angiogenic biomarkers for prediction of early preeclampsia onset in high-risk women

8/10/2019 Angiogenic Biomarkers for Prediction of Early Preeclampsia Onset in High-risk Women

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http://informahealthcare.com/jmfISSN: 1476-7058 (print), 1476-4954 (electronic)

J Matern Fetal Neonatal Med, 2014; 27(10): 10381048

! 2014 Informa UK Ltd. DOI: 10.3109/14767058.2013.847415

ORIGINAL ARTICLE

Angiogenic biomarkers for prediction of early preeclampsia onset inhigh-risk women

Tiffany A. Moore Simas1,2, Sybil L. Crawford3, Susanne Bathgate4, Jing Yan5, Laura Robidoux6, Melissa Moore7, andSharon E. Maynard8,9

1Department of Obstetrics and Gynecology, University of Massachusetts Medical School/UMass Memorial Health Care, Worcester, MA, USA,2Department of Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA, 3Department of Medicine, Division of Preventive and

Behavioral Medicine, University of Massachusetts Medical School, Worcester, MA, USA, 4Department of Obstetrics and Gynecology, George

Washington University, Washington, DC, USA, 5Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical

School, Worcester, MA, USA, 6Department of Obstetrics and Gynecology, UMass Memorial Health Care, Worcester, MA, USA, 7Department of

Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA, USA, 8Department of Medicine, Division of

Preventive and Behavioral Medicine, George Washington University, Washington, DC, USA, and 9Department of Medicine, Division of Nephrology,

Lehigh Valley Health Network, Allentown, PA, USA

Abstract

Objective: Chronic hypertension, pregestational diabetes mellitus, history of prior preeclampsiaand obese nulliparity are maternal conditions associated with increased preeclampsia risk.

Whether altered maternal angiogenic factor levels allow for prediction of pending disease

is unclear. Our objective was to evaluate angiogenic factors for early preeclampsia prediction

in high-risk women.

Methods:Serial serum specimens were collected from 157 women at high preeclampsia risk and50 low-risk controls between 23 and 36 weeks gestation in 3 windows (2327.6, 2831.6, and

3235.6 weeks) in a two-center observational cohort. Soluble fms-like tyrosine kinase-1 (sFlt1),placental growth factor (PlGF) and soluble endoglin (sEng) were measured by ELISA.

Results:Multivariate parsimonious logistic regression analyses using backward elimination forprediction of early-preeclampsia (diagnosed534 weeks) found the best-fitting model included

the predictors (1) sFlt1 measured in the second window (2831.6 weeks) with AUC 0.85,sensitivity 67% and specificity 96% and (2) sFlt1 measured in the first window (2327.6 weeks)

and sEng change between first and second window with AUC 0.91, sensitivity 86% and

specificity 96%.

Conclusions:Two-stage sampling screening protocol utilizing sFlt1 and sEng is promising forprediction of preeclampsia diagnosed before 34 weeks. Larger studies are needed to confirm

these findings.

Keywords

Angiogenic factors, placenta growth factor

(PlGF), preeclampsia, soluble endoglin(sEng), soluble fms-like tyrosine kinase 1

(sFlt1)

History

Received 25 April 2013

Revised 3 August 2013

Accepted 18 September 2013Published online 29 October 2013

Introduction

Preeclampsia, a multifactorial, multiorgan disease, compli-

cates 58% of pregnancies and is characterized by new

hypertension and proteinuria onset after 20 weeks gestation

in previously normal pregnancies [13]. Most preeclampsia

cases occur in healthy, nulliparous women; however, womenwith chronic hypertension, diabetes mellitus, obesity, multiple

gestations or preeclampsia in a prior pregnancy, have

substantially increased risk [46].

Altered placental expression of soluble fms-like tyrosine

kinase-1 (sFlt1), soluble endoglin (sEng) and placental

growth factor (PlGF) contribute to preeclampsia pathogenesis

[79]. Maternal serum levels are altered prior to clinically

evident disease in both normal and high-risk women [1015].

Despite promise of early detection, the development ofaccurate biomarkers for preeclampsia prediction has remained

elusive but desirable given its theoretical benefits, especially

to high-risk groups.

The goal of this study was to determine the predictive

value of serum angiogenic factor levels for early preeclampsia

prediction in high-risk women. Our prior work indicated

that (1) a combination of three angiogenic biomarkers (sFlt1,

PlGF, and sEng), assayed in late second trimester is highly

predictive of early (534 weeks) preeclampsia in high-risk

women in advance of clinical symptoms and (2) the within-

woman rate of change of angiogenic biomarkers from the

second to third trimesters of pregnancy is highly predictive

Address for correspondence: Tiffany A. Moore Simas, MD, MPH, Med,Division of Obstetrics and Gynecology Research, Department ofObstetrics and Gynecology, University of Massachusetts MedicalSchool, Memorial Campus, 119 Belmont Street, Jaquith Building2nd floor Rm. 2-008, Worcester, MA 01605, USA. Tel: 1(508)334-6678. Fax: 1(508)334-9277. E-mail: [email protected]
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of overall preeclampsia risk also in advance of clinical signs

and symptoms [10]. We sought to expand this work and

determine the sensitivity and specificity of a test proposed to

inform clinical decision-making.

