# Differential DNA Methylation in Placenta Associated With Maternal Blood Pressure During Pregnancy

Tsegaselassie Workalemahu, Marion Ouidir, Deepika Shrestha, Jing Wu, Katherine L Grantz, Fasil Tekola-Ayele

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Tsegaselassie Workalemahu, Marion Ouidir, Deepika Shrestha, Jing Wu, Katherine L Grantz, et al.. Differential DNA Methylation in Placenta Associated With Maternal Blood Pressure During Pregnancy. Hypertension, 2021, 75 (4), pp.1117 - 1124. 10.1161/hypertensionaha.119.14509. hal-04662409

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# Differential DNA methylation in placenta associated with maternal blood pressure during pregnancy

Tsegaselassie Workalemahu, PhD, M.S.¹, Marion Ouidir, PhD¹, Deepika Shrestha, PhD¹, Jing Wu, PhD², Katherine L. Grantz, M.D., M.S.¹, Fasil Tekola-Ayele, PhD¹

¹Epidemiology Branch, Division of Intramural Population Health Research, *Eunice Kennedy Shriver* National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

²Division of Intramural Population Health Research, *Eunice Kennedy Shriver* National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA

## Abstract

Abnormal blood pressure during pregnancy is associated with impaired fetal growth, predisposing the offspring to cardiometabolic abnormalities over the life-course. Placental DNA methylation may be the regulatory pathway through which maternal blood pressure influences fetal and adult health outcomes. Epigenome-wide association study of 301 participants with placenta sample examined associations between DNA methylation and mmHg increases in systolic and diastolic blood pressure in each trimester. Findings were further examined using gene expression, gene pathway and functional annotation analyses. CpGs known to be associated with cardiometabolic traits were evaluated. Increased maternal systolic and diastolic blood pressure were associated with methylation of 3 CpGs in the first, 6 CpGs in the second, and 15 CpGs in the third trimester at 5% FDR (P-values ranging from 6.6×10⁻¹⁵ to 2.3×10⁻⁷). Several CpGs were enriched in pathways including cardiovascular-metabolic development (P=1.0×10⁻⁴⁵). Increased systolic and diastolic blood pressure were associated with increased CpG methylation and gene expression at *COL12A1*, a collagen family gene known for regulatory functions in the heart. Out of 304 previously reported CpGs known to be associated with cardiometabolic traits, 36 placental CpGs were associated with systolic and diastolic blood pressure in our data. The present study provides the first evidence for associations between placental DNA methylation and increased maternal blood pressure during pregnancy at genes implicated in cardiometabolic diseases. Identification of blood pressure-associated methylated sites in the placenta may provide clues to early origins of cardiometabolic dysfunction and inform guidelines for early prevention.

Clinical Trial Registration: [ClinicalTrials.gov](http://ClinicalTrials.gov), NCT00912132.

## Keywords

blood pressure; DNA methylation; placenta; pregnancy; gene expression; cardiometabolic

<footnote>Corresponding author details: Fasil Tekola-Ayele (F.T.-A.); ayeleft@mail.nih.gov; 6710B Rockledge Dr., RM 3204, Bethesda MD 20892, Tel: 301-827-6518.</footnote>

<footnote>Disclosures</footnote>

<footnote>None</footnote>
## Introduction

Abnormal blood pressure during gestation is associated with impaired fetal growth,¹, ² and increases the lifetime risk of hypertension and cardiovascular diseases in the offspring.³, ⁵ Pregnancy-related hypertensive disorders including preeclampsia and gestational hypertension are associated with higher blood pressure in childhood⁶ and may result in persistent dysfunction in systemic and pulmonary circulation in the child.⁷ Placental perfusion is controlled by maternal blood flow and fetal circulation.⁸ Insufficient perfusion leads to secretion of proinflammatory molecules that damage maternal endothelial cells and disrupt implantation.⁸, ⁹ Disruptions in placental pathophysiology may alter arterial structure in the child and is associated with hypertension in adulthood.¹⁰ However, little is understood about the mechanisms that may explain the developmental origins of the cardiometabolic manifestations in the offspring.

