between groups. To visualize the quantitative clustering relationship of multiple metabolites across samples, a heatmap was presented for eicosanoid metabolomics data (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Utilizing hierarchical cluster analysis on the top 20 metabolites exhibiting the greatest variability, the samples from both controls and severe preeclampsia were grouped into two clusters, albeit with some degree of overlap (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). We then calculated the VIP scores, and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC displays the top 15 metabolites contributing most to the classification. It is noteworthy that metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 primarily represent COX and LOX metabolites of AA.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA illustrates the metabolic pathways of products derived from AA, DHA, and EPA.[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] The levels of 10 metabolites, with an increase in 6 and a decrease in 4, differed statistically between groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among the 6 increased metabolites, two (TXB\u003csub\u003e2\u003c/sub\u003e and PGE\u003csub\u003e2\u003c/sub\u003e) are AA-COX-derived, two (12-HETE and 15 HETE) are AA-12/15-LOX-derived, one (14-HDoHE) is DHA-12/15-LOX-derived, and one (11-HETE) is AA-derived auto-oxidation product. Of the 4 decreased metabolites, two (16,17-EDP and 19,20-EDP) are DHA-CYP450-derived, one (5,6-DHET) is AA-CYP450-derived, and one (5-HEPE) is EPA-5 LOX-derived. These consistent findings indicate an increased activation of COX and 12/15-LOX pathways, alongside a compromised CYP450 pathway, underlying the disturbed circulating eicosanoid metabolism in first trimester before the clinical onset of severe preeclampsia.\u003c/p\u003e\n \u003cp\u003eWe next assessed correlations between eicosanoid metabolites, presenting the correlation coefficient matrix in \u003cstrong\u003eAdditional file 1: Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e. As anticipated, metabolites within the same major pathways exhibited positive correlations. For instance, there were positive correlations observed in AA-COX/LOX-derived products (TXB2, PGE2, 12-HETE and 15-HETE) and in DHA-CYP450-derived products (16,17-EDP and 19,20-EDP). Conversely, metabolites from pathways competing for the same substrate displayed negative correlations. For example, AA-CYP450-derived product (5,6-DHET) exhibited negative correlations with AA-COX-derived products (TXB\u003csub\u003e2\u003c/sub\u003e, PGE\u003csub\u003e2\u003c/sub\u003e, 12-HETE, 15-HETE), and DHA-12/15-LOX-derived product (14-HDoHE) showed negative correlations with DHA-CYP450-derived products (16,17-EDP, 19,20-EDP). Notably, DHA and EPA, as well as DHA-CYP450/auto-oxidation-derived metabolites (16,17-EDP, 19,20-EDP, 4-HDoHE, 8-HDoHE, 16-HDoHE and 20-HDoHE), exhibited negative correlations with AA-COX-derived products (TXB\u003csub\u003e2\u003c/sub\u003e, PGE\u003csub\u003e2\u003c/sub\u003e, 12-HETE, 15-HETE).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction and Performance of Prediction Models for Severe Preeclampsia\u003c/h2\u003e\n \u003cp\u003eWe then conducted a univariate logistic regression analysis to examine the association between 10 metabolites that statistically differed and the risk of severe preeclampsia. As depicted in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, six metabolites (14-HDoHE, 12-HETE, TXB\u003csub\u003e2\u003c/sub\u003e, PGE\u003csub\u003e2\u003c/sub\u003e, 11-HETE and 15-HETE) exhibited statistically significant relationships with severe preeclampsia, with an AUC ranging from 0.65 to 0.71. To further investigate the predictive potential associated with these metabolites, we analyzed the ratios of upregulated to downregulated metabolites. This analysis takes into consideration the inherent imbalances within the key pathways of eicosanoid metabolism. These ratios included: AA-COX-derived/AA-CYP450-derived metabolites (TXB2/5,6-DHET and PGE2/5,6-DHET), DHA-12/15-LOX-derived/DHA-CYP450-derived metabolites (14-HDoHE/16,17-EDP and 14-HDoHE/19,20-EDP), as well as other combinations. The XGBoost model revealed that the 14-HDoHE/19,20-EDP ratio achieved the highest score in feature importance (\u003cstrong\u003eAdditional file 1: Fig. S3;\u003c/strong\u003e the 14-HDoHE/16,17-EDP ratio was omitted due to its substantial collinearity with the 14-HDoHE/19,20-EDP ratio).