metabolomic analysis for first-trimester trisomy 18 detection · 2017. 11. 1. · metabolomic...

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GENETICS

Metabolomic analysis for first-trimester trisomy 18 detectionRayO. Bahado-Singh,MD,MBA; Ranjit Akolekar,MD; Anushka Chelliah,MD; Rupasri Mandal, PhD; Edison Dong, PhD;Michael Kruger, MS; David S. Wishart, PhD; Kypros Nicolaides, MD

OBJECTIVE: The purpose of this study was to determine whether age had a 73.3% sensitivity and 96.6% specificity for trisomy 18
nuclear magnetic resonanceebased metabolomic markers in first-trimester maternal serum can detect fetuses with trisomy 18.
STUDY DESIGN: This was a study of pregnancies between 11 weeksand 13 weeks 6 days’ gestation. We analyzed 30 cases of trisomy 18and a total of 114 euploid cases. Nuclear magnetic resonanceebasedmetabolomic analysis was performed. A further analysis was performedthat compared 30 cases with trisomy 18 and 30 trisomy 21 (T21) cases.

RESULTS: Metabolomic markers were sensitive for trisomy 18detection. A combination of 2-hydroxybutyrate, glycerol and maternal

From the Department of Obstetrics andGynecology, Oakland University WilliamBeaumont School of Medicine, Royal Oak,MI (Dr Bahado-Singh); the Department ofObstetrics and Gynecology, King’s CollegeHospital, London, England, UK (Drs Akolekarand Nicolaides); the Department of Obstetricsand Gynecology,Wayne State University Schoolof Medicine, Detroit, MI (Dr Chelliah andMr Kruger); and the Departments of BiologicalSciences (Drs Mandal, Dong, and Wishart) andComputing Sciences (Dr Wishart), University ofAlberta, Edmonton, AB, Canada.

Received Nov. 18, 2012; revised Feb. 28, 2013;accepted March 21, 2013.

Supported in part by a grant from the FetalMedicine Foundation (Charity No. 1037116).

The authors report no conflict of interest.

Presented at the 33rd annual meetingof the Society for Maternal-Fetal Medicine,San Francisco, CA, Feb. 11-16, 2013.

Reprints: Ray O. Bahado-Singh, MD, MBA,Beaumont Health System, 3535 West13 Mile Rd., Suite 233, Royal Oak, MI [email protected].

0002-9378/$36.00ª 2013 Mosby, Inc. All rights reserved.http://dx.doi.org/10.1016/j.ajog.2013.03.028

detection, with an area under the receiver operating curve: 0.92 (P<.001). Other metabolite markers, which include trimethylamine, weresensitive for distinguishing trisomy 18 from T21 cases.

CONCLUSION: This is the first report of prenatal trisomy 18 detectionthat has been based on metabolomic analysis. Preliminary resultssuggest that such markers are sensitive not only for the detectionof fetal trisomy 18 but also for distinguishing this aneuploidyfrom T21.

Key words: metabolomics, screening, trisomy 18

Cite this article as: Bahado-Singh RO, Akolekar R, Chelliah A, et al. Metabolomic analysis for first-trimester trisomy 18 detection. Am J Obstet Gynecol 2013;209:65.e1-9.

etabolomics involves the com-
M prehensive cataloging and char-acterization of the small molecules thatare found in cells, organs, and tissues.Metabolomics has allowed scientists todevelop an increasingly detailed picture
of the biochemical activity of cells inboth their normal and diseased states.The impact of external influences (eg,toxins, medications, and environmentalchanges) can be mapped based on met-abolic profiles or metabolic signaturesthat have been measured by modernmetabolomic techniques. Advances ininstrumentation and improvements tobioinformatic tools are now permittingvery extensive analysis of human bio-fluids with the use of these techniques.1,2

Metabolomics has shown significantpromise for the discovery of new bio-markers for the detection of a number ofcomplex clinical disorders.3 However, theuse of metabolomics in obstetrics is arelatively new phenomenon. Neverthe-less, a number of recent studies haveshown its value in the prediction of ordetection of biomarkers for preeclamp-sia and preterm labor.4-7 More recently,we identified a novel set of maternalserum metabolomic markers for thefirst-trimester detection of fetuses withDown syndrome.8 Trisomy 18 is the sec-ond most common aneuploidy afterDown syndrome.Maternal first-trimesterscreening for trisomy 18 detection hasbeen an area of research interest for manyyears9 and the standard of clinical care formore than a decade.10,11