DNA methylation at cytosine-(phosphate)-guanine (CpG) dinucleotide markers is associated with alterations in vascular function in response to elevated blood pressure.¹¹, ¹² Maternal cardiometabolic factors are associated with DNA methylation in the placenta.¹³⁻¹⁵ Differential DNA methylation in first trimester placental trophoblasts of women with preeclampsia alters expression of genes, (e.g. cadherin 11 [CDH1]), demonstrating that epigenetic modifications early in pregnancy can have effects on placental function.¹³, ¹⁶ Discovering placental epigenetic changes associated with maternal blood pressure may facilitate efforts to identify diagnostic markers and clinical therapeutics, informing guidelines for early prevention of cardiometabolic dysfunctions.¹², ¹⁷ However, limited research exists in assessing the relationship between DNA methylation and blood pressure levels,¹¹ and there are no placental epigenome-wide studies (EWASs) of blood pressure during pregnancy.

The objectives of this study are: 1) to perform EWASs examining the associations of maternal systolic blood pressure (SBP) and diastolic blood pressure (DBP) during 1st, 2nd, and 3rd trimesters of pregnancy with CpG methylation in the placenta, 2) to test gene expression changes per millimeters of mercury (mmHg) increase in SBP and DBP at each trimester 3) and to evaluate associations of previously known cardiometabolic trait-associated CpGs with blood pressure in an attempt to shed light on the mechanisms of developmental origins of cardiometabolic diseases.

## Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request. The DNA methylation data are available through dbGaP with accession number phs001717.v1.p1.

## Study Design and Population

The current study included 312 women from the *Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies*, a cohort study of women without major pre-existing medical conditions who provided placenta samples at delivery.¹⁸, ¹⁹ The study population is described in detail in the Online Supplement Methods
section. The study was approved by institutional review boards at the NICHD and each of the participating clinical sites. Written informed consent was obtained from each woman who participated in the study.

## Measurement of Maternal Variables

SBP and DBP measurements in mmHg were abstracted from routine blood pressure measurements taken during prenatal visits and measurements scheduled at 8–13, 16–22, 24–29, and 34–37 weeks of gestation. The 1st, 2nd and 3rd trimester SBP was calculated to be the average of record-abstracted SBP measurements taken during 0–13 weeks, 14–27 weeks and 28–40 weeks of gestation, respectively.²⁰⁻²² Trimester-specific DBP was also calculated similarly. Gestational age was determined using the date of the last menstrual period and confirmed by ultrasound.¹⁸, ¹⁹

## Placental DNA and RNA Extraction and Measurement

Placental parenchymal biopsies were obtained at the fetal side from 312 mothers. After quality control (detailed in the Online Supplement Methods), 301 mothers with placenta sample were included in the EWAS. DNA was extracted as previously described.²³ DNA methylation was assayed using Illumina's Infinium Human Methylation450 Beadchip (Illumina Inc., San Diego, CA) array. Standard Illumina protocols were followed for background correction, and normalization to internal control probes.²⁴ RNA was extracted from 80 placentas using TRIZOL reagent (Invitrogen, MA), and sequenced using the Illumina HiSeq2000 system. The expression of the transcripts were quantified using Salmon.²⁵ Details on quality control and quantification of RNA and methylation data are provided in the Online Supplement Methods section.

Placental SNP genotyping was done using HumanOmni2.5 Beadchips (Illumina Inc., San Diego, CA). Genotype-based principal components (PCs) were calculated from genome-wide autosomal SNP data. The R package "prcomp" was used to calculate PCs on the samples' percent methylation profiles.²⁶ A total of 75 samples with both methylation and RNA-seq data were included in the association tests for correlation between DNA methylation and gene expression, as well as covariate-adjusted associations of SBP and DBP with gene expression.

## Epigenome-wide Analyses

We performed six epigenome-wide analyses by fitting robust linear regression models for each CpG site as the response variable on the *M*-value scale and each of the 1st, 2nd and 3rd trimester SBP and DBP variables as the primary predictors. All analyses included maternal age, race/ethnicity, fetal sex, five methylation sample plate (to adjust for potential batch effects), ten genotype-based PCs, three methylation-based PCs and putative cell-mixture estimated using surrogate variable analysis (SVA)²⁷ components (n=20) as adjustment factors in the models. Adjustment for genotype-based PCs has been demonstrated to provide additional benefit in accounting for population stratification.²⁸ We also included race/ethnicity in our models to potentially account for socio-economic differences. The quantile-quantile plots of *P*-values are presented in Figure S1. Additionally, we implemented
Differentially Methylated Regions analysis described in our Online Supplement Methods section.²⁹