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analyses and AUC for potential biomarkers for PE\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBiomarker\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI) *\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14-HDoHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45 (0.27\u0026ndash;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71 (0.60\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12-HETE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.97 (1.23\u0026ndash;3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69 (0.57\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTXB\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.01 (1.22\u0026ndash;3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69 (0.58\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePGE\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.90 (1.17\u0026ndash;3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68 (0.56\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11-HETE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88 (1.16\u0026ndash;3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65 (0.53\u0026ndash;0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15-HETE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88 (1.16\u0026ndash;3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66 (0.54\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,17-EDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82 (0.54\u0026ndash;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55 (0.43\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,20-EDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79 (0.51\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60 (0.47\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,6-DHET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78 (0.51\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56 (0.43\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-HEPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94 (0.61\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53 (0.40\u0026ndash;0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e* The unit is 1 SD after the normalization transformation. AUC: area under the curve; CI: confidence interval; OR: odds ratio\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGiven the well-established status and clinical validation of the Fetal Medicine Foundation (FMF) preeclampsia risk calculator (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.fetalmedicine.com\u003c/span\u003e\u003c/span\u003e) as a model relying on clinical variables for predicting preeclampsia, we chose to utilize it as the base model. The clinical variables integrated into the FMF model, adapted to our cohort\u0026apos;s information availability, included age, BMI, conception method, medical history, obstetric history, and biophysical measurements (average arterial pressure), which yielded an AUC of 0.77 (95% CI: 0.67 to 0.87). We further investigated whether the incorporation of a single eicosanoid metabolite ratio could augment predictive value, ensuring the preservation of model parsimony. The AUC for the single metabolite ratio-based model ranged from 0.66 (15-HETE/5-HEPE) to 0.75 (14-HDoHE/16,17 EDP and 14-HDoHE/19,20 EDP), as shown in \u003cstrong\u003eAdditional file 1: Tables S1\u003c/strong\u003e to \u003cstrong\u003eS6\u003c/strong\u003e. The incorporation of ratios into the FMF model significantly improved its performance, resulting in an enhanced AUC ranging from 0.81 to 0.87. Specifically, there was a significant improvement in AUC from 0.77 (the FMF model alone) to 0.87 by integrating the 14-HDoHE/19,20 EDP ratio (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u0026Delta;AUC\u0026thinsp;=\u0026thinsp;0.10, 95% CI: 0.03 to 0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Notably, the addition of the 14-HDoHE/19,20 EDP ratio not only improved the net reclassification and integrated discrimination improvement but also enhanced the overall model fit and calibration (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cstrong\u003eAdditional file 1: Fig. S4\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBased on a large prospective cohort, by using a nested case-control design, this study focused on the circulating eicosanoid metabolites in blood samples collected in pregnant women during the first trimester who later developed severe preeclampsia. Through targeted metabolic profiling, we observed distinctive features in women who later experienced severe preeclampsia, as compared with the non-complicated controls. Our analysis revealed an increased activation of COX and 12/15-LOX pathways, alongside a compromised CYP450 pathway, as the underlying mechanisms in the altered eicosanoid metabolomics preceding the clinical onset of severe preeclampsia. Furthermore, the metabolite ratios representing the metabolic shift from heightened pathways (COX and 12/15-LOX) to the compromised pathway (CYP450) enzymes not only exhibited clinically accepted discrimination in univariable models but also provided additive value over the established FMF first-trimester prediction algorithms. Therefore, based on the principles of parsimony and clinical interpretability, this study uncovered novel prediction models for severe preeclampsia, emphasizing eicosanoid metabolic profiling. Additionally, our findings provide mechanistic evidence supporting early aspirin use for COX pathway inhibition and suggest that rebalancing the 12/15-LOX and CYP450 pathways may be a potential strategy for preventing severe preeclampsia.