Our objectives were, first, to deter-mine whether maternal first-trimester

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serum from trisomy 18 pregnancieshad a qualitatively or quantitatively dis-tinct metabolomic profile relative tonormal pregnancies. Second, we wantedto evaluate the sensitivity and specificityof serum metabolite markers for thedetection of trisomy 18. Finally, wewanted to compare trisomy 18 withpreviously reported cases of Down syn-drome to determine whether these 2aneuploidies could be distinguished withthe use of first-trimester maternal serummetabolomic analysis.8

MATERIALS AND METHODS

Patients and specimen samples were ob-tained as part of an ongoing prospectivestudy by the Fetal Medicine Foundationin London, England. The study wasapproved by the institutional reviewboard of King’s College Hospital, London,England. In this study, an averagerisk population of British women werescreened and recruited prospectively from2003-2009. Written consent was obtainedfrom all participating women. The detailsof the study population, consent ap-proval by institutional authorities, spec-imen collection, storage, transportation,and metabolomic analysis have been de-scribed in a previous publication.7 Briefly,maternal demographic data, sonogra-phic measurements, and karyotype datawere entered prospectively in a database.

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TABLE 1Comparison of ultrasound and demographic markers

ParameterTrisomy 18(n [ 30)a

Control cases(n [ 60)a P value

Crown rump length, mm 58.7 � 6.5 65.5 � 8.8 < .001

Delta nuchal translucency, mm 4.0 � 2.6 0.7 � 0.3 < .001

Maternal age, y 36.7 � 5.2 31.3 � 6.4 < .001

Maternal weight, kg 69.7 � 20.6 66.4 � 13.7 .29

Maternal race, % .001

White 27.0 � 28.7 67.0 � 71.3

Nonwhite 3.0 � 10.0 47.0 � 41.2

Parity: nulliparous, % 9.0 � 30.0 52.0 � 45.6 .149a Data are given as mean � SD.

Bahado-Singh. Metabolomics and trisomy 18. Am J Obstet Gynecol 2013.

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Nuchal translucency measurement wasperformed; pregnancy-associated plasmaproteineA and free-b human chorionicgonadotropin measured as a part ofroutine clinical screening for aneuploidy.Delta nuchal translucency (DNT) valuesor the difference between the observedNT measurement and the median ex-pected value based on the crown rumplength (CRL) were calculated.12 Maternalvenous blood was collected in plainvacutainer tubes (Becton Dickinson UKLimited, Oxfordshire, UK), processedwithin 15minutes of blood collection, andcentrifuged at 3000 rpm for 10minutes toseparate serum from packed cells. Theserum that was obtained was separatedinto 0.5-mL aliquots and stored ate80�Cuntil subsequent analysis. None of thesamples had been thawed and refrozenpreviously. The serum samples weretransported by air courier to the labora-tory in Alberta, Canada, for metabolomictesting; the samples were packed in dry iceand maintained in a frozen state at alltimes. Case and control specimens for theprimary study were collected within 1week of each other. The caseswith trisomy18 had a fetal karyotype confirmed basedon chorionic villus sampling; controlsubjects were selected from women whodelivered live born neonates at full termwith a normal karyotype or a normalphenotypic examination.

The study was limited to 11-13 weeks6 days’ gestation. An important advan-tage of metabolomic analysis is that a

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relatively large number of markers can beidentified simultaneously that leads tothe development of several differentpredictive algorithms. We looked atmetabolomics-only algorithms and al-gorithms that combined metaboliteswith maternal demographic and othermarkers. In the primary metabolomicanalysis, we conducted a case-controlledstudy of 30 cases with trisomy 18 and60 matched euploid control specimens.Subsequently, to increase the studypower and to facilitate the performancelogistic regression analyses with an ex-panded number of predictive variables,an additional set of 54 euploid controlspecimens from a previously reportedtrisomy 21 study was also added forfurther analyses.8 Only those measuredmetabolites that were common to each ofthe trisomy 18 and expanded controlcases were considered for this particularanalysis. In addition, the metabolomicanalysis was limited to cases in which alldemographic and sonographic measure-ments were available. This resulted in atotal of 30 cases with trisomy 18 and 114normal control subjects. For the sake ofuniformity, the 30 cases with trisomy 18and combined 114 control subjects werealso used for TheGmax analyses (version11.9.23; Available at: www.thegmax.com.Accessed April 30, 2013) so that the re-sults of standard logistic regression andTheGmax analyses could be compared.Specimen collection, processing, andmetabolomic analysis for the 54 control

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subjects who were used in the trisomy21 study were performed with the samestandards and were conducted at thesame time.8

