First-trimester metabolomic detection of late-onset preeclampsia

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OBSTETRICS

First-trimester metabolomic detectionof late-onset preeclampsiaRay O. Bahado-Singh, MD, MBA; Ranjit Akolekar, MD; Rupasri Mandal, PhD; Edison Dong, BSc;Jianguo Xia, PhD; Michael Kruger, MS; David S. Wishart, PhD; Kypros Nicolaides, MD

OBJECTIVE: We sought to identify first-trimester maternal serum bio-markers for the prediction of late-onset preeclampsia (PE) usingmetabolomic analysis.

STUDY DESIGN: In a case-control study, nuclear magnetic resonance–ased metabolomic analysis was performed on first-trimester maternalerum between 11�0-13�6 weeks of gestation. There were 30 casesf late-onset PE, ie, requiring delivery �37 weeks, and 59 unaffectedontrols. The concentrations of 40 metabolites were compared be-ween the 2 groups. We also compared 30 early-onset cases to the

2013;208:58.e1-7.

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occurs �34-35 weeksSee Journal Club, page 87

58.e1 American Journal of Obstetrics & Gynecology JANUARY 2013

RESULTS: A total of 14 metabolites were significantly elevated and 3 sig-nificantly reduced in first-trimester serum of late-onset PE patients. A com-plex model consisting of multiple metabolites and maternal demographic char-acteristics had a 76.6% sensitivity at 100% specificity for PE detection. Asimplified model using fewer predictors yielded 60% sensitivity at 96.6% spec-ificity. Strong separation of late- vs early-onset PE groups was achieved.

CONCLUSION: Significant differences in the first-trimester metaboliteswere noted in women who went on to developed late-onset PE and be-tween early- and late-onset PE.

Key words: metabolomics, preeclampsia prediction
ate-onset group.
Cite this article as: Bahado-Singh RO, Akolekar R, Mandal R, et al. First-trimester metabolomic detection of late-onset preeclampsia. Am J Obstet Gynecol

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Metabolomics, a relatively recentaddition to the “omics” family,

involves the high-throughput character-ization and interpretation of the small-molecule metabolites (�1500 d) pro-duced by cells, tissues, and organisms.To date, �8000 human metabolites from

80 chemical classes have been identi-ed or catalogued.1 As technology im-

From the Department of Obstetrics andGynecology, Wayne State University, Detroit,MI (Dr Bahado-Singh and Mr Kruger); HarrisBirthright Research Centre for Fetal Medicine,King’s College Hospital, London, UnitedKingdom (Drs Akolekar and Nicolaides); andthe Departments of Biological Sciences (DrsMandal and Wishart and Mr Dong) andComputing Sciences (Drs Xia and Wishart),University of Alberta, Edmonton, Alberta,Canada.

Received June 18, 2012; revised Nov. 4, 2012;accepted Nov. 8, 2012.

This study was partly supported by a grantfrom the Fetal Medicine Foundation, CharityNumber 1037116.

The authors report no conflict of interest.

Reprints not available from the authors.

0002-9378/$36.00© 2013 Published by Mosby, Inc.http://dx.doi.org/10.1016/j.ajog.2012.11.003

proves it is expected that this numbercould grow by a factor of �10.2 Becauseof the wide chemical diversity of metab-olites, their tight coupling with environ-mental interactions (food, drugs, gutmicrobiota), and their huge phenoty-pic-dependent concentration variations(�106), metabolomics offers a powerful,quantitative route to describe the actualphenotype of cells, tissues, or organismsin both normal and diseased states. Re-cently, significant advances have oc-curred both in metabolite identificationtechniques1,2 and computational tech-

iques3 for analyzing the large volume ofdata generated by metabolomic studies.There is currently tremendous interest inthe use of metabolomics for the charac-terization and early diagnosis of complexdiseases.4

Preeclampsia (PE) is a common obstet-ric disorder characterized by hypertensionand proteinuria during pregnancy. It is acause of significant morbidities, affectingthe health of both the mother5 and fetus.However, its causes and pathophysiol-ogy largely remain a mystery. It now ap-pears that PE is at least 2 fairly distinctdisorders, an early-onset and a late-onsetform.6,7 The early-onset variety typically

of pregnancy, and

is associated with significant fetal mor-bidities. The pathophysiology is thoughtto be failure of trophoblast invasion ofthe maternal spiral arteriole8 resulting inmaintenance of high maternal vascularresistance. This is consistent with thehigh frequency of placental underperfu-sion reported9 in this disorder.