## Gene Expression Analyses

First, we tested the Pearson correlation between the DNA methylation levels (M-values) of the top-significant CpGs and mRNA levels of their corresponding mapped genes. Second, we tested the associations between the mRNA levels of the genes with SBP and DBP measurements using DESeq2,³⁰ adjusting for maternal age, race/ethnicity, fetal sex, and 10 genotype-based PCs. The Benjamini-Hochberg adjusted P-value of the Wald test was used to correct for multiple testing and evaluate significant associations (P-value<0.05) between the blood pressure measurements and mRNA levels (hereafter referred as gene expression).

## Canonical Pathway and Functional Annotation Analyses

To evaluate whether the top-significant CpGs are abundant in island and transcript start site (TSS) promoter regions, the chi-squared test was used. Functions of genes near the top 200 CpGs selected based on nominal EWAS P-value were explored to identify the most enriched canonical pathways and gene networks using Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, CA, USA, www.qiagen.com/ingenuity). Further details on pathway and functional annotation analyses are provided in the Online Supplement Methods section.

## Evaluation of previously reported cardiometabolic trait-associated CpGs

Previously reported CpGs that were associated with cardiometabolic traits by EWASs were identified through PubMed using keywords and MeSH terms on DNA methylation or epigenetics and blood pressure, hypertension or preeclampsia studies published between January 01, 2013 and January 01, 2019.¹¹, ¹³⁻¹⁵, ¹⁷, ³¹⁻³⁴ We identified 312 cardiometabolic trait-associated CpGs, out of which 304 unique CpG sites were found in our data. To evaluate the associations of these 304 CpGs with SBP and DBP in our study, we considered CpGs with P-values <0.05 and effect directions (hyper- or hypo-methylation status) identical to the reported effect to be statistically significant (details are provided in the Online Supplement Methods section).

## Results

### Participants' Characteristics

A total of 301 pregnant women who self-identified their race/ethnicity as non-Hispanic White (25.6%), non-Hispanic Black (23.9%), Hispanic (33.9%) and Asian/Pacific Islander (16.6%) participated in the study. Women were on average (mean ± s.d.) 27.7±5.3 years old and delivered at 39.5±1.1 gestational weeks (Table 1, S1 and S2). There were 8 (2.7%) gestational hypertensive and 5 (1.7%) preeclamptic women. The mean SBP at 1st, 2nd, and 3rd trimester was 109.3±10.7, 108.7±9.0 and 111.1±9.0 mmHG, respectively, and the mean DBP was 66.6±7.5, 64.7±6.0, 67.1±6.3 mmHG, respectively.
Associations of maternal SBP and DBP during pregnancy with DNA Methylation in
Placenta

Maternal SBP and DBP during pregnancy were associated with DNA methylation at a total of 24 CpG sites in placenta (3 in the 1st, 6 in the 2nd and 15 in the 3rd trimester) (BACON-corrected FDR P-value<0.05; Empirical P-value ranged from 6.6×10⁻¹⁵ to 2.3×10⁻⁷) (Table 2, S3, Figure S2). Notably, higher 1st trimester SBP and higher 1st trimester DBP were associated with increased methylation at cg06880028 (COL12A1/FILIP1). Moreover, higher SBP or DBP at both 2nd and 3rd trimesters were significantly associated with increased methylation at cg14203118 (EEF1D), cg04867934 (RABGAP1L), cg09559971 (TRAF7 and SNORD60), and cg19565306 (RABL2B), and decreased methylation at cg02918224 (TP53INP2). Out of the 24 CpGs that were significantly associated with SBP and DBP in our data, 18 were enriched in CpG island (Chi-squared test P=0.002) and 11 were in promoter transcript start site (TSS) (Chi-squared P=1.4×10⁻⁶) regions. Among the top-significant CpGs in Table 2, cg10752545 (NAV2), cg05903251 (TBL3), cg06570818 (AGPAT1/RNF5), cg03712991 (SNX18), cg24195524 (RNF5/RNF5P1), cg17813879 (SH2D4A), cg14203118 (EEF1D), and cg13392293 (CDK5RAP2) showed directionally consistent associations with change in blood pressure in repeated measurement analyses after Bonferroni-correction (Table S4). Significantly differentially methylated regions associated with blood pressure for regions that span our top-significant CpGs in Table 2 included NAV2, HOXC4, TP53INP2, AGPAT1, RNF5, and RNF5P1 (Adjusted P-value<0.05) (Table S5).

