The predictive value of early pregnancy markers for the risk of preeclampsia in women with twin pregnancies

The predictive value of early pregnancy markers for the risk of preeclampsia in women with twin pregnancies | 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 The predictive value of early pregnancy markers for the risk of preeclampsia in women with twin pregnancies Chao Wang, Tingyu Hu, Jiangnan Wu, Meng Yu, Ting Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7574644/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 13 You are reading this latest preprint version Abstract Background Predicting pre-eclampsia (PE) risk in twin pregnancies enables timely intervention and disease management. We developed a predictive model integrating baseline characteristics, uterine artery pulsatility index (UtA-PI), and serum biomarkers (uE3, AFP, inhibin A) to identify high-risk patients for early intervention. Methods This prospective cohort study enrolled 339 twin pregnancies receiving antenatal care at a tertiary hospital (January 2019–March 2024). Serum AFP, uE3, inhibin-A levels, and bilateral UtA-PI were measured at 11–16 weeks’ gestation. A nomogram prediction model for PE risk was constructed. Results PE was diagnosed in 43 of 339 women (incidence: 12.68%; 95% CI: 9.12–16.25%). Six variables met inclusion criteria (P < 0.10 in univariate/multivariate analyses): IVF conception, pre-pregnancy BMI, right UtA-PI, serum inhibin A, serum uE3, and chorionicity. The model identified lower pre-pregnancy BMI, elevated right UtA-PI, and dysregulated serum biomarkers (uE3, inhibin A) as key predictors of PE. The nomogram demonstrated moderate discriminative ability (C-index: 0.74; 95% CI: 0.67–0.81). Calibration curves indicated excellent agreement between predicted and observed risk, while decision and clinical impact curves confirmed clinical utility. Conclusions Integrating IVF status, chorionicity, early-pregnancy serum biomarkers (inhibin A, uE3), and right UtA-PI effectively predicts PE risk in twin pregnancies. Preeclampsia Twin-pregnancy Inbihin A uE3 UtA-PI Nomogram model Figures Figure 1 Figure 2 Figure 3 1.Introduction Preeclampsia (PE), a major pregnancy-specific disorder, poses significant threats to both maternal and fetal health. Globally, PE accounts for approximately 14% of maternal mortality and 15% of preterm births, resulting in an estimated 76,000 maternal and 500,000 fetal deaths annually[ 1 ]. Furthermore, PE can lead to severe maternal and fetal complications, significantly impacting the long-term health of both mother and child[ 2 , 3 ]. Early intervention, such as first-trimester low-dose aspirin prophylaxis, can effectively prevent PE onset and progression. This reduces the incidence of fetal growth restriction, placental abruption, and other adverse outcomes while prolonging gestation[ 4 ]. However, identifying the target population for intervention remains challenging since PE is typically diagnosed only after clinical symptoms manifest. Consequently, an effective predictive model for PE risk is essential to enable timely intervention. Fawaz Azizieh and colleagues suggested that inflammatory responses and placental abnormalities in PE lead to alterations in multiple serum biomarker levels, which may precede clinical symptoms onset[ 5 ]. Maternal serum biomarkers (e.g., alpha-fetoprotein (AFP), unconjugated estriol (uE3), inhibin A) and uterine artery pulsatility index (UtA-PI) show promising predictive value for PE in singleton pregnancies[ 6 – 8 ]. Crucially, twin pregnancies carry a two- to three-fold increased risk of PE with earlier onset and more severe consequences than singleton pregnancies[ 9 ]. Despite this elevated risk, few studies have focused on developing PE prediction models specifically for twin pregnancies. In singleton pregnancies, maternal serum AFP, uE3, and inhibin A levels, along with UtA-PI differ significantly between PE and normal pregnancies, demonstrating promising predictive value. However, the behavior and predictive utility of these biomarkers in twin pregnancies complicated by PE, which typically exhibits earlier onset and more severe consequences, remain unclear. Therefore, we conducted a prospective cohort study to evaluate the predictive value of maternal serum biomarkers (AFP, uE3, inhibin A) and UtA-PI for PE risk in twin pregnancies. 2. Methods and Materials 2.1 Study design and data collection This was a prospective cohort study. Twin pregnancies were recruited from women who received regular antenatal care in the Maternal and Child Healthcare Hospital of Changning District Shanghai China between January 2019 and March 2024. Women were excluded if they had a singleton pregnancy, chronic hypertension, prior history of preeclampsia, pregestational diabetes mellitus, gestationaldiabetes mellitus, thrombophilia or other maternal chronic diseases. If the fetuses were found any abnormality (structural abnormality or chromosomal), they were excluded either. All pregnant women were recruited between 11 and 13 weeks of gestation. General baseline information of the subjects was collected after enrollment. They then received UtA Doppler ultrasonography measurement and underwent the integrated test for fetal Downs syndrome in which the plasma levels of AFP, uE3, inhibin A were measured. The study was approved by the ethics committee of the hospital (CNFBLLKT-2023-004). Informed consent was obtained from all participants prior to enrollment. 2.2 Serum biomarkers Maternal venous blood samples (3–5 mL) were collected from fasting participants between 11 and 16 weeks of gestation. Samples were centrifuged at 4000 rpm (approximately 3000 × g) for 10 minutes at 25°C and relative humidity < 40% to isolate serum. Serum aliquots were stored at -80°C until analysis. Quantitative measurement of maternal serum biomarkers, including AFP, uE3, and inhibin A, was performed using the following automated chemiluminescent immunoassays on the Beckman Coulter Access platform: Access INHIBIN A, Access Unconjugated Estriol, and Access AFP (Beckman Coulter, Brea, CA, USA). 2.3 Uterine artery Doppler ultrasonography The UtA Doppler transabdominal ultrasonography with color flow mapping was performed at 11 + 0 to 16 + 0 weeks of gestation. The bilateral UtA-PIs were recorded according to the Fetal Medicine Foundation guidelines[ 10 ]. 2.4 PE definition Preeclampsia was defined according to the Diagnosis and treatment of hypertension and pre-eclampsia in pregnancy: a clinical practice guideline in China (2020)[ 11 ]. In this study, PE included the new onset of both gestational hypertension and significant proteinuris after 20 weeks of gestation in a previously normotensive women. Gestational hypertension was defined as repeated systolic blood pressure measurements of ≥ 140 mmHg and/or diastolic blood pressure measurements of ≥ 90 mmHg which was firstly found after 20 weeks of gestation and recover to normal blood pressure after 12 weeks after child birth. Proteinuria was defined as ≥ 300 mg proteinin a 24-hour urine collection, or repeated urinalysis showing ≥ 1 + protein, or a urine protien/creatinine ratio ≥ 0.3. 2.5 Statistical analysis The participants were divided into groups based on the occurrence of PE. Categorical variables were presented as n (%), and the incidence of PE (95% confidence interval, 95%CI) was calculated across variable subgroups. Categorical variables were compared between PE and non-PE groups using chi-square test or Fisher’s exact tests, as appropriate. Continuous variables were presented as median (interquartile range, IQR), and differences between PE and non-PE groups were compared using the Mann-Whitney U test. Prior to model fitting, the measurements of the five measured markers (AFP, left and right PI, uE3, and inhibin A) were standardized using data in the non-PE group to ensure comparability of effect sizes and improve numerical stability. Logistic regression models were performed to estimate the associations between various variables and the risk of PE, with odds ratios (ORs) and 95% CIs. In the multivariable model, maternal age (years), chorionicity (monochorionic or dichorionic), IVF (yes or no), parity (0 or ≥ 1), pre-pregnancy BMI (kg/m 2 ), and the five measured markers (AFP, left and right PI, uE3, and inhibin A) were included to estimate adjusted ORs (95% CIs). Variables with P < 0.10 in either univariate or multivariate analyses were considered potential predictors of PE risk and were visualized using a nomogram. The predictive performance of the nomogram model was assessed using receiver operating characteristic (ROC) curve analysis, while calibration was assessed using the Hosmer-Lemeshow test, with closer agreement between observed and predicted probabilities indicating better model fit. In addition, decision curve analysis and clinical impact curve analysis were performed to quantify the clinical utility of the model by estimating the net benefit and classification rates across all decision thresholds. All analyses were performed using R software (version 4.3.1), with a two-sided P-value < 0.05 considered statistically significant. 