Placental growth factor as a predictive marker of preeclampsia in twin pregnancy

Placental growth factor as a predictive marker of preeclampsia in twin pregnancy | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Placental growth factor as a predictive marker of preeclampsia in twin pregnancy Guijie Qi, Ling Yao, Zhiming Liu, Wanru Guo, Heng Liu, Jinghua Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3029973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Placental growth factor (PLGF) has been reported to predict the absence of preeclampsia (PE) in singleton pregnancies. Thus, this study aims to evaluate the predictive value of the PLGF in twin pregnancies. Methods: Twin pregnancy with clinically suspected PE (24 weeks 0 days to 36 weeks 6 days of gestation) was enrolled in this study. The threshold of PLGF was determined on the basis of a receiver-operating characteristic curve to predict PE. Results: A cutoff value of 215 pg/mL for PLGF indicates a good predictive performance for PE. An area under the curve of 0.863 with 86% sensitivity and 80% specificity was also obtained. Conclusion: Effective screening for PE can be provided by the PLGF assay in twin pregnancies with clinically suspected PE. preeclampsia twin pregnancy placental growth factor Figures Figure 1 Figure 2 Figure 3 Background Preeclampsia (PE), complicating 2–8% of all pregnancies, remains a major cause of maternal morbidity and mortality worldwide [ 1 – 3 ]. Recent publications have confirmed that the level of placental growth factor (PLGF) is related to PE, and a PLGF cutoff value of 100 pg/mL can predict the short-term absence of PE and preterm birth in singleton pregnancies [ 4 – 9 ]. Twin pregnancies have a 2–3 times higher risk for PE than singleton pregnancies, whereas only little research has studied the predictive value of the PLGF in twin pregnancies [ 10 – 11 ]. Thus, timely diagnosing or excluding PE is of great importance in twin pregnancies. In this study, we aimed to evaluate whether PLGF is also applicable to twin pregnancies in predicting the absence of PE and to explore the optimal cutoff value of PLGF in predicting the absence of PE. Methods Study design and participants A single-center, prospective study was conducted in the Hospital of Maternal and Health of Tangshan to evaluate the use of the PLGF for the prediction of the absence of PE in twin pregnancies. Women aged 18 years and older (24 weeks 0 days to 36 weeks 6 days of gestation at the first visit) who presented with suspected PE, with a live twin pregnancy, were invited to participate. Suspected PE was defined if one or more of the following signs or symptoms are met: headache with visual disturbances, epigastric or right upper-quadrant pain, dipstick proteinuria, new-onset or worsening of existing hypertension, a headache with visual disturbances, abnormal maternal blood tests that were suggestive of disease (such as hepatic or thrombocytopenia or renal dysfunction), and fetal growth restriction. This study was approved by the ethics committee of the Hospital of Maternal and Health of Tangshan, and all parturients signed informed consent. Definitions and outcome measures The outcome was the diagnosis of PE, as defined by the international Society for the Study of Hypertension in Pregnancy 2018 recommendations [ 12 ]. Materials For the measurement of PLGF, samples were inverted at least five times after blood drawing and left to clot at room temperature for 30 min before centrifugation. Samples were collected and stored at 2°C–8℃ until analysis.Plasma was analyzed for PLGF in batch using the Triage PLGF Test at a clinical laboratory, and assays were performed using an automated biochemistry analyzer (iRaTe 300; Aucheer, China). All samples were detected within 24 h. Statistical analysis Chi-square test, T-test, and ANOVA were used for data analysis using SPSS 19.0. All statistical tests were bilateral, and the difference was considered statistically significant when P < 0.05. Results Demographics and characteristics Between July 2020 and February 2022, a total of 298 twin gestations were enrolled, of which 141 were suspected with PE and eligible for analysis. Baseline characteristics and laboratory findings based on the clinical diagnosis at time of study entry are shown in Table 1 . A total of 36 developed PEs are observed, and the incidence of PE is 25.53%. Table 1 Baseline characteristics of the study participants No preeclampsia (n = 105) Preeclampsia (n = 36) P -value P -value* Age 31.28 ± 4.28 31.22 ± 4.31 0.948 0.758 Gestational age at delivery 36.34 ± 1.42 35.41 ± 1.74 0.002 0.005 Gestational age at screening 30.89 ± 4.69 31.16 ± 4.21 0.759 0.959 Weight(kg) 74.73 ± 12.20 82.59 ± 12.88 0.002 0.002 BMI 28.36 ± 4.25 31.68 ± 4.65 < 0.001 < 0.001 Systolic blood pressure (mmHg) 122.50 ± 12.83 132.11 ± 13.35 < 0.001 < 0.001 Diastolic blood pressure (mmHg) 76.59 ± 10.36 83.00 ± 13.33 0.004 0.005 MAP 91.89 ± 10.38 99.37 ± 