Canonical Pathway Analysis

Genes near top 200 EWAS CpGs were enriched in disease pathways including cardiovascular and metabolic diseases (Table S6). These included 15 molecules from 1st trimester SBP EWAS that were involved in cardiovascular disease functions. Additionally, 13 molecules from 1st trimester DBP EWAS were involved in cardiovascular system development and function. From 2nd trimester SBP EWAS, 17 molecules were involved in cardiovascular disease and 15 molecules were involved in cardiac arrhythmia, cardiovascular and metabolic disease functions. Lastly, metabolic disease function was among the top canonical pathways that involved 31 molecules identified from 3rd trimester SBP EWAS.

Functional Annotations

In the mQTL database, 201 SNPs were reported to be cis-meQTL with cg06880028 (COL12A1; FILIP1), which was associated with 1st trimester SBP and DBP in our study. Another locus (rs4417778) was cis-meQTL with cg02918224 (TP53INP2), which was associated with 2nd and 3rd trimester DBP and 3rd trimester SBP in our study (Table S7). The identified cis-meQTL SNPs overlap with regulatory motifs and showed the highest enrichment of cell type-specific enhancers (after Bonferroni correction of the binomial test p-values, P<3.9×10⁻⁴) for fetal heart and psoas muscle cells (Table S8). The expression of genes (n=28) near top-significant CpGs (n=25) in 53 different tissues from GTEx v7 Ensembl version 92 database are shown in Figure S3. FILIP1 gene is highly expressed in the artery, left ventricle of the heart and uterine tissues (Figure S4). COL12A1's RNA
expression is highest in the endometrium and its protein is moderately expressed in the placenta (Figure S5).

## Correlations between DNA methylation and Gene Expression in Placenta

Higher 3rd trimester SBP and DBP were significantly associated with higher expression of *COL12A1* in placenta (log2-fold change=0.03 [P=0.03] and log2-fold change=0.05 [P=0.03] per 1 mmHG increase in SBP and DBP, respectively) (Table S9). No significant correlations were found between DNA methylation and gene expression levels of the annotated genes for each of the 24 CpG sites (Figure S6). However, there was a positive relationship between DNA methylation at cg06880028 (which was hypermethylated with higher BP) and *COL12A1* gene expression levels (*r*=0.16, *P*=0.16) (Figure S6).

## Evaluation of Previously Reported Cardiometabolic-trait-associated CpG Sites

Out of 304 CpG sites that were implicated in cardiometabolic traits in previous EWASs and evaluated in our study, 36 CpGs (11.8%) showed directionally consistent associations with maternal SBP and DBP in our data. These included 12 CpGs in placenta associated with preeclampsia, two CpGs in maternal leukocyte associated with preeclampsia, one CpG in peripheral blood associated with hypertensive pregnancy, one CpG in adult blood associated with SBP and DBP, and 20 CpGs in adult blood associated with myocardial infarction (Table S10-S11). Figure 1 shows a Venn Diagram of the CpGs that exhibited overlapping associations with cardiometabolic traits in published EWAS and with maternal SBP and DBP during pregnancy in our study.

## Discussion

To our knowledge, this is the first EWAS of trimester-specific maternal blood pressure in placenta. We identified 24 novel CpG sites associated with trimester-specific maternal systolic and diastolic blood pressures. These blood pressure-associated placental CpGs were abundant in CpG islands and promoter transcription start sites, which suggest their potential roles in functional regulation of gene transcription.³⁵ The genes annotating the top 200 EWAS CpGs showed enrichment in pathways including cardiovascular-metabolic development. Nearly 12% of CpGs previously implicated in cardiometabolic traits were found to be associated with maternal SBP and DBP in our data. These findings provide clues to specific epigenetic modifications in the placenta that may mediate the effect of maternal blood pressure on fetal development and disease risk in later life.³²