3.Results 3.1 Basic characteristics A total of 562 twin-pregnant women were recruited between January 2019 and March 2024 who received regular antenatal care in the Maternal and Child Healthcare Hospital of Changning District Shanghai China. According to the exclusion criteria, 223 were excluded and 339 were targeted finally in our study (Fig. 1 ). Forty-three were diagnosed with PE, with an incidence rate of 12.68% (95% CI: 9.12%-16.25%). There was no statistically significant difference in the chorionicity, parity and maternal age between the PE and non-PE groups. The incidence of PE was higher in the IVF group than in the natural conception group (18.8% vs. 9.0%, P = 0.009), and the pre-pregnancy BMI in the PE group was slightly lower than that in the non-PE group (20.57 vs. 21.26, P = 0.049) (Table 1 ). Table 1 Basic characteristics of women with twin pregnancies Variables Non-PE(N = 296) PE (N = 43) Total (N = 339) P value Chorionicity, n (incidence, 95% CI) 0.56 Monochorionic 77 (85.6) 13 (14.4, 7.0-21.9) 90 Dichorionic 219 (88.0) 30 (12.0, 8.0-16.1) 249 IVF, n (incidence, 95% CI) 0.009 No 192 (91.0) 19 (9.0, 5.1–12.9) 211 Yes 104 (81.3) 24 (18.8, 11.9–25.6) 128 Parity, n (incidence, 95% CI) 0.50 0 250 (86.8) 38 (13.2, 9.3–17.1) 288 ≥ 1 46 (90.2) 5 (9.8, 1.4–18.3) 51 Pre-pregnancy BMI, kg/m 2 , median (IQR) 21.26 (19.83–23.88) 20.57 (19.22–22.04) 21.11 (19.72–23.74) 0.049 Age, years,median (IQR) 35 (32–37) 35 (32–38) 35 (32–37) 0.89 AFP, ng/ml, median (IQR) 44.69 (34.86–58.51) 41.15 (32.67–53.63) 44.55 (34.78–58.20) 0.48 Left PI, median (IQR) 1.32 (1.02–1.67) 1.39 (1.15–1.73) 1.32 (1.04–1.68) 0.27 Right PI, median (IQR) 1.34 (1.01–1.70) 1.47 (1.22–1.85) 1.37 (1.02–1.73) 0.043 uE3, ng/ml, median (IQR) 1.15 (0.94–1.48) 1.01 (0.87–1.32) 1.13 (0.92–1.47) 0.061 Inhibin A, pg/ml, median (IQR) 207.70 (162.35-279.35) 217.60 (168.30-305.70) 210.8 0 (162.70-283.50) 0.16 Of the five indicators, the right UtA-PI measured at 12 weeks' gestation in the PE group was significantly higher than that in the non-PE group (1.47 vs. 1.34, P = 0.043). The P value for the difference in uE3 between the groups was 0.061, while the differences in the other three indicators, including AFP, inbihin A and left UtA-PI, were not statistically significant (Table 1 ). 3.2 Factors associated with PE Logistic regression analysis showed that potential factors associated with PE (e.g., P < 0.10) included IVF, pre-pregnancy BMI, right UtA-PI and inbihin A. In the adjusted model, pregnant women with dichorionic twins had a lower risk of PE relative to that in women with monochorionic twins (P = 0.055). In addition, uE3 was associated with PE (Table 2 ). Therefore, the above six variables were screened as predictive variables in nomogram model. The regression coefficients for each variables in the final nomogram model were presented in Table 3 . Table 2 Association between variables and risk of preeclampsia Variables Unadjusted OR (95% CI) P value Adjusted OR (95% CI) P value Chorionicity Monochorionic Reference Reference Dichorionic 0.81 (0.40–1.64) 0.56 0.43 (0.18–1.02) 0.055 IVF No Reference Reference Yes 2.33 (1.22–4.46) 0.01 3.87 (1.64–9.12) 0.002 Parity 0 Reference Reference ≥ 1 0.72 (0.27–1.91) 0.50 1.18 (0.38–3.62) 0.78 Pre-pregnancy BMI, per kg/m2 0.90 (0.80–1.01) 0.076 0.89 (0.78–1.01) 0.061 Age, per year 1.02 (0.94–1.11) 0.59 0.99 (0.90–1.09) 0.79 Left PI Z score, per SD 1.18 (0.86–1.60) 0.30 1.08 (0.72–1.61) 0.72 Right PI Z score, per SD 1.32 (0.97–1.81) 0.08 1.23 (0.83–1.82) 0.30 uE3 Z score, per SD 0.14 (0.012–1.59) 0.11 0.07 (0.004–1.21) 0.067 AFP Z score, per SD 0.92 (0.64–1.32) 0.64 1.02 (0.66–1.56) 0.94 Inhibin A Z score, per SD 1.38 (1.09–1.75) 0.009 1.48 (1.13–1.94) 0.005 Table 3 Factors included into the nomogram model Variables β SE Adjusted OR (95% CI) P value Intercept 0.311 1.361 1.365 0.819 Chorionicity Monochorionic Reference Dichorionic -0.833 0.437 0.43 (0.19–1.03) 0.057 IVF No Reference Yes 1.301 0.402 3.67 (1.67–8.08) 0.001 Pre-pregnancy BMI, per kg/m 2 -0.120 0.063 0.89 (0.78-1.00) 0.058 Right PI Z score, per SD 0.238 0.173 1.27 (0.90–1.78) 0.168 uE3 Z score, per SD -2.588 1.325 0.08 (0.006–1.01) 0.051 Inhibin A Z score, per SD 0.389 0.135 1.48 (1.13–1.92) 0.004 3.3 Predictive model for PE in twin pregnancies Figure 2 showed the nomogram model predicting the risk of PE by integrating six key clinical and biomarkers. The model highlighted that lower pre-pregnancy BMI, abnormal placental resistance (right PI Z-score), and dysregulated biomarkers (uE3 and Inhibin A Z-scores) are particularly strong risk contributors. The C-index was 0.74 (95% CI: 0.67–0.81), indicating a moderate discriminative ability (Fig. 3 A). Calibration curves showed close agreement between predicted and actual probability, suggesting excellent model fit (Fig. 3 B). Decision curve analysis and clinical impact curve analysis confirmed clinical utility (Fig. 3 C & D). 4. Discussion 4.1 Major Findings We developed a predictive nomogram for preeclampsia (PE) in twin pregnancies using six clinically accessible variables. Key findings identified IVF conception and monochorionic placentation as significant risk factors. Biomarker analysis revealed elevated maternal serum inhibin A and a trend towards lower uE3 in PE pregnancies, consistent with placental dysfunction mechanisms. Paradoxically, higher pre-pregnancy BMI appeared potentially protective. Elevated right UtA-PI was associated with PE, aligning with some prior studies, though not significant as an independent predictor. The integrated model demonstrated clinically useful discriminative performance. 4.2 Comparison with Previous Studies Preeclampsia is usually associated with placental dysfunction, reduction in placental volume, intrauterine growth restriction, abnormal uterine and umbilical artery Doppler ultrasonography findings, low birth weight, multi-organ dysfunction, perinatal death, and adverse maternal and neonatal outcomes[ 12 ]. Twin pregnancy is an important risk factor for PE, therefore, exploring the risk factors and constructing a prediction model for PE in this population is critical for early detection and intervention of the disorder. Inhibin A is a key hormone in the hypothalamic-pituitary-gonadal axis. During pregnancy, inhibin A in maternal serum, amniotic fluid, and fetal circulatory is mainly synthesized by placental syncytiotrophoblasts, which plays a crucial role in reproductive endocrine regulation, endometrial decidualization, embryo implantation, and trophoblast proliferation and differentiation[ 13 ]. In this cohort, we observed that serum biomarkers (e.g., uE3 and inhibin A) measured in early pregnancy were associated with the risk of PE. In addition, these two biomarkers also make a large contribution to the prediction model. The finding aligns consistently with established literature. Park HJ et al. reported elevated serum inhibin A levels in singleton PE cases[ 14 ]. Svirsky et al. found that twin pregnancies with PE also exhibited significantly higher inhibin A levels in early and mid-pregnancy[ 15 ]. A large-scale study by the British Medical Council (BMC) supported that elevated inhibin A level in mid-trimester was significantly associated with placental dysfunction and increased PE risk[ 16 ]. During pregnancy, placenta-derived uE3 is not excreted through maternal kidneys, with up to 90% originating from fetal adrenal synthesis, making serum uE3 a reliable indicator of placental function. Normally, maternal serum uE3 level increases with gestational age, but in PE, placental dysfunction leads to decreased uE3 levels[ 7 ]. In PE the vascular endothelium