Methods

Setting and subjects

All women receiving prenatal care at UMass Memorial

Health Care or George Washington University betweenSeptember 2007 and June 2010 were considered for enroll-

ment. The Institutional Review Boards of the University

of Massachusetts Medical School and George Washington

University approved the study, and all subjects provided

informed consent.

Inclusion criteria were pregnancy528 weeks and eligibil-

ity into either (1) the low-risk cohort or (2) the high-risk

cohort. Low-risk subjects included women with BMI526kg/

m2, singleton gestations and without preeclampsia risk factors

or exclusionary parameters. High-risk subjects included

women with 1 preeclampsia risk factor: nulliparous with

pre-pregnancy BMI 30 kg/m2, pre-gestational diabetes

(Type 1 or 2), chronic hypertension, multiple gestation, and/or previous preeclampsia. Prior studies by this group advised

post-hoc exclusion of multiples due to altered angiogenic

profile independent of preeclampsia risk [16].

Exclusion criteria for either cohort included: (1) age520

or 440 years, (2) pre-existing proteinuria with baseline

measurement 300 mg/24h from timed collection or random

spot protein: creatinine 0.3 mg/mg, (3) lupus or anti-

phospholipid antibody syndrome, (4) compliance concern,

(5) anti-retroviral medication use, (6) organ transplantation,

(7) illicit drug abuse or methadone maintenance, (8) expected

delivery outside participating facilities, and/or (9) non-

English speaking.

Baseline clinical and demographic data and medicalhistory were collected upon enrollment. Gestational age was

calculated from first trimester ultrasound or clinical dating

concurrent with second trimester ultrasound [17].

Serum sampling and immunoassay

Serum was collected approximately every 4 weeks between

23rd and 36th weeks. All specimens were collected prior to

preeclampsia diagnosis. After phlebotomy, blood samples

were centrifuged, aliquoted, and frozen at 80 C until assay;

each specimen was thawed only once. Enzyme-linked

immunosorbent assays (R&D Systems, Minneapolis, MN)

for human sFlt1, PlGF and sEng were performed in duplicateby an investigator blinded to outcomes. Inter-assay and intra-

assay variation coefficients were 4.9% and 2.5% for sFlt1,

8.3% and 1.8% for PlGF and 3.7% and 2.6% for sEng.

Individual biomarkers were combined into ratios reflective of

overall angiogenic balance: (sFlt1 sEng)/PlGF, sEng/PlGF,

sFlt1/PlGF, PlGF/(sEng sFlt1), and sEng sFlt1.

Diagnosis of preeclampsia

After delivery, records were reviewed to determine whether

hypertensive disorders of pregnancy (HDoP) developed and if

so, gestational age at diagnosis. HDoP was defined according

to published guidelines [1,2]. Hypertension was either systolic

140 mmHg or diastolic blood pressure 90 mmHg on 2

occasions4h apart [18]. Proteinuria was 300 mg protein/

24 h, spot protein:creatinine0.30 mg/mg, or dipstick1

on 2 occasions 4 h apart. Gestational hypertension (gHTN)

was new-onset hypertension without proteinuria after 20

weeks. Preeclampsia was new-onset hypertension and pro-

teinuria after 20 weeks. In women with chronic hypertension,

preeclampsia diagnosis required new-onset proteinuria after

20 weeks. Preeclampsia was considered early-onset (early-PE) if clinically apparent disease was diagnosed534 weeks

and late-onset (late-PE) if diagnosed 34 weeks. The

nomenclature of early and late-onset refers to the diagnosis

of clinically apparent disease (i.e. when subject met blood

pressure and proteinuria definitional criteria regarding pre-

eclampsia diagnosis) and not onset of disease as this cannot

be known.

Statistical analysis

The high-risk cohort was divided into four groups based

on pregnancy outcome: no HDoP (HR-noHDoP), gHTN

(HR-gHTN), late-PE (HR-latePE) and early-PE (HR-earlyPE). Low-risk controls without HDoP were analyzed as

a 5th group (LR-noHDoP). Low-risk controls with HDoP

were excluded from analyses. Continuous variables were

summarized by mean and standard deviation (SD) with pair-

wise comparisons using Wilcoxon two-sample rank sum tests.

Categorical variables were summarized using frequency

measures, and compared using Fishers exact test.

The five groups had biomarker comparisons at each of

three gestational windows, 2327.6, 2831.6, and 3235.6

weeks. After log-transforming each biomarker (after adding

an offset of 1 to avoid negative log-transformed values) to

handle right-skewness, multiples of the median (MoM) were

calculated by dividing observed values by the expected

median, which was estimated using data from the LR-

noHDoP group as follows. First, nonparametric loess

smoothing [19] indicated a quadratic trajectory over time

for each log-transformed biomarker. Second, we estimated a

quantile regression [20] for each log-transformed biomarker

as a function of (gestational age) (gestational age) [2]. This

equation was used to generate expected medians for each

log-transformed observation based on the observed gesta-

tional age; other more complex functions of gestational age

were included as predictors but did not improve model fit,

nor were they statistically significant. To compare the five

groups regarding trends over time in biomarkers, linear mixedmodels [21] were estimated for each MoM-transformed

biomarker as a function of gestational age window (categor-

ical), group, and the windowgroup interaction. Rather than

assuming linear biomarker trajectories by treating gestational

age as a continuous variable, we allowed for non-linear

trajectories by categorizing gestational age into windows.

These models suggested that the five groups could be

distinguished based on first window measurement and the

within-woman change between first and second windows.