We observed corroborating evidence from DNA methylation, gene expression, and meQTL analyses for the role of the *COL12A1* locus in blood pressure regulation. Specifically, higher SBP and DBP were associated with higher methylation at cg06880028 (near *COL12A1* or *FILIP1*) in the 1st trimester, and higher expression of *COL12A1* gene in the 3rd trimester. Several common variants were cis-meQTL with cg06880028 in blood across pregnancy, childhood, adolescence and middle age, suggesting the role of genetic regulation on DNA methylation across the life-course.³⁶ *COL12A1* encodes the alpha chain of type XII collagen, which is highly expressed in the endometrium and is involved in embryonic development.³⁷ Weaker expression of collagen XII has been found in the intima of
atherosclerotic plaques.³⁸ In mice, reduced mRNA expression of *COL12A1* involved in endothelial-to-mesenchymal transition (EMT) was observed in the heart endothelial cells.³⁹ EMT is a fundamental process in establishing an early placental blood supply, and in atherosclerotic lesions associated with plaque stability.⁴⁰ EMT-derived fibroblastic-like cells can enhance clinical disease progression towards a collagen-poor, rapture prone plaque phenotype.⁴⁰ Together, these data suggest type XII collagen, involved in EMT, may play a role in preventing formation of atherosclerosis.

*FILIP1* encodes filamin A binding protein and is highly expressed in fetal heart cells. *FILIP1* transcripts related to fatty acid metabolism can be observed as early as 10 weeks during fetal development and were shown to increase as fetal heart matures.⁴¹ Fetal cardiomyocyte growth regulation is critical since the number of cardiac cells at birth predetermines the number of cardiomyocytes in later life,⁴² a known predictor of cardiac disease.⁴³ Our study’s finding of associations of higher SBP and DBP in the 1st trimester with higher methylation of CpGs near *FILIP1* was also corroborated by positive but non-significant correlation between 3rd trimester SBP and DBP and expression of the *FILIP1* gene.

There are a few placenta-specific EWASs in blood pressure-related traits such as preeclampsia and gestational hypertension.¹³⁻¹⁵ Yeung et al¹⁵ identified 63 differentially methylated CpGs in the placenta associated with preeclampsia, 11 of which showed directionally consistent associations with higher SBP and DBP across the trimesters in our study. Anton et al¹³ identified 229 CpGs differentially methylated in preterm preeclamptic placentas (. Notable findings from our study when evaluating the reported CpGs include, associations of increased methylation of cg26624576 (*CDH11*) with increased SBP and DBP across the trimesters. *CDH11* (Cadherin-11) regulates anchoring trophoblasts to the decidua.⁴⁴ Hypermethylation of cg26624576 in preeclamptic placenta has been validated by pyrosequencing by Anton et al¹³, and contributes to the decreased trophoblast invasiveness associated with preeclampsia.¹³

In maternal leukocytes, increased methylations of cg24030449 (*FGF20*) and cg01485645 (*MLLT6*) were observed among women with preeclampsia.³³ In our study, increased methylation of the same CpGs in placenta was associated with increased SBP in the 2nd and 3rd trimesters. *FGF20* is involved in embryonic development, cell growth, and tissue repair.

In offspring blood, lower methylation of cg09735274 (*TRIM31*) was observed in hypertensive pregnancies.³² In our study, we increased 3rd trimester SBP and DBP was associated with lower methylation of cg09735274 in placenta. *TRIM31* gene expression in placenta has been associated with preeclampsia.⁴⁵ It is noteworthy that some of the differentially methylated CpG cites that were associated with levels of blood pressure in our study overlapped in their associations with hypertensive disorders of pregnancy.

In adult blood of 17,010 individuals, higher SBP and DBP has been associated with lower methylation of cg14476101 (*PHGDH*).¹¹ In our study, higher 3rd trimester DBP was associated with lower methylation of cg14476101 in placenta. Among Swedish adults with history of myocardial infarction (MI), 211 CpG sites were differentially methylated in blood,
¹⁷ of which 20 CpG sites showed directionally consistent associations with increased SBP and DBP in our study. In particular, methylation of cg03135061 (*DYSF*) was associated with MI,¹⁷ and increased maternal 1st trimester DBP was associated with increased methylation of the same CpG in our study. Additionally, MI-associated increased methylations of two CpGs (cg19866478 and cg08301572), near zinc finger protein genes (*ZNF480* and *ZNF730*), were associated with increased trimester-specific SBP and DBP in our study. Zinc finger proteins are involved in regulation of several cellular processes and development of diabetes.⁴⁶

