congenital heart defects and maternal genetic, metabolic, and lifestyle factors

Congenital Heart Defects and Maternal Genetic,Metabolic, and Lifestyle Factors

Charlotte A. Hobbs, Stewart L. MacLeod, S. Jill James, and Mario A. Cleves*

University of Arkansas for Medical Sciences, College of Medicine, Department of Pediatrics,Arkansas Children’s Hospital Research Institute, Little Rock, AR

Received 28 October 2010; Revised 16 December 2010; Accepted 17 December 2010

BACKGROUND: The purpose of this study was to identify metabolic, genetic, and lifestyle factors that dis-criminate between women who have pregnancies affected by congenital heart defects (CHDs) from thosewho have unaffected pregnancies. METHODS: We analyzed the concentrations of 13 serum biomarkers, 3functional genetic variants, and 4 lifestyle factors among 417 women with CHD-affected pregnancies and250 controls. To identify risk factors that discriminated between cases and controls, we used logistic regres-sion followed by recursive partitioning to identify non-linear interactions. A receiver operating characteristic(ROC) curve was constructed to evaluate the discriminatory accuracy of the final model. RESULTS: A combi-nation of risk factors discriminated women who had pregnancies affected by CHDs from those who hadunaffected pregnancies. Among 21 possible determinants, serum concentrations of homocysteine and methi-onine, and reduced:oxidized glutathione ratios (GSH:GSSG) had the greatest discriminatory power. Recur-sive partition modeling resulted in five terminal nodes each illustrating the interplay of these three bio-markers. Women with elevated homocysteine and low GSH:GSSG had the highest risk of having CHD-affected pregnancy, whereas women with low homocysteine, high methionine, and high GSH:GSSG had thelowest risk. The corresponding area under the ROC curve was 81.6% (95% confidence interval [CI], 78.1–85.2%), indicating high ability to discriminate between cases and controls. CONCLUSION: High homocys-teine, low methionine, and a reduced GSH:GSSG ratio were the strongest discriminating factors betweencases and controls. Measurement of total homocysteine, methionine, and total and reduced glutathione inreproductive aged women may play a role in primary prevention strategies targeted at CHDs. Birth DefectsResearch (Part A) 91:195–203, 2011. � 2011 Wiley-Liss, Inc.

Key words: folic acid; methionine; homocysteine; SNPs; recursive partitioning; malformations

INTRODUCTION

Congenital heart defects (CHDs) are among the mostprevalent and serious of birth defects (Christianson et al.,2006). CHDs are the leading cause of death from congeni-tal malformations (Jenkins et al., 2007). The majority ofinfants with CHDs have isolated conditions that are notassociated with identifiable syndromes, chromosomalabnormalities, or other structural birth defects, and themajority of CHDs occur in the absence of known terato-gen exposure (Botto et al., 2003). Most CHDs are thoughtto occur as a result of a complex interaction betweenenvironmental exposures, lifestyle factors, and genomicand epigenomic susceptibilities.

Previous work by our team and others has describedan association between CHDs and metabolic alterationsand common genetic variants in folate and glutathionerelated pathways (Hobbs et al., 2005a; Hobbs et al.,

2006b; van Beynum et al., 2007; Goldmuntz et al., 2008;van Driel et al., 2008; Shaw et al., 2009). For example,women pregnant with fetuses with CHD and women

Disclosures: The findings and conclusions in this report are those of theauthors and do not necessarily represent the views of the Centers for DiseaseControl and Prevention or the National Institutes of Health.Conflict of interest: none declared.*Correspondence to: Mario Cleves, Arkansas Center for Birth DefectsResearch and Prevention, 13 Children’s Way, Slot 512-40, Little Rock, AR72202. E-mail: [email protected]

Funding: This work was supported by the Centers for Disease Control andPrevention (Cooperative Agreement No. 3U50DD613236-10W1 to C. A.Hobbs); the Eunice Kennedy Shriver National Institutes of Child Health andHuman Development (5R01HD039054-09 to C. A. Hobbs); and the ArkansasBiosciences Institute, the major research component of the Tobacco Settle-ment Proceeds Act of 2000 (to C. A. Hobbs).

Published online 7 March 2011 in Wiley Online Library (wileyonlinelibrary.com).DOI: 10.1002/bdra.20784

Birth Defects Research (Part A): Clinical and Molecular Teratology 91:195�203 (2011)

� 2011 Wiley-Liss, Inc. Birth Defects Research (Part A) 91:195�203 (2011)

who gave birth to infants with CHD have higher homo-cysteine and reduced glutathione levels than controlwomen (Wenstrom et al., 2001; Hobbs et al., 2005a;Hobbs et al., 2005b; Hobbs et al., 2006b). Studies investi-gating the association between CHDs and commongenetic variants in genes encoding for critical enzymes infolate and glutathione-related pathways have yieldedinconsistent results (van Beynum et al., 2007; Goldmuntzet al., 2008; Shaw et al., 2009).