Consequently, for each biomarker, we estimated multinomial

logistic regression models to predict the five-category HDoP

outcome as a function of both the first MoM-transformed

DOI: 10.3109/14767058.2013.847415 Preeclampsia in high-risk women 1039


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biomarker in the first window and the within-woman change

between the first and second windows. Odds ratios for each

predictor were calculated based on 1 SD change in biomarker

predictors. A multivariate model was estimated including

first-window and change in sFlt1, sEng, and PlGF as

candidate predictors and using backward elimination to

obtain a parsimonious set of predictors. We also estimated

parallel binomial logistic regression models for early-onset

preeclampsia versus other. For the latter models, we

calculated the area under the receiver operating characteristiccurve (AUC) with MannWhitney 95% confidence intervals

[22], as well as optimal sensitivity and specificity at the

maximum value of the Youden index [23] and exact 95%

confidence intervals. In supplemental analyses in high-risk

participants, we estimated the joint associations of risk

factors chronic hypertension/diabetes, prior preeclampsia,

and obesity/nulliparity with early preeclampsia using

logistic regression, adjusting for first window measurement

and within-woman change in each biomarker.

Results

Of the 380 patients invited to participate, 299 (78.7%)consented. Of these, 92 were subsequently excluded due to

ineligibility by criteria (n 12), absence of samples (n 28),

missing dating criteria (n 1), twin conception reduced to

singleton (n 1), multiple gestation (n 41) and low-risk but

developed HDoP (n 9, 15.3%). Analyses therefore included

50 low-risk controls without HDoP (LR-noHDoP) and 157

HR singletons (see Figure 1). Within the high-risk cohort,

95 (60.5%) did not develop HDoP, 42 (26.8%) developed

gHTN, 10 (6.4%) developed late-PE and 10 (6.4%) developed

early-PE. Only serum specimens obtained prior to HDoP

diagnosis were utilized in analyses.

Table 1 shows baseline characteristics. By design,

LR-noHDoP had normal and overweight BMIs were normo-

tensive and were without chronic hypertension, pregestational

diabetes or preeclampsia history. High-risk subjects that

developed gHTN were younger, had lower gravity, lower

mean BMI and by definition had less chronic hypertension

and less anti-hypertensive medications, than subjects in the

high-risk cohort that did not develop HDoP. High-risk

subjects that developed late-onset preeclampsia differed

significantly from high-risk subjects that did not develop

HDoP only in nulliparity frequency. There were no statistic-

ally significant differences in high-risk subjects that devel-

oped early-onset preeclampsia compared to those that did not

develop HDoP.Using linear mixed modeling, we compared unadjusted

mean MoM for each biomarker by gestational age window,

with the reference gestational age median calculated using the

LR-noHDoP cohort. Figure 2 compares biomarker levels

expressed as mean MoMs in subjects by cohort (LR versus

HR) and HDoP status (none versus gHTN versus latePE

versus earlyPE) for each biomarker (sEng, PlGF, sFlt1) and

angiogenic ratio (sFlt1 sEng)/PlGF at each time window.

Compared to the other four groups, the HR-earlyPE group is

statistically significantly different by pair-wise comparisons

in all windows for sFlt1, sEng and (sFlt1 sEng)/PlGF.

The LRC and HR-noHDoP subgroups track closely together

near mean of 1 across all biomarkers and are not statistically

significantly different from each other by pair-wise compari-

sons with exception of PlGF in window 3. The HR-latePE

and HR-gHTN subgroups similarly track closely together and

are not statistically significantly different from each other

at any windows across any biomarkers. The HR-latePE group

is significantly different from the HR-noHDoP group in

the 3235.6 week gestational window for sEng and sFlt1,

but not for PlGF or the angiogenic ratio. Adjusting for risk

factors does not significantly affect results (data not shown).Unadjusted mean MoM for alternative angiogenic ratios

sEng/PlGF, sFlt1/PlGF, PlGF/(sEng sFlt1), and sEng

sFlt1 are provided in Supplemental Figure S1. Differences

between groups were similar to those observed for

(sFlt1 sEng)/PlGF.

Based on these results, we modeled the five-category

HDoP grouping as a function of first-window biomarker and

change in biomarker between first and second windows, using

multinomial logistic regression. Odds ratios (reference:

LR-noHDoP) were calculated based on a 1 SD change in

biomarker predictors. Lower PlGF, higher sFlt1, and higher

angiogenic ratio (sFlt1 sEng)/PlGF measured in window 1

were associated with increased odds for early-onset pre-eclampsia; greater changes in sEng, sFlt1, and angiogenic

ratio (sFlt1 sEng)/PlGF from window 1 to 2 also were

associated with increased odds for early-onset preeclampsia

(Table 2A). Regarding late-onset preeclampsia, lower PlGF

and higher angiogenic ratio (sFlt1 sEng)/PlGF in window 1

were associated with increased odds; however, changes in

biomarker levels from first to the second sampling were

not robust predictors (Table 2A). Backward elimination

omitting the ratio to avoid colinearity was utilized to

determine the most parsimonious model when considering

individual contributions of all biomarkers; the first PlGF

measurement and within-woman sFlt1 change were the only

predictors retained. In the high-risk cohort, PlGF is predictive

of gHTN and late-onset preeclampsia and the sFlt1 change

is predictive of early-onset disease (Table 2B).