12.14 < 0.001 < 0.001 PLGF(pg/mL) 483.13 ± 509.22 197.94 ± 186.65 0.001 < 0.001 PLGF MOM 1.26 ± 1.23 0.54 ± 0.55 < 0.001 =3 32 (30.48%) 8 (22.22%) Parity 0.195 - 0 71 (67.62%) 31 (86.11%) 1 25 (23.81%) 4 (11.11%) >=2 9 (8.57%) 1 (2.78%) Conception 0.511 - spontaneous 62 (59.05%) 19 (52.78%) assisted 43 (40.95%) 17 (47.22%) Diabetes mellitus 0.63 - no 90 (85.71%) 32 (88.89%) yes 15 (14.29%) 4 (11.11%) Thyroid abnormalities 0.295 - no 89 (84.76%) 33 (91.67%) yes 16 (15.24%) 3 (8.33%) Anemia 0.783 - no 95 (90.48%) 32 (88.89%) yes 10 (9.52%) 4 (11.11%) Scarred uterus 0.251 - no 81 (77.14%) 31 (86.11%) yes 24 (22.86%) 5 (13.89%) Previous adverse pregnancy outcomes 0.262 - no 97 (92.38%) 31 (86.11%) yes 8 (7.62%) 5 (13.89%) Epatitis 0.099 0.16 no 104 (99.05%) 34 (94.44%) yes 1 (0.95%) 2 (5.56%) Results are presented as mean ± SD. P < 0.05 is considered significant PLGF in twin pregnancies developed PE versus undeveloped PE In twin pregnancies without PE, the PLGF level and PLGF multiple of median (MoM) were significantly higher than that in twin pregnancies with PE (Figs. 1 .a and b). The results showed that after 24 weeks of gestation, PLGF showed well separation between the two groups, indicating that PLGF could be used to predict the presence of PE in twin pregnant women suspected of PE after 24 weeks of gestation. PLGF cutoff of 215 pg/mL in twin pregnancies The optimal cutoff value of PLGF for PE was further determined by analyzing the ROC curve of PE (Fig. 2 ). The optimal cutoff was 215 pg/mL for PLGF or 0.564 for PLGF MoM. No significant difference in the effect of PLGF concentration (AUC, 0.863; sensitivity, 86.1%; specificity, 80%; accuracy, 81.6%) and PLGF MOM (AUC, 0.865; sensitivity, 83.3%; specificity, 81.0%; accuracy, 81.6%) was observed in the prediction of PE (Table 2 ). Table 2 The performance of PlGF and PlGF MoM in predicting preeclampsia Method Risk Cutoff AUC Specificity Sensitivity Accuracy PlGF(pg/mL) 215.0 0.863 0.800 0.861 0.816 PlGF MOM 0.564 0.865 0.810 0.833 0.816 AUC Area under receiver operating curve We also compared the prediction performance for PE using a cutoff value of 215 pg/mL using PLGF percentiles at different gestational intervals. As shown in Table 3 , the cutoff of the 15th percentile had a good predictive performance (66.7% sensitivity, 90.5% specificity, 84.4% accuracy) but not superior to the performance of the cutoff value of 215 pg/mL. Table 3 The performance of PlGF and PlGF percentile at different gestational intervals in predicting preeclampsia Method Risk Cutoff Specificity Sensitivity Accuracy PlGF(pg/mL) 215.0 0.800 0.861 0.816 PlGF percentile 5th 0.971 0.306 0.801 10th 0.933 0.444 0.809 15th 0.905 0.667 0.844 20th 0.866 0.694 0.823 25th 0.838 0.722 0.809 MAP slightly increase the predictive performance for PE Given that blood pressure was measured by all participants, and the mean arterial pressure (MAP) was considered as a critical marker for predicting PE at the first trimester [ 13 ], whether adding MAP would improve the performance remained unclear. The result showed that MAP slightly increase the predictive performance for PE by PLGF (AUC 0.891 vs. AUC 0.863, Fig. 3 ). The optimal calculation model of PLGF plus MAP was presented as follows: YPLGF + MAP = exp(− 7.91318 + 0.07924*MAP − 0.00789*PLGF) Discussion The present study established a cutoff point of 215 pg/mL of PLGF, which was evaluated using the Aucheer PLGF immunoassay, as a useful predictor of clinical signs of the disease and short-term absence of PE in women with twin pregnancies. The AUC of PLGF is 0.863, and the prediction accuracy is 81.6%, with 86% sensitivity and 80% specificity. MAP can slightly increase the prediction performance, and the AUC of PLGF plus MAP is 0.891. PE is a complex syndrome, which is associated with high risks of pregnancy-associated morbidity and mortality, and the management of this complex syndrome must be improved. Previous studies have shown that proteinuria and high blood pressure have a poor predictive value for PE and its associated adverse outcomes [ 14 ]. Antiangiogenic and angiogenic factors have been implicated in the pathophysiology of PE [ 6 ]. In general, the PLGF has shown good diagnostic performance compared with other biomarkers [ 15 ]. Duhig et al. found that PLGF alone predicted delivery within 14 days among women with confirmed PE in singleton pregnancies [ 8 ]. In the present study, we also found that the predictive performance of low PLGF shows well predictive performance for the absence of PE in twin pregnancies. Our study had several limitations. First, an observational study was conducted, and randomized trials were necessary to determine if the cutoff, when used in clinical practice as opposed to the current standard of care, could reduce hospitalizations and costs, with similar or better outcomes in regard to fetal and maternal adverse outcomes. In addition, the data were validated using Aucheer immunoassays, and