Lifestyle factors that may modify folate and/or gluta-thione metabolism, such as smoking, alcohol, multivita-min supplement use, and body mass index, have beenreported to be associated with CHDs (Shaw et al., 1995;Jenkins et al., 2007; Grewal et al., 2008; Malik et al., 2008;Verkleij–Hagoort et al., 2008; Stothard et al., 2009). Mater-nal intake of folic acid containing supplements has beendemonstrated to reduce the occurrence of some CHDs(Shaw et al., 1995; Botto et al., 2003; van Beynum et al.,2010).

Cardiogenesis and the occurrence of CHDs areextremely complex biologic processes. Previous studiesthat have investigated the role of multiple factors in fo-late and glutathione pathways on the occurrence ofCHDs have identified main effects and first order interac-tions (Hobbs et al., 2005a; Hobbs et al., 2006a; Hobbset al., 2006b; van Beynum et al., 2006; Jongbloet, 2007).Less is understood about the relative importance of indi-vidual determinants of CHDs and the complex interac-tions between determinants. In this study, our objectiveswere (1) to estimate the association between CHDs andmultiple factors that may perturb folate and glutathionemetabolism, and (2) to determine whether we can dis-criminate women who are at risk for having pregnanciesaffected by CHDs from those women who are not, basedon metabolic alterations, common genetic variants, andlifestyle factors.

METHODSCase Selection

Cases were identified and ascertained through theArkansas Reproductive Health Monitoring System, astatewide birth defects registry. Inclusion criteria forcases were as follows: residents of Arkansas at the end ofindex pregnancy and at study enrollment; live-borninfants; pregnancy ended between February 1998 andSeptember 2008; physician diagnosis of a nonsyndromicseptal, conotruncal, or right-sided or left-sided obstruc-tive heart defect that was confirmed by prenatal or post-natal echocardiogram, surgery, or autopsy report, or allthree; English or Spanish speaking; and participant in theNational Birth Defects Prevention Study (NBDPS). Detailsregarding the NBDPS methodology have previously beenpublished (Yoon et al., 2001). Briefly, the NBDPS is anongoing multisite population-based case-control/case-pa-rental study intended to identify the etiology of morethan 30 nonsyndromic structural birth defects, includingcardiac defects. An expert panel of pediatric clinical dys-morphologists developed uniform diagnostic criteria foreach phenotype. An expert team specializing in pediatriccardiology reviewed all cardiac cases (Botto et al., 2007).Infants who had a known single-gene disorder or chro-mosomal abnormality, including microdeletion of q22consistent with DiGeorge syndrome, were excluded from

the NBDPS. Infants who were adopted or in foster carewere ineligible to participate in the NBDPS study.For the current study, only nonsyndromic CHDs were

included. Infants who had a noncardiac structural mal-formation in addition to the cardiac malformation werealso excluded as were those who had complex CHD.

Selection of Controls

Controls were English-speaking or Spanish-speakingArkansas residents who had live births that were unaf-fected by any birth defect and randomly selected from allbirth certificates registered at the Arkansas Departmentof Health with birth dates between June 1998 and May2008. Only singleton live births were included in theanalysis.

Maternal Interview and Home Visit Data

At the Arkansas Center for Birth Defects Research andPrevention, research nurses administered a structuredcomputer-assisted telephone interview with each NBDPSparticipant. After the interview, participants are mailed akit to collect buccal cell samples.After NBDPS participation, a research nurse contacted

each CHD-eligible participant and control mother by tele-phone to schedule a home visit. During home visits, thenurse obtained written informed consent, performed avenipuncture to obtain blood, and collected informationabout current use of multivitamins, cigarettes, alcohol,and caffeine. NBDPS participation rate in Arkansas forthe study period was 75.4% for cases and 68.3% for con-trols. Of mothers that were eligible for home visits,approximately 77% of cases and 56% of controls agreedto the home visit and provided biologic samples forDNA extraction and measurement of plasma metabolites.Cases and controls were excluded if they were preg-

nant or lactating at the time of the blood draw, were tak-ing any known folate antagonist medications, or hadtype I or type II diabetes diagnosed before the indexpregnancy. The study protocol and provisions forinformed consent were reviewed and approved by theInstitutional Review Board at the University of Arkansasfor Medical Sciences.