To assess biomarker performance for early-onset predic-

tion (versus no early preeclampsia), we conducted parallel

binary logistic regression analyses as a function of the first

observation of each biomarker and angiogenic ratios from

window 1, window 2, and change between windows 1 and 2

(Table 3), from which we calculated sensitivity and specifi-

city. These analyses showed that second window biomarkers

tend to be more predictive of early onset preeclampsia as

compared with first window biomarkers or biomarker change

from first to second window. Models using both first windowbiomarker and change in biomarkers from first to second

window tended to perform similarly to models using second

window biomarker alone, in analyses when only a single

biomarker or single ratio is utilized.

We then considered models that potentially included

multiple biomarkers/ratios. We estimated two parsimonious

models derived via backwards elimination utilizing second

window biomarker/ratios alone (Model 1), and first window

biomarker & change from first to second window (Model 2).

For Model 1, second window sFlt1 was the only biomarker

retained. For Model 2, first window sFlt1 and change in sEng

were the only biomarkers retained. Model 2 (AUC 0.91,

1040 T. A. Moore Simas et al. J Matern Fetal Neonatal Med, 2014; 27(10): 10381048


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sensitivity 86%, specificity 96%) was more predictive of

early preeclampsia as compared with Model 1 (AUC 0.85,

sensitivity 67%, specificity 96%;Table 4).

Calculated sensitivities and specificities for any HDoP

and any preeclampsia were significantly lower than for early-

onset. AUC, sensitivity and specificity for binomial logistic

regressions of HDoP outcomes as function of window 1

(sFlt1 sEng)/PlGF and (sFlt1 sEng)/PlGF change from

window 1 to 2 are presented here for comparison with those

presented inTable 3for early-preeclampsia: any HDoP (AUC

0.78, sensitivity 76%, specificity 73%) and any preeclampsia

(AUC 0.84, sensitivity 86% and 85%).

Figure 1. Study flow chart. Included abbreviations: GWU, George Washington University; UMass, UMass Memorial Health Care; LRC, Low-RiskCohort; HRC, High-Risk Cohort; HDoP, Hypertensive Disorders of Pregnancy; gHTN, Gestational hypertension; PE, Preeclampsia; MHTN,mild hypertension; SHTN, severe hypertension; MPE, mild preeclampsia; SPE, severe preeclampsia; GA, gestational age. Late onset preeclampsiawas diagnosed 34 weeks gestational age and early onset was prior to 34 weeks.



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Table1.

Demographiccharacteristicsofs

tudysubjects.

High-risksingletoncohort(N

157)(1

PEriskfactorincludingcHTN,

DM1orDM

2,

historyPE,obese/nullipara)

Hypertensivediseaseofpregnancy(N

62)

Preeclampsia(N

20)

Demographiccharacteristics

Allsubjects

(N

207)*

Lowriskhealthy

cohort-noHDoP(n

50)*

NoHDoP

(n

95)*

Gestational

HTN(n

42)*

Lateonset34weeks

(n

10)*

Earlyonset534weeks

(N

10)*

Maternalage(years)

31.4

5.2

31.1

5.5

32.0

5.0

30.2

4.9y

31.6

6.7

32.9

5.5

Gravity

2.6

1.8

2.4

1.3

2.9

1.7

2.2

1.3y

2.1

1.2

3.7

4.2

Essentialnulliparity

98(47.6

)

23(46.9

)

40(42.1

)

24(57.1)

8(80.0

)y

3(30.0

)

Bodymassindex(kg/m2)

32.0

8.2

24.9

2.2

33.1

8.0

30.2

4.9y

31.6

6.7

33.5

7.6

Underweight(BMI518.5

)

1(0.5

)

0(0.0

)

1(1.1

)

0(0.0

)

0(0.0

)

0(0.0

)

Normalweight(BMI18.524.9

)

36(17.5

)

22(44.9

)

12(12.6

)

1(2.4

)

0(0.0

)

1(10.0

)

Overweight(BMI25.029.9

)

64(31.1

)

27(55.1

)

24(25.3

)

6(14.3

)

4(40.0

)

3(30.0

)

Obese(BMI

30.0

)

105(51.0

)

0(0.0

)

58(61.1

)

35(83.3)

6(60.0

)

6(6.0

)

SystolicBPatenrollment(mmHg)

119.5

14.2

109.1

9.5

122.7

15.9

122.3

10.4

119.1

9.0

128.1

10.3

DiastolicBPatenrollment(mmHg)

73.1

10.6

66.3

6.7

75.1

11.2

75.9

7.1

71.4

12.0

77.2

17.1

Anti-hypertensivemedicationsatenrollme

nt

39(18.8

)

0(0.0

)

33(34.7

)

0(0.0

)y

2(20.0

)

4(40.0

)

Race-ethnicgroup

Non-Hispanicwhite

117(56.5

)

31(62.0

)

49(51.6

)

25(59.5)

7(70.0

)

5(50.0

)

Black

64(30.9

)

11(22.0

)

10(10.5

)

3(7.1

)

1(10.0

)

4(40.0

)

Hispanicwhite

20(9.7

)

5(10.0

)

34(35.8

)

14(33.3)

2(20.0

)

0(0.0

)

Other

6(2.9

)

3(6.0

)

2(2.1

)

0(0.0

)

0(0.0

)

1(10.0

)

Smokingstatus

Currentsmoker

14(6.8

)

5(10.0

)

5(5.3

)

4(9.5

)