the optimal cutoff point for the PLGF may differ when other assays are used. The present study suggests that a PLGF cutoff value of 215 pg/mL could be used to predict the presence or absence of PE in women with twin pregnancies who are clinically suspected of having PE. Conclusions Our data showed that effective screening for PE can be provided by the PLGF assay in twin pregnancies with clinically suspected PE. A cutoff point of 215 pg/mL for PLGF, assessed using Aucheer PLGF immunoassays, is identified as a useful predictor for predicting PE. Abbreviations PLGF Placental growth factor PE preeclampsia ISSHP international Society for the Study of Hypertension in Pregnancy MoM Multiple Of Median AUC Area under receiver operating curve MAP mean arterial pressure. Declarations Acknowledgements We thank to doctors in the obstetrical clinic of obstetrical clinic doctor of Maternal and Child Health hospital for its linguistic assistance during the preparation of this manuscript. Authors’ contributions Qi GJ and Zhang JH contributed to the study design, initial drafting, and data extraction and revised the manuscript.Guo WR and Liu H provided important intellectual input.Yao,L and Liu ZM critically revised the manuscript.All authors approved the final version of the manuscript for publication. Funding The study was funded by a grant from the Medical Association Commission of Hebei Province (No. 20231749) which allowed all steps of the study to be conducted. These steps were study design, data gathering, analysis and interpretation of data, and the writing of the manuscript. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Informed written consent was obtained from all participants under the protocols approved by the Ethical Committee of The Hospital of Maternal and Health of Tangshan, China.(No. 2022-011-01).All methods were carried out in accordance with relevant guidelines and regulations. Consent for Publication. Not applicable. Competing interests The authors declare that they have no competing interests. References Ananth CV, Keyes KM, Wapner RJ. Pre-eclampsia rates in the United States, 1980–2010: age-period-cohort analysis. BMJ. 2013;347:f6564. Duley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol. 2009;33:130. Magee LA, Brown MA, Hall DR, Gupte S, Hennessy A, Karumanchi SA, Kenny LC, McCarthy F, Myers J, Poon LC, Rana S, Saito S, Staff AC, Tsigas E, von Dadelszen P. The 2021 International Society for the Study of Hypertension in Pregnancyclassification, diagnosis & management recommendations for international practice.Pregnancy Hypertens 2022;27: 148. Wiles K, Chappell LC, Lightstone L, Bramham K. Updates in Diagnosis and Management of Preeclampsia in Women with CKD. Clin J Am Soc Nephrol. 2020;15(9):1371–80. Torry DS, Wang HS, Wang TH, Caudle MR, Torry RJ. Preeclampsia is associated with reduced serum levels of placenta growth factor. Am J Obstet Gynecol. 1998;179(6 Pt 1):1539–44. Levine RJ, Maynard SE, Qian C, Lim KH, England LJ, Schisterman EF, Thadhani R, Sachs BP, Epstein FH, Sibai BM, Sukhatme VP, Karumanchi SA. Circulating angiogenic factors and the risk of preeclampsia. N Engl J Med. 2004;350(7):672–83. Chappell LC, Duckworth S, Seed PT, Griffin M, Myers J, Mackillop L, Simpson N, Waugh J, Anumba D, Kenny LC, Redman CWG, Shennan AH. Diagnostic Accuracy of Placental Growth Factor in Women With Suspected Preeclampsia: A Prospective Multicenter Study. Circulation. 2013;128(19):2121–31. Duhig KE, Myers J, Seed PT, Sparkers J, Lowe J, Hunter RM, Shennan AH, Chappell LC, et al. Placental growth factor testing to assess women with suspectedpre-eclampsia: a multicentre, pragmatic, stepped-wedge cluster-randomised controlled trial. Lancet. 2019;393(10183):1807–18. Duhig KE, Webster LM, Sharp A, Gill C, Seed PT, Shennan AH, Myers JE, Chappell LC. Diagnostic accuracy of repeat placental growth factor measurements in women with suspected preeclampsia: A case series study. Acta Obstet Gynecol Scand. 2020;99(8):994–1002. R Kaaja. Predictors and risk factors of pre-eclampsia. Minerva Ginecol. 2008;60(5):421–9. Laine K, Murzakanova G, Sole KB, Pay AD, Heradstveit S, Raisanen S. Prevalence and Risk of Pre-Eclampsia and Gestational Hypertension in Twin Pregnancies: A Population-Based Register Study. BMJ Open. 2019;9:e029908. Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, Hall DR, Warren CE, Adoyi G, Ishaku S. International Society for the Study of Hypertension in Pregnancy (ISSHP).The hypertensive disorders of pregnancy: ISSHP classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens. 2018;13:291–310. Tan MY, Syngelaki A, Poon LC, Rolnik DL, O'Gorman N, Delgado JL, Akolekar R, Konstantinidou L, Tsavdaridou M, Galeva S, Ajdacka U, Molina FS, Persico N, Jani JC, Plasencia W, Greco E, Papaioannou G, Wright A, Wright D, Nicolaides KH. Screening for pre-eclampsia by maternal factors and biomarkers at 11–13 weeks’ gestation. Ultrasound Obstet Gynecol. 