Preeclampsia is the leading cause of fetal growth restriction, indicated premature delivery, and is responsible for more than 50,000 maternal deaths annually worldwide.1,2 A screening test that could identify women early in pregnancy who would later have development of preeclampsia would allow increased surveillance of those at risk and reduce surveillance for those unlikely to have development of the syndrome. Identification of an at-risk population would foster investigative studies and clinical trials.
Although defined by hypertension and proteinuria, preeclampsia involves multiple organ systems (eg, renal, liver, brain, vascular, coagulation, placenta) that may define different pathophysiological phenotypes. Thus, phenotype-specific3 panels of biomarkers may be necessary to identify those at risk before appearance of overt disease (hypertension and proteinuria).
We describe an observational study in nulliparous women at low risk. Biomarkers chosen are based on the potential different underlying pathophysiologies of preeclampsia. We measured maternal blood concentrations of the syncytiotrophoblast proteins ADAM-124 (which cleaves insulin-like growth factor-binding protein-3 and insulin-like growth factor- binding protein-4) and pregnancy-associated plasma protein-A5 (which cleaves insulin-like growth factor-binding protein-46 and may regulate trophoblast invasion) together with the galectin placental protein 13,7 a marker of trophoblast function. Preeclampsia also is associated with altered expression of placental-derived proangiogenic and antiangiogenic proteins placental growth factor, a vascular endothelial growth factor family member, soluble fms-like tyrosine kinase-1, the soluble form of Flt-1 vascular endothelial growth factor receptor, and soluble endoglin, a transforming growth factor-β coreceptor. Disturbances in the coagulation system are reported in preeclampsia, particularly decreased platelet number and increased platelet volume;8,9 hence, we performed a complete blood count including platelet parameters. The primary objective was to identify clinical characteristics and biochemical markers in first-trimester samples that would possibly predict the subsequent development of preeclampsia.
MATERIALS AND METHODS
The Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network conducted this study as a planned observational cohort of a previously reported, larger, randomized, controlled trial to estimate whether antioxidant supplementation (1,000 mg vitamin C and 400 international units of vitamin E) prevented preeclampsia in nulliparous women at low risk for development of the syndrome.10 Women in the randomized clinical trial were eligible to participate in this cohort if their gestational age at enrollment was between 9 weeks 0 days and 12 weeks 6 days. Written informed consent was obtained from every participant and the study was approved by the Institutional Review Board at each clinical site and the data coordinating center.
Clinical information including demographics, medical, obstetrical, family, social, and sexual history was obtained at the time of enrollment by personal interview and chart review. Anthropometric measurements were taken for determination of BMI and body fat by the waist-to-hip ratio at enrollment. Four measures of blood pressure were assessed: 1) systolic blood pressure, 2) diastolic blood pressure, 3) mean arterial pressure [diastolic+1/3(systolic−diastolic) ], and 4) pulse pressure (systolic–diastolic). A complete blood count was performed and additional blood was collected and stored for future evaluation of biochemical assays.
After enrollment but before study drug initiation, blood was collected into ethylenediaminetetraacetic acid and serum tubes, plasma- or serum-separated, aliquoted, and stored at −70°C until analysis. For the biochemical marker analysis, a case-control study was performed: 174 women with preeclampsia diagnosed and an approximately 3:1 random sample of 509 normotensive nonproteinuric women matched by center and gestational age at enrollment.
Three biomarkers, ADAM-12, pregnancy-associated plasma protein-A, and placental protein-13, were measured in serum by NTD Laboratories. Enzyme-linked immunosorbent assay kits were used with interassay coefficients of variation of 7% at 146 ng/mL, 4% at 2,243 milliunits/mL, and 3% at 115 pg/mL, respectively. Three biomarkers, soluble fms-like tyrosine kinase-1, endoglin, and placental growth factor, were measured in ethylenediaminetetraacetic acid plasma using Luminex assays developed by Rules Based Medicine. The interassay coefficients of variation were 9% at 1.378 ng/mL for soluble fms-like tyrosine kinase-1, 8% at 3.91 ng/mL for Endoglin, and 10% at 476 pg/mL for placental growth factor.