0(0.0

)

0(0.0

)

Liveswithsmoker

23(11.2

)

5(10.0

)

9(9.6

)

5(11.9

)

2(20.0

)

2(20.0

)

Chronichypertension

46(22.2

)

NA

38(40.0

)

0(0.0

)y

3(30.0

)

5(50.0

)

Pregestationaldiabetes(DM1orDM2)

27(13.0

)

NA

18(19.0

)

4(9.5

)

3(30.0

)

2(20.0

)

Historyofpriorpreeclampsia

52(25.1

)

NA

31(32.6

)

16(38.1)

1(10.0

)

4(40.0

)

Obese(BMI

30)nullipara

67(32.4

)

NA

36(37.9

)

23(54.8)

6(60.0

)

2(20.0

)

PE,preeclampsia;cHTN,chronichypertension;DM,

diabetesmellitus;HDoP,

hypertensivedisorderofpregnancy;kg/m2,

kilogr

ams/meter

2;BMI,bodymassindex;BP,bloodpressure;mmHg,millimeters

mercury.

*Dataaremean

standarddeviationornumber(%).

yp50.0

5forcomparisonswithinhigh-risk

cohort;comparisonbetweenHDoPgroups

(gestationalHTNorPreEgroupsindividua

lly)tohigh-riskgroupwithoutHDoP(gray

column)



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Linear mixed models for log-transformed biomarkers and

logistic regressions were all re-run omitting low-risk women

and using HR-noHDoP as reference, with and without

controlling for high-risk inclusion factors; results were

generally similar and consistent (data not shown) and not

unexpected given similarities in biomarker profiles between

LR-noHDoP and HR-noHDoP groups (Figure 2). Similarly,

models were re-run omitting low-risk women and using only

the HR-noHDoP subjects without small for gestational age

(SGA) neonates or without missing information pertinent to

determination of SGA status as the reference group (n 86),

with and without controlling for high-risk inclusion factors;

results were generally similar and consistent (date not shown).

Of note, MoM logsFlt1 and MoM logsEng were highly

correlated with a Pearson correlation coefficient of 0.62.

Among high-risk women, risk factors were not statistically

significantly related to early-onset preeclampsia, both before

and after adjustment for biomarkers, and adjustment for

risk factors had little impact on associations of biomarkers

with early-onset preeclampsia (data not shown).

Figure 2. Maternal serum levels of sEng (A), PlGF (B), sFlt1 (C) and the angiogenic ratio of (sFlt1 sEng):PlGF (D) by gestational age. Unadjusted

means of multiples of median analyses shown for specimens drawn during three gestational age windows. Subjects in low-risk singleton cohort withouthypertensive disorders of pregnancy (HDoP) provided reference median. Women in high-risk singleton cohort were categorized into those who didnot develop HDoP, those that developed gestational hypertension and those that developed preeclampsia with onset either before 34 weeks or after34 weeks. The key indicates which line corresponds to which group and how many specimens were contributed by how many women in eachgestational age window.

Graph Key Cohort

# Specimens / # Women

23-27.6 wks 28-31.6 wks 32-35.6 wks

Low Risk Cohort no HDoP 48 / 48 43 / 41 38 / 37

High risk no HDoP 83 / 81 70 / 69 71 / 69

High risk gestational hypertension 38 / 38 35 / 35 33 / 32

High risk late onset (34 wks) preeclampsia

8 / 8 11 / 10 9 / 9

High risk

early onset (< 34 wks) preeclampsia

10 / 10 9 / 9 4 / 4



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Discussion

Here, we demonstrated that angiogenic biomarkers sampled

in late second and early third trimesters are highly predictive

of preeclampsia diagnosed534 weeks in a high-risk cohort,

using low-risk women as a reference group. Specifically,

a two-stage screening protocol with sFlt1 measured in late

second trimester and sEng change between late second and

early third trimester had a 91% AUC, 86% sensitivity and 96%

specificity for the prediction of early-onset preeclampsia.

Consistent with studies by other investigators [2426], we

found angiogenic biomarkers were most predictive of early

onset preeclampsia, and less robust for prediction of

gHTN or preeclampsia more generally. Since adjustment forpreeclampsia risk factors did not significantly affect our

results, it can be concluded that biomarker profiles are greater

risk indicators than maternal risk factors themselves.

Prediction of preeclampsia using angiogenic biomarkers

has potential utility in several different patient populations.

The large majority of preeclampsia occurs in women without

any known risk factors, thus the case for preeclampsia

screening in healthy nulliparous women is strong. Several

prospective, longitudinal studies have evaluated angiogenic

biomarkers in healthy nulliparous women [2729]. Although

fewer in number, women with preeclampsia risk factors such

as chronic hypertension, diabetes mellitus, and prior

Table 2. Multinomial logistic regression models for pregnancy induced hypertension outcomes.