The late-onset form is considered to beore of a maternal constitutional disor-

er10 due to underlying maternal micro-vascular disorders such as hypertensionor a genetic predisposition in which poortrophoblast invasion is thought to play aless significant role. Late-onset PE is sig-nificantly more common and while it of-ten has a mild course can be associatedwith significant clinical morbidities.11 Its therefore important to investigate itsathogenesis and if possible to developiomarker predictors of this disorder.Studies have now confirmed the clinical

easibility of first-trimester screening forarly-, late-, and intermediate-onset vari-ties of PE using demographic, clinical,iomarker, and uterine artery Doppler in-ormation.12,13 Recently, the National Col-aborating Center for Women’s and Chil-ren’s Health in the United Kingdom

ssued clinical guidelines14 for routine
early prenatal screening for PE based on
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maternal demographic, historical, andclinical characteristics. It is possible that,in the future, combining clinical withbiomarker predictors could further en-hance screening accuracy. Our primaryobjective was to evaluate the use ofmetabolomics to identify first-trimesterbiomarkers of late-onset PE. Second-arily, we evaluated the diagnostic accu-racy of these markers for late-onset PEprediction. Finally, we evaluated the ca-pability of metabolomics for distin-guishing late- from early-onset PE.

MATERIALS AND METHODSStudy populationThis study is part of an ongoing prospec-tive study being conducted by the FetalMedicine Foundation, London, UnitedKingdom, for the first-trimester predic-tion of important fetal and obstetric disor-ders. Institutional review board project#02-03-033 approval was obtained onMarch 14, 2003. The details of patient eval-uation and study methods have been ex-tensively described in a prior report ofmetabolomic prediction of early-onsetPE.15 A routine population of Britishwomen was prospectively screened fromMarch 2003 through September 2009and they all gave written consent to partic-ipate in the study, which was approved by

TABLE 1Demographic and other characterisonset preeclampsia vs control grou

Parameter

No. of cases...................................................................................................................

Maternal age, y, mean (SD)...................................................................................................................

Racial origin, n (%)..........................................................................................................

White..........................................................................................................

Black..........................................................................................................

Asian..........................................................................................................

Mixed...................................................................................................................

Nullipara, n (%)...................................................................................................................

Weight, kg, mean (SD)...................................................................................................................

Crown-rump length, mm, mean (SD)...................................................................................................................

Uterine pulsatility index, MoM, mean (SD)...................................................................................................................

MoM, multiples of median.

Bahado-Singh. Late-onset preeclampsia, metabolomics. A

the King’s College Hospital Research Eth- t

ics Committee. Briefly, women were re-cruited at 11�0�13�6 weeks’ gestation.

aternal characteristics and medical his-ory were documented and first-trimes-er ultrasound, including crown-rumpength (CRL) and uterine artery Dopplerulsatility index (PI), was measured.ata collection was planned before lab-ratory testing. The lower of the left andight uterine artery Doppler PI value wassed for PE prediction. Maternal serumamples were also obtained and stored at

80°C for subsequent laboratory analysis.he long-term objective of the project is toevelop and evaluate new markers and ex-

sting biomarkers of PE. Apart from thereviously published study on early-onsetE15 these cases represent the first metabo-

omic analysis of PE from this studyopulation.A total of 30 singleton pregnancies that

ubsequently developed late-onset PE re-uiring delivery �34 weeks formed thetudy group and were matched with 60 un-ffected controls. These cases were not pre-iously used in any prior publication. Theate-onset PE cases were selected at ran-om from our database of available storedamples. Each case of late-onset PE was

atched with 2 controls who delivered ahenotypically normal neonate with ap-ropriate birth weight for gestational age at

s: late-

te-onseteeclampsia Control P value

59 —..................................................................................................................