The primary outcome was the development of preeclampsia including mild and severe preeclampsia, hemolysis, elevated liver enzymes, low platelet (HELLP) syndrome, and eclampsia. Mild preeclampsia was defined as mild pregnancy-associated hypertension (140–159 systolic or 90–109 diastolic on two occasions 2–240 hours apart) and proteinuria (300–4,999 mg total protein per 24 hours, 2+ or higher on dipstick testing, or a protein-to-creatinine ratio of 0.35 or higher). Severe preeclampsia was defined as preeclampsia with either severe pregnancy-associated hypertension (160 or more systolic or 110 or more diastolic on two occasions 2–240 hours apart, or a single occurrence treated with antihypertensive medications) or protein excretion of 5 g or more in a 24-hour urine sample or as mild pregnancy-associated hypertension with oliguria (less than 500 mL), pulmonary edema, or thrombocytopenia (platelet count of less than 100,000 per mm3). For this analysis, severe preeclampsia, HELLP syndrome, and eclampsia were combined as severe preeclampsia. Early-onset preeclampsia was defined as the development of preeclampsia before 34 weeks of gestation. Preeclampsia was confirmed through central review using a standardized protocol by three reviewers not associated with the clinical site of origin of deidentified medical records of all women with pregnancy-associated hypertension.
For any given biomarker, the sample size of 683 with approximately 3:1 control group women to case group women yields 95% power to detect an odds ratio of 2 if approximately 25% of the control group women are exposed (ie, above the 75th percentile). This sample size also yields more than 80% power to detect as little as a 0.25 standard deviation in difference between case group women and control group women when treating a biomarker as a continuous variable.
Categorical variables were compared using the χ2 test and continuous variables were compared using the Wilcoxon rank-sum test. Multiples of the median were computed for each biomarker by dividing the observed measurement by the expected median, which was derived from multiple regression of gestational age at sample collection, maternal weight in kilograms, racial group, and smoking during pregnancy in the women who did not have elevated blood pressure, proteinuria, or a small-for-gestational-age neonate. All variables that were significant with P<.1 were included in the expected median model. The 75th percentile cut-off was defined using the women who did not have elevated blood pressure, proteinuria, or a small-for-gestational-age neonate.
Logistic regression analysis was used to determine which factors were significantly associated with preeclampsia. The final model was selected using backward elimination in which all variables were initially included in the model and then selectively removed if not significant (P<.10). The performance of screening for individual markers and the final multivariable model were determined by receiver-operating characteristics curves and by calculating the sensitivity for a fixed 80% specificity. The specificity was set at 80% to maintain a reasonable false-positive rate.
Unless specifically noted, a nominal P<.05 was considered to indicate statistical significance and no adjustments were made for multiple comparisons. Analyses were performed using SAS software.
Figure 1 shows the enrollment and follow-up of the women who participated in the original trial and the observational cohort in 16 clinical centers between April 2004 and February 2008. Of the 2,434 women enrolled into the observational cohort, 40 (1.6%) were lost to follow-up, resulting in a final cohort of 2,394. The median gestational age at enrollment was 11.6 (range 8.7–13.9) weeks. The overall incidence of preeclampsia (7.4%) was not different between those allocated to antioxidants or to placebo (7.9% compared with 6.8%, respectively; P=.32). Data were available for 176 women who had development of preeclampsia, of whom 72 had development of severe preeclampsia. Only 18 women had development of early-onset preeclampsia, a number too small to perform multivariable analysis.