Metabolomic data collection was per-formed with the Varian Inova 500 MHzNMR (nuclear magnetic resonance)spectrometer (International EquipmentTrading Ltd, Vernon Hills, IL) as previ-ously described.7 Plasma samples werefiltered through 3-kd cutoff centrifugalfilter units (Amicon Microcon YM-3;Sigma-Aldrich, St. Louis, MO) to re-move plasma proteins. Aliquots (350 mL)of each plasma sample were transferredinto the centrifuge filter devices andspun (10,000 rpm for 20 minutes)to remove macromolecules (primarilyprotein and lipoproteins) from the sam-ple. If the total volume of the samplewas <300 mL, an appropriate amountfrom a 50-mmol NaH2PO4 buffer (pH 7)was added until the total volume ofthe sample was 300 mL. Any samplethat had to have buffer added to bringthe solution volume to 300 mL was an-notated with the dilution factor, andmetabolite concentrations were cor-rected in the subsequent analysis. Sub-sequently, 35 mL of D2O and 15 mL of astandard buffer solution (11.667 mmoldisodium-2, 2-dimethyl-2-silcepentane-5-sulphonate, 730 mmol imidazole, and0.47% NaN3 in H2O) was added to thesample.

The plasma sample (350 mL) wasthen transferred to a standard microcellNMR tube (Shigemi, Inc, Allison Park,PA) for subsequent spectral analysis. All1H-NMR spectra were collected on a500-MHz Inova (Varian Inc, Palo Alto,CA) spectrometer equippedwith a 5-mmITCN Z-gradient PFG cold-probe. Thesinglet produced by the disodium-2,2-dimethyl-2-silcepentane-5-sulphonatemethyl groups was used as an internalstandard for chemical shift referencing(set to 0 ppm) and for quantification. All1H-NMR spectra were processed andanalyzed with the Chenomx NMR SuiteProfessional Software package (version7.1; Chenomx Inc, Edmonton, Alberta,Canada) that allows for qualitative andquantitative analysis of an NMR spec-trum by manually fitting spectral sig-natures from an internal database to

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FIGURE 1Partial least squares discriminant analysis

A, 2-dimensional and B, 3-dimensional plots of maternal serum metabolites from trisomy 18 and

control cases. Cases with trisomy 18 are in red ; control cases are in green. Clustering and

segregation of the 2 patient groups indicate that significant discrimination of groups is achieved with

metabolomics (from the primary analysis: trisomy 18, 30 cases; euploid, 60 control cases). We

performed 2000 permutations or resamplings (P ¼ .0095).


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the observed spectrum. Specifically, thespectral fitting for all detected metabo-lites was done with the use of the stan-dard Chenomx 500 MHz metabolitelibrary. Typically, 90% of the visiblepeaks were assigned to a compound,and >90% of the spectral area couldbe fit routinely with the use of theChenomx spectral analysis software.It has been shown previously thatthis fitting procedure provides absoluteconcentration accuracy of �90%. Eachspectrum was processed and analyzedby at least 2 NMR spectroscopists tominimize compound misidentificationandmisquantification. Metabolomic dataanalysis was performed with Metab-oAnalyst, which is aweb-based server thatsupports a wide range of univariate andmultivariate statistical techniques.13 Lognormalization of the metabolomic datawas performed before further analysis.Principal component analysis (PCA) isa multivariate statistical technique thatwe used to identify combinations ofmetabolites (principal components) thatbest distinguish trisomy 18 from euploidcases.14 On the PCA plot, the first prin-cipal component, which is the mostimportant discriminator, is representedon the x-axis; the second and third prin-cipal components, in order of discrimi-nating power, are plotted on the y- andz-axis in the 2- and 3-dimensional PCAplots, respectively.

Partial least squares discriminant anal-ysis (PLS-DA) further rotates the prin-cipal components that are identified onthe PCA plot to find the combination ofmetabolites that optimally distinguishescases from control subjects.14 Permuta-tion testing was performed with randomrelabeling of the metabolomic data andthe rerunning of the PLS-DA to mini-mize the probability that any observedseparation between the 2 groups couldbe due to chance. A total of 2000repeated relabelings were performed;the probability value that correspondedto the probability that the observedgroup separation was due to chance wasdetermined. MetaboAnalyst was usedfor both PCA and PLS-DA. A variableimportance in projection (VIP) plot,which indicates the contribution ofindividual metabolites in the separation

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FIGURE 2VIP plot of cases with trisomy 18 vs euploid cases

The most discriminating metabolites for distinguishing trisomy 18 vs euploid cases are shown in the

upper right hand corner of the plot. The legend to the right of the plot indicates whether the

metabolite is increased or decreased in the case and control samples (early trisomy 18, 30 cases;

control, 60 cases).