.2 (6.4) 30.8 (5.6) .81..................................................................................................................

.02..................................................................................................................

(46.7) 44 (74.6)..................................................................................................................

(46.7) 14 (23.7)..................................................................................................................

(0) 1 (1.7)..................................................................................................................

(6.7) 0 (0)..................................................................................................................

(40) 31 (52.5) .37..................................................................................................................

.9 (15.7) 67.7 (12.2) .03..................................................................................................................

.0 (9.1) 62.7 (7.6) .69..................................................................................................................

.07 (0.35) 0.98 (0.31) .22..................................................................................................................

Obstet Gynecol 2013.

erm and did not develop any hypertensive c

JANUARY 2013 Ameri

isorder of pregnancy and who had bloodollected within 3 days of assessment of theate-onset PE case. There was no evidentource of bias in the selection of cases orontrols. The definition of PE used washat proposed by the International Societyor the Study of Hypertension in Preg-ancy,16 namely systolic pressure �140m Hg or diastolic pressure �90 mm Hg

n �2 occasions 4 hours apart �20 weeksf gestation, in women who were previ-usly normotensive. Proteinuria was de-ned as a total of 300 mg in a 24-hour urineollection or 2 readings of at least 2� pro-einuria on a midstream or catheterizedrine specimen in the absence of a 24-our urine collection must also haveeen present in addition to the hyperten-ion. Proteinuria must also have beenresent in addition to the hypertension

or the diagnosis of PE. No HELLP syn-rome or gestational hypertension casesere included.Nuclear magnetic resonance (NMR)

pectrometry was used for metabolitedentification in the specimen samples.ample preparation and NMR spectros-opy methods were performed as de-ailed previously using a 500-MHz Var-an Inova NMR spectrometer (Variannc, Palo Alto, CA).15 Forty metabolitesn each serum sample were identifiednd quantified in each case and controlample using commercial (NMR Suite.1; Chenomx Inc, Edmonton, Alberta,anada) spectral fitting software con-

aining an NMR spectral reference li-rary of �200 compounds. On initialetabolomic analysis, readers were

linded to patient status.

Statistical analysisRecommended statistical procedures formetabolomic analysis3,17 were followed.Log scaling was used for normalization ofall metabolomic data. Principal compo-nent analysis (PCA) and partial leastsquares discriminant analysis (PLS-DA)were performed to identify patterns.3 PCAs an unsupervised classification techniqueor transforming a complex collection ofata points such that the important prop-rties of the sample can be more simplyisplayed along the X- and Y-axes. Two

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Bahado-Singh. Late-onset preeclampsia, metabolomics. Am J Obstet Gynecol 2013. (continued )

Research Obstetrics www.AJOG.org

are significant metabolite differences be-tween the normal and control groups.

PLS-DA is used to enhance the separa-tion between the groups by summarizingthe data into a few latent variables thatmaximize covariance between the re-sponse and the predictors.18 To mini-

ize the possibility that the observedeparation on PLS-DA is due to chance,ermutation testing was performed.his involved repeated (2000 times) data

ampling, with different random label-ng. A significant P value indicates thathe separation observed between groupss very unlikely to be due to chance. The

etaboAnalyst computer program wassed to perform (PCA and PLS-DA)nalyses.19 A variable importance in pro-

jection (VIP) plot18 is a plot ranking themetabolites based on their importancein discriminating study from controlgroups. Metabolites with the highestvalues on X-and Y-axes are the mostpowerful group discriminators.