Clinical characteristics obtained at their first-trimester enrollment visit are reported in Table 1. Univariable analysis revealed that maternal age, race, marital status, years of education, primary source of medical payment, prenatal caregiver, BMI, and systolic blood pressure at enrollment were significantly associated with the development of preeclampsia (Table 1). These clinical characteristics were evaluated in a logistic regression model to predict preeclampsia. Of the four measures of blood pressure evaluated, systolic blood pressure was the most significant (P=.001) and had the highest area under the curve (AUC=0.58, 95% confidence interval [CI] 0.53–0.62). Backward elimination resulted in a final model that included African American race (adjusted odds ratio 1.7, 95% CI 1.1–2.6), Hispanic race (adjusted odds ratio 1.5, 95% CI 1.0–2.5), systolic blood pressure (adjusted odds ratio 1.1, 95% CI 1.1–1.2 per 5-unit increase), and BMI (adjusted odds ratio 1.1, 95% CI 1.0–1.3 per 5-unit increase) at enrollment, and education level (adjusted odds ratio 0.9, 95% CI 0.9–1.0 per 1-unit increase). This model had a sensitivity of 36% (95% CI 29–44) and specificity of 80% for development of preeclampsia; the area under the curve was 0.65 (95% CI 0.61–0.69). When development of severe preeclampsia was considered, pulse pressure showed the most significant association (AUC 0.60, 95% CI 0.53–0.66) of the four blood pressure measures. Prenatal caregiver, infection, the use of prenatal vitamins, pulse pressure, years of education, and number of sex partners were found to be significantly associated with development of severe preeclampsia. The final clinical model for prediction of severe preeclampsia yielded an AUC of 0.67 (95% CI 0.61–0.74) with a sensitivity of 42% (95% CI 30–54) at 80% specificity.
Mean platelet volume was significantly higher in the first trimester in women who later had development of preeclampsia; however, there was no difference in white blood count, red blood count, hemoglobin, hematocrit, and platelet count (Table 2). Mean platelet volume differed among the racial groups (Hispanic median 10.6 fl, range 6.7–14.1, African American median 9.0 fl, range 6.3–14.0, and white median 8.6 fl, range 6.4–13.4, respectively; P<.001) and was slightly higher with increasing BMI. The proportion of women with mean platelet volume multiples of the median at or above the 75th percentile was higher among women with preeclampsia compared with normotensive nonproteinuric women (31.6% compared with 24.3%; P=.03). However, the area under the receiver-operating characteristics curve for mean platelet volume multiples of the median as a continuous measure was 0.54 (95% CI 0.49–0.58), with a sensitivity of 25% (95% CI 19–32) for 80% specificity. Mean platelet volume multiples of the median were not found to be associated with development of severe preeclampsia (P=.68).
Biomarker concentrations were measured in 174 women who later had development of preeclampsia and 509 normotensive and nonproteinuric women. Comparison of first-trimester concentrations (multiples of the median) revealed that whereas ADAM-12 was significantly higher and pregnancy-associated plasma protein-A was significantly lower, there was no difference for placental protein 13 in those patients who went on to have development of preeclampsia compared with women who remained normotensive and nonproteinuric (Table 3). The proportion of women with an ADAM-12 multiples of the median at or above the 75th percentile was higher among women with preeclampsia compared with normotensive nonproteinuric women (35.5% compared with 25.0%, P=.009).
Among the angiogenic markers, placental growth factor was significantly lower in women who went on to have development of preeclampsia compared with women who remained normotensive and nonproteinuric; however, sFlt and endoglin were not different (Table 3). Placental growth factor had the highest AUC (0.61), with a sensitivity of 32% (95% CI 25–39) for 80% specificity (Table 3). The proportion of women with a placental growth factor multiple of the median below the 25th percentile was higher among women with preeclampsia compared with normotensive nonproteinuric women (38.5% compared with 25.0%; P<.001).
When all six first-trimester biomarkers were combined, the AUC increased to 0.66 (95% CI 0.62–0.71). However, this yielded a sensitivity of 38% (95% CI 31–46), for a fixed 80% specificity.