VIP, variable importance in projection.



of cases from control subjects was alsoconstructed.14

Finally, we determined whether se-rum metabolite markers were able to

TABLE 2Metabolites for trisomy 18 detectioncasesa: logistic regression method

Model Sensitivity, % S

2-hydroxybutyrate, glycerol 53.3 8

2-hydroxybutyrate, glycerol,maternal ageb

73.3 9

CI, confidence interval.

a Trisomy 18, 30 cases; matched normal control, 60 cases; b D

Bahado-Singh. Metabolomics and trisomy 18. Am J Obstet


distinguish pregnancies with trisomy 18from pregnancies with trisomy 21. PCAand PLS-DA were performed with theuse of the metabolomic data for 30 cases

ematched study and control

pecificity, %Area under thecurve (95% CI) P value

4.6 0.78 (0.68e0.88) < .001

6.6 0.92 (0.85e0.98) < .001

ataset used for this particular calculation.

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with trisomy 18 and for the 30 maternalserum specimens with trisomy 21 thathad been reported in a previous study.8

As noted previously, the specimens fortrisomy 18 and 21 were collected duringthe same period from the same patientpopulation and underwent metabolomicanalysis at approximately the same timein the same laboratory. AVIP curve wasalso generated to determine the meta-bolites that best distinguished pregnancyspecimens with trisomy 18 from speci-mens with trisomy 21.

The means and standard deviationsof all measured serum metabolite con-centrations were compared between thetrisomy 18 and control groups. Usinglogistic regression, we determined theindividual maternal risk of having apregnancy with trisomy 18 based onmetabolites, demographics, and ultra-sound measurements. Receiver operatorcharacteristic curves along with areasunder the receiver operator characteristiccurve (AUC) were determined. The sen-sitivity and specificity of several differentalgorithms for trisomy 18 detection werealso calculated. In all cases, a probabilityvalue of< .05 was considered statisticallysignificant.

Algorithms for trisomy 18 predictionwere also generated with the use ofgenetic programming along with theircorresponding sensitivity, specificity,AUC, and probability values. Geneticprogramming mimics the principle ofevolutionary genetics (ie, selection,recombination, and mutation) to de-velop rules for the optimal prediction ofan outcome of interest with the use ofmultiple and diverse prediction variables.The principles of genetic programminghave been summarized in some of ourprevious publications.7,15 Genetic pro-gramming analysis is a robust techniqueand is not limited by a lack of normality(ie, a Gaussian distribution) in the data,by sample size, or by missing data and isused currently in diverse fields from thepharmaceutical industry to banking. Weused a commercial computer programcalled TheGmax. We also tested for apossible correlation between maternaldemographic characteristics, gestationalage, and significant metabolites in bothtrisomy 18 and normal cases.

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TABLE 3Maternal first-trimester serum metabolite concentrations

Metabolite

Concentration, mmol/La

P valueTrisomy 18 (n [ 30) Control cases (n [ 114)