Biomarkers (Study cohorts and p value)

Odds ratio (95% CI) for biomarkervalue in first window,*

computed for 1SD differencez

Odds ratio (95% CI) for changebetween windows 1 and 2,y

computed for 1SD differencez

AsEndoglin (1 SD increase)

Low risk controls (n 39)x Reference ReferenceHR-no HDoP (n56)x 0.54 (0.31, 0.94) 1.24 (0.66, 2.32)HR-gHTN (n 31)x 0.69 (0.39, 1.21) 2.44 (1.28, 4.62)HR-late PE (n8)x 1.01 (0.50, 2.07) 2.36 (1.02, 5.47)

HR-early PE (n6)x 1.41 (0.69, 2.90) 4.16 (1.65, 10.45)p Value 0.0945 0.0105

PlGF (1 SD decrease)Low risk controls (n 39)x Reference ReferenceHR-no HDoP (n56)x 1.33 (0.86, 2.07) 0.94 (0.63, 1.40)HR-gHTN (n 31)x 3.18 (1.72, 5.90) 1.63 (0.89, 2.98)HR-late PE (n8)x 5.55 (1.86, 16.55) 0.99 (0.43, 2.29)HR-early PE (n6)x 10.11 (2.29, 44.68) 4.15 (0.78, 22.03)p Value 0.0001 0.1991

sFlt1 (1 SD increase)Low risk controls (n 39)x Reference ReferenceHR-no HDoP (n56)x 1.22 (0.75, 1.99) 0.85 (0.44, 1.67)HR-gHTN (n 31)x 1.08 (0.61, 1.89) 2.40 (1.22, 4.72)HR-late PE (n8)x 1.38 (0.62, 3.07) 2.37 (0.98, 5.71)HR-early PE (n6)x 2.94 (1.29, 6.70) 4.52 (1.80, 11.37)

p Value 0.1232 0.0024Angiogenic ratio (1 SD increase) [(sFlt1 sEng)/PlGF]

Low risk controls (n 39)x Reference ReferenceHR-no HDoP (n56)x 1.11 (0.61, 2.05) 0.97 (0.56, 1.69)HR-gHTN (n 31)x 2.02 (1.05, 3.91) 1.92 (1.04, 3.52)HR-late PE (n8)x 3.46 (1.60, 7.50) 1.20 (0.58, 2.51)HR-early PE (n6)x 4.54 (1.79, 11.47) 3.13 (1.25, 7.83)p-value 0.0025 0.0455

BPlGF (1 SD decrease) sFlt1 (1 SD increase)

Low risk controls (n39)x Reference ReferenceHR-no HDoP (n56)x 1.38 (0.89, 2.14) 0.74 (0.40, 1.39)HR-gHTN (n 31)x 2.53 (1.37, 4.65) 1.86 (0.94, 3.68)HR-late PE (n8)x 4.90 (1.60, 15.00) 1.59 (0.62, 4.07)HR-early PE (n6)x 3.82 (0.93, 15.77) 3.51 (1.44, 8.55)p Value 0.0080 0.0125

A: Separately for each biomarker, include two predictors simultaneously: (a) Multiple of Median (MoM) value of log (angiogenicbiomarker 1) at window 1 (2327.6 weeks) and (b) change from window 1 to window 2 (2831.6 weeks).

B: Multivariate parsimonious model from backward elimination, including sEng, sFlt1, and PlGF as candidate predictors.MoM, Multiple of the median; sEng, soluble endoglin; sFlt1, soluble fms-like tyrosine kinase 1; PlGF, placental growth factor; CI,

confidence interval; SD, standard deviation; HR, high risk; HDoP, hypertensive disorder of pregnancy; gHTN, gestationalhypertension; PE, preeclampsia.

*First observation used in gestational age window 2327.6 weeks.yChange in first observations in window 1 (2327.6 weeks) and window 2 (2831.6 weeks).zDirection of standard deviation reflects preeclampsia risk and thus was an increase for sEng, sFlt1 and the angiogenic ratio and a

decrease for PlGF.xNumber of subjects in each category different than total cohort due to missing specimens at designated time points.Bold faced font indicates statistical significance with p-values that are50.05 or odds ratios with 95% confidence intervals that do not

cross 1.



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preeclampsia are at significantly increased risk for pre-

eclampsia development. Given the high incidence of pre-

eclampsia in these high-risk women and at times, the

difficulty in making the diagnosis, the potential benefit of

an accurate screening test is also great in this population. In

this and our prior work [10,15], we have chosen to focus on

this relatively understudied group of high-risk women.

Several investigators have reported sequential measure-

ment of biomarkers and gestational change in biomarker

levels for preeclampsia prediction [13,24,30]. These studies,

together with our prior work regarding a two-staged screening

protocol [10,15], informed our current analytic approach.

For models using only a single biomarker or single ratio,

prediction models using second window levels alone

Table 3. Binomial logistic regression models for early onset preeclampsia (n6) versus no earlypreeclampsia (n134) for single biomarkers and angiogenic ratios.

AUC % sensitivity % specificity

sEng:First window only 0.78 66.7 96.3Second window only 0.85 83.3 94.0Change from first to second only 0.86 66.7 92.5First window & change 0.86 83.3 94.0

PlGF:

First window only 0.76 50.0 96.3Second window only 0.84 83.3 88.1Change from first to second only 0.63 33.3 97.8First window & change 0.84 83.3 83.6

sFlt1:First window only 0.74 66.7 87.3Second window only 0.85 66.7 95.5Change from first to second only 0.82 83.3 83.6First window & change 0.86 83.3 88.8

(sFlt1 sEng)/PlGF:First window only 0.77 66.7 97.0Second window only 0.83 83.3 91.8Change from first to second only 0.76 66.7 88.8First window & change 0.83 83.3 91.0

sEng/PlGF:First window only 0.78 66.7 96.3Second window only 0.83 83.3 91.8Change from first to second only 0.77 66.7 87.3First window & change 0.83 83.3 91.8

sFlt1/PlGF:First window only 0.78 66.7 96.3Second window only 0.84 83.3 89.6Change from first to second only 0.77 66.7 90.3First window & change 0.84 83.3 86.6