Biomarker Measurements

As previously mentioned, participants were part of anongoing case-control study in which Arkansas partici-pants in the NBDPS were re-contacted and home visitswere made to collect additional nutritional data and bio-logic samples. We previously reported on the plasmaconcentrations of biomarkers of the folate pathway(Hobbs et al., 2005a) and transsulfuration pathways(Hobbs et al., 2006a) among participants with a preg-nancy ending between February 1998 and July 2004. Inthe current analyses, we extended that analysis byincluding all women who participated between February1998 and September 2008 that had biomarker measure-ments and DNA available for genotyping the three sin-gle-nucleotide polymorphisms (SNPs) in the three candi-date pathways illustrated in Figure 1.Methods for sample preparation and measurement of

total homocysteine, cysteine, glutathione (GSH), and oxi-dized glutathione concentrations have been describedpreviously (Hobbs et al., 2005b).

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Birth Defects Research (Part A) 91:195�203 (2011)

Single-Nucleotide Polymorphism Genotyping

DNA was extracted from maternal buccal cell samplesor peripheral blood lymphocytes by use of Pure GeneDNA purification reagents (Gentra Systems, Minneapolis,MN) according to the manufacturer’s protocol. GenomicDNA was quantified by use of the Applied BiosystemsTaqMan RNaseP Detection Reagents (Applied Biosys-tems, Foster City, CA) using a standard curve of genomicDNA. The standard curve samples as well as thegenomic DNA samples of unknown concentration weresubjected to an initial denaturation at 958C for 10 minutesfollowed by 40 polymerase chain reaction (PCR) cycles(958C for 15 seconds; and 608C for 1 minute) in an ABIPRISM 7900HT real-time PCR instrument. DNA concen-trations were calculated from the standard curve usingApplied Biosystems software. Genomic DNA (10 to 15ng) was used as a template for whole genome amplifica-tion (WGA) by use of the Sigma GenomePlex WGA kitaccording to the protocol provided by the manufacturer.WGA product was quantified as above and 10 ng wereused for each genotyping assay. PCR of polymorphic al-leles was accomplished by the use of TaqMan Assays onan ABI 7900 Sequence Detection instrument (AppliedBiosystems) for the following polymorphisms: betaine-homocysteine methyltransferase (BHMT) NM001713,G742A, rs3733890; methylenetetrahydrofolate reductase(MTHFR) NM005957, C677T, rs1801133; transcobalamin II(TCII) NM 000355, C777G, rs1801198. PCR conditionswere 958C for 10 minutes followed by 40 cycles of 958Cfor 15 seconds, then 608C for 1 minute. A post-PCR allelicdiscrimination read was performed using ABI software.

The MTHFR gene encodes for methylenetetrahydrofo-late reductase that catalyzes the conversion of 5,10-meth-ylenetetrahydrofolate to 5-methyltetrahydrofolate and is

essential for re-methylation of homocysteine to methio-nine. The SNP, MTHFR, and 677C?T (rs1801133), andhas been associated with decreased enzyme activity(Hobbs et al., 2006b). Additionally, vitamin B12, methyl-ated cobalamin, is also necessary for re-methylation ofhomocysteine. To enter cells or pass through the placen-tal boundary, cobalamin must be bound to transcobala-min. Evidence from previous studies demonstrated thatthe 776 C?G (rs1801198) SNP in cobalamin transportergene, TCII, results in diminished concentrations of cellu-lar and plasma transcobalamin and consequentlydecreased availability of vitamin B12 (Lievers et al., 2002;von Castel–Dunwoody et al., 2005). The BHMT gene con-verts homocysteine to methionine using betaine as thecarbon donor. A common polymorphism, 742G?A(rs3733890), is believed to decreased BHMT activity,resulting in elevated homocysteine levels and thus,increasing susceptibility to birth defects (Morin et al.,2003; Zhu et al., 2005; Shaw et al., 2009).

Statistical Methods

Sociodemographic, lifestyle characteristics, and SNP al-lele frequencies of case and control subjects were com-pared to the Fisher exact test for categorical variables orwith the Mann–Whitney U/Wilcoxon rank-sum test forcontinuous variables. All plasma biomarkers exhibitedpositively skewed distributions; therefore, to achieve nor-mality, biomarker data were log-transformed (naturallog) before analysis. Normality of the transformed datawas verified by using the Anderson–Darling test(Anderson and Darling, 1952). Outlier analysis was per-formed for each biomarker, stratified by case-control sta-tus. Mean log-transformed biomarker concentrations of

Figure 1. Candidate pathways. THF, tetrahydrofolate; 5,10-CH2-THF, 5,10-methylene tetrahydrofolate; 5-CH3-THF, 5-methyltetrahydrofo-late; MTHFR, methylenetetrahydrofolate reductase; TCII, transcobalamin II; BHMT, betaine homocysteine methyltransferase; SAM, S-adenosylmethionine; SAH, S-adenosylhomocysteine; CysGly, cysteinylglycine; GluCys, glutamylcysteineglycine; GSH, reduced glutathi-one; GSSG, oxidized glutathione.