Epigenetic marks have been shown to mediate the effect of pregnancy hypertension on risk of vascular disease later in life.³² These marks can mediate gene expression patterns that control embryonic development and organogenesis,⁴⁷ playing a central role in the developmental programming of adult disease.³² Our finding for blood pressure-CpGs methylation associations in the placenta as well as evidence from published cardiometabolic traits associated with epigenetic modifications in several tissues and stages of life support the developmental origins and health and diseases (DoHaD) hypothesis.³⁴

Our study has several limitations. Placental DNA methylation and gene expression profile varies during pregnancy.⁴⁸ Therefore, there may be a potential complex relationship between maternal blood pressure and placental DNA methylation levels across gestation that could not be distinguished by our study for which all placentas were collected at or near term. We are not able to distinguish whether differential methylation is a cause or consequence of maternal blood pressure change.³² We observed outliers and large ranges of values in gene expression (e.g. *RBX1*) that may be due to cell-type heterogeneity by sample. Potential heterogeneity in cell composition of our placenta samples may have been exacerbated by placental histopathological differences between sub-groups of individuals, however, in our previous study that used the same study participants,⁴⁹ we did not find evidence for differences of gross placental histopathological characteristics by race/ethnicity or sex.

To account for confounding by cell-type, we adjusted for cell-type heterogeneity based on data-driven estimation using the SVA method. Ability to adjust for genotype-based ancestry was another important strength in our study as it allowed effective removal of bias due to population stratification.²⁸ With further application of a Bayesian method, the inflation factors of *P*-values in the association results were minimized.⁵⁰ Moreover, our EWAS findings were corroborated with downstream functional annotations and directionally consistent replications of adult BP-related CpGs. Finally, our study included diverse race/ethnic study participants with longitudinal measurement of maternal blood pressure throughout pregnancy.

## Conclusions

In this study we report evidence for novel placental epigenetic changes associated with increased maternal blood pressure during pregnancy at genes including *COL12A1* and *FILIP1* that are implicated in cardiometabolic diseases. Epigenetic changes may explain the mechanisms of the associations between increased maternal blood pressure, fetal development and adult cardiometabolic diseases. Identification of blood pressure-induced
methylated sites in the placenta may provide clues to early origins of cardiometabolic diseases and inform guidelines for early prevention.

## Perspectives

Placental DNA methylation is a suggested regulatory mechanism through which maternal blood pressure influences fetal and adult health outcomes. We conducted the first epigenome-wide association study to examine the associations of maternal systolic and diastolic blood pressure during 1st, 2nd, and 3rd trimesters of pregnancy with DNA methylation in the placenta. Findings reported herein provide strong evidence for placental DNA methylation of genes involved in cardiovascular-metabolic development pathways. Placental CpGs that were known to be associated with cardiometabolic factors in other studies, conferred associations with systolic and diastolic blood pressure in our data. Findings on blood pressure-associated methylated sites in the placenta may provide clues to early origins of cardiometabolic disease risk and inform guidelines for early prevention.

## Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

## Acknowledgments

We acknowledge the study participants of the NICHD Fetal Growth Studies. We thank research teams at all participating clinical centers (Christina Care Health Systems, Columbia University, Fountain Valley Hospital, California, Long Beach Memorial Medical Center, New York Hospital, Queens, Northwestern University, University of Alabama at Birmingham, University of California, Irvine, Medical University of South Carolina, Saint Peters University Hospital, Tufts University, and Women and Infants Hospital of Rhode Island). We acknowledge the Wadsworth Center, C-TASC and The EMMES Corporations in providing data and imaging support. This work utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov).

## Sources of Funding

This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health including American Recovery and Reinvestment Act funding via contract numbers HHSN275200800013C; HHSN275200800002I; HHSN27500006; HHSN275200800003IC; HHSN275200800014C; HHSN275200800012C; HHSN275200800028C; HHSN275201000009C and HHSN27500008. Additional support was obtained from the NIH Office of the Director, the National Institute on Minority Health and Health Disparities and the National Institute of Diabetes and Digestive and Kidney Diseases.

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Novelty and Significance

What is new?

* This is the first placental EWAS of maternal blood pressure during pregnancy.