When development of severe preeclampsia was considered, only ADAM-12, pregnancy-associated plasma protein-A, and placental growth factor were found to be significantly different in the first trimester in those women who had development of severe preeclampsia (all P≤.001) compared with normotensive women. When all six biomarkers were combined, an AUC of 0.72 (95% CI 0.65–0.78) with a sensitivity of 48% (95% CI 35–60) for a fixed 80% specificity for predicting severe preeclampsia was found.
Clinical and biochemical factors individually identified as significant predictors of preeclampsia were initially included in the multivariable model. In combination, two factors were no longer significant: Hispanic race and mean platelet volume multiples of the median. The final predictive model that included African American race, systolic blood pressure, and BMI at enrollment, education level, ADAM-12 multiples of the median, pregnancy-associated plasma protein-A multiples of the median, and placental growth factor multiples of the median had an AUC of 0.73 (95% CI 0.69–0.77) with a sensitivity of 46% (95% CI 38–54) for 80% specificity (Table 4, Fig. 2). ADAM-12 multiples of the median and pregnancy-associated plasma protein-A multiples of the median were highly correlated (r=0.46; P<.001). Removal of ADAM-12 multiples of the median and pregnancy-associated plasma protein-A individually from the final model resulted in a slight decrease in the AUC 0.72 (95% CI 0.67–0.76) for ADAM-12 and 0.72 (95% CI 0.68–0.76) for pregnancy-associated plasma protein-A). The AUC when both biochemical markers were removed was 0.71 (95% CI 0.67–0.76) with a sensitivity of 46% (95% CI 38–54) for 80% specificity.
A final predictive model was also constructed for development of severe preeclampsia. Variables remaining in the final model included clinical (obstetric caregiver and pulse pressure) and biomarker (ADAM-12, pregnancy-associated plasma protein-A, and placental growth factor) data. This gave an AUC of 0.75 (95% CI 0.68–0.81) with a sensitivity of 55% (95% CI 43–67) at a fixed 80% specificity.
This study was performed in a nulliparous population, with collection of a comprehensive clinical data set and with a standardized definition of preeclampsia that allowed for a rigorous evaluation. Despite evaluation of multiple first-trimester clinical and biochemical parameters, we were unable to identify an algorithm that could predict subsequent preeclampsia with clinically useful sensitivity and specificity. When calculating sensitivity with specificity set at 80%, we correctly identified less than half of those women who had development of preeclampsia, ie, less than the flip of a coin, and just more than half of those who had development of severe preeclampsia. The small number of patients with early-onset preeclampsia unfortunately precluded analysis for this group with a high incidence of maternal and fetal morbidity.
Multivariable analysis demonstrated that African American or Hispanic race, systolic blood pressure, BMI at enrollment, and education level were the strongest clinical predictors of development of preeclampsia, but with only 36% sensitivity at 80% specificity. In contrast to previous findings, diastolic blood pressure at enrollment11 was not significantly higher. Hispanic ethnicity is not traditionally recognized as a risk factor for preeclampsia but is related to an increased incidence of obesity, metabolic syndrome, and diabetes.12 Recent reports utilizing clinical variables collected at less than 15 or 16 weeks of gestation similarly noted their low sensitivity for prediction of preeclampsia.13,14 When predicting severe preeclampsia, the multivariable analysis of clinical predictors was improved to a sensitivity of 42% at 80% specificity, although still not clinically useful.
Preeclampsia is proposed to have an immune component with a protective effect of exposure to paternal antigens,15,16 but we found no relationship between number of partners or the duration of the sexual relationship and development of preeclampsia. Nor was there an association of smoking, a family history of preeclampsia or cardiovascular disease, and development of the syndrome. This contrasts with the reports from Chesley17 and, more recently, with the fact that a positive family history was associated with a 20%–30% risk of development of preeclampsia.18 Our failure to find such associations may reflect our racially and ethnically heterogeneous population with a smaller proportion of smokers (16.9%) and a larger number of obese (22.5%) and overweight (26.0%) women.