In comparing the concentrations ofmetabolites between groups, outlier test-ing was performed using Dixon Q test.20

The Dixon Q test is used for identifica-tion of outliers in the dataset and re-places that value with the one closest toit. Replacement of outliers helps to meetthe assumption of normal distributionand equal variance between groups. Only asinglevalue(forvaline)wasadjusted inthisfashion, however. Kolmogorov-Smirnovand Shapiro-Wilk tests of normal distribu-tion were performed. Metabolite concen-trations in late-onset PE vs controls werecompared using the 2-tailed t test. Mann-

hitney U test was used in comparing me-abolite concentrations between groupshat were not normally distributed. Otherndependent variables including fetal CRL,terine artery Doppler, PI, and maternalge, parity, weight, ethnicity, smoking, andedical disorders were included in the

enetic computing analyses along with theetabolite concentrations for PE

rediction.Genetic programming is a branch of

volutionary computing while geneticomputing is a branch of genetic program-ing. The advantage of genetic computing

ies in its ability to handle nonnormallyistributed outcome measures and the

arge volume of data generated from “om-

58.e3 American Journal of Obstetrics & Gynecolog

TABLE 2Serum metabolite concentrations by nuclear magnetic resonance

Metabolite

Late-onset PE, mean(SD) (concentrationin �mol/L)

Controls, mean (SD)(concentration in�mol/L) P value

Foldchange

No. of cases 30 59 — —..............................................................................................................................................................................................................................................

Glycerol 800.7 (541.7) 312 (296.8) � .001 2.4..............................................................................................................................................................................................................................................

1-methylhistidine 70.3 (40.0) 38.9 (20.3) � .001 1.7..............................................................................................................................................................................................................................................

Valine 142.5 (50.6) 121.6 (43.3) � .05 1.1..............................................................................................................................................................................................................................................

Carnitine 46.8 (24.8) 27.8 (20.0) .001 1.7..............................................................................................................................................................................................................................................

Acetone 22.1 (11.4) 14.9 (8.5) .003 1.6..............................................................................................................................................................................................................................................

Trimethylamine 6.03 (2.0) 7.6 (3.3) .005 0.88..............................................................................................................................................................................................................................................

Isopropanol 10.7 (4.6) 7.7 (4.8) .006 1.4..............................................................................................................................................................................................................................................

Pyruvate 83.1 (45.8) 62.1 (24.1) .006 1.3..............................................................................................................................................................................................................................................

Hydroxyisovalerate_3 6.5 (3.3) 4.7 (2.5) .008 1.4..............................................................................................................................................................................................................................................

Acetamide 11.9 (7.8) 16.1 (6.4) .008 0.73..............................................................................................................................................................................................................................................

Glucose 4312.9 (1783.0) 3362.4 (765.9) .008 1.2..............................................................................................................................................................................................................................................

Dimethylamine 3.2 (1.7) 4.4 (2.1) .012 0.74..............................................................................................................................................................................................................................................

Hydroxybutyrate_2 28.0 (14.4) 21.2 (7.5) .02 1.3..............................................................................................................................................................................................................................................

Creatinine 63.2 (16.5) 55.1 (14.7) .021 1.1..............................................................................................................................................................................................................................................

Creatine 41.5 (5.9) 33.4 (15.6) .024 1.2..............................................................................................................................................................................................................................................

Citrate 85.9 (26.9) 74.1 (23.5) .028 1.3..............................................................................................................................................................................................................................................

Hydroxybutyrate_3 49.9 (46.7) 29.7 (19.1) .038 1.4..............................................................................................................................................................................................................................................

Leucine 114.5 (98.5) 87.1 (61.9) .112 1.2..............................................................................................................................................................................................................................................

Acetate 80.6 (101.5) 49.1 (52.5) .12 1.6..............................................................................................................................................................................................................................................

Betaine 33.3 (23.6) 21.6 (9.4) .14 1.5..............................................................................................................................................................................................................................................

Glutamine 253.1 (131.1) 218.5 (66.9) .182 1.2..............................................................................................................................................................................................................................................

Ethanol 67.7 (42.6) 56.1 (37.2) .19 1.2..............................................................................................................................................................................................................................................

Ornithine 36.8 (17.4) 42.3 (22.5) .24 0.87..............................................................................................................................................................................................................................................

Acetoacetate 18.9 (9.8) 16.5 (9.6) .27 1.1..............................................................................................................................................................................................................................................

Alanine 366.8 (204.8) 323.8 (151.2) .27 1.1..............................................................................................................................................................................................................................................

Lactate 1213.1 (564.7) 1100.9 (689.3) .44 1.1..............................................................................................................................................................................................................................................