2018;52(2):186–95. O'Gorman N, Wright D, Poon LC, Rolnik DL, Syngelaki A, de Alvarado M, Carbone IF, Dutemeyer V, Fiolna M, Frick A, Karagiotis N, Mastrodima S, de Paco Matallana C, Papaioannou G, Pazos A, Plasencia W, Nicolaides KH. Multicenter screening for preeclampsia by maternal factors and biomarkers at 11–13 weeks’gestation: comparison to NICE guidelines and ACOG recommendations. Ultrasound Obstet Gynecol. 2017;49(6):756–60. Zhong Y, Zhu F, Ding Y. Serum screening in first trimester to predict pre-eclampsia, small for gestational age and preterm delivery:Systematic review and meta-analysis. BMC Pregnancy Childbirth. 2015;15:191. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3029973","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":212540486,"identity":"2be54976-2b3c-4f74-868a-290aba552589","order_by":0,"name":"Guijie Qi","email":"","orcid":"","institution":"Department of Genetics, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guijie","middleName":"","lastName":"Qi","suffix":""},{"id":212540487,"identity":"65d8f506-f6ba-4dc7-9327-6f1193b9f297","order_by":1,"name":"Ling Yao","email":"","orcid":"","institution":"Department of Genetics, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Yao","suffix":""},{"id":212540488,"identity":"4b1f50e5-125d-4225-8964-9a884c4d482c","order_by":2,"name":"Zhiming Liu","email":"","orcid":"","institution":"Department of Genetics, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Liu","suffix":""},{"id":212540489,"identity":"12898a2e-f59a-417b-956d-d8642d261e87","order_by":3,"name":"Wanru Guo","email":"","orcid":"","institution":"Department of Genetics, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanru","middleName":"","lastName":"Guo","suffix":""},{"id":212540490,"identity":"9146e901-0c37-443f-9e66-494495519e92","order_by":4,"name":"Heng Liu","email":"","orcid":"","institution":"Department of Genetics, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Liu","suffix":""},{"id":212540491,"identity":"f472a1bb-1832-480d-86f7-2720b3975bc0","order_by":5,"name":"Jinghua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACxmYGBmYQwwBEJFTY8PDzN5Ci5cOZNBnJGQcI2wTXwjiz5bCNQUMCAeXtvIdfF7bZ2Zuznz38mrfhPI8BwwHGDx9z8DmML816Zlsys2VPXpo1747bPObMDcySM7fh08JjZszbxsxmcCAHyDhzm8ey4QAbMy9hLfU8BuffgBjneAwOJBDUYvyYt+2whMGNHOOHM9sOEKXFjJnn3HEDgxtvzICBnMwjOeNgM16/GPafMf7MU1Ztb3A+x/hDQoWdPT9/88EPH/FpaWBgk4CyYQzGBtzqgUAeGDUfoGw4YxSMglEwCkYBCgAA6ZVQMDleqfkAAAAASUVORK5CYII=","orcid":"","institution":"Department of Clinical Laboratory, The Hospital of Maternal and Health of Tangshan","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jinghua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-06-06 14:44:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3029973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3029973/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39259446,"identity":"93e7032c-f2ab-4d9e-9ba3-6ae6a2c351c4","added_by":"auto","created_at":"2023-06-28 20:23:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlGF level and PlGF MoM in women with preeclampsia and without preeclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) shows the PLGF level and (b) shows the PLGF MoM. The bottom edge and the top edge of eachbox represents the minimum value and the 90% percentile, respectively. The band within the box represents the median value.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3029973/v1/2a1a04fef5f45845bb13a934.png"},{"id":39259448,"identity":"7ccae21b-7310-4031-ac46-1315d878d194","added_by":"auto","created_at":"2023-06-28 20:23:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of PlGF level and PlGF MoM in predicting preeclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive performance of a cutoff point of 215pg/mL or 0.564 of PLGF MoM for predicting preeclampsia. Area under receiver operating curve (AUC)denotes area under the curve.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3029973/v1/c19332464855952eefbc1442.png"},{"id":39260092,"identity":"81c6e63e-7ef5-44ad-a3e0-70a8dbcf6afa","added_by":"auto","created_at":"2023-06-28 20:31:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive performance of PlGF and PLGF plus MAP in predicting preeclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGreen line indicate the performance of PlGF plus MAP(AUC:0.891), which ia slightly better than the performance of PlGF only ( red line)(AUC:0.863). MAP only performed worse in predicting preeclampsia(AUC:0.735).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3029973/v1/aa4f4dde8405f10d1b76686e.png"},{"id":47047992,"identity":"14d3f27c-a4f0-43e2-9b69-874d867c2b8f","added_by":"auto","created_at":"2023-11-25 01:37:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":539160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3029973/v1/301e0513-cf65-4da1-a326-6ffa7efcb094.