197CONGENTIAL HEART DEFECTS AND LIFESTYLE FACTORS


case and control subjects were compared by using a ttest, and linear regression was used to adjust these com-parisons for age, race, educational level, householdincome, multivitamin supplement intake, number of ciga-rettes smoked daily, alcohol consumption, caffeine intake,and interval between the end of pregnancy and the blooddraw. Adjusted odds ratios (ORs) and 95% confidenceintervals (CIs) for the association between CHD and life-style, SNPs and biomarkers were calculated using multi-variable adjusted logistic regressions.

Because of the possibility of complex non-linear inter-actions between patient characteristics, genetic variants,and biomarkers not detectable via logistic regression, re-cursive partitioning was used to explore these relation-ships. All patient characteristics, genetic variants, andbiomarkers were entered into the recursive partitioningalgorithm. Recursive partitioning is a multivariable anal-ysis method useful in uncovering obscure patterns orsubgroups within data. It relates an outcome (case-con-trol status) to a set of independent predictors (patientcharacteristics, genetic variants, and biomarkers). Themethod partitions the data into homogeneous groups ornodes, while maximizing the differences between nodes.The recursive algorithm we implemented used the Ginicriterion for splitting nodes. Only independent predictorsthat best discriminate between nodes were retained inthe final mode. Because an external dataset was not avail-able for replication, and concerns about model over-fit-ting, a 10-fold cross-validation was used for tree valida-tion (Zhang, 1998). Results from recursive partitioningare routinely presented as a decision tree or a classifica-tion algorithm.

The overall ability of the recursive partitioning algo-rithm to discriminate between cases and controls wasquantified by the area under the receiver operating char-acteristic (ROC) curve. The ROC curve, a plot of the sen-sitivity versus 1 – specificity of a discriminatory test, is afrequently used tool for evaluating the effectiveness ofdiagnostic and prognostic tests to correctly discriminatebetween diseased and non-diseased individuals, whereasthe area under this curve (AUC) provides an estimate ofthis discriminating ability. The AUC takes values rangingfrom 0.5, indicating no discriminatory power, to 1, indi-cating perfect discrimination of cases and controls. ROCcurves were constructed using both the non-parametrictrapezoidal rule and by computing maximum likelihoodestimates of the parameters of a smooth fitting ROCcurve (Dorfman and Alf, 1969; Cleves, 1999).

Data management and most analysis were performedusing Stata version 11.0 (Stata Corporation, College Sta-tion, TX). Recursive partitioning was performed utilizingthe algorithm implemented in the CTMBR program(http://peace.med.yale.edu/pub/mcart).

RESULTS

A total of 667 mothers were included in this study; 417cases and 250 controls. The distribution of CHD pheno-types are presented in Table 1. Selected characteristics ofcases and controls are presented in Table 2. The vast ma-jority of participants, 504 (75.6%), were white. At thetime of the blood draw, 63.5% of cases were <30 yearsold and less than half (48.4%) of the cases reported drink-ing alcohol. There was no significant difference betweenthe percentage of cases (23.7%) and controls (26.0%) that

reported regular multivitamin use at the time of theblood draw. Smoking did not vary significantly (p 50.1435) between cases (27.8%) and controls (22.4%) andneither did caffeine intake. None of the three genetic var-iants examined were associated with CHDs.Unconditional logistic regression was used to model

the association between CHD and maternal genetic, met-abolic, and lifestyle factors. As presented in Table 3, afteradjusting for all other variables in the model, elevatedhomocysteine (OR, 5 1.47; 95% CI, 1.22–1.77) was associ-ated with CHD-affected pregnancies. Whereas, decreasedmethionine (OR, 5 0.89; 95% CI, 0.83–0.96), S-adenosyl-methionine:S-adenosylhomocysteine ratio (OR, 5 0.57;95% CI, 0.41–0.77), glutamylcysteine (OR, 5 0.55; 95% CI,0.37–0.82), and GSH:GSSG (OR, 5 0.94; 95% CI, 0.91–0.97) were associated with CHD-affected pregnancies. Asin univariable analyses, none of the genetic variants wereassociated with CHDs. Because we were interested in thecomplex interactions between risk factors and the effectof these interactions on the occurrence of CHDs, recur-sive partitioning was performed.Complex non-linear interactions between four lifestyle