Of the blood parameters, only mean platelet volume, a sensitive indicator of platelet activation and consumption,19 was different at enrollment, being modestly greater in those who subsequently had development of preeclampsia compared with those who remained normotensive. Mean platelet volume is higher in patients with hypertension and prehypertension,20 in insulin-resistant, nonobese, nondiabetic patients with coronary artery disease,21 and in those with diabetes mellitus, hypertension, hypercholesterolemia, smoking, and obesity, which are all reported risk factors for preeclampsia. Cross-sectional studies of mean platelet volume in women with established preeclampsia revealed high sensitivity and specificity.8 We found that mean platelet volume multiples of the median was significantly higher in the first trimester in women destined to have development of preeclampsia preceding the clinical onset by 16–18 weeks of gestation, but with poor sensitivity and specificity. Mean platelet volume multiples of the median, however, was not significantly higher in women who later had development of severe preeclampsia, suggesting platelet activation is not common to all cases of preeclampsia. Unlike previous reports,22 the ratio of platelet volume-to-number did not relate to development of preeclampsia (data not shown). The association of platelet volume with African American and Hispanic race, first-trimester BMI, and systolic blood pressure indicates that we may be identifying susceptible women with subclinical vascular dysfunction.23
Microarray analysis revealed ADAM-12 to be the most highly upregulated gene transcript in placental tissue24 from women with established preeclampsia, with corresponding increases in ADAM-12 in maternal serum. Our finding of significantly increased serum concentrations of ADAM-12 in the first trimester contradicts reports of decreased ADAM-1225,26 in women who subsequently had development of preeclampsia. This may be attributable to differing patient populations; however, we agree with previous studies that another proteinase, pregnancy-associated plasma protein-A, was significantly lower in the first trimester27,28 in women who had development of preeclampsia compared with those who did not. Although significantly different at 9–12 weeks of gestation, the sensitivity, either singly or combined of ADAM-12 or pregnancy-associated plasma protein-A, for prediction of subsequent preeclampsia did not have clinical utility, in agreement with previous smaller studies.26
In contrast to the reports that first-trimester placental protein 13 is significantly reduced29–31 in those patients who went on to have development of preeclampsia, we find no significant difference. A smaller retrospective study reported similar negative findings for placental protein 13, although it found, as we did, a positive association for pregnancy-associated plasma protein-A32 Our finding of no increase in predictive power by combined measurement of placental protein 13 and pregnancy-associated plasma protein-A again agrees with recent studies.33,34
Significant changes in the ratio of proangiogenic (placental growth factor) to antiangiogenic (soluble fms-like tyrosine kinase-1 and endoglin) markers precede the clinical presentation of preeclampsia by several weeks.35 Placental growth factor has the best predictive power in the first trimester, but with low sensitivity and combination of angiogenic and antiangiogenic markers, ie, placental growth factor-to-sFlt and placental growth factor-to-endoglin ratios did not appreciably increase the sensitivity (data not shown). The low predictive capability of suites of trophoblast function or placental proangiogenic and antiangiogenic markers indicates either that the involvement of these agents in the pathophysiology of preeclampsia is a late event or that there are several pathologic phenotypes leading to the syndrome of preeclampsia.3,36
The low sensitivity (46.1%) from this multivariable analysis of clinical risk factors and biochemical markers points to the heterogeneity of preeclampsia, the difficulty of identifying at-risk patients among a low-risk group, and defining an enriched population for study. When new biomarkers are advanced, they may similarly lack sensitivity if the syndrome has several pathologic phenotypes. Defining the clinical outcomes of patients identified by abnormal biomarker values in the first trimester may prove useful in elucidating if preeclampsia has several different underlying pathologies. Phenotyping and predicting disease based on biomarkers may prove more useful for diagnosis of disease and outcome in individuals rather than a clinical diagnosis of the preeclampsia syndrome.