2-hydroxybutyrate 26.96 � 13.11 16.93 � 8.17 < .001

3-hydroxybutyrate 54.57 � 64.53 32.9 � 53.42 < .004

3-hydroxyisovalerate 7.38� 4.18 5.24� 3.84 .009

Acetate 28.26 � 12.83 22.9 � 31.703 .79

Acetoacetate 19.56 � 10.63 16.15 � 9.18 .08

Acetone 22.19 � 10.86 15.98 � 9.44 < .001

Alanine 293.4 � 83.44 258.62 � 85.38 < .015

Arginine 110.97 � 26.59 122.82 � 39.7 < .04

Asparagine 39.13 � 16.35 31.89 � 13.6 < .04

Betaine 26.51 � 11.92 25.27 � 10.03 .98

Carnitine 26.04 � 10.5 24.3 � 11.7 .256

Choline 10.2 � 2.2 31.6 � 100.6 .875

Citrate 77.6 � 19.1 66.1 � 22.9 .008

Creatine 30.3 � 12.8 35.6 � 15.15 .054

Creatinine 55.9 � 14.0 53.7 � 14.6 .496

Ethanol 58.8 � 72.3 63.7 � 54.1 .346

Formate 19.1 � 21.3 18.2 � 11.2 .483

Glucose 4133.0 � 903.7 3848.99 � 783.5 .124

Glutamine 292.9 � 77.3 250.2 � 77.3 .007

Glycerol 281.8 � 111.7 194.3 � 157.44 < .001

Glycine 261.6 (99.4) 209.8 (74.9) .001

Isobutyrate 6.77 � 2.5 5.6 � 4.7 .003

Isoleucine 41.1 � 10.7 38.6 � 16.6 .54

Lactate 1247.6 � 900.3 946.4 � 451.0 .03

Leucine 72.95 � 24.0 71.6 � 40.6 .04

Malonate 14.8 � 7.5 15.0 � 8.5 .54

Methionine 20.15 � 7.05 26.09 � 11.26 .007

Ornithine 36.83 � 13.32 28.20 � 10.61 .001

Phenylalanine 74.91 � 34.67 58.24 � 28.67 .003

Proline 135.31 � 42.98 125.42 � 48.53 .141

Propylene-glycol 9.66 � 4.10 7.94 � 3.69 .019

Pyruvate 74.66 � 35.11 59.70 � 23.3 .04

Serine 118.06 � 37.23 132.03 � 50.64 .067

Succinate 6.90 � 6.33 6.50 � 10.83 .018

Threonine 104.34 � 48.09 108.80 � 42.53 .36

Tyrosine 56.9 � 15.2 55.82 � 19.6 .46

Valine 121.9 � 44.00 119.6 � 38.1 .53

1-methylthistidine 51.2 � 17.6 43.4 � 22.3 .018a Data are given as mean� SD; 30 cases of trisomy 18 and 114 normal control cases (latter group includes 54 cases from aprevious study8).



RESULTS

In our initial metabolomic analysis, the30 cases with trisomy 18 were comparedwith 60 matched normal control sub-jects. Table 1 gives a comparison of thematernal demographic characteristics oftrisomy 18 and healthy euploid cases. Asseen from these data, CRL was lower inthe affected cases, and DNT and meanmaternal age were higher in the affectedcases. There was a significant differencein maternal racial heritage in the 2groups with a disproportionately highpercentage of white pregnancies amongthe euploid group. There were no sig-nificant differences in the frequency ofsmokers and medical disorders betweenthe 2 groups (not shown). In Figure 1,the 2- and 3-dimensional PLS-DA plotsprovide significant visual evidence ofclustering or separation of the 30 tri-somy 18 vs the 60 matched euploidcontrol subjects. Permutation analysiswith 2000 resamplings was performed todetermine whether the observed sepa-ration was due to chance. The results ofthe permutation analysis indicated thatthe probability that the observed sepa-ration or discrimination between tri-somy 18 and normal control specimenswas due to chance was .0095.

The VIP plot in Figure 2 indicatesthat 2-hydroxybutyrate was the mostdiscriminating metabolite for the sepa-ration of cases with trisomy 18 fromeuploid control specimens in the primaryanalyses. The heatmap on the right of theY-axis indicates that 2-hydroxybutyratewas elevated in trisomy 18 comparedwith the control specimens.

We performed analyses in the pri-mary study group (trisomy 18, 30 cases;euploid, 60 control subjects) to assess theperformance of metabolomic markers inthis smaller dataset in which cases wereexactly matched with control subjects.The purpose of this exercise was 2-fold:First, we wanted to minimize any un-recognized bias that would affect theresults (the smaller number of subjectsin this group limits the number of pre-dictive variables that can be consideredin each regression analysis); second,we wanted to determine whether thefindings in this matched group were

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TABLE 4Metabolites alone and combined with delta nuchal translucency fortrisomy 18 detectioneexpanded study groupa: logistic regressionmethod

Model Sensitivity, % Specificity, %Area under thecurve (95% CI) P value

2-hydroxybutyrate 53.3 86.0 0.80 (0.73e0.89) < .001

2-hydroxybutyrate,maternal age

63.3 95.6 0.89 (0.83e0.95) < .001

2-hydroxybutyrate,delta nuchaltranslucency

90.0 100.0 0.96 (0.90e1.0) < .001


a Trisomy 18, 30 cases; normal controls, 114 cases.



consistent with that of the expandedstudy group in which a larger number ofpredictive markers can be consideredsimultaneously in the regression anal-ysis and be presented subsequently. Asshown in Table 2, the combination of2-hydroxybutyrate and glycerol was sen-sitive for trisomy 18 detection. Maternalrace did not contribute to trisomy 18prediction when considered with thesemarkers.

For the second series of analyses, the30 cases with trisomy 18 were comparedwith an expanded number of euploidcontrol subjects (n ¼ 114). As notedearlier, the purpose of evaluating anexpanded patient group is to strengthenthe power for logistic regression ana-lyses. Table 3 compares the metaboliteconcentrations between these 2 groups.Of a total of 38 metabolites that wereevaluated in this expanded analysis, 21

TABLE 5Metabolites and trisomy 18 detectionTheGmax (parsimonious model)

Model Sensitivity, % S

2-hydroxybutyrate 66.7

Acetone and glucose 43.8

Delta nuchal translucencyand glucose

100.0 1


a Trisomy 18, 30 cases; normal controls, 114 cases.