factors (maternal smoking, multivitamin supplement use,alcohol intake, and body mass index), three commongenetic variants (MTHFR 677 C>T, BHMT 742 G>A, andTCII 776 C>G), and the 14 metabolites included in Table 4were explored via recursive partitioning (Table 5 andFig. 2). The final recursive partitioning analysis generateda regression tree with four levels and five terminal nodes.Of the possible 21 characteristics that could discriminatebetween cases and controls, homocysteine, GSH:GSSG re-dox ratio, and methionine were selected as the most im-portant predictors. Odds ratios were computed for eachterminal node by comparing them to the node deemed asmost benign characterized by women with homocysteine<8.97 lM, methionine �18.43 lM, and a GSH:GSSG ratio�22.85. Compared to this reference node, women withhomocysteine �8.97 and a GSH:GSSG ratio <23.16 wereover 54 times more likely to have a CHD-affected preg-nancy (OR, 5 54.05; 95% CI, 20.96–174.73), whereaswomen with homocysteine <8.97 and methionine <18.43were over 14 times more likely to have a CHD-affectedpregnancy (OR, 5 14.56; 95% CI, 6.38–36.98). Womenwith homocysteine �8.97 lM and a GSH:GSSG ratio�23.16 were almost 6 times more likely to have a CHD-affected pregnancy than those in the reference node (OR,5 5.65; 95% CI, 3.27–9.86), whereas women with homo-cysteine concentrations <8.97 lM, methionine concentra-tions �18.43 lM, and GSH:GSSG ratio <22.85 were 4times more likely to have a CHD-affected pregnancythan women in the reference node (OR, 5 4.06; 95% CI,

Table 1Distribution of CHD Phenotypes of Case Offspring

CHD Phenotype* Number of patients Percentage

Conotruncal 61 14.63Right and left obstructive 123 29.50Septal 233 55.88

*Offspring with more than one phenotype were classified in theorder listed in the table. For example, an offspring with bothconotruncal and septal defects was counted as a conotruncal.CHD, congenital heart defect.

198 HOBBS ET AL.


2.57–6.44). In summary, five terminal nodes were identi-fied representing a combination of three metabolic varia-bles: homocysteine, GSH:GSSG, and methionine.

ROC curves (Fig. 3) were constructed and AUC com-puted from the results of the recursive partitioning analy-sis to evaluate the discriminatory ability of the final re-cursive partitioning algorithm. AUC ranges from 0.5

(chance) to 1 (perfect discrimination/accuracy) with areasabove 0.7 representing moderate discriminative abilityand above 0.8 representing strong discriminative ability.The AUC of the ROC curve constructed using the trape-zoidal method was 0.79 (95% CI, 0.76–0.82), whereas theAUC of the ROC curve constructed using the maximumlikelihood method was 0.82 (95% CI, 0.78–0.85), indicat-

Table 2Selected Characteristics of Cases and Controls at the Time of Study Participation

Cases (N 5 417) Controls (N 5 250) p value

Race [n (%)]White 323 (77.5) 181 (72.4)African American 54 (12.9) 45 (18.0)Hispanic 32 (7.7) 21 (8.4)Others 8 (1.9) 3 (1.2) 0.2937

Age [n (%)]Less than 30 years 265 (63.5) 167 (66.8)30 or more years 152 (36.5) 83 (33.2) 0.4038

Education [n (%)]0 to 11 year 58 (13.9) 44 (17.6)High school diploma/GRE 140 (33.6) 80 (32.0)Some college/technical 110 (26.4) 70 (28.0)Bachelor degree 84 (20.1) 37 (14.8)Graduate degree 23 (5.5) 19 (7.6) 0.2628Missing 2 (0.5) 0 (0.0)

Household income [n (%)]0 to $10,000 87 (20.9) 44 (17.6)$10,000 to $20,000 66 (15.8) 36 (14.4)$20,000 to $30,000 63 (15.1) 43 (17.2)$30,000 to $40,000 45 (10.8) 34 (13.6)$40,000 to $50,000 49 (11.8) 26 (10.4)>$50,000 88 (21.1) 51 (20.4) 0.6983Missing 19 (4.6) 16 (6.4)

Smoking [n (%)]No 301 (72.2) 194 (77.6)Yes 116 (27.8) 56 (22.4) 0.1434

Drinking [n (%)]No 213 (51.1) 114 (45.6)Yes 202 (48.4) 135 (54.0) 0.1736Missing 2 (0.5) 1 (0.4)