\u003c/p\u003e \u003cp\u003eTowards the end of the first trimester (10 to 12 weeks), the intrauterine environment undergoes substantial changes, as featured by the initiation of maternal arterial circulation and the shift towards hemotrophic nutrition.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] During this critical transition period, the abrupt rise in oxygen level elicits the production of reactive oxygen species and inflammatory response. With crucial roles in pro- and anti-inflammatory processes, eicosanoids are gaining attention for their potential implications in pregnancy-associated disorders.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Previous efforts have been made to explore the feasibility of using early trimester circulating markers measured during this critical period to predict subsequent risk of preeclampsia.[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] These studies utilized either non-targeted metabolomic approaches,[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] multi-omics approaches,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] or concentrated on specific eicosanoid metabolites,[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] thereby precluding the presentation of an overview of eicosanoids metabolomics. To the best our knowledge, the present study represents the first effort to explore the predictive capacity of eicosanoids in early pregnancy for subsequent preeclampsia prediction. This is achieved through the utilization of targeted eicosanoid metabolomic approaches in a hypothesis-driven manner, leveraging data from a large perspective cohort.\u003c/p\u003e \u003cp\u003eOur finding that the AA-COX pathway becomes activated (elevated TXB\u003csub\u003e2\u003c/sub\u003e, a stable metabolite of TXA\u003csub\u003e2\u003c/sub\u003e) in the first trimester among pregnant women who later experience severe preeclampsia offers support for initiating low-dose aspirin in early pregnancy for high-risk populations. This observation, combined with previous report relied on the longitudinal measurements of urinary TXA\u003csub\u003e2\u003c/sub\u003e/PGI\u003csub\u003e2\u003c/sub\u003e levels,[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] our earlier work on pregnant women with established preeclamptic symptoms,[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and recent findings from a multi-omics study,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] fortifies the evidence indicating that AA-COX pathway activation is unique to early pregnancy. This collective evidence provides a mechanistic explanation for the ineffectiveness of delayed initiation of aspirin beyond this crucial transition period (before 16 weeks of gestation) for preventing preeclampsia.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] As the results of AA-COX pathway activation, PGE\u003csub\u003e2\u003c/sub\u003e, another product derived from the AA-COX pathway, exhibits increased levels before the onset of severe preeclampsia. While this observation may appear contradictory to previous findings in studies relying on measurements from preeclamptic patients,[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] it could probably represent a compensatory mechanism aimed at mitigating the vasoconstriction and platelet hyper-aggregability induced by elevated TXA\u003csub\u003e2\u003c/sub\u003e in the early pregnancy.\u003c/p\u003e \u003cp\u003eWe observed the consistent elevation of circulating levels of 12-HETE and 15-HETE preceding the onset of severe preeclampsia symptoms, suggesting the activation of the AA-12/15-LOX pathway in the first trimester. Furthermore, we found that the level of 11-HETE, a metabolite originating from AA auto-oxidation (occasionally proposed in literature as a COX side-product, serving as an indicator of COX activity),[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] displayed an increase in pregnant women who subsequently developed severe preeclampsia. HETEs, particularly 12-HETE and 15-HETE, play a pivotal role in inflammation, oxidative stress, and endothelial dysfunction, thereby influencing key processes involved in the pathogenesis of cardiovascular disease.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Alterations in certain HETEs among preeclamptic patients have been reported in previous studies, including elevated levels of 12-HETE and 15-HETE in placental tissue,[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] increased human umbilical arterial 15-HETE,[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and elevated serum levels of 15-HETE in preeclamptic patients compared to those with normotension.