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Placental growth factor as a predictive marker of preeclampsia in twin pregnancy","fulltext":[{"header":"Background","content":"\u003cp\u003ePreeclampsia (PE), complicating 2\u0026ndash;8% of all pregnancies, remains a major cause of maternal morbidity and mortality worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent publications have confirmed that the level of placental growth factor (PLGF) is related to PE, and a PLGF cutoff value of 100 pg/mL can predict the short-term absence of PE and preterm birth in singleton pregnancies [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Twin pregnancies have a 2\u0026ndash;3 times higher risk for PE than singleton pregnancies, whereas only little research has studied the predictive value of the PLGF in twin pregnancies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, timely diagnosing or excluding PE is of great importance in twin pregnancies.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to evaluate whether PLGF is also applicable to twin pregnancies in predicting the absence of PE and to explore the optimal cutoff value of PLGF in predicting the absence of PE.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eA single-center, prospective study was conducted in the Hospital of Maternal and Health of Tangshan to evaluate the use of the PLGF for the prediction of the absence of PE in twin pregnancies. Women aged 18 years and older (24 weeks 0 days to 36 weeks 6 days of gestation at the first visit) who presented with suspected PE, with a live twin pregnancy, were invited to participate. Suspected PE was defined if one or more of the following signs or symptoms are met: headache with visual disturbances, epigastric or right upper-quadrant pain, dipstick proteinuria, new-onset or worsening of existing hypertension, a headache with visual disturbances, abnormal maternal blood tests that were suggestive of disease (such as hepatic or thrombocytopenia or renal dysfunction), and fetal growth restriction. This study was approved by the ethics committee of the Hospital of Maternal and Health of Tangshan, and all parturients signed informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions and outcome measures\u003c/h2\u003e \u003cp\u003eThe outcome was the diagnosis of PE, as defined by the international Society for the Study of Hypertension in Pregnancy 2018 recommendations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003eFor the measurement of PLGF, samples were inverted at least five times after blood drawing and left to clot at room temperature for 30 min before centrifugation. Samples were collected and stored at 2\u0026deg;C\u0026ndash;8℃ until analysis.Plasma was analyzed for PLGF in batch using the Triage PLGF Test at a clinical laboratory, and assays were performed using an automated biochemistry analyzer (iRaTe 300; Aucheer, China). All samples were detected within 24 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eChi-square test, T-test, and ANOVA were used for data analysis using SPSS 19.0. All statistical tests were bilateral, and the difference was considered statistically significant when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographics and characteristics\u003c/h2\u003e \u003cp\u003eBetween July 2020 and February 2022, a total of 298 twin gestations were enrolled, of which 141 were suspected with PE and eligible for analysis. Baseline characteristics and laboratory findings based on the clinical diagnosis at time of study entry are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 36 developed PEs are observed, and the incidence of PE is 25.53%.\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\u003eBaseline characteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo preeclampsia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.73\u0026thinsp;\u0026plusmn;\u0026thinsp;12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.59\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.50\u0026thinsp;\u0026plusmn;\u0026thinsp;12.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.11\u0026thinsp;\u0026plusmn;\u0026thinsp;13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.59\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.89\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLGF(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e483.13\u0026thinsp;\u0026plusmn;\u0026thinsp;509.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197.94\u0026thinsp;\u0026plusmn;\u0026thinsp;186.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLGF MOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (44.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (44.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (24.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (30.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (22.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (67.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (86.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (23.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003espontaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (59.