impairment leads to placental ischemia and hypoxia. This triggers the trophoblast apoptosis, consequently resulting in decreased serum uE3 levels[ 17 ]. Uterine artery hemodynamics revealed complex patterns. The direction of pulsatility index change observed in our study aligns with Rizzo et al.'s twin pregnancy report[ 18 ]. But conflicts with Svirsky et al.'s findings of lower first-trimester PI[ 9 ]. This heterogeneity contrasts with the consistent high-resistance pattern seen in singleton preeclampsia[ 19 , 20 ]and underscores the distinct hemodynamic profile of twin pregnancies. Alpha-fetoprotein (AFP) is a normal serum protein during fetal development and a small amount of AFP transfer to the maternal circulation via the placenta[ 17 ]. Some researches demonstrate that in PE, the transfer of fetal AFP to the maternal is reduced due to systemic maternal vasospasm[ 7 ]. But in this study, there is no difference in AFP level between PE and normal pregnancies. These may be due to that in twin pregnancy, the AFP produced in twins and the elevated total AFP level may weaken the impact of the placenta vasospasm in PE. The observed relationship between higher pre-pregnancy BMI and reduced preeclampsia risk contradicts most previous reports[ 21 , 22 ]. This discrepancy may reflect unique pathophysiological mechanisms in twin pregnancies or methodological limitations, such as some extreme high or low pre-pregnancy BMI that we omitted in our study because of the comparatively small amount of samples in this research. In our study, the result showed that twin pregnancy by IVF may have double even triple possibility to develop into PE. Different from natural pregnancy, maternity patients who conceived through IVF may experience adverse pregnancy outcomes such as hypertensive disorders in pregnancy due to biological, medical and sociological factors[ 23 ]. Mukhopadhaya and Arulkumaran[ 24 ] and Sibai[ 25 ] believe that the IVF technology increase the risk of adverse pregnancy outcomes such as maternal pre-eclampsia and gestational hypertension. While Sun et al[ 26 ] as well as Isaksson et al[ 27 ] offer an opposite opinion. The procedure of superovulation as part of IVF technology was not able to fully simulate the physiological state of hormone. It was reported that onset of pre-eclampsia may be related to the unusually high levels of HCG on the trigger day which activated the renin-angiotensin-aldosterone system[ 28 ]. HCG-induced high hormone levels post the trigger day also played a role in occurrence of pre-eclampsia. Moreover, other complications such as multiple pregnancy, or the embryos obtained from donated eggs will also increase the risk of hypertensive disorders in pregnancy[ 29 ]. To our knowledge, most of previous studies showed no differences in PE occurrence in MCP and DCP[ 30 , 31 ]. While others observed the opposite results[ 32 – 34 ]. Our study found that DCP may be a protective factors for PE in twin pregnancy, however there were no statistically significant. Studies with larger samples are warranted to confirm the relativity between chorionicity and risk of PE. 4.3 Clinical Significance The developed nomogram model provides a clinically implementable tool for preeclampsia risk stratification in twin pregnancies. Its practical value stems from utilizing routinely available parameters obtainable during standard prenatal care, requiring no specialized testing beyond current practice. This model enables early identification of high-risk twin pregnancies, facilitating targeted surveillance protocols and timely intervention strategies (e.g., low-dose aspirin). The established clinical utility through decision curve analysis supports its potential to improve perinatal outcomes by optimizing resource allocation and preventive management for this vulnerable population. 4.4 Study Strengths and Limitations This investigation offers several strengths, including its dedicated focus on the high-risk twin pregnancy population where validated prediction tools are notably lacking. The model integrates multidimensional parameters routinely available in clinical practice, enhancing its translational potential. Methodological limitations warrant consideration. The single-center recruitment strategy introduces potential selection bias and may restrict generalizability. The cohort size constrains subgroup analyses and may have limited power to detect modest effect sizes. External validation across diverse healthcare settings remains essential before widespread implementation. Future multicenter studies should further explore the unexpected BMI relationship and chorionicity. 5. Conclusions This study developed and validated a nomogram prediction model for preeclampsia in twin pregnancies, incorporating basic characteristics, serum biomarkers in early pregnancy, and doppler indexes. The findings provide evidence for better understanding of PE in twin pregnancies and a clinically applicable tool for early identification of PE. Further studies on the model’s clinical applicability in larger cohorts are warranted. Declarations Conflicts of Interest All the authors declare that there are no conflicts of interest. Funding Statement The study was funded by the Scientific Research Project of Changning District Science and Technology Commission (CNKW2022Y35). Author Contribution Chao Wang and Meng Yu wrote the main manuscript text, and Jiangnan Wu performed the statistical analyses and prepared tables and figures. Chao Wang and Ting Peng conceived of the study. Tingyu Hu participated in the study design. Ting Peng and Meng Yu jointly supervised this work . All authors read and approved the final manuscript. Data Availability In this manuscript, full datasets are not publicly available for protecting the confidentiality of all the participants. However, the data that support the findings of this study are available on request from the corresponding author. Inquiries should be communicated with the corresponding author, and we will consider all sufficiently specified and reasonable requests. References Henderson, J.T., et al., Preeclampsia Screening: Evidence Report and Systematic Review for the US Preventive Services Task Force . Jama, 2017. 317(16): p. 1668–1683. Miller, E.C., et al., Risk Factors for Pregnancy-Associated Stroke in Women With Preeclampsia . Stroke, 2017. 48(7): p. 1752–1759. Monroe, J.C., K.M. Naugle, and K.E. 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Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 20 Dec, 2025 Reviews received at journal 22 Nov, 2025 Reviews received at journal 18 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 31 Oct, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 09 Sep, 2025 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. 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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-7574644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542934454,"identity":"72412657-67c6-4f36-adc5-8d56ff07fa23","order_by":0,"name":"Chao Wang","email":"","orcid":"","institution":"Shanghai Changning Maternity and Infant Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""},{"id":542934455,"identity":"4e0befa0-01b7-49da-a285-c5fce3f38da4","order_by":1,"name":"Tingyu Hu","email":"","orcid":"","institution":"Shanghai Changning Maternity and Infant Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingyu","middleName":"","lastName":"Hu","suffix":""},{"id":542934456,"identity":"0d146d3d-2b66-4aa1-a4ca-9b91ecd5b6f9","order_by":2,"name":"Jiangnan Wu","email":"","orcid":"","institution":"Obstetrics and Gynecology Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jiangnan","middleName":"","lastName":"Wu","suffix":""},{"id":542934457,"identity":"c5b82b96-05f5-431c-8a10-baa8a98c34af","order_by":3,"name":"Meng Yu","email":"","orcid":"","institution":"Shanghai Changning Maternity and Infant Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Yu","suffix":""},{"id":542934460,"identity":"819fcdcd-bb64-49d1-a4b5-904c2ea565c4","order_by":4,"name":"Ting Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACfvYzBgc+VNjIsbG3HyBOi2RPjuHBGWfSjPl4ziQQp8Xgho7xYd6Ww4nzJBwMiNXCY3BwZkNaepsEQwLDj4ptxGjh/3Dg4w6b3DbpxgOMPWduE2vLmbTcNpkDCcyMbURoMQNqOczbdjidTSLBgDgtxjd0wFoSiNcCDGQDUCAbtgED+SBRfuF3P2P8ARiV8vLt7Qcf/KggQgsKOECi+lEwCkbBKBgFuAAApWVFNkiK9bAAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Changning