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Noteworthy is the fact that both the pharmacological inhibition of 12/15-LOX in angiotensin II-infused ovariectomized female mice[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the genetic deletion of macrophage 12/15-LOX in high-salt-fed mice[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] ameliorate hypertensive responses. This suggests that the enhanced activity of the AA-12/15-LOX pathway is implicated in the pathogenesis of hypertension. Therefore, our observations in early pregnancy preceding preeclampsia onset, other group\u0026rsquo;s findings in preeclamptic patients, and findings in hypertension animal models, collectively imply that the sustained activation of the AA-12/15-LOX pathway precedes and accompanies the development of preeclampsia and is linked to hypertension in non-pregnant state. Moreover, the increased production of 14-HDoHE, a metabolite of DHA via 12/15-LOX, serves as additional evidence for the activation of the 12/15-LOX pathway.\u003c/p\u003e \u003cp\u003eWe observed decreased CYP450 pathway activation in women later developed severe preeclampsia, as evident by the decreased levels of 5,6-DHET (a stable metabolite form of 5,6-EET) and the decreased levels of 16,17-EDP of 19,20-EDP, the products of AA-CYP450 and DHA-CYP450 pathways, respectively. Our findings seem contradictory to a previous report, which showed higher levels of 5,6-(EET and DHET) in the circulation of women with preeclampsia starting from the first three months of pregnancy,[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] as well as the elevated CYP2J2 isoform in the preeclamptic placenta. As the products of AA-CYP450 pathway, EETs are generally thought to mediate vasodilation and anti-inflammatory effect, therefore confers protection against hypertension. CYP2J2 exhibited notable regioselectivity in metabolizing ω-3 PUFAs, specifically, demonstrating exclusivity in converting DHA to 19,20-EDP. Thus, the consistent reduction in products from AA-CYP450 and DHA-CYP450 provides robust evidence supporting the hypothesis that CYP450 is less likely to be activated during early pregnancy in women who later developed severe preeclampsia. Moreover, a recent work showed that increased EETs in preeclamptic placenta and umbilical cord (~\u0026thinsp;33.8 weeks of gestation) are likely due to reduced soluble hydrolase (metabolizing EETs to DHETs), not the increased expression of CYP2J2.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] Accordingly, EETs increase patients with established symptoms of preeclampsia may exert compensatory activity toward the vasoconstrictor, antiangiogenic activities.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe exact mechanisms underlying the altered eicosanoid metabolomics remain unexplored. Recently, ferroptosis, a form of cell death dependent on iron and mediated by lipid peroxidation, has gained prominence in its role as a crucial pathological process connecting to preeclampsia.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The 12/15-LOX as a class of non-heme iron-containing enzymes, play an important role in promoting ferroptosis by facilitating the peroxidation of PUFA-phosphatidylethanolamines complex in ferroptotic process.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] In our recent genome-wide association study (GWAS) conducted on the present cohort, we pinpointed three potential single-nucleotide polymorphisms (SNPs) associated with preeclampsia risk (\u003cem\u003eHSF2\u003c/em\u003e, \u003cem\u003eGJA1\u003c/em\u003e, and \u003cem\u003eTRIM36\u003c/em\u003e).[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Notably, none of these SNPs has been validated in a recent preeclampsia GWAS involving the Finnish and Estonian populations.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] An intriguing observation is that \u003cem\u003eTRIM36\u003c/em\u003e (tripartite motif-containing 36), encoding a microtubule-associated E3 ligase, was also found to be implicated in ferroptosis.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] This evidence contributes to the growing body of support, indicating the involvement of ferroptosis in the pathogenesis of preeclampsia.\u003c/p\u003e \u003cp\u003eOur findings indicating increased activities in the AA-COX and AA/DHA-12/15-LOX pathways preceding the clinical onset of severe preeclampsia not only advocate for the early implementation of COX inhibition (such as low-dose aspirin) but also propose the potential strategies of rebalancing of the metabolic shift by modulating heightened AA/DHA-12/15-LOX activity. This modulation could involve interventions like supplementing with ω-3 PUFAs, recognized for their competition with AA for the same metabolic enzymes and their role in nutritional control of ferroptosis.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Another strategy could be the utilization of medications known for their safety during pregnancy, which exhibit inhibitory effects on AA/DHA metabolism, with a specific focus on targeting 12/15-LOX.