Methionine 24.7 (7.4) 23.6 (6.5) .48 1.0..............................................................................................................................................................................................................................................

Threonine 157.2 (60.2) 166.2 (62.5) .5 0.94..............................................................................................................................................................................................................................................

Propylene glycol 11.1 (5.0) 11.8 (4.9) .51 0.93..............................................................................................................................................................................................................................................

Formate 27.0 (13.8) 29.0 (17.8) .6 1.02..............................................................................................................................................................................................................................................

Tyrosine 65.1 (23.7) 62.3 (21.7) .6 1.04..............................................................................................................................................................................................................................................

Proline 172.2 (57.7) 165.7 (56.4) .61 1.04..............................................................................................................................................................................................................................................

Serine 148.4 (103.4) 158.6 (92.2) .635 0.93..............................................................................................................................................................................................................................................

Arginine 136.3 (55.5) 131.2 (35.9) .65 1.04..............................................................................................................................................................................................................................................

Asparagine 31.3 (11.3) 32.4 (13.7) .71 0.96..............................................................................................................................................................................................................................................

Phenylalanine 78.0 (45.9) 80.9 (45.4) .78 0.96..............................................................................................................................................................................................................................................

Glycine 238.4 (129.3) 244.0 (115.7) .84 0.97..............................................................................................................................................................................................................................................

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ics” laboratory analyses21 and it has previ-ously been successfully used for metabolo-mic analysis.15,21 Genetic computingenerates rules by which an optimal num-er of variables can be selected from a largeumber of exploratory variables for theptimal prediction of the outcomes of in-erest. The Gmax computer program, ver-ion 11.09.23 (Genetic Computing Consul-ants Limited, London, UK) was used forenetic computing analysis.

In addition, using a limited number ofndependent biochemical and demo-raphic predictor variables, logistic re-ression was used to generate probabilityquations for the prediction of PE. Basedn the regression equations generated,

TABLE 2Serum metabolite concentrations by

Metabolite

Late-onset PE, mea(SD) (concentrationin �mol/L)

Choline 172.3 (341.5)...................................................................................................................

Succinate 13.2 (13.8)...................................................................................................................

Malonate 23.1 (14.3)...................................................................................................................

Isobutyrate 7.6 (2.5)...................................................................................................................

PE, preeclampsia.


FIGURE 1Principal componentsanalysis plot

Principal component analysis plot showing someseparation between late-onset preeclampsia(PE) (green) and control (red) for nuclear mag-netic resonance spectrometry.Bahado-Singh. Late-onset preeclampsia, metabolomics.Am J Obstet Gynecol 2013.

ndividual probability for developingate-onset PE was calculated. Receiver op-rating characteristics curves with sensitiv-ty plotted against false-positive rates (1-pecificity) were generated along with the5% confidence interval and/or P valuesor the area under the curve. For the pri-

ary analysis, power analysis indicatedhat a minimum of 17 cases was needed inach group to have 80% power for a-sided P � .0001.After the primary analysis, a normal

roup of 60 control cases previously re-orted for the early-onset PE metabolo-ic study15 derived from the same pa-

ient population was subsequently addedo the current study group of 30 casesnd 59 control patients to permit suffi-ient power to perform the regressionnalyses.22 Finally, we used PCA and

PLS-DA analyses to determine whetherlate-onset PE could be differentiatedfrom early-onset PE by directly compar-ing the 30 early-onset PE cases reportedin a prior publication15 with the 30 late-

nset PE cases in the current study.

RESULTSResults for a total of 30 cases and 59 (of60) controls used for the primary analy-sis are reported, as insufficient volume ofserum was available for metabolomicanalysis in one of the control samples.Table 1 compares the maternal age,weight, race, and gestational age at bloodcollection was determined by and repre-sented by CRL measurements, betweenlate-onset PE and normal cases. Therewas a significant difference in maternalrace with a lower percentage of whites

clear magnetic resonance (continued)

Controls, mean (SD)(concentration in�mol/L) P value

Foldchange

185.7 (351.6) .87 0.93..................................................................................................................