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (52.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eassisted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (40.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (47.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (85.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (88.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (84.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (91.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (15.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (90.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (88.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScarred uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (77.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (86.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (22.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (13.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious adverse pregnancy outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (92.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (86.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (13.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (99.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (94.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eResults are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePLGF in twin pregnancies developed PE versus undeveloped PE\u003c/h2\u003e \u003cp\u003eIn twin pregnancies without PE, the PLGF level and PLGF multiple of median (MoM) were significantly higher than that in twin pregnancies with PE (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a and b). The results showed that after 24 weeks of gestation, PLGF showed well separation between the two groups, indicating that PLGF could be used to predict the presence of PE in twin pregnant women suspected of PE after 24 weeks of gestation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePLGF cutoff of 215 pg/mL in twin pregnancies\u003c/h2\u003e \u003cp\u003eThe optimal cutoff value of PLGF for PE was further determined by analyzing the ROC curve of PE (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimal cutoff was 215 pg/mL for PLGF or 0.564 for PLGF MoM. No significant difference in the effect of PLGF concentration (AUC, 0.863; sensitivity, 86.1%; specificity, 80%; accuracy, 81.6%) and PLGF MOM (AUC, 0.865; sensitivity, 83.3%; specificity, 81.0%; accuracy, 81.6%) was observed in the prediction of PE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eThe performance of PlGF and PlGF MoM in predicting 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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Cutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlGF(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlGF MOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAUC\u003c/em\u003e Area under receiver operating curve\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also compared the prediction performance for PE using a cutoff value of 215 pg/mL using PLGF percentiles at different gestational intervals. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the cutoff of the 15th percentile had a good predictive performance (66.7% sensitivity, 90.5% specificity, 84.4% accuracy) but not superior to the performance of the cutoff value of 215 pg/mL.\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\u003eThe performance of PlGF and PlGF percentile at different gestational intervals in predicting preeclampsia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Cutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlGF(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePlGF percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\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\u003eMAP slightly increase the predictive performance for PE\u003c/h2\u003e \u003cp\u003eGiven that blood pressure was measured by all participants, and the mean arterial pressure (MAP) was considered as a critical marker for predicting PE at the first trimester [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], whether adding MAP would improve the performance remained unclear. The result showed that MAP slightly increase the predictive performance for PE by PLGF (AUC 0.891 vs. AUC 0.863, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The optimal calculation model of PLGF plus MAP was presented as follows: YPLGF\u0026thinsp;+\u0026thinsp;MAP\u0026thinsp;=\u0026thinsp;exp(\u0026minus;\u0026thinsp;7.91318\u0026thinsp;+\u0026thinsp;0.07924*MAP\u0026thinsp;\u0026minus;\u0026thinsp;0.00789*PLGF)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study established a cutoff point of 215 pg/mL of PLGF, which was evaluated using the Aucheer PLGF immunoassay, as a useful predictor of clinical signs of the disease and short-term absence of PE in women with twin pregnancies. The AUC of PLGF is 0.863, and the prediction accuracy is 81.6%, with 86% sensitivity and 80% specificity. MAP can slightly increase the prediction performance, and the AUC of PLGF plus MAP is 0.891.