\u003c/p\u003e \u003cp\u003eThis study's robustness is evident in its incorporation into a sizable prospective cohort, where blood samples were collected preceding disease onset. Furthermore, the development of clinically interpretable models was achieved through a hypothesis-driven methodology. We acknowledged the following limitations. First, our findings are only based on a Chinese population. It is crucial to validate these findings in external cohorts and diverse ethnic populations. Second, our analysis relies on circulating metabolomics, and while we have noted an imbalance in the activities of three key eicosanoid metabolism pathways, the specific enzymes responsible for these metabolic shifts still need to be elucidated. Third, despite our primary focus on the early prediction of severe preeclampsia, unraveling the therapeutic possibilities linked to changes in circulating eicosanoid metabolomics necessitates a clearer understanding of the dynamic fluctuations in key enzyme activities and metabolite levels.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a prospective cohort, by using a targeted metabolomic approach, this study revealed the predictive capacities of first-trimester circulating eicosanoid metabolite alterations for severe preeclampsia, with added value over established models. The identified metabolic pathways, characterized by heightened activation of COX and 12/15-LOX pathways and compromised CYP450 pathway, provide novel preventive targets for severe preeclampsia during early pregnancy. External validation in diverse ethnic group is warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003esPE: severe preeclampsia\u003c/p\u003e\n\u003cp\u003eHDP: hypertensive disorders in pregnancy\u003c/p\u003e\n\u003cp\u003eAA: arachidonic acid\u003c/p\u003e\n\u003cp\u003eDHA: docosahexaenoic acid\u003c/p\u003e\n\u003cp\u003eEPA: eicosapentaenoic acid\u003c/p\u003e\n\u003cp\u003ePUFA: polyunsaturated fatty acid\u003c/p\u003e\n\u003cp\u003eCOX: cyclooxygenase\u003c/p\u003e\n\u003cp\u003eCYP450: Cytochrome P450\u003c/p\u003e\n\u003cp\u003eLOX: lipoxygenase\u003c/p\u003e\n\u003cp\u003eTXB\u003csub\u003e2\u003c/sub\u003e: thromboxane B\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003ePGE\u003csub\u003e2\u003c/sub\u003e: prostaglandin E\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eCYP2J2: CYP subfamily 2J polypeptide 2\u003c/p\u003e\n\u003cp\u003eHETE: hydroxyeicosatetraenoic acid\u003c/p\u003e\n\u003cp\u003eHEPE: hydroxyeicosapentaenoic acid\u003c/p\u003e\n\u003cp\u003eHDoHE: hydroxydocosahexaenoic acid\u003c/p\u003e\n\u003cp\u003eEDP: epoxydocosapentaenoic acid\u003c/p\u003e\n\u003cp\u003eDHET: dihydroxyeicosatrienoic acid\u003c/p\u003e\n\u003cp\u003eEET: epoxyeicosatrienoic acid\u003c/p\u003e\n\u003cp\u003eFMF: Fetal Medicine Foundation\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eMBP: mean blood pressure\u003c/p\u003e\n\u003cp\u003eFC: fold change\u003c/p\u003e\n\u003cp\u003eVIP: variable importance in projection\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003ePPV: positive predictive value\u003c/p\u003e\n\u003cp\u003eNPV: negative predictive value\u003c/p\u003e\n\u003cp\u003eXGBoost: extreme gradient boosting\u003c/p\u003e\n\u003cp\u003eGWAS: genome-wide association study\u003c/p\u003e\n\u003cp\u003eSNPs: single-nucleotide polymorphisms\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to all who provided their support and insights for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Y.M, X.Z; Methodology: Y.M, L.L, Y. F, J.Y; Data collection: L.Z, X.N, S.C; Funding acquisition: X.Z, Q.Y, H.C; Supervision: Y.Y, C.H, H.C; Writing: Y.M, L.L, X.Z; Review and editing: All author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (82321001,\u0026nbsp;72274133),\u0026nbsp;Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-069C) and the Double First-Class Project of Tianjin Medical University (SYL001-303078100822).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data, analytic methods, and study materials will be made available for onsite audits by third parties for the purposes of reproducing the results or replicating the procedure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten consent was obtained from all participants. The research protocol was approved by the Ethics Committee of Characteristic Medical Center of the CPAPF (PJHEC-2015-A1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMagee LA, Nicolaides KH, von Dadelszen P. Preeclampsia. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2022;386:1817-1832\u003c/li\u003e\n\u003cli\u003eDimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV, et al. Pre-eclampsia. \u003cem\u003eNature reviews. Disease primers\u003c/em\u003e. 2023;9:8\u003c/li\u003e\n\u003cli\u003eSitras V, Paulssen RH, Gronaas H, Leirvik J, Hanssen TA, Vartun A, et al. 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Omega-3 fatty acid epoxides are autocrine mediators that control the magnitude of ige-mediated mast cell activation. \u003cem\u003eNature medicine\u003c/em\u003e. 2017;23:1287-1297\u003c/li\u003e\n\u003cli\u003eGumilar KE, Priangga B, Lu CH, Dachlan EG, Tan M. Iron metabolism and ferroptosis: A pathway for understanding preeclampsia. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e. 2023;167:115565\u003c/li\u003e\n\u003cli\u003eMajed BH, Khalil RA. Molecular mechanisms regulating the vascular prostacyclin pathways and their adaptation during pregnancy and in the newborn. \u003cem\u003ePharmacol Rev\u003c/em\u003e. 2012;64:540-582\u003c/li\u003e\n\u003cli\u003eRolnik DL, Nicolaides KH, Poon LC. Prevention of preeclampsia with aspirin. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. 2022;226:S1108-S1119\u003c/li\u003e\n\u003cli\u003eClark BA, Ludmir J, Epstein FH, Alvarez J, Tavara L, Bazul J, et al. 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Ovariectomy via 12/15-lipoxygenase augments angiotensin ii-induced hypertension and its pathogenesis in female mice. \u003cem\u003eHypertension\u003c/em\u003e. 2023;80:1245-1257\u003c/li\u003e\n\u003cli\u003eKriska T, Cepura C, Magier D, Siangjong L, Gauthier KM, Campbell WB. Mice lacking macrophage 12/15-lipoxygenase are resistant to experimental hypertension. \u003cem\u003eAm J Physiol Heart Circ Physiol\u003c/em\u003e. 2012;302:H2428-2438\u003c/li\u003e\n\u003cli\u003eHerse F, Lamarca B, Hubel CA, Kaartokallio T, Lokki AI, Ekholm E, et al. Cytochrome p450 subfamily 2j polypeptide 2 expression and circulating epoxyeicosatrienoic metabolites in preeclampsia. \u003cem\u003eCirculation\u003c/em\u003e. 2012;126:2990-2999\u003c/li\u003e\n\u003cli\u003eDalle Vedove F, Fava C, Jiang H, Zanconato G, Quilley J, Brunelli M, et al. 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Genetic risk factors associated with preeclampsia and hypertensive disorders of pregnancy. \u003cem\u003eJAMA Cardiol\u003c/em\u003e. 2023;8:674-683\u003c/li\u003e\n\u003cli\u003eLiu X, Yan C, Chang C, Meng F, Shen W, Wang S, et al. Foxa2 suppression by trim36 exerts anti-tumor role in colorectal cancer via inducing nrf2/gpx4-regulated ferroptosis. \u003cem\u003eAdv Sci (Weinh)\u003c/em\u003e. 2023;10:e2304521\u003c/li\u003e\n\u003cli\u003eMishima E, Conrad M. Nutritional and metabolic control of ferroptosis. \u003cem\u003eAnnu Rev Nutr\u003c/em\u003e. 2022;42:275-309\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Preeclampsia, Prediction Model, Eicosanoids, Polyunsaturated Fatty Acid","lastPublishedDoi":"10.21203/rs.3.rs-4132010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4132010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The metabolic profiles of eicosanoids before the clinical onset of preeclampsia remain incompletely understood. This study aimed to use a targeted metabolomic approach to identify eicosanoid metabolites in first-trimester blood samples and assess their potential to predict severe preeclampsia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We carried out a nested case-control study focusing on eicosanoid metabolites within a prospective cohort of 5,809 pregnant women. The study analyzed 45 participants who subsequently developed severe preeclampsia and 41 controls with uncomplicated pregnancies. Metabolomic data were examined, and the predictive performance of these metabolites was evaluated using receiver operating characteristic (ROC) curves.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among 40 eicosanoids metabolites quantified, the levels of 10 metabolites differed statistically between groups. Further analysis revealed an increased activation of cyclooxygenase (COX) and 12/15-lipoxygenase (LOX) pathways, alongside a compromised cytochrome P450 (CYP450) pathway, as the underlying mechanisms in the altered eicosanoid metabolomics preceding the clinical onset of severe preeclampsia. Notably, ratios of metabolites indicating a shift from heightened (COX and 12/15-LOX) to compromised (CYP450) pathways demonstrated clinically relevant predictive potential: the performance of the Fetal Medicine Foundation first-trimester preeclampsia screening algorithms (area under curve [AUC] = 0.77, 95% confidence interval [CI]: 0.67 to 0.87) was significantly improved by incorporating these ratios, with the highest increment achieved by the 14-hydroxy-docosahexaenoic acid/19,20-epoxydocosapentaenoic acid ratio (AUC = 0.87, 95% CI: 0.80 to 0.94; ΔAUC = 0.10, 95% CI: 0.03 to 0.18, \u003cem\u003eP \u003c/em\u003e= 0.008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our findings revealed novel prediction models for severe preeclampsia based on first-trimester eicosanoid metabolomics, and provide mechanistic evidence supporting early aspirin use for COX pathway inhibition and suggest that rebalancing the 12/15-LOX and CYP450 pathways may be a potential strategy for preventing severe preeclampsia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: Chinese Clinical Trial Registry Identifier: ChiCTR-EOC-15007644\u003c/p\u003e","manuscriptTitle":"First-Trimester Serum Targeted Metabolomics for Eicosanoids Reveals Predictive Potential and Preventive Targets for Severe Preeclampsia: A Nested Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:06:17","doi":"10.21203/rs.3.rs-4132010/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b70063ee-7b3d-44af-b7ac-b4c5c2a9ea96","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T07:58:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 13:06:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4132010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4132010","identity":"rs-4132010","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}