Maternity and Infant Health Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-09-09 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07:45:23","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98337,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7574644/v1/581e77a824f3dab81b0abf85.html"},{"id":95818634,"identity":"653fe4f7-a08c-4e4d-9d12-847596197ec6","added_by":"auto","created_at":"2025-11-13 10:21:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150533,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7574644/v1/4ff788f9c12d7063f028e455.png"},{"id":95795794,"identity":"db8d77a1-e85a-4288-b909-765dbdde25f8","added_by":"auto","created_at":"2025-11-13 07:45:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":55117,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram model predicting the risk of PE\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7574644/v1/bd8993a9b7932164ff4506d7.png"},{"id":95795793,"identity":"d1858edf-a90f-421b-aaba-3fa40763c7d6","added_by":"auto","created_at":"2025-11-13 07:45:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118615,"visible":true,"origin":"","legend":"\u003cp\u003eA: The ROC curve of the model B: The Calibration curve of the model.C:The decision curve analysis of the model D: The clinical imapct curve of the model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7574644/v1/4ce5f32518b2a1a69437cd65.png"},{"id":105223455,"identity":"53ecc517-2cd7-4154-9632-fdb97f295fe6","added_by":"auto","created_at":"2026-03-23 16:06:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1072669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7574644/v1/b97a2b64-1101-4335-98e7-f6a1e7adff3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The predictive value of early pregnancy markers for the risk of preeclampsia in women with twin pregnancies","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003ePreeclampsia (PE), a major pregnancy-specific disorder, poses significant threats to both maternal and fetal health. Globally, PE accounts for approximately 14% of maternal mortality and 15% of preterm births, resulting in an estimated 76,000 maternal and 500,000 fetal deaths annually[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, PE can lead to severe maternal and fetal complications, significantly impacting the long-term health of both mother and child[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEarly intervention, such as first-trimester low-dose aspirin prophylaxis, can effectively prevent PE onset and progression. This reduces the incidence of fetal growth restriction, placental abruption, and other adverse outcomes while prolonging gestation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, identifying the target population for intervention remains challenging since PE is typically diagnosed only after clinical symptoms manifest. Consequently, an effective predictive model for PE risk is essential to enable timely intervention.\u003c/p\u003e\u003cp\u003eFawaz Azizieh and colleagues suggested that inflammatory responses and placental abnormalities in PE lead to alterations in multiple serum biomarker levels, which may precede clinical symptoms onset[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Maternal serum biomarkers (e.g., alpha-fetoprotein (AFP), unconjugated estriol (uE3), inhibin A) and uterine artery pulsatility index (UtA-PI) show promising predictive value for PE in singleton pregnancies[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Crucially, twin pregnancies carry a two- to three-fold increased risk of PE with earlier onset and more severe consequences than singleton pregnancies[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite this elevated risk, few studies have focused on developing PE prediction models specifically for twin pregnancies.\u003c/p\u003e\u003cp\u003eIn singleton pregnancies, maternal serum AFP, uE3, and inhibin A levels, along with UtA-PI differ significantly between PE and normal pregnancies, demonstrating promising predictive value. However, the behavior and predictive utility of these biomarkers in twin pregnancies complicated by PE, which typically exhibits earlier onset and more severe consequences, remain unclear. Therefore, we conducted a prospective cohort study to evaluate the predictive value of maternal serum biomarkers (AFP, uE3, inhibin A) and UtA-PI for PE risk in twin pregnancies.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and data collection\u003c/h2\u003e\u003cp\u003eThis was a prospective cohort study. Twin pregnancies were recruited from women who received regular antenatal care in the Maternal and Child Healthcare Hospital of Changning District Shanghai China between January 2019 and March 2024. Women were excluded if they had a singleton pregnancy, chronic hypertension, prior history of preeclampsia, pregestational diabetes mellitus, gestationaldiabetes mellitus, thrombophilia or other maternal chronic diseases. If the fetuses were found any abnormality (structural abnormality or chromosomal), they were excluded either.\u003c/p\u003e\u003cp\u003eAll pregnant women were recruited between 11 and 13 weeks of gestation. General baseline information of the subjects was collected after enrollment. They then received UtA Doppler ultrasonography measurement and underwent the integrated test for fetal Downs syndrome in which the plasma levels of AFP, uE3, inhibin A were measured. The study was approved by the ethics committee of the hospital (CNFBLLKT-2023-004). Informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Serum biomarkers\u003c/h2\u003e\u003cp\u003eMaternal venous blood samples (3\u0026ndash;5 mL) were collected from fasting participants between 11 and 16 weeks of gestation. Samples were centrifuged at 4000 rpm (approximately 3000 \u0026times; g) for 10 minutes at 25\u0026deg;C and relative humidity\u0026thinsp;\u0026lt;\u0026thinsp;40% to isolate serum. Serum aliquots were stored at -80\u0026deg;C until analysis. Quantitative measurement of maternal serum biomarkers, including AFP, uE3, and inhibin A, was performed using the following automated chemiluminescent immunoassays on the Beckman Coulter Access platform: Access INHIBIN A, Access Unconjugated Estriol, and Access AFP (Beckman Coulter, Brea, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Uterine artery Doppler ultrasonography\u003c/h2\u003e\u003cp\u003eThe UtA Doppler transabdominal ultrasonography with color flow mapping was performed at 11\u0026thinsp;+\u0026thinsp;0 to 16\u0026thinsp;+\u0026thinsp;0 weeks of gestation. The bilateral UtA-PIs were recorded according to the Fetal Medicine Foundation guidelines[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 PE definition\u003c/h2\u003e\u003cp\u003ePreeclampsia was defined according to the Diagnosis and treatment of hypertension and pre-eclampsia in pregnancy: a clinical practice guideline in China (2020)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, PE included the new onset of both gestational hypertension and significant proteinuris after 20 weeks of gestation in a previously normotensive women. Gestational hypertension was defined as repeated systolic blood pressure measurements of \u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure measurements of \u0026ge;\u0026thinsp;90 mmHg which was firstly found after 20 weeks of gestation and recover to normal blood pressure after 12 weeks after child birth. Proteinuria was defined as \u0026ge;\u0026thinsp;300 mg proteinin a 24-hour urine collection, or repeated urinalysis showing\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;protein, or a urine protien/creatinine ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe participants were divided into groups based on the occurrence of PE. Categorical variables were presented as n (%), and the incidence of PE (95% confidence interval, 95%CI) was calculated across variable subgroups. Categorical variables were compared between PE and non-PE groups using chi-square test or Fisher\u0026rsquo;s exact tests, as appropriate. Continuous variables were presented as median (interquartile range, IQR), and differences between PE and non-PE groups were compared using the Mann-Whitney U test.