PlGF/(sEng sFlt1):First window only 0.77 66.7 99.3Second window only 0.84 83.3 91.8Change from first to second only 0.67 50.0 89.6First window & change 0.84 83.3 87.3

sEng sFlt1:First window only 0.77 66.7 97.0

Second window only 0.86 83.3 90.3Change from first to second only 0.76 66.7 92.5First window & change 0.86 83.3 94.8

Area under the ROC (AUC), sensitivity, and specificity are given for each individual biomarker andangiogenic ratios for four predictors: First window (2327.6 weeks) biomarker level or ratio, secondwindow (2831.6 weeks) biomarker level or ratio, biomarker or ratio change from first to second window,and both first window level and change from first to second window.

Table 4. Multivariate parsimonious models from backward elimination, including sEng, sFlt1, and PlGF as candidate predictors forearly-onset preeclampsia.

AUC % sensitivity % specificity

Model 1: second window sFlt1 only 0.85 66.7 95.5

Model 2: first window sFlt1 & change in sEng (from first to second window) 0.91 85.7 95.5

Two reduced models were derived: Model 1 was derived using second window (2831.6 weeks) biomarkers alone: sFlt1 was the onlybiomarker retained. Model 2 was derived using first window (2327.6 weeks) biomarkers and change from first to second window: firstwindow sFlt1 and change in sEng were retained.



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performed as well as models which included both first

window levels and change in levels from first to second

window. However, when models potentially including mul-

tiple biomarkers/ratios were considered, we found best

prediction of early-onset preeclampsia with a model that

included both first window sFlt1 and change in sEng from

windows 1 to 2 (Table 4, Model 2). Whether the added

expense and logistics of obtaining serum samples at two

different gestational ages justifies the improved predictive

performance above that achieved with second windowbiomarkers alone remains uncertain and merits further

investigation with regards to both clinical utility and cost

effectiveness.

Although not incorporated in our study design, when

evaluating the merits of a two-staged approach, consideration

should be given to inclusion of placental dysfunction signs

like abnormal Doppler velocimetry [3134] in prediction

models. Additionally, one must acknowledge that aberrant

plasma or serum angiogenic biomarker patterns are not

specific to preeclampsia but have been associated with other

poor obstetric outcomes like preterm delivery [35,36], fetal

death [37], delivery of small for gestational age neonates

[3840], mirror syndrome (Ballantynes syndrome) [41],and twin to twin transfusion syndrome [42], among others.

The application of a MoM approach to angiogenic

biomarker measurement in the setting of preeclampsia was

pioneered by Su et al. in 2001 [43] and more recently utilized

by Chaiworapongsa and colleagues in a study of utility of

angiogenic biomarkers among women presenting to obstetric

triage with suspected preeclampsia [44]. We utilized this

MoM approach, with median derived from a low-risk cohort

without HDoP, since angiogenic biomarker levels vary

considerably based on gestational age at time of sampling.

A recent meta-analysis reported low sensitivities for

angiogenic biomarkers sampled 520 weeks for preeclamp-

sia prediction (regardless of gestational age at diagnosis)

among low-risk pregnancies [45]. These results, represent-

ing findings from 430 studies in low-risk women, have

tempered enthusiasm for the clinical utility of angiogenic

biomarkers in prediction. However, we continue to be

optimistic regarding a clinically useful role in targeting

high-risk subjects as they are the most complex to

diagnose and the most likely to have severe disease and

early diagnosis. Our study adds to the limited data

available for preeclampsia prediction in high-risk women.

Including subjects with prior preeclampsia, chronic hyper-

tension or both and measuring PlGF, inhibin A and sFlt1

at 1219 and 2428 weeks, Sibai et al. similarly foundhigh prediction for early-onset (527 weeks, AUC 93%)

preeclampsia but weak prediction for late-onset (37

weeks) disease [46]. In a secondary analysis of the

NICHD Maternal Fetal Medicine Units Network trial of

aspirin to prevent preeclampsia in high-risk pregnancies

complicated by diabetes, hypertension, multiples and prior

preeclampsia, biomarker patterns consistent with those in

low-risk women who develop preeclampsia were identified;

however, limited credibility was given to their clinical

usefulness for prediction [47]. Concordant with our prior

and current findings, Powers et al. [47] and Sibai et al.

[46] found rate of biomarker change predictive in some

high-risk subgroups where analyses were separated by risk

factor [47], and more robustly, for early-onset disease [46].

Throughout the results, we presented data using low-risk

subjects without HDoP as the comparator group so as to

create predictive models. We also re-ran all analyses using the

high-risk group that did not develop HDoP and the subset of

this group that did not develop SGA and found very similar

results regardless of reference group (data not shown). Similar

to our study design, Dwyer [48] and Verlohren [49] included

healthy control comparison cohorts. Dwyer investigated ahigh-risk cohort with underlying risk for vascular disease

(hypertension, lupus, pre-gestational diabetes, history of

preeclampsia and others) and comparisons were made to a

healthy normal-risk group that were absent of any of the

high-risk conditions. Dwyer found significant alterations in

sFlt1 and PlGF from the second trimester onward in women

who later developed preeclamspia [48], but did not perform

prediction modeling. Verlohren showed the sFlt1/PlGF ratio

differs among women with chronic hypertension, gestational

hypertension, and preeclampsia in comparison to healthy

controls defined as singletons with normal pregnancy out-

comes; and further, that the angiogenic ratio may be useful for

identification of women at risk for imminent delivery [49].However, their study did not measure biomarker levels prior

to preeclampsia onset, and prediction of preeclampsia was not

a goal of the study.