What is relevant?

* Higher maternal blood pressure during pregnancy is associated with DNA methylation changes in the placenta.

* Placental DNA methylation loci have relevance for cardiometabolic diseases.

Summary

* Placental methylation signatures of higher blood pressure may underlie molecular pathways in early origins of cardiometabolic disease risk.
<figure><img src="image_2.png" /><figcaption>**Figure 1.**

Illustration of overlaps between significantly differentially methylated CpGs in the present study and previously known cardiometabolic trait-related CpGs.</figcaption></figure>
<caption>Table 1.</caption>

Characteristics of study participants

<table><thead><tr><th>Characteristics</th><th>Mean ± SD or n (%) (n=301)</th></tr></thead><tbody><tr><td>Maternal age, y</td><td>27.7 ± 5.3</td></tr><tr><td>Gestational age at delivery, wk</td><td>39.5 ± 1.1</td></tr><tr><td>Self-reported race/ethnicity, n (%)</td><td></td></tr><tr><td>White</td><td>77 (25.6)</td></tr><tr><td>Black</td><td>72 (23.9)</td></tr><tr><td>Hispanic</td><td>102 (33.9)</td></tr><tr><td>Asian/Pacific Islander</td><td>50 (16.6)</td></tr><tr><td>Gestational hypertension, n (%)</td><td>8 (2.7)</td></tr><tr><td>Preeclampsia, n (%)</td><td>5 (1.7)</td></tr><tr><td>Systolic blood pressure, mmHG</td><td></td></tr><tr><td>1<sup>st</sup> Trimester (0–13 wk)</td><td>109.3 ± 10.7</td></tr><tr><td>2<sup>nd</sup> Trimester (14–27 wk)</td><td>108.7 ± 9.0</td></tr><tr><td>3<sup>rd</sup> Trimester (28–40 wk)</td><td>111.1 ± 9.0</td></tr><tr><td>Diastolic blood pressure, mmHG</td><td></td></tr><tr><td>1<sup>st</sup> Trimester (0–13 wk)</td><td>66.6 ± 7.5</td></tr><tr><td>2<sup>nd</sup> Trimester (14–27 wk)</td><td>64.7 ± 6.0</td></tr><tr><td>3<sup>rd</sup> Trimester (28–40 wk)</td><td>67.1 ± 6.3</td></tr></tbody></table>
<table><caption>Table 2.
Top-significant CpGs (FDR *P* <0.05) associated with maternal SBP and DBP during pregnancy</caption><thead><tr><th>CpG</th><th>Gene</th><th>Chr: Position *</th><th>Relation to Gene/Feature</th><th>Relation to Island</th><th>Δβ<sup>†</sup></th><th>P<sup>‡</sup></th><th>FDR<sup>§</sup></th><th>Gestation Week||</th><th>Phenotype</th></tr></thead><tbody><tr><td>cg11702145</td><td>HCN3</td><td>1:155247214</td><td>TSS200</td><td>Island</td><td>0.021</td><td>1.6E-08</td><td>2.1E-02</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg04867934</td><td>RABGAPIL</td><td>1:174129130</td><td>5'UTR</td><td>Island</td><td>0.039</td><td>1.5E-08</td><td>1.2E-03</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg23618323</td><td>EDEM3</td><td>1:184723951</td><td>1stExon; 5'UTR</td><td>Island</td><td>0.015</td><td>9.0E-08</td><td>1.6E-04</td><td>14-27 wk</td><td>SBP</td></tr><tr><td>cg10752545</td><td>NAV2; LOC100126784</td><td>11:19734701</td><td>TSS200; Body</td><td>Island</td><td>0.016</td><td>2.4E-08</td><td>7.6E-03</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg26201952</td><td>HOXC4</td><td>12:5446253</td><td>Nearest gene</td><td>N_Shore</td><td>0.027</td><td>4.4E-11</td><td>2.4E-06</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg05130406</td><td>CAPS2</td><td>12:75723912</td><td>TSS200</td><td>Island</td><td>0.041</td><td>1.4E-11</td><td>7.6E-04</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg23314283</td><td>SUGT1</td><td>13:53226751</td><td>5'UTR</td><td>Island</td><td>0.003</td><td>1.8E-08</td><td>2.4E-03</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg05903251</td><td>TBL3</td><td>16:2022307</td><td>Body</td><td>Island</td><td>0.005</td><td>9.0