\u003c/p\u003e\u003cp\u003ePrior to model fitting, the measurements of the five measured markers (AFP, left and right PI, uE3, and inhibin A) were standardized using data in the non-PE group to ensure comparability of effect sizes and improve numerical stability. Logistic regression models were performed to estimate the associations between various variables and the risk of PE, with odds ratios (ORs) and 95% CIs. In the multivariable model, maternal age (years), chorionicity (monochorionic or dichorionic), IVF (yes or no), parity (0 or \u0026ge;\u0026thinsp;1), pre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), and the five measured markers (AFP, left and right PI, uE3, and inhibin A) were included to estimate adjusted ORs (95% CIs).\u003c/p\u003e\u003cp\u003eVariables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in either univariate or multivariate analyses were considered potential predictors of PE risk and were visualized using a nomogram. The predictive performance of the nomogram model was assessed using receiver operating characteristic (ROC) curve analysis, while calibration was assessed using the Hosmer-Lemeshow test, with closer agreement between observed and predicted probabilities indicating better model fit. In addition, decision curve analysis and clinical impact curve analysis were performed to quantify the clinical utility of the model by estimating the net benefit and classification rates across all decision thresholds.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R software (version 4.3.1), with a two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Basic characteristics\u003c/h2\u003e\u003cp\u003eA total of 562 twin-pregnant women were recruited between January 2019 and March 2024 who received regular antenatal care in the Maternal and Child Healthcare Hospital of Changning District Shanghai China. According to the exclusion criteria, 223 were excluded and 339 were targeted finally in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Forty-three were diagnosed with PE, with an incidence rate of 12.68% (95% CI: 9.12%-16.25%). There was no statistically significant difference in the chorionicity, parity and maternal age between the PE and non-PE groups. The incidence of PE was higher in the IVF group than in the natural conception group (18.8% vs. 9.0%, P\u0026thinsp;=\u0026thinsp;0.009), and the pre-pregnancy BMI in the PE group was slightly lower than that in the non-PE group (20.57 vs. 21.26, P\u0026thinsp;=\u0026thinsp;0.049) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic characteristics of women with twin pregnancies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-PE(N\u0026thinsp;=\u0026thinsp;296)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePE (N\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;339)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChorionicity, n (incidence, 95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonochorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (85.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (14.4, 7.0-21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDichorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (88.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (12.0, 8.0-16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIVF, n (incidence, 95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192 (91.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (9.0, 5.1\u0026ndash;12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (81.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (18.8, 11.9\u0026ndash;25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParity, n (incidence, 95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250 (86.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (13.2, 9.3\u0026ndash;17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (90.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (9.8, 1.4\u0026ndash;18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI, kg/m\u003csup\u003e2\u003c/sup\u003e, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.26 (19.83\u0026ndash;23.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.57 (19.22\u0026ndash;22.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.11 (19.72\u0026ndash;23.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, years,median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (32\u0026ndash;37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (32\u0026ndash;38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (32\u0026ndash;37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAFP, ng/ml, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.69 (34.86\u0026ndash;58.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.15 (32.67\u0026ndash;53.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.55 (34.78\u0026ndash;58.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLeft PI, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32 (1.02\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39 (1.15\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32 (1.04\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRight PI, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34 (1.01\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 (1.22\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.37 (1.02\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003euE3, ng/ml, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15 (0.94\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.87\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13 (0.92\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInhibin A, pg/ml, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207.70 (162.35-279.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e217.60 (168.30-305.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e210.8 0 (162.70-283.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOf the five indicators, the right UtA-PI measured at 12 weeks' gestation in the PE group was significantly higher than that in the non-PE group (1.47 vs. 1.34, P\u0026thinsp;=\u0026thinsp;0.043). The P value for the difference in uE3 between the groups was 0.061, while the differences in the other three indicators, including AFP, inbihin A and left UtA-PI, were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Factors associated with PE\u003c/h2\u003e\u003cp\u003eLogistic regression analysis showed that potential factors associated with PE (e.g., P\u0026thinsp;\u0026lt;\u0026thinsp;0.10) included IVF, pre-pregnancy BMI, right UtA-PI and inbihin A. In the adjusted model, pregnant women with dichorionic twins had a lower risk of PE relative to that in women with monochorionic twins (P\u0026thinsp;=\u0026thinsp;0.055). In addition, uE3 was associated with PE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, the above six variables were screened as predictive variables in nomogram model. The regression coefficients for each variables in the final nomogram model were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between variables and risk of preeclampsia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChorionicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonochorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDichorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81 (0.40\u0026ndash;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 (0.18\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIVF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.33 (1.22\u0026ndash;4.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.87 (1.64\u0026ndash;9.