Bahado-Singh. Metabolomics and trisomy 18. Am J Obstet


metabolites were present in significantlydifferent maternal serum concentrationsbetween the 2 groups. Several logisticregression analyses were performed onthis expanded dataset. The independentvariables that were used in the logisticregression analysis were chosen on thebasis of the most discriminating meta-bolite that was indicated by VIP analysisand important maternal demographicmarkers (such as age, race, and fetalgestational age). These are used currentlyin standard clinical screening. Thesewere used to develop prediction algo-rithms from which AUC curves alongwith the sensitivity and specificity werecalculated. As shown in Table 3, ametabolite-only algorithm that wasbased on 2-hydroxybutyrate had a 53.3%sensitivity and 86% specificity for tri-somy 18 detection.When this metabolitewas combined with maternal age, the

expanded study groupa:

pecificity, %Area under thecurve (95% CI) P value

80.7 0.80 < .001

98.2 0.81 < .0001

00.0 1.0 < .00001

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sensitivity increased to 63.3% and spec-ificity to 95.6%. Maternal race didnot contribute significantly to trisomy18 prediction in these analyses. When2-hydroxybutrate was combined withDNT, a sensitivity of 90.0% and a spec-ificity of 100.0%were achieved (Table 4).

Genetic programming analyses wereperformed on the expanded patientgroup of 30 cases with trisomy 18 and114 euploid cases. Table 5 shows thediagnostic performance with the use of alimited combination of metabolites.Finally, with an expanded number ofmetabolites (glycine, 2-hydroxybutyrate,ornithine, lactate, pyruvate, and gluta-mine), maternal age, weight, andethnicity explained 94.4% of the differ-ence between cases of euploid and caseswith trisomy 18. A 73.3% sensitivity and100% specificity were achieved for tri-somy 18 detection, with an AUC of 0.94(P < .00001).

A logical question to ask is whetherserum metabolomics can distinguish tri-somy 18 from trisomy 21 in first-trimesterpregnancies. Towards this end, a total of30 trisomy 21 pregnancy samples werecompared with 30 trisomy 18 specimens.As emphasized previously, these speci-mens were collected, processed, andstored and underwent metabolomic ana-lyses with identical protocols and atessentially the same time. Our resultsshow that NMR-based metabolomicssuccessfully discriminated first-trimestertrisomy 18 from trisomy 21 pregnancies.The clustering and segregation of the 2groups can be seen in the PCA andPLS-DA plots in Figures 3. Permutationanalysis that used 2000 resamplingsshowed that the observed separation be-tween the 2 aneuploidies had a probabilityvalue of< .0005 of being by chance. Basedon the VIP plot (Figure 4), trimethyl-amine appears to be the most importantor informative metabolite for distin-guishing trisomy 18 from Down syn-drome specimens, followed by threonine,creatine and formate. Trimethylaminewas elevated in cases with trisomy 18; theother 3 metabolites were reduced com-pared with trisomy 21 pregnancies.

Based on Spearman’s rho, t-test,and Mann-Whitney U testing, there wasno significant correlation between the

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FIGURE 3Discrimination of trisomy 18 from trisomy 21 pregnancies


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most discriminating metabolites (ie,2-hydroxybutyrate, ethanol, or formateconcentrations) and CRL, maternal race,or maternal age in either the trisomy 18or normal groups. CRL, which is themost accurate measure of gestationalage, did not persist in either the logis-tic regression analyses or genetic pro-gramming analyses as a significantindependent predictor of trisomy 18,despite the significant reduction of CRLvalues in the aneuploidy group (Tables 4and 5). Thus, differences in metaboliteconcentrations between the case andcontrol groups could not be explained bygestational age or by any maternal de-mographic characteristics in either theprimary or extended analysis groups.Also, as noted previously, there was nosignificant difference in maternal weightbetween the groups (Table 1). The ge-netic programming analyses consideredall metabolomic, maternal demographic,and ultrasound markers simultaneouslyand in an unbiased fashion; however,maternal weight still did not show up as asignificant contributor to the predictionalgorithms.