Body mass index [n (%)]Missing 12 (2.9) 16 (6.4)Normal 155 (37.2) 91 (36.4)Obese 136 (32.6) 80 (32.0)Underweight 8 (1.9) 8 (3.2)Overweight 106 (25.4) 55 (22.0) 0.6313

MTHFR C677TC 316 (65.6%) 169 (68.7%)T 166 (34.4%) 77 (31.3%) 0.3956

TCII C776GC 265 (56.1%) 142 (61.7%)G 207 (43.9%) 88 (38.3%) 0.1587

BHMT G742AG 341 (73.2%) 169 (72.8%)A 125 (26.8%) 63 (27.2%) 0.9260

Vitamin supplements [n (%)]No 316 (75.8) 182 (72.8)Yes 99 (23.7) 65 (26.0) 0.5149Missing 2 (0.5) 3 (1.2)

Daily caffeine (mg)* 27.5 (4.4–93.7) 20.6 (3.9–61.5) 0.1681Drinks/week among drinkers 0.5 (0.2–2.0) 0.4 (0.2–1.3) 0.5498Cigarettes/day among smokers 10.0 (5.5–20.0) 10.0 (5.0–15.0) 0.2100Pregnancy to participation (mo) 14.9 (9.8–21.7) 14.2 (8.4–24.4) 0.9030

*Median; interquantile range in parentheses (all such values).MTHFR, methylenetetrahydrofolate reductase gene; BHMT, betaine homocysteine methyltransferase

gene; TCII, transcobalamin II gene.



ing high ability to discriminate between cases and con-trols.

DISCUSSION

In our study of CHD, we determined that amongpotential genetic, lifestyle, and metabolic risk factors,high serum homocysteine, low plasma GSH:GSSG ratio,and low plasma methionine levels were the strongest fac-tors to discriminate between cases and controls. Wheremany earlier studies have examined the impact of func-tional SNPs (van Beynum et al., 2007; Verkleij–Hagoortet al., 2008), lifestyle factors (Kallen, 1999; Grewal et al.,2008; Malik et al., 2008; Stothard et al., 2009), and metab-olites (Hobbs et al., 2005a; Hobbs et al., 2005b) as inde-pendent risk factors, in the present study, we were ableto perform a combined analysis by recursive partitioningto identify those maternal factors that most strongly

influenced the risk of having a CHD-affected pregnancy.Recursive partitioning also allowed us to identify cut-offvalues for each metabolite that provided the best discrim-ination between cases and controls.Our analysis included genotype data for three SNPs

that are known to change the activity of enzymes in thefolate pathway. Maternal genotype was not independ-ently associated with birth defect risk for any of thegenetic polymorphisms studied. This finding is in agree-ment with a recent meta-analysis of homocysteine levelsand MTHFR polymorphisms in both orofacial clefts andCHDs (Verkleij–Hagoort et al., 2007). We found no signif-icant contribution of these genetic factors for the predic-tion of case/control status.As stated above, we found that metabolite levels for

homocysteine and methionine and the ratio forGSH:GSSG were the best predictors of case/control sta-tus. Although, through these metabolites we were able todiscriminate between CHD cases and controls, theseresults do not necessarily imply these metabolites ascomponents of the causal mechanism for CHDs.Alterations in metabolite levels reflect variations in en-

zymatic activity that may be encoded by SNPs and/ormodified by lifestyle factors. Specific combinations ofexposures and genetically determined enzyme activity re-sponsible for determining metabolite levels result in dif-ferences in biomarkers that confer a higher risk of CHDs.Some limitations of our study have to be considered.

First, maternal blood draw occurred several months to 3years after the end of pregnancy. Alterations in maternalmetabolites that we observed in this study may not havebeen present during cardiogenesis. However, our find-ings are substantiated by multiple other studies thatfound that increases in homocysteine during pregnancywere associated with CHDs (Wenstrom et al., 2001) aswere increases in homocysteine at 15 months post-par-tum (van Driel et al., 2008). Second, the three functionalgenetic variants included in the present study were notindependently associated with CHDs. It is possible thatother common or rare genetic variants involved in homo-cysteine, methionine, and glutathione metabolism mayhave a stronger association with CHD than the threeselected variants tested. Future studies that include amore comprehensive approach to identify genetic var-iants, gene-gene and gene-environment interactions thatare associated with CHDs are needed. Third, some CHDphenotypes may share different maternal risk factors,thus combining all phenotypes may obscure some associ-ations. Future studies should also explore this possibility.Fourth, although the classification algorithm was vali-dated internally using a 10-fold cross-validation method,the lack of an external dataset on which to independentlyvalidate findings limits the generalizability of computedcut-points. Fifth, because the classification algorithm wasdeveloped based on women who agreed to participate inthe NBDPS, did not have type I or type II diabetes beforethe index pregnancy and had singleton live births, it isunknown how the metabolites from the classification treewould perform as discriminatory factors for CHDs in thegeneral population of pregnant women. Although casesand controls were excluded if they were pregnant or lac-tating at the time of the blood draw, six women whomay have been pregnant after the index pregnancy, andbefore blood collection, were not identified and thus notexcluded from the analysis. If such pregnancies occurred,