13.4 (12.9) .9 0.83..................................................................................................................

23.1 (8.7) .97 0.98..................................................................................................................

7.6 (3.2) 1 1.0..................................................................................................................


and higher percentage of blacks in the f

JANUARY 2013 Ameri

late-onset PE group as well as greaterbody weight in the PE group (Table 1). InTable 2 we assessed the pairwise differ-ences between individual metaboliteconcentrations for controls and late-on-set PE patients. A total of 17 metaboliteswere present in significantly differentconcentrations in late-onset PE vs con-trol groups. Fourteen of these 17 metab-olite concentrations were increased inthe late-onset PE group while 3 were re-duced compared to normals. The P val-ues determined via a Student t test provethat for 17 of the metabolites, the indi-vidual concentration differences are sig-nificant (meaningful and reproducible)at levels determined by at P� .05. The foldchanges are also presented in Table 2.Some separation and discrimination isachieved between the cases of late-onsetPE and the controls from the PCA anal-ysis of the NMR data is shown in Figure 1.

he separation, however, did not appearramatic. The PLS-DA analysis resulted

n a detectable separation between late-nset PE group (in green) compared toormal cases (in red) (Figure 2). Permu-

ation testing revealed that the observedeparation of the late-onset PE from theormal group was highly unlikely to beue to chance (P � .0005). Figure 3 dis-lays the VIP plot. Glycerol, carnitine,ethylhistidine, and acetone appeared

o be the most important metabolites foristinguishing late-onset PE from nor-al cases, based on VIP analysis. A heatap is shown on the right of the VIP

lot. Red indicates that a particular me-abolite concentration is increased inate-onset PE cases while green indicateseduced concentration compared toormal controls.In Table 3, using genetic computing

nalysis of the primary dataset, ie, 30 late-nset PE cases and 59 controls, high diag-ostic accuracy for late-onset PE detectionas achieved. First-trimester uterine arteryoppler measurements were not signifi-

antly different between case and controlroups (Table 1) and did not improve theodel for late-onset PE prediction. Logis-

ic regression-based prediction, using anxpanded number of subjects, ie, 30 late-nset PE and a total of 119 normal cases,imilarly showed good diagnostic accuracy

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respective derived probability equationsfor PE are also provided in the legend ofTable 4. For the regression-based analysis alimited number of predictors, glycerol andcarnitine, were chosen based on the VIPplot (Figure 3) along with either maternalrace or alternatively body weight. The lat-ter 2 are known to be risk factors for late-onset PE.23 One-methylhistidine was alsocombined with glycine and carnitine toform a biochemistry-only algorithm.There was no significant correlation notedbetween maternal weight or racial statusand glycine, carnitine, or 1-methylhisti-dine in either the late-onset PE (P � .37) ornormal (P � .36) patient group. Neithermaternal weight nor race appeared to sig-nificantly enhance PE prediction overthese metabolites alone (Table 4) in the re-gression-based analyses.

Importantly, significant discriminationbetween early- and late-onset PE wasachieved using metabolomics (Figure 4,A). This result appears to support thecurrent view that these are different dis-orders. Glycerol, acetate, trimethyl-amine, and succinate appeared to be themost important metabolites for distin-guishing the 2 types of PE based on VIPanalysis shown in Figure 4, B.

COMMENTUsing NMR-based metabolomic analy-sis we found 17 metabolites that werepresent in statistically significantly dif-ferent concentrations in late-onset PEcases compared to normal controls. Us-ing metabolites by themselves or com-bined with traditional maternal demo-graphic and clinical markers, significantdiagnostic accuracy for late-onset PE de-tection was achieved. Late-onset PE ismore common than the early-onset va-riety and can be associated with signifi-cant morbidity,11 justifying interest in itsdetection. We were also able to distin-guish early- from late-onset PE usingmetabolomics. The heterogeneous etiol-ogy10 of late-onset PE has posed a partic-

lar challenge in developing predictiveiomarkers. This heterogeneity is how-ver a strength and not a limitation foretabolomic analysis. The findings are

ll the more interesting given the long
nterval between testing and disease d

anifestation. Metabolites are known toe stable when stored at �80°C and sincehe cases and controls were collected athe same time and stored under identicalonditions variability due to storage isnlikely to have accounted for the ob-erved differences between groups. First-rimester prediction of PE could poten-ially facilitate the use of prophylacticspirin �16 weeks of gestation.