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72 (0.27\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18 (0.38\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI, per kg/m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.80\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.78\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, per year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (0.94\u0026ndash;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99 (0.90\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLeft PI Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18 (0.86\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08 (0.72\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRight PI Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32 (0.97\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23 (0.83\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003euE3 Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14 (0.012\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07 (0.004\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAFP Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92 (0.64\u0026ndash;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02 (0.66\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInhibin A Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38 (1.09\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48 (1.13\u0026ndash;1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors included into the nomogram model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChorionicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonochorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDichorionic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 (0.19\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIVF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.67 (1.67\u0026ndash;8.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePre-pregnancy BMI, per kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.78-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRight PI Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27 (0.90\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003euE3 Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08 (0.006\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInhibin A Z score, per SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48 (1.13\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Predictive model for PE in twin pregnancies\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the nomogram model predicting the risk of PE by integrating six key clinical and biomarkers. The model highlighted that lower pre-pregnancy BMI, abnormal placental resistance (right PI Z-score), and dysregulated biomarkers (uE3 and Inhibin A Z-scores) are particularly strong risk contributors. The C-index was 0.74 (95% CI: 0.67\u0026ndash;0.81), indicating a moderate discriminative ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Calibration curves showed close agreement between predicted and actual probability, suggesting excellent model fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Decision curve analysis and clinical impact curve analysis confirmed clinical utility (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u0026amp; D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Major Findings\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWe developed a predictive nomogram for preeclampsia (PE) in twin pregnancies using six clinically accessible variables. Key findings identified IVF conception and monochorionic placentation as significant risk factors. Biomarker analysis revealed elevated maternal serum inhibin A and a trend towards lower uE3 in PE pregnancies, consistent with placental dysfunction mechanisms. Paradoxically, higher pre-pregnancy BMI appeared potentially protective. Elevated right UtA-PI was associated with PE, aligning with some prior studies, though not significant as an independent predictor. The integrated model demonstrated clinically useful discriminative performance.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Comparison with Previous Studies\u003c/h2\u003e\u003cp\u003ePreeclampsia is usually associated with placental dysfunction, reduction in placental volume, intrauterine growth restriction, abnormal uterine and umbilical artery Doppler ultrasonography findings, low birth weight, multi-organ dysfunction, perinatal death, and adverse maternal and neonatal outcomes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Twin pregnancy is an important risk factor for PE, therefore, exploring the risk factors and constructing a prediction model for PE in this population is critical for early detection and intervention of the disorder.\u003c/p\u003e\u003cp\u003eInhibin A is a key hormone in the hypothalamic-pituitary-gonadal axis. During pregnancy, inhibin A in maternal serum, amniotic fluid, and fetal circulatory is mainly synthesized by placental syncytiotrophoblasts, which plays a crucial role in reproductive endocrine regulation, endometrial decidualization, embryo implantation, and trophoblast proliferation and differentiation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this cohort, we observed that serum biomarkers (e.g., uE3 and inhibin A) measured in early pregnancy were associated with the risk of PE. In addition, these two biomarkers also make a large contribution to the prediction model. The finding aligns consistently with established literature. Park HJ et al. reported elevated serum inhibin A levels in singleton PE cases[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Svirsky et al. found that twin pregnancies with PE also exhibited significantly higher inhibin A levels in early and mid-pregnancy[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A large-scale study by the British Medical Council (BMC) supported that elevated inhibin A level in mid-trimester was significantly associated with placental dysfunction and increased PE risk[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDuring pregnancy, placenta-derived uE3 is not excreted through maternal kidneys, with up to 90% originating from fetal adrenal synthesis, making serum uE3 a reliable indicator of placental function. Normally, maternal serum uE3 level increases with gestational age, but in PE, placental dysfunction leads to decreased uE3 levels[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In PE the vascular endothelium impairment leads to placental ischemia and hypoxia. This triggers the trophoblast apoptosis, consequently resulting in decreased serum uE3 levels[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUterine artery hemodynamics revealed complex patterns. The direction of pulsatility index change observed in our study aligns with Rizzo et al.'s twin pregnancy report[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. But conflicts with Svirsky et al.'s findings of lower first-trimester PI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This heterogeneity contrasts with the consistent high-resistance pattern seen in singleton preeclampsia[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]and underscores the distinct hemodynamic profile of twin pregnancies.\u003c/p\u003e\u003cp\u003eAlpha-fetoprotein (AFP) is a normal serum protein during fetal development and a small amount of AFP transfer to the maternal circulation via the placenta[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Some researches demonstrate that in PE, the transfer of fetal AFP to the maternal is reduced due to systemic maternal vasospasm[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. But in this study, there is no difference in AFP level between PE and normal pregnancies. These may be due to that in twin pregnancy, the AFP produced in twins and the elevated total AFP level may weaken the impact of the placenta vasospasm in PE.\u003c/p\u003e\u003cp\u003eThe observed relationship between higher pre-pregnancy BMI and reduced preeclampsia risk contradicts most previous reports[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This discrepancy may reflect unique pathophysiological mechanisms in twin pregnancies or methodological limitations, such as some extreme high or low pre-pregnancy BMI that we omitted in our study because of the comparatively small amount of samples in this research.