Table 3Adjusted odds ratios and 95% confidence intervals forthe association between congenital heart defects and

lifestyle, SNPs and biomarkers*

Odds ratio95% Confidence

interval

Homocysteine (lM) 1.47 (1.22–1.77)Methionine (lM) 0.89 (0.83–0.96)SAM/SAH 0.57 (0.41–0.77)Adenosine (lM) 4.22 (0.41–43.25)Folic acid (mg/L) 1.03 (0.96–1.12)Vitamin B12 (ng/L) 1.00 (1.00–1.00)CysGly (lM) 1.02 (0.97–1.06)GluCys (lM) 0.55 (0.37–0.82)Cysteine (lM) 1.00 (0.99–1.01)GSH/GSSG 0.94 (0.91–0.97)MTHFR C677T

CC ReferenceCT 0.88 (0.43–1.77)TT 1.29 (0.50–3.35)

TCII C776GCC ReferenceCG 0.64 (0.30–1.36)GG 0.77 (0.31–1.92)

BHMT G742AAA ReferenceGA 0.65 (0.18–2.41)GG 0.64 (0.18–2.30)

Body mass index 0.99 (0.94–1.03)Age 0.99 (0.93–1.05)Cigarettes per day 1.02 (0.96–1.07)Drinks per week 0.93 (0.82–1.04)Vitamin supplements

No ReferenceYes 0.69 (0.32–1.48)

*Estimates adjusted for all covariates in the table, except forthe MTHFR C677T polymorphism which was adjusted for allcovariates other than serum folic acid , and the TCII G742Apolymorphism which was adjusted for all covariates other thanserum vitamin B12.

SNP, single nucleotide polymorphism; SAM, S-adenosylmethi-onine; SAH, S-adenosylhomocysteine; CysGly, cysteinylglycine;GluCys, glutamylcysteineglycine; GSH, reduced glutathione;GSSG, oxidized glutathione; MTHFR, methylenetetrahydrofolatereductase gene; BHMT, betaine homocysteine methyltransferasegene; TCII, transcobalamin II gene.

200 HOBBS ET AL.


Table 4Plasma biomarker summary statistics and crude and adjusted p values for comparison of log-transformed plasma

concentrations between cases and controls

Biomarker

Cases (N 5 417) Controls (N 5 250)

p value* p value**Mean (SD) Median Range Mean (SD) Median Range

Homocysteine (lM) 9.19 (2.41) 8.76 (4.41–21.22) 7.55 (1.64) 7.54 (4.13–13.91) <0.0001 <0.0001Methionine (lM) 22.41 (5.05) 21.66 (13.71–40.41) 24.87 (4.70) 24.26 (15.18–39.12) <0.0001 <0.0001SAM (nM) 73.57 (15.90) 73.18 (37.67–144.20) 78.50 (14.65) 77.34 (48.13–123.90) <0.0001 0.0002SAH (nM) 29.72 (10.15) 28.08 (12.15–87.27) 23.49 (6.89) 22.73 (9.21–48.75) <0.0001 <0.0001SAM/SAH 2.76 (1.10) 2.59 (0.69–7.30) 3.65 (1.31) 3.46 (1.27–8.38) <0.0001 <0.0001Adenosine (lM) 0.28 (0.17) 0.24 (0.06–1.14) 0.25 (0.13) 0.23 (0.07–0.87) 0.0255 0.0721Folic acid (mg/L) 11.90 (5.19) 11.06 (1.86–39.20) 13.05 (5.86) 12.11 (3.31–40.12) 0.0081 0.0068Vitamin B12 (ng/L) 453.90 (197.58) 425.00 (38.24–1524.23) 483.89 (229.67) 432.75 (98.79–1256.00) 0.2144 0.4217GSH (lM) 6.23 (1.81) 6.12 (1.48–17.36) 6.57 (1.70) 6.43 (2.19–14.77) 0.0047 0.0019CysGly (lM) 44.80 (7.72) 44.54 (26.57–66.95) 43.43 (7.42) 42.27 (27.67–65.56) 0.0284 0.0554GluCys (lM) 2.49 (0.83) 2.38 (0.83–5.73) 2.79 (1.06) 2.65 (1.03–7.43) 0.0001 <0.0001Cysteine (lM) 232.00 (27.71) 230.40 (148.81–323.31) 228.18 (27.02) 227.63 (170.86–293.21) 0.0837 0.2943GSSG(lM) 0.313 (0.152) 0.284 (0.064–1.025) 0.24 (0.09) 0.22 (0.09–0.64) <0.0001 <0.0001GSH/GSSG 23.60 (11.35) 20.67 (4.27–80.00) 30.98 (12.45) 28.72 (7.85–83.63) <0.0001 <0.0001