The fact that the fold changes in metab-lite concentrations seen in our study areot very large is not unexpected. Bloodust be maintained in very tight homeo-

tasis. In some cases, a 2-fold change in vi-al metabolite levels (eg, glucose or creati-ine) represents the difference betweenealth and disease. A 3-fold change canean the difference between life and

eath. Further, late-onset PE is an ex-remely heterogenous disorder,10 thus aingle biomarker that definitively distin-uishes (very high fold change) this disor-er from normal would therefore be bothiologically implausible and would obviatehe need for metabolomic analysis. Astated, the advantage of metabolomic anal-sis for complex disorders is the ability todentify a constellation of markers that in-

FIGURE 2Partial least squares plots of metab

, Two-dimensional (2D) and B, 3-dimensionaeparations using nuclear magnetic resonance–reeclampsia (PE) cases (green) versus controlsroups indicate that significant discrimination ofion differences. (30 early-onset PE cases/59 co2000 permutations or resamplings were perforahado-Singh. Late-onset preeclampsia, metabolomics. Am J

ividually have modest diagnostic accu-

y JANUARY 2013

acy but when combined is strongly pre-ictive of the disorder.The diagnostic accuracy of the metab-

lites combined with race and/or weight

ic data

D) partial least squares discriminant analysissed metabolomic measurements of late-onset). Clear clustering and segregation of 2 patient

ups is achieved based on metabolite concentra-ls.)(P � .0005).

tet Gynecol 2013.

FIGURE 3VIP plot: most discriminatingmetabolites in descendingorder of importance

Color boxes indicate whether metabolite concen-tration is increased (red) or decreased (green) inpreeclampsia (PE) versus control. (30 early-on-set PE cases/59 controls.)VIP, variable importance in projection.

Bahado-Singh. Late-onset preeclampsia, metabolomics.Am J Obstet Gynecol 2013.

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found in this study appears higher thanthat reported for widely available tradi-tional clinical markers used by them-selves. In a large prospective study offirst-trimester prediction of late-onsetPE, maternal risk factors and systolic, di-astolic, and mean arterial pressures hadsensitivities of 41.4%, 31.2%, 33.6%, and36.7%, respectively, at a false-positiverate of 10%.23 The concentrations of the

most metabolomic markers wereound to be independent of maternaleight or ethnicity. We also presented

vidence that metabolomic analysis canistinguish cases destined to developarly- vs late-onset PE cases.

Very little has been published on these of metabolomics for PE prediction.he limited data available, includingurs, uniformly support the value of thisiscovery tool.24-26 Interestingly, thereas little overlap between the metabo-

ites of significant diagnostic value in theurrent manuscript compared to theseublications. The explanation for differ-nces could be due to a number of factors.

TABLE 3Prediction of late-onset preeclampon genetic computing (primary dat

Model Sensitivity, %

Parsimoniousb 60...................................................................................................................

Complexc 76.7...................................................................................................................

AUC, area under curve.a 30 early-onset preeclampsia cases/59 controls; b Methylhis

and others (pyruvate, Hydroxybutyrate_3, 1-methylhistidineaccount for 5.1% of model prediction of preeclampsia.


TABLE 4Prediction of late-onset preeclamplogistic regression model (expande

Model Sensitivity, %

Glycerolb 40...................................................................................................................

Glycerol and weightc 40...................................................................................................................

Glycerol, 1-methylhistidined 56.7...................................................................................................................

Respective probability equations based on the regression anaAUC, area under curve; CI, confidence interval.a Sixty normal cases added from prior publication15 (tota

considered in regression: glycerol, carnitine, and white/c Predictors considered in regression: glycerol, carnit0.033*weight; d Predictors considered in regression: gly0.002*glycerol � 0.032*methylhistidine-4.04.