\u003c/p\u003e\u003cp\u003eIn our study, the result showed that twin pregnancy by IVF may have double even triple possibility to develop into PE. Different from natural pregnancy, maternity patients who conceived through IVF may experience adverse pregnancy outcomes such as hypertensive disorders in pregnancy due to biological, medical and sociological factors[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mukhopadhaya and Arulkumaran[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and Sibai[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] believe that the IVF technology increase the risk of adverse pregnancy outcomes such as maternal pre-eclampsia and gestational hypertension. While Sun et al[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] as well as Isaksson et al[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] offer an opposite opinion. The procedure of superovulation as part of IVF technology was not able to fully simulate the physiological state of hormone. It was reported that onset of pre-eclampsia may be related to the unusually high levels of HCG on the trigger day which activated the renin-angiotensin-aldosterone system[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. HCG-induced high hormone levels post the trigger day also played a role in occurrence of pre-eclampsia. Moreover, other complications such as multiple pregnancy, or the embryos obtained from donated eggs will also increase the risk of hypertensive disorders in pregnancy[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To our knowledge, most of previous studies showed no differences in PE occurrence in MCP and DCP[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While others observed the opposite results[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study found that DCP may be a protective factors for PE in twin pregnancy, however there were no statistically significant. Studies with larger samples are warranted to confirm the relativity between chorionicity and risk of PE.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Clinical Significance\u003c/h2\u003e\u003cp\u003eThe developed nomogram model provides a clinically implementable tool for preeclampsia risk stratification in twin pregnancies. Its practical value stems from utilizing routinely available parameters obtainable during standard prenatal care, requiring no specialized testing beyond current practice.\u003c/p\u003e\u003cp\u003eThis model enables early identification of high-risk twin pregnancies, facilitating targeted surveillance protocols and timely intervention strategies (e.g., low-dose aspirin). The established clinical utility through decision curve analysis supports its potential to improve perinatal outcomes by optimizing resource allocation and preventive management for this vulnerable population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Study Strengths and Limitations\u003c/h2\u003e\u003cp\u003eThis investigation offers several strengths, including its dedicated focus on the high-risk twin pregnancy population where validated prediction tools are notably lacking. The model integrates multidimensional parameters routinely available in clinical practice, enhancing its translational potential.\u003c/p\u003e\u003cp\u003eMethodological limitations warrant consideration. The single-center recruitment strategy introduces potential selection bias and may restrict generalizability. The cohort size constrains subgroup analyses and may have limited power to detect modest effect sizes. External validation across diverse healthcare settings remains essential before widespread implementation. Future multicenter studies should further explore the unexpected BMI relationship and chorionicity.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study developed and validated a nomogram prediction model for preeclampsia in twin pregnancies, incorporating basic characteristics, serum biomarkers in early pregnancy, and doppler indexes. The findings provide evidence for better understanding of PE in twin pregnancies and a clinically applicable tool for early identification of PE. Further studies on the model\u0026rsquo;s clinical applicability in larger cohorts are warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eAll the authors declare that there are no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThe study was funded by the Scientific Research Project of Changning District Science and Technology Commission (CNKW2022Y35).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChao Wang and Meng Yu wrote the main manuscript text, and Jiangnan Wu performed the statistical analyses and prepared tables and figures. Chao Wang and Ting Peng conceived of the study. Tingyu Hu participated in the study design. Ting Peng and Meng Yu jointly supervised this work . All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eIn this manuscript, full datasets are not publicly available for protecting the confidentiality of all the participants. However, the data that support the findings of this study are available on request from the corresponding author. Inquiries should be communicated with the corresponding author, and we will consider all sufficiently specified and reasonable requests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHenderson, J.T., et al., \u003cem\u003ePreeclampsia Screening: Evidence Report and Systematic Review for the US Preventive Services Task Force\u003c/em\u003e. Jama, 2017. 317(16): p. 1668\u0026ndash;1683.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller, E.C., et al., \u003cem\u003eRisk Factors for Pregnancy-Associated Stroke in Women With Preeclampsia\u003c/em\u003e. Stroke, 2017. 48(7): p. 1752\u0026ndash;1759.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonroe, J.C., K.M. Naugle, and K.E. Naugle, \u003cem\u003eEffect of Acute Bouts of Volume-Matched High-Intensity Resistance Training Protocols on Blood Glucose Levels\u003c/em\u003e. J Strength Cond Res, 2020. 34(2): p. 445\u0026ndash;450.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrivedi, N.A., \u003cem\u003eA meta-analysis of low-dose aspirin for prevention of preeclampsia\u003c/em\u003e. J Postgrad Med, 2011. 57(2): p. 91\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzizieh, F., R. Raghupathy, and M. Makhseed, \u003cem\u003eMaternal cytokine production patterns in women with pre-eclampsia\u003c/em\u003e. Am J Reprod Immunol, 2005. 54(1): p. 30\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasci, J., et al., \u003cem\u003eAlpha-fetoprotein (AFP) is reduced at 36 weeks' gestation in pregnancies destined to deliver small for gestational age infants\u003c/em\u003e. Eur J Obstet Gynecol Reprod Biol, 2025. 308: p. 266\u0026ndash;268.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, W. and H. Liang, \u003cem\u003eThe role of serum markers PAPP-A β-hCG, AFP, and uE3 in predicting the risk of preeclampsia in early, middle, and late pregnancy\u003c/em\u003e. Technol Health Care, 2023. 31(3): p. 1027\u0026ndash;1037.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, T., et al., \u003cem\u003eModified multiple marker aneuploidy screening as a primary screening test for preeclampsia\u003c/em\u003e. BMC Pregnancy Childbirth, 2022. 22(1): p. 190.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvirsky, R., et al., \u003cem\u003eFirst trimester markers of preeclampsia in twins: maternal mean arterial pressure and uterine artery Doppler pulsatility index\u003c/em\u003e. Prenat Diagn, 2014. 34(10): p. 956\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, C.K., et al., \u003cem\u003eAn integrated model for the prediction of preeclampsia using maternal factors and uterine artery Doppler velocimetry in unselected low-risk women\u003c/em\u003e. Am J Obstet Gynecol, 2005. 193(2): p. 429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cem\u003e[Diagnosis and treatment of hypertension and pre-eclampsia in pregnancy: a clinical practice guideline in China(2020)].\u003c/em\u003e Zhonghua Fu Chan Ke Za Zhi, 2020. 55(4): p. 227\u0026ndash;238.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObed, S. and A. Patience, \u003cem\u003eBirth weight and ponderal index in pre-eclampsia: a comparative study\u003c/em\u003e. Ghana Med J, 2006. 40(1): p. 8\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWelt, C.K., et al., \u003cem\u003eSerum inhibin B in polycystic ovary syndrome: regulation by insulin and luteinizing hormone\u003c/em\u003e. J Clin Endocrinol Metab, 2002. 87(12): p. 5559\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark, H.J., et al., \u003cem\u003eScreening models using multiple markers for early detection of late-onset preeclampsia in low-risk pregnancy\u003c/em\u003e. BMC Pregnancy Childbirth, 2014. 14: p. 35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvirsky, R., et al., \u003cem\u003eFirst and second trimester maternal serum inhibin A levels in twins with pre-eclampsia\u003c/em\u003e. Prenat Diagn, 2016. 36(11): p. 1071\u0026ndash;1074.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingnoi, W., et al., \u003cem\u003eA cohort study of the association between maternal serum Inhibin-A and adverse pregnancy outcomes: a population-based study\u003c/em\u003e. BMC Pregnancy Childbirth, 2019. 19(1): p. 124.