1. World Health Organization. Estimates of maternal mortality: a new approach by WHO and UNICED. Geneva (Switzerland): World Health Organization; 1996.
2. Which anticonvulsant for women with eclampsia? Evidence from the Collaborative Eclampsia Trial. Lancet 1995;345:1455–63.
3. Myatt L, Carpenter L. Prediction of preeclampsia. In: Lyall F, Belfort M, editors. Preeclampsia etiology and clinical practice. Cambridge (UK): Cambridge University Press; 2007.
4. Irwin JC, Suen LF, Cheng BH, Martin R, Cannon P, Deal CL, et al.. Human placental trophoblasts secrete a disintegrin metalloproteinase very similar to the insulin-like growth factor binding protein-3 protease in human pregnancy serum. Endocrinology 2000;141:666–74.
5. Tornehave D, Chemnitz J, Teisner B, Folkersen J, Westergaard JG. Immunohistochemical demonstration of pregnancy-associated plasma protein A (PAPP-A) in the syncytiotrophoblast of the normal placenta at different gestational ages. Placenta 1984;5:427–31.
6. Irwin JC, Suen LF, Martina NA, Mark SP, Giudice LC. Role of the IGF system in trophoblast invasion and pre-eclampsia. Hum Reprod 1999;14(Suppl 2):90–6.
7. Bohn H, Kraus W, Winckler W. Purification and characterization of two new soluble placental tissue proteins (PP13 and PP17). Oncodev Biol Med 1983;4:343–50.
8. Howarth S, Marshall LR, Barr AL, Evans S, Pontre M, Ryan N. Platelet indices during normal pregnancy and pre-eclampsia. Br J Biomed Sci 1999;56:20–2.
9. Norris LA, Sheppard BL, Burke G, Bonnar J. Platelet activation in normotensive and hypertensive pregnancies complicated by intrauterine growth retardation. Br J Obstet Gynaecol 1994;101:209–14.
10. Roberts JM, Myatt L, Spong CY, Thom EA, Hauth JC, Leveno KJ, et al.. Vitamins C and E to prevent complications of pregnancy-associated hypertension. N Engl J Med 2010;362:1282–91.
11. Thadhani R, Ecker JL, Kettyle E, Sandler L, Frigoletto FD. Pulse pressure and risk of preeclampsia: a prospective study. Obstet Gynecol 2001;97:515–20.
12. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med 2003;163:427–36.
13. North RA, McCowan LM, Dekker GA, Poston L, Chan EH, Stewart AW, et al.. Clinical risk prediction for pre-eclampsia in nulliparous women: development of model in international prospective cohort. BMJ 2011;342:d1875.
14. Nijdam ME, Janssen KJ, Moons KG, Grobbee DE, van der Post JA, Bots ML, et al.. Prediction model for hypertension in pregnancy in nulliparous women using information obtained at the first antenatal visit. J Hypertens 2010;28:119–26.
15. Dekker GA, Robillard PY, Hulsey TC. Immune maladaptation in the etiology of preeclampsia: a review of corroborative epidemiologic studies. Obstet Gynecol Surv 1998;53:377–82.
16. Kho EM, McCowan LM, North RA, Roberts CT, Chan E, Black MA, et al.. Duration of sexual relationship and its effect on preeclampsia and small for gestational age perinatal outcome. J Reprod Immunol 2009;82:66–73.
17. Chesley LC, Annitto JE, Cosgrove RA. The familial factor in toxemia of pregnancy. Obstet Gynecol 1968;32:303–11.
18. Cincotta RB, Brennecke SP. Family history of pre-eclampsia as a predictor for pre-eclampsia in primigravidas. Int J Gynaecol Obstet 1998;60:23–7.
19. Tygart SG, McRoyan DK, Spinnato JA, McRoyan CJ, Kitay DZ. Longitudinal study of platelet indices during normal pregnancy. Am J Obstet Gynecol 1986;154:883–7.