This study has limitations. First, our study design focused

the analytic cohorts at the ends of the preeclampsia risk

spectrum. Our low risk subjects did not have any HDoP risk

factors and were excluded if HDoP developed; this resulted in

a purposeful 0% HDoP prevalence among the low risk

analytic cohort. Conversely, our high-risk cohort was very

high risk as evidenced by a 39.5% incidence of HDoP, with

12.7% preeclampsia and 6.4% early preeclampsia diagnosis.

This rate is similar to that reported in high-risk women in

other studies [50] and lower than Dwyer et al., who with many

similar risk factors, reported a 25% preeclampsia rate, with

75% delivered preterm, prior to 37 weeks [48]. This design

approach facilitated predictive model evaluation by allowing

the comparison group to be free of HDoP. However, it can

limit translation from the research to clinical arena as

differences in groups may be augmented by their designed

purity. Additionally, our results cannot be applied to low-risk

pregnancies, which account for the majority of preeclampsia

cases.

Second, it is difficult to dichotomize HDoP into a yes/no

variable when it is a complex disease spectrum. This

spectrum of disease severity is reflected in biomarkeralterations, as shown in Table 2, with increasing odds of

progressively more severe HDoP disease with lower PlGF and

greater change sEng and sFlt1. However, dichotomy is

required for test performance determinations via sensitivity

and specificity. Out of necessity, cut-offs and definitions

according to standards were applied [1,2]; despite this, similar

to observations regarding maternal hyperglycemia and

adverse pregnancy outcomes [51], risk thresholds were not

obvious. As delivery is the only definitive cure and as new

potentially temporizing treatments like extracorporeal

removal of sFlt1 [52] are high-risk and experimental, we

choose to focus the main dichotomy on early-onset disease.



10/11

Early preeclampsia would most benefit from prediction as

interventions like betamethasone for promotion of fetal lung

maturity, increased surveillance, and experimental trials

would be most beneficial and justifiable.

Finally, the sample size is relatively small. With the

exception of Powers [47] and Sibais [46] secondary analyses

of large randomized trials, total high-risk subjects contribut-

ing to the existing literature in this arena remains relatively

sparse. Our study contributes to this limited literature and

provides further insight regarding angiogenic biomarkerpredictive ability.

Prediction, whether by serum markers alone or with

clinical parameters [53], remains a desirable goal for hyper-

tensive pregnancy disorders. In addition to prediction, reliable

biomarkers would allow for monitoring of targeted prevention

or treatment strategies in women at highest risk. A role for

angiogenic biomarkers in preeclampsia prediction remains

plausible. Although preeclampsia risk is affected by co-

morbidities like diabetes, chronic hypertension and prior

preeclampsia, the fact that adjusted and unadjusted analyses

were similar implies that a single predictive model incorpor-

ating serum parameters may be appropriate regardless of

clinical parameters that increase preeclampsia risk status;thus, potentially simplifying screening algorithms in complex

populations.

Declaration of interests

Research reported in this publication was supported by the

National Center for Advancing Translational Sciences of the

National Institutes of Health under award number(s)

UL1RR031982 and UL1TR000161, and by the Wilson

Genetics Endowment of the George Washington University

School of Medicine and Health Sciences. The content is

solely the responsibility of the authors and does not

necessarily represent the official views of the funding

agencies. Dr. Maynard is named as a co-inventor on a

patent filed by Beth Israel Deaconess Medical Center for the

use of angiogenesis-related proteins for diagnosis and

treatment of preeclampsia. All other authors declare no

conflicts of interests. The authors alone are responsible for

the content and writing of this article. A portion of the

findings in this manuscript were presented at the Society for

Gynecologic Investigation 59th Annual Scientific

Meeting, San Diego, CA, March 2124, 2012 and published

in the supplementary edition of Reproductive Sciences as

follows:

(1) Maynard S, Crawford S, Bathgate S, et al. Mid-gestationangiogenic biomarker levels are increased in women at

high-risk for preeclampsia. Reprod Sci 2012:19, No 3

(Supplement): T326.

(2) Moore Simas T, Crawford S, Bathgate S, et al.

Angiogenic biomarkers are altered prior to preeclampsia

onset in high-risk women. Reprod Sci 2012;19, No 3

(Supplement): T327.

(3) Maynard S, Crawford S, Bathgate S, et al. Mid-gestation

angiogenic biomarker levels are increased in women at

high-risk for preeclampsia. Society for Gynecologic

Investigation (SGI) 59th Annual Scientific Meeting,

San Diego, CA, March 2124, 2012.

(4) Moore Simas T, Crawford S, Bathgate S, et al.

Angiogenic biomarkers are altered prior to preeclampsia

onset in high-risk women. Society for Gynecologic

Investigation (SGI) 59th Annual Scientific Meeting,

San Diego, CA, March 2124, 2012.

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Supplementary material available online

Supplementary Figure S1


angiogenic biomarkers for prediction of early preeclampsia onset in high-risk women

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