COMMENT

This is the first report to have shownthat metabolomic markers in first-trimester maternal serum can be usedto distinguish trisomy 18 from nor-mal pregnancies. Unlike conventionalscreening, metabolomics has the powerto detect a large number of predictivemarkers simultaneously that makes itpossible to develop multiple different

Two-dimensional A, principal component anal-ysis and B, partial least squares discriminant

analysis plots of maternal serum metabolites for

first-trimester trisomy 18 vs trisomy 21. Cases

with trisomy 18 are in red ; trisomy 21 cases are

in green. Clustering and segregation of the 2

patient groups are demonstrated. Permutation

analysis indicates that that the observed sepa-

ration has a probability value of .0005 of

being due to chance (30 trisomy 18 cases; 30

trisomy 21 cases).

PC, principal component.

Bahado-Singh. Metabolomics and trisomy 18. Am J ObstetGynecol 2013.


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FIGURE 4VIP plot of trisomy 18 vs trisomy 21 cases

The most discriminating metabolites (trimethylamine, threonine, and creatine) for distinguishing

trisomy 18 from trisomy 21 cases are shown in the upper right hand corner of the plot. The legend to

the right of the plot indicates whether the metabolite is increased or decreased in the case and

control samples (trisomy 18, 30 cases; 30 trisomy 21 cases).

VIP, variable importance in projection.



algorithms with good to excellent diag-nostic accuracies. Various combinationsof metabolites, maternal demographiccharacteristics, and DNTmeasurementstherefore were modeled in both thecase control and expanded patientgroups. VIP analysis indicated that 2-hydroxybutyrate was the most discrimi-nating metabolite for distinguishingtrisomy 18 from normal pregnancy.When 2-hydroxybutyrate was combinedwith glycerol and maternal age, we ach-ieved a 73.3% sensitivity and a 96.6%specificity for trisomy 18 detection(Table 2). When glucose was combinedwith DNT measurements, a level of100.0% sensitivity and specificity was


achieved in the expanded patient group(Table 5).Because trisomy 18 conventionally is

combined with trisomy 21 screening forclinical prenatal aneuploidy assessment,an interesting question was whethermetabolomics could distinguish be-tween the 2 disorders. Our study foundthat significant discrimination betweentrisomy 18 and trisomy 21 cases wasachievable. The most discriminatingmetabolites for this purpose were tri-methylamine, threonine, and creatine.Although not the primary objective of

the study, we attempted to determinewhether metabolomics could providefurther insights into the pathophysiologic

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condition of pregnancies with trisomy 18.We reviewed the Human MetabolomeDatabase (www.hmdb.ca; version 2.5)16

which summarizes the role of mostknown metabolites in the human body.As noted earlier, 2-hydroxybutyrate wasfound to be increased in the maternalserum of pregnancies with trisomy 18.According to the Human MetabolomeDatabase, 2-hydroxybutyrate is releasedas a by-product of the conversion ofcystathionine to cysteine in the trans-sulfuration pathway of homocysteinemetabolism. Cysteine is the substrate forthe production of the antioxidant gluta-thione. The elevated 2-hydroxybutyratelevels could represent an antioxidantresponse to oxidative stress. The basis forsuch a metabolic response in pregnancieswith trisomy 18 is unclear. The authorswere unable to identify any previouslypublished literature that addressed thisissue.

The question may be posed about thenecessity of the performance of meta-bolomic analysis of pregnancies withtrisomy 18, given the high diagnosticaccuracy currently being reported fornoninvasive molecular testing in preg-nancy. Metabolomics has the potential toaddress other scientifically intriguingquestions that go beyond the detection oftrisomy 18. For example, the mechanismby which the presence of an extra com-plement of chromosome 18 disrupts cellfunction remains unanswered. Althoughthis was not our study objective, ourpreliminary data suggest that oxidativedysfunction could play a role at thecellular level. We could find no previousreport in the literature that has addressedthis issue. This observation is intriguingbecause our previous metabolomic anal-ysis indicated that increased oxidativestress is a feature of Down syndrome,8

which is another common aneuploidy.Metabolomics represents a paradigmshift to conventional screening. Multiplecombinations of different metabolitescan be identified, each with good accu-racy for the prediction of the same dis-order, which represents a proverbialexcess of wealth. One of our algorithmshad 100% sensitivity and 100% speci-ficity in this preliminary study. Of course,a much larger number of cases and

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controls, preferably obtained prospec-tively, would be required for precisesensitivity and specificity estimates. Wedid not analyze the additional impactof standard biochemical markers (suchas free ß-human chorionic gonado-tropin and pregnancy-associated plasmaproteineA) if theywere to be added to thepredictive models. These conceivablycould lead to additional biomarker com-binations with high accuracy for trisomy18. From a more utilitarian perspective,metabolomic analysis potentially couldlead to the availability of accurate andaffordable tests, particularly when cost isan issue.