20. Varol E, Akcay S, Icli A, Yucel H, Ozkan E, Erdogan D, et al.. Mean platelet volume in patients with prehypertension and hypertension. Clin Hemorheol Microcirc 2010;45:67–72.
21. Varol E, Akcay S, Ozaydin M, Erdogan D, Dogan A, Altinbas A. Mean platelet volume is associated with insulin resistance in non-obese, non-diabetic patients with coronary artery disease. J Cardiol 2010;56:154–8.
22. von Dadelszen P, Magee LA, Devarakonda RM, Hamilton T, Ainsworth LM, Yin R, et al.. The prediction of adverse maternal outcomes in preeclampsia. J Obstet Gynaecol Can 2004;26:871–9.
23. Roberts JM, Hubel CA. The two stage model of preeclampsia: variations on the theme. Placenta 2009;30(Suppl A):S32–7.
24. Gack S, Marme A, Marme F, Wrobel G, Vonderstrass B, Bastert G, et al.. Preeclampsia: increased expression of soluble ADAM 12. J Mol Med 2005;83:887–96.
25. Laigaard J, Sorensen T, Placing S, Holck P, Frohlich C, Wojdemann KR, et al.. Reduction of the disintegrin and metalloprotease ADAM12 in preeclampsia. Obstet Gynecol 2005;106:144–9.
26. Spencer K, Cowans NJ, Stamatopoulou A. ADAM12s in maternal serum as a potential marker of pre-eclampsia. Prenat Diagn 2008;28:212–6.
27. Poon LC, Stratieva V, Piras S, Piri S, Nicolaides KH. Hypertensive disorders in pregnancy: combined screening by uterine artery Doppler, blood pressure and serum PAPP-A at 11–13 weeks. Prenat Diagn 2010;30:216–23.
28. Spencer K, Cowans NJ, Nicolaides KH. Low levels of maternal serum PAPP-A in the first trimester and the risk of pre-eclampsia. Prenat Diagn 2008;28:7–10.
29. Gonen R, Shahar R, Grimpel YI, Chefetz I, Sammar M, Meiri H, et al.. Placental protein 13 as an early marker for pre-eclampsia: a prospective longitudinal study. BJOG 2008;115:1465–72.
30. Chafetz I, Kuhnreich I, Sammar M, Tal Y, Gibor Y, Meiri H, et al.. First-trimester placental protein 13 screening for preeclampsia and intrauterine growth restriction. Am J Obstet Gynecol 2007;197:35e1–7.
31. Huppertz B, Sammar M, Chefetz I, Neumaier-Wagner P, Bartz C, Meiri H. Longitudinal determination of serum placental protein 13 during development of preeclampsia. Fetal Diagn Ther 2008;24:230–6.
32. Stamatopoulou A, Cowans NJ, Matwejew E, von Kaisenberg C, Spencer K. Placental protein-13 and pregnancy-associated plasma protein-A as first trimester screening markers for hypertensive disorders and small for gestational age outcomes. Hypertens Pregnancy 2011;30:384–95.
33. Spencer K, Cowans NJ, Chefetz I, Tal J, Meiri H. First-trimester maternal serum PP-13, PAPP-A and second-trimester uterine artery Doppler pulsatility index as markers of pre-eclampsia. Ultrasound Obstet Gynecol 2007;29:128–34.
34. Akolekar R, Syngelaki A, Beta J, Kocylowski R, Nicolaides KH. Maternal serum placental protein 13 at 11–13 weeks of gestation in preeclampsia. Prenat Diagn 2009;29:1103–8.
35. Levine RJ, Lam C, Qian C, Yu KF, Maynard SE, Sachs BP, et al.. Soluble endoglin and other circulating antiangiogenic factors in preeclampsia. N Engl J Med 2006;355:992–1005.
36. Myatt L, Miodovnik M. Prediction of preeclampsia. Semin Perinatol 1999;23:45–57.
Supplemental Digital Content
© 2012 by The American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.