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYaron, Y., et al., \u003cem\u003eSecond-trimester maternal serum marker screening: maternal serum alpha-fetoprotein, beta-human chorionic gonadotropin, estriol, and their various combinations as predictors of pregnancy outcome\u003c/em\u003e. Am J Obstet Gynecol, 1999. 181(4): p. 968\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRizzo, G., et al., \u003cem\u003eUterine artery Doppler evaluation in twin pregnancies at 11\u0026thinsp;+\u0026thinsp;0 to 13\u0026thinsp;+\u0026thinsp;6 weeks of gestation\u003c/em\u003e. Ultrasound Obstet Gynecol, 2014. 44(5): p. 557\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuhig, K., B. Vandermolen, and A. Shennan, \u003cem\u003eRecent advances in the diagnosis and management of pre-eclampsia\u003c/em\u003e. F1000Res, 2018. 7: p. 242.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright, D., et al., \u003cem\u003eA competing risks model in early screening for preeclampsia\u003c/em\u003e. Fetal Diagn Ther, 2012. 32(3): p. 171\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, L., et al., \u003cem\u003eRelationship between prepregnancy BMI and gestational weight gain(GWG) with preeclampsia: a study based on restricted cubic spline\u003c/em\u003e. BMC Pregnancy Childbirth, 2025. 25(1): p. 360.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarber, E., et al., \u003cem\u003ePregnancy and placental outcomes according to maternal BMI in women with preeclampsia: a retrospective cohort study\u003c/em\u003e. Arch Gynecol Obstet, 2024. 309(6): p. 2521\u0026ndash;2528.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong, F., et al., \u003cem\u003eCorrelation of hypertensive disorders in pregnancy with procedures of in vitro fertilization and pregnancy outcomes\u003c/em\u003e. Exp Ther Med, 2017. 14(6): p. 5405\u0026ndash;5410.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePiri, M., et al., \u003cem\u003eEffects of nano-berberine and berberine loaded on green synthesized selenium nanoparticles on cryopreservation and in vitro fertilization of goat sperm\u003c/em\u003e. Sci Rep, 2024. 14(1): p. 24171.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSibai, B.M., \u003cem\u003eSubfertility/infertility and assisted reproductive conception are independent risk factors for pre-eclampsia\u003c/em\u003e. Bjog, 2015. 122(7): p. 923.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun, L.M., et al., \u003cem\u003eAssisted reproductive technology and placenta-mediated adverse pregnancy outcomes\u003c/em\u003e. Obstet Gynecol, 2009. 114(4): p. 818\u0026ndash;824.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsaksson, R., M. Gissler, and A. Tiitinen, \u003cem\u003eObstetric outcome among women with unexplained infertility after IVF: a matched case-control study\u003c/em\u003e. Hum Reprod, 2002. 17(7): p. 1755\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMikat, B., et al., \u003cem\u003eβhCG and PAPP-A in first trimester: predictive factors for preeclampsia?\u003c/em\u003e Hypertens Pregnancy, 2012. 31(2): p. 261\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePecks, U., N. Maass, and J. Neulen, \u003cem\u003eOocyte donation: a risk factor for pregnancy-induced hypertension: a meta-analysis and case series\u003c/em\u003e. Dtsch Arztebl Int, 2011. 108(3): p. 23\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarter, E.B., et al., \u003cem\u003eThe impact of chorionicity on maternal pregnancy outcomes\u003c/em\u003e. Am J Obstet Gynecol, 2015. 213(3): p. 390.e1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeduc, L., L. Takser, and D. Rinfret, \u003cem\u003ePersistance of adverse obstetric and neonatal outcomes in monochorionic twins after exclusion of disorders unique to monochorionic placentation\u003c/em\u003e. Am J Obstet Gynecol, 2005. 193(5): p. 1670\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampbell, D.M. and A. Templeton, \u003cem\u003eMaternal complications of twin pregnancy\u003c/em\u003e. Int J Gynaecol Obstet, 2004. 84(1): p. 71\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSparks, T.N., et al., \u003cem\u003eDoes risk of preeclampsia differ by twin chorionicity?\u003c/em\u003e J Matern Fetal Neonatal Med, 2013. 26(13): p. 1273\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarno, L., et al., \u003cem\u003eRisk of preeclampsia: comparison between dichorionic and monochorionic twin pregnancies\u003c/em\u003e. J Matern Fetal Neonatal Med, 2014. 27(10): p. 1080\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Preeclampsia, Twin-pregnancy, Inbihin A, uE3, UtA-PI, Nomogram model","lastPublishedDoi":"10.21203/rs.3.rs-7574644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7574644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePredicting pre-eclampsia (PE) risk in twin pregnancies enables timely intervention and disease management. We developed a predictive model integrating baseline characteristics, uterine artery pulsatility index (UtA-PI), and serum biomarkers (uE3, AFP, inhibin A) to identify high-risk patients for early intervention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e This prospective cohort study enrolled 339 twin pregnancies receiving antenatal care at a tertiary hospital (January 2019\u0026ndash;March 2024). Serum AFP, uE3, inhibin-A levels, and bilateral UtA-PI were measured at 11\u0026ndash;16 weeks\u0026rsquo; gestation. A nomogram prediction model for PE risk was constructed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePE was diagnosed in 43 of 339 women (incidence: 12.68%; 95% CI: 9.12\u0026ndash;16.25%). Six variables met inclusion criteria (P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in univariate/multivariate analyses): IVF conception, pre-pregnancy BMI, right UtA-PI, serum inhibin A, serum uE3, and chorionicity. The model identified lower pre-pregnancy BMI, elevated right UtA-PI, and dysregulated serum biomarkers (uE3, inhibin A) as key predictors of PE. The nomogram demonstrated moderate discriminative ability (C-index: 0.74; 95% CI: 0.67\u0026ndash;0.81). Calibration curves indicated excellent agreement between predicted and observed risk, while decision and clinical impact curves confirmed clinical utility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIntegrating IVF status, chorionicity, early-pregnancy serum biomarkers (inhibin A, uE3), and right UtA-PI effectively predicts PE risk in twin pregnancies.\u003c/p\u003e","manuscriptTitle":"The predictive value of early pregnancy markers for the risk of preeclampsia in women with twin pregnancies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:45:18","doi":"10.21203/rs.3.rs-7574644/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-20T07:20:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-22T19:19:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T05:04:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T04:34:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T07:15:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175233014844575431744188307729455470822","date":"2025-11-13T04:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43200634878063580230142260304660970311","date":"2025-11-08T02:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226712292615418291058079606825188967540","date":"2025-11-02T19:42:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10495119940084365822033418738368789863","date":"2025-10-31T16:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T10:19:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T14:55:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T14:10:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-09T13:52:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56dc389c-dc0a-417b-b1dc-fabb3ec27552","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:03:34+00:00","versionOfRecord":{"articleIdentity":"rs-7574644","link":"https://doi.org/10.1186/s40001-026-04242-x","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-03-20 15:59:47","publishedOnDateReadable":"March 20th, 2026"},"versionCreatedAt":"2025-11-13 07:45:18","video":"","vorDoi":"10.1186/s40001-026-04242-x","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04242-x","workflowStages":[]},"version":"v1","identity":"rs-7574644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7574644","identity":"rs-7574644","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}