Bahado-Singh. Late-onset preeclampsia, metabolomics. Am J

e used a quantitative NMR-based plat-orm that precisely determined the meta-olite concentrations while Kenny et al24

used a semiquantitative liquid chroma-tography (LC)-mass spectrometry (MS)-based platform. Despite some overlap, dif-ferent metabolomic platforms used in the2 studies are known to identify largelydifferent metabolites.1,3 For example,

MR identifies polar compounds, whileC-MS identifies nonpolar compounds.n addition, the metabolites identified byhe LC-MS study of Kenny et al24 were ob-

tainedviamassmatchingonly,whereas themetabolites identified/quantified in thecurrent study were identified based onauthentic standards and comprehensivespectral matching. Similarly, anotherstudy by Kenny et al25 used an electros-pray ionization MS method and the me-tabolites found to be significant predic-tors of PE were not surprisingly largelydifferent from those reported here. Astudy by Odibo et al26 using an LC/MS-MS platform reported accurate first-trimester prediction of PE. Again the

basedta)

ecificity, % AUC P value

.6 0.885 � .00001..................................................................................................................

0.96 � .00001..................................................................................................................

, glycerol, weight, race, acetoacetate; c Valine, weight, race,cerol, trimethylamine, medical disorder). Other metabolites


based onataseta)

ecificity, % AUC (95% CI) P value

.1 0.79 (0.692–0.888) � .001..................................................................................................................

0.796 (0.698–0.894) � .001..................................................................................................................

0.783 (0.667–0.898) � .001..................................................................................................................

.

late-onset preeclampsia and 119 normals); b Predictorswhite race. Prob (preeclampsia) � 0.002*glycerol-2.60;and weight. Prob (preeclampsia) � 0.002*glycerol �l, carnitine and 1-methylhistidine. Prob (preeclampsia) �


JANUARY 2013 Ameri

significant metabolites identified wereprimarily amino acids and largely dif-fered from those reported here. Theirmanuscript focused primarily on early-onset PE cases. At this stage, the differentcapabilities of the metabolomic plat-forms is an overall positive as it allows amore comprehensive search for poten-tially useful markers. Ultimately, largeprospective studies would be needed toidentify the optimal combination ofclinically useful markers for PE screen-ing and which platform might be moreadvantageous clinically.

Although not a primary objective ofthis study, metabolomics is known to bea powerful tool for helping to elucidatethe pathogenesis of complex disorders. Areview of the Human MetabolomicsDatabase1 was performed to determinethe role of some of the metabolitesnoted to be abnormal in late-onset PE.Two metabolites based on the VIP plot(Figure 3 and Table 2) were particu-larly notable for their differences be-tween the PE and normal groups: glyc-erol and carnitine. Glycerol is a3-carbon alcohol that forms the back-bone of glycolipids. It is therefore im-portant in lipid metabolism. Abnor-malities of lipid metabolism arerecognized to be a feature of PE and arethought to play a role in its pathogen-esis.27 Further, there is a well-docu-

ented relationship between maternalbesity and an increased risk of late-nset PE,28 which we also found in this

study. Carnitine is a quaternary am-monium compound that is responsiblefor the transport of lipids from the cy-toplasm into the mitochondria for en-ergy metabolism. It is made primarilyin the liver and kidneys, which are bothsignificantly affected in PE. Carnitinesinhibit oxidative stress and preventlipid peroxidation. Both of these areimportant pathological processes inPE. Increased carnitine productioncould be a response plausibly intendedto counter excessive oxidative metabo-lism of lipids. Based on the metabolo-mic data found here, a picture of a cen-tral disturbance in lipid metabolism inlate-onset PE emerges.

In conclusion, first-trimester metabo-

siaase

Sp

96.........

100.........

tidine, gly

siad d

Sp

94.........

95.........

95.........

lyses

l 30non-ine,cero

lomic markers appeared preliminarily to



be useful in discriminating late-onset PEfrom normal controls or early-onset PEcases. Based on these promising prelim-inary results, the value of metabolomicsfor clinical prediction and diagnosis ofPE should be further investigated. f

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First-trimester metabolomic detection of late-onset preeclampsia

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