Although this is an interesting ques-tion, we cannot say whether the observedchanges in metabolite concentrationsarose primarily from the fetal, placental,or maternal compartment. This was notan objective of our study, and the currentstudy design precludes us from makingsuch a distinction.

In conclusion, using both a case con-trol and an expanded dataset, we re-port the identification of metabolomicmarkers that, by themselves or combinedwith maternal age or nuchal trans-lucency measurement, can distinguishpregnancies with trisomy 18 fromnormalfirst-trimester pregnancies with accuracy.We used statistical methods that are rec-ommended for both metabolomic dataand standard biomarker analyses.We alsoused genetic programming, which is apowerful analytic and feature selectionmethod, to confirm these associations.When metabolites were combined with

NTmeasurements the detection rate fortrisomy 18 was significantly increased.We did not evaluate the effect ofcombining these metabolites with estab-lished first-trimester protein biochemicalmarkers (such as pregnancy-associatedplasma proteineA and free behumanchorionic gonadotropin) for trisomy 18prediction. Our preliminary assessmentof the trisomy 18 maternal serum meta-bolic profile suggests the possibility ofincreased antioxidant response in preg-nancies with trisomy 18. Finally, ouranalysis indicates that the first-trimesterpregnancies with trisomy 18 and 21 canbe distinguished potentially with the useof easily measureable maternal serummetabolite markers. -

REFERENCES

1. Psychogios N, Hau DD, Peng J, et al. Thehuman serum metabolome. PLoS One 2011;6:e16957.2. Mandel R, Guo HC, Chaudhary KK, et al.Multiplatform characterization of the human cere-brospinal fluidmetabolome: a comprehensive andquantitative update. Genome Med 2012;4:38.3. Xu XH, Huang Y, Wang G, Chen SD. Metab-olomics: a novel approach to identify potentialdiagnostic biomarkers and pathogenesis in Alz-heimer’s disease. Neurosci Bull 2012;28:641-8.4. Kenny LC, Broadhurst DI, Dunn W, et al.Robust early pregnancy prediction of laterpreeclampsia using metabolomic biomarkers.Hypertension 2010;56:741-9.5. Romero R, Mazaki-Tovi S, Kusanovich JP,et al. Metabolomics in premature labor: a novelapproach for identifying patients at risk for pre-term delivery. J Matern Fetal Neonatal Med2010;23:1344-59.6. OdiboAO,GoetzingerKR,OdiboL,Cahill AG,Macones GA. First-trimester prediction of

JULY 2013 Ame

preeclampsia using metabolomic biomarkers: adiscovery phase study. Prenat Diagn 2011;31:990-4.7. Bahado-Singh RO, Akolekar R, Mandal R,et al. Metabolomics and first trimester predictionof early-onset preeclampsia. J Matern FetalNeonatal Med 2012;25:1840-7.8. Bahado-Singh R, Akolekar R, Mandal R, et al.Metabolomic analysis for first-trimester Downsyndrome prediction. Am J Obstet Gynecol2013;208:371.e1-8.9. Kagan KO, Wright D, Maiz N, Pandeva I,Nicolaides KH. Screening for trisomy 18by maternal age, fetal nuchal translucency,free beta-human chorionic gonadotropinand pregnancy-associated plasma protein-A. Ultrasound Obstet Gynecol 2008;32:488-92.10. National Institute for Health and CareExcellence (NICE). Antenatal care: routine carefor the healthy pregnant woman. NICE guideline2008. Available at: www.guidance.nice.org.uk.Accessed April 23, 2013.11. American College of Obstetricians andGynecologists. Screening for fetal chromosomalabnormalities. ACOG practice bulletin no. 77.Obstet Gynecol 2007;109:217-27.12. Wright D, Kagan KO, Molina FS, Gazzoni A,Nicolaides KH. A mixture model of nuchaltranslucency thickness in screening for chro-mosomal defects. Ultrasound Obstet Gynecol2008;31:376-83.13. Xia J, Mandal R, Sineinkov IV, Broadhurst D,Wishart D. MetaboAnalyst 2.0: a comprehensiveserver for metabolomic data analysis. NucleicAcids Res 2012;40:W127-33.14. Wishart DS. Computational approaches tometabolomics methods. Mol Biol 2010;593:283-313.15. Goodcare R. Making sense of the meta-bolome using evolutionary computation: seeingthe wood with the trees. J Exp Bot 2005;56:245-54.16. Wishart DS, Knox C, Guo AC, et al. HMDB:a knowledgebase for the human metabolome.Nucleic Acids Res 2009;37:D603-10.


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