\u003c/p\u003e \u003cp\u003ePE is a complex syndrome, which is associated with high risks of pregnancy-associated morbidity and mortality, and the management of this complex syndrome must be improved. Previous studies have shown that proteinuria and high blood pressure have a poor predictive value for PE and its associated adverse outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Antiangiogenic and angiogenic factors have been implicated in the pathophysiology of PE [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In general, the PLGF has shown good diagnostic performance compared with other biomarkers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Duhig et al. found that PLGF alone predicted delivery within 14 days among women with confirmed PE in singleton pregnancies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the present study, we also found that the predictive performance of low PLGF shows well predictive performance for the absence of PE in twin pregnancies.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, an observational study was conducted, and randomized trials were necessary to determine if the cutoff, when used in clinical practice as opposed to the current standard of care, could reduce hospitalizations and costs, with similar or better outcomes in regard to fetal and maternal adverse outcomes. In addition, the data were validated using Aucheer immunoassays, and the optimal cutoff point for the PLGF may differ when other assays are used. The present study suggests that a PLGF cutoff value of 215 pg/mL could be used to predict the presence or absence of PE in women with twin pregnancies who are clinically suspected of having PE.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur data showed that effective screening for PE can be provided by the PLGF assay in twin pregnancies with clinically suspected PE. A cutoff point of 215 pg/mL for PLGF, assessed using Aucheer PLGF immunoassays, is identified as a useful predictor for predicting PE.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLGF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlacental growth factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epreeclampsia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISSHP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational Society for the Study of Hypertension in Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple Of Median\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under receiver operating curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean arterial pressure.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank to doctors in the obstetrical clinic of obstetrical clinic doctor of Maternal and Child Health hospital for its linguistic assistance during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQi GJ and Zhang JH contributed to the study design, initial drafting, and data extraction and revised the manuscript.Guo WR and Liu H provided important intellectual input.Yao,L and Liu ZM critically revised the manuscript.All authors approved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by a grant from the Medical Association Commission of Hebei Province (No. 20231749) which allowed all steps of the study to be conducted. These steps were study design, data gathering, analysis and interpretation of data, and the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from all participants under the protocols approved by the Ethical Committee of The Hospital of Maternal and Health of Tangshan, China.(No. 2022-011-01).All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnanth CV, Keyes KM, Wapner RJ. Pre-eclampsia rates in the United States, 1980\u0026ndash;2010: age-period-cohort analysis. BMJ. 2013;347:f6564.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuley L. The global impact of pre-eclampsia and eclampsia. Semin Perinatol. 2009;33:130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagee LA, Brown MA, Hall DR, Gupte S, Hennessy A, Karumanchi SA, Kenny LC, McCarthy F, Myers J, Poon LC, Rana S, Saito S, Staff AC, Tsigas E, von Dadelszen P. The 2021 International Society for the Study of Hypertension in Pregnancyclassification, diagnosis \u0026amp; management recommendations for international practice.Pregnancy Hypertens 2022;27: 148.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiles K, Chappell LC, Lightstone L, Bramham K. Updates in Diagnosis and Management of Preeclampsia in Women with CKD. Clin J Am Soc Nephrol. 2020;15(9):1371\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorry DS, Wang HS, Wang TH, Caudle MR, Torry RJ. Preeclampsia is associated with reduced serum levels of placenta growth factor. Am J Obstet Gynecol. 