*two-sample t test using log-transformed data.**Adjusted for age, race, BMI, number of cigarettes smoked per day, alcohol consumption, vitamin intake, caffeine intake and the

interval between the end of pregnancy and study participation SAM, S-adenosylmethionine; SAH, S-adenosylhomocysteine; SD, SD;GSH, reduced glutathione; CysGly, cysteinylglycine; GluCys, glutamylcysteineglycine; GSSG, oxidized glutathione.

Table 5Estimated odds ratios (OR) and 95% CI for the terminal nodes of the final recursive partition

Node N Cases (N 5 417) Controls (N 5 250) OR (95% CI)

Homocysteine <8.97 and methionine �18.43 andGSH:GSSG ratio �22.85

227 71 (17.0%) 156 (37.3%) Reference

Homocysteine <8.97 and methionine <18.43 61 53 (12.7%) 8 (1.9%) 14.56 (6.38–36.98)Homocysteine �8.97 and GSH:GSSG ratio <23.16 128 123 (29.5%) 5 (1.2%) 54.05 (20.96–174.73)Homocysteine �8.97 and GSH:GSSG ratio �23.16 100 72 (17.2%) 28 (6.7%) 5.65 (3.27–9.86)Homocysteine <8.97 and methionine �18.43 andGSH:GSSG ratio <22.85

151 98 (23.4%) 53 (12.7%) 4.06 (2.57–6.44)

OR, odds ratio; CI, confidence interval; GSH, reduced glutathione; GSSG, oxidized glutathione.

Figure 2. Classification tree generated from recursive partitioning. GSH, reduced glutathione; GSSG, oxidized glutathione.

plasma biomarker levels could potentially be more rele-vant to intervening pregnancies. Last, the threshold levelsof biomarker concentrations that mostly strongly discri-minated between cases and controls were selectedthrough recursive partitioning and do not necessarilyreflect levels that would typically be considered clinicallysignificant in the general population. For example, thethreshold level of homocysteine concentrations was8.97lm yet most clinicians and laboratories would con-sider concentrations >13lm in non-pregnant reproduc-tive aged women to be elevated. Before defining specificrecommendations for the clinical measurement of homo-cysteine before pregnancy, prospective studies such asthe National Children’s Study may need to confirmresults from case-control studies and/or expert panelsand professional organizations need to develop evidence-based guidelines. (Refsum et al., 2004; Lyerly et al., 2009).

Ideally, primary prevention of CHDs will be the mosteffective strategy to minimize the morbidity and mortal-ity associated with CHDs. Primary prevention of CHDsis a major challenge because of the lack of informationabout modifiable risk factors and the difficulties inherentin studying a relatively rare outcome with limited finan-cial and human resources (Jenkins et al., 2007). For manydecades, it has been recognized that most CHDs are dueto a complex interaction between environmental, lifestyle,and biologic factors. In the past decade, since the comple-tion of the human genome project, tools to unravel thegenetic component of the etiology of complex conditionshave become available. In addition to new laboratorytechnologies, the use of powerful statistical models to testfor interactive effects is increasingly required and repre-sents the strength of our study. Our finding that plasmabiomarker levels for homocysteine and methionine andthe ratio for GSH:GSSH were the best predictors for casestatus suggest a strategy for CHD prevention throughmodulation of modifiable dietary factors and supplementuse that have a direct or indirect effect on these bio-markers. For example, folate supplementation will reduce

homocysteine (Molloy, 2010), and antioxidants, vitaminsC and E, will reduce oxidative stress (Fiore and Capasso,2008; Alpsoy et al., 2009).Our study will need to be replicated, but if findings

are consistent across studies, clinical strategies for riskidentification, stratification, and intervention may beintroduced. As evidence converges demonstrating thatincreased total homocysteine and other metabolic andgenomic factors are associated with specific and multipleadverse reproductive outcomes, strategies will need to bedeveloped to translate these findings to optimize andpersonalize reproductive care in the preconceptional pe-riod.

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congenital heart defects and maternal genetic, metabolic, and lifestyle factors

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