1998;179(6 Pt 1):1539\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine RJ, Maynard SE, Qian C, Lim KH, England LJ, Schisterman EF, Thadhani R, Sachs BP, Epstein FH, Sibai BM, Sukhatme VP, Karumanchi SA. Circulating angiogenic factors and the risk of preeclampsia. N Engl J Med. 2004;350(7):672\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChappell LC, Duckworth S, Seed PT, Griffin M, Myers J, Mackillop L, Simpson N, Waugh J, Anumba D, Kenny LC, Redman CWG, Shennan AH. Diagnostic Accuracy of Placental Growth Factor in Women With Suspected Preeclampsia: A Prospective Multicenter Study. Circulation. 2013;128(19):2121\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuhig KE, Myers J, Seed PT, Sparkers J, Lowe J, Hunter RM, Shennan AH, Chappell LC, et al. Placental growth factor testing to assess women with suspectedpre-eclampsia: a multicentre, pragmatic, stepped-wedge cluster-randomised controlled trial. Lancet. 2019;393(10183):1807\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuhig KE, Webster LM, Sharp A, Gill C, Seed PT, Shennan AH, Myers JE, Chappell LC. Diagnostic accuracy of repeat placental growth factor measurements in women with suspected preeclampsia: A case series study. Acta Obstet Gynecol Scand. 2020;99(8):994\u0026ndash;1002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Kaaja. Predictors and risk factors of pre-eclampsia. Minerva Ginecol. 2008;60(5):421\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaine K, Murzakanova G, Sole KB, Pay AD, Heradstveit S, Raisanen S. Prevalence and Risk of Pre-Eclampsia and Gestational Hypertension in Twin Pregnancies: A Population-Based Register Study. BMJ Open. 2019;9:e029908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, Hall DR, Warren CE, Adoyi G, Ishaku S. International Society for the Study of Hypertension in Pregnancy (ISSHP).The hypertensive disorders of pregnancy: ISSHP classification, diagnosis \u0026amp; management recommendations for international practice. Pregnancy Hypertens. 2018;13:291\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan MY, Syngelaki A, Poon LC, Rolnik DL, O'Gorman N, Delgado JL, Akolekar R, Konstantinidou L, Tsavdaridou M, Galeva S, Ajdacka U, Molina FS, Persico N, Jani JC, Plasencia W, Greco E, Papaioannou G, Wright A, Wright D, Nicolaides KH. Screening for pre-eclampsia by maternal factors and biomarkers at 11\u0026ndash;13 weeks\u0026rsquo; gestation. Ultrasound Obstet Gynecol. 2018;52(2):186\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Gorman N, Wright D, Poon LC, Rolnik DL, Syngelaki A, de Alvarado M, Carbone IF, Dutemeyer V, Fiolna M, Frick A, Karagiotis N, Mastrodima S, de Paco Matallana C, Papaioannou G, Pazos A, Plasencia W, Nicolaides KH. Multicenter screening for preeclampsia by maternal factors and biomarkers at 11\u0026ndash;13 weeks\u0026rsquo;gestation: comparison to NICE guidelines and ACOG recommendations. Ultrasound Obstet Gynecol. 2017;49(6):756\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong Y, Zhu F, Ding Y. Serum screening in first trimester to predict pre-eclampsia, small for gestational age and preterm delivery:Systematic review and meta-analysis. BMC Pregnancy Childbirth. 2015;15:191.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"preeclampsia, twin pregnancy, placental growth factor","lastPublishedDoi":"10.21203/rs.3.rs-3029973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3029973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Placental growth factor (PLGF) has been reported to predict the absence of preeclampsia (PE) in singleton pregnancies. Thus, this study aims to evaluate the predictive value of the PLGF in twin pregnancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Twin pregnancy with clinically suspected PE (24 weeks 0 days to 36 weeks 6 days of gestation) was enrolled in this study. The threshold of PLGF was determined on the basis of a receiver-operating characteristic curve to predict PE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA cutoff value of 215 pg/mL for PLGF indicates a good predictive performance for PE. An area under the curve of 0.863 with 86% sensitivity and 80% specificity was also obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eEffective screening for PE can be provided by the PLGF assay in twin pregnancies with clinically suspected PE.\u003c/p\u003e","manuscriptTitle":"Placental growth factor as a predictive marker of preeclampsia in twin pregnancy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-28 20:23:01","doi":"10.21203/rs.3.rs-3029973/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40cb487b-12af-4e18-aa45-712539086491","owner":[],"postedDate":"June 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-25T01:29:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-28 20:23:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3029973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3029973","identity":"rs-3029973","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}