E-10</td><td>2.5E-04</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg09559971</td><td>TRAF7; SNORD60</td><td>16:2205259</td><td>TSS1500</td><td>Island</td><td>0.009</td><td>2.9E-08</td><td>6.0E-03</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg00912407</td><td>ZDHHC7</td><td>16:84998665</td><td>Unclassified</td><td>OpenSea</td><td>0.015</td><td>8.4E-09</td><td>3.7E-03</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg23685529</td><td>C17orf49</td><td>17:6918055</td><td>TSS200</td><td>Island</td><td>0.017</td><td>3.7E-11</td><td>5.5E-03</td><td>0-13 wk</td><td>SBP</td></tr><tr><td>cg13679837</td><td>XRN2</td><td>20:21283918</td><td>TSS200</td><td>Island</td><td>0.025</td><td>1.7E-15</td><td>3.3E-02</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg02918224</td><td>TP53INP2</td><td>20:33298052</td><td>Nearest gene</td><td>Island</td><td>0.043</td><td>3.6E-14</td><td>1.1E-02</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg20351327</td><td>EIF3D</td><td>22:36925411</td><td>TSS200</td><td>Island</td><td>0.029</td><td>1.0E-11</td><td>1.0E-07</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg00912407</td><td>ZDHHC7</td><td>16:84998665</td><td>Unclassified</td><td>OpenSea</td><td>0.043</td><td>8.1E-13</td><td>1.2E-07</td><td>14-27 wk</td><td>DBP</td></tr><tr><td>cg23685529</td><td>C17orf49</td><td>17:6918055</td><td>TSS200</td><td>Island</td><td>0.049</td><td>1.2E-12</td><td>2.2E-08</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg13679837</td><td>XRN2</td><td>20:21283918</td><td>TSS200</td><td>Island</td><td>0.002</td><td>3.4E-09</td><td>2.1E-02</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg02918224</td><td>TP53INP2</td><td>20:33298052</td><td>Nearest gene</td><td>Island</td><td>0.004</td><td>1.5E-09</td><td>6.3E-03</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg00912407</td><td>ZDHHC7</td><td>16:84998665</td><td>Unclassified</td><td>OpenSea</td><td>0.004</td><td>6.0E-10</td><td>3.7E-02</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg23685529</td><td>C17orf49</td><td>17:6918055</td><td>TSS200</td><td>Island</td><td>0.01</td><td>3.7E-09</td><td>1.1E-02</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg13679837</td><td>XRN2</td><td>20:21283918</td><td>TSS200</td><td>Island</td><td>0.009</td><td>2.3E-12</td><td>2.4E-04</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg02918224</td><td>TP53INP2</td><td>20:33298052</td><td>Nearest gene</td><td>Island</td><td>0.012</td><td>4.3E-08</td><td>3.0E-02</td><td>14-27 wk</td><td>DBP</td></tr><tr><td>cg20351327</td><td>EIF3D</td><td>22:36925411</td><td>TSS200</td><td>Island</td><td>0.013</td><td>1.3E-09</td><td>3.7E-03</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg00912407</td><td>ZDHHC7</td><td>16:84998665</td><td>Unclassified</td><td>OpenSea</td><td>0.017</td><td>3.2E-13</td><td>3.4E-04</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg23685529</td><td>C17orf49</td><td>17:6918055</td><td>TSS200</td><td>Island</td><td>0.024</td><td>7.7E-09</td><td>5.8E-03</td><td>28-40 wk</td><td>DBP</td></tr></tbody></table>
<table><thead><tr><th>CpG</th><th>Gene</th><th>Chr: Position *</th><th>Relation to Gene/Feature</th><th>Relation to Island</th><th>Δβ<sup>†</sup></th><th>p<sup>‡</sup></th><th>FDR<sup>§</sup></th><th>Gestation Week ||</th><th>Phenotype</th></tr></thead><tbody><tr><td>cg19565306</td><td>RPL23AP82; RABL2B</td><td>22:51222011</td><td>Body; 1stExon; TSS200; 5'UTR</td><td>Island</td><td>0.028</td><td>4.0E-08</td><td>1.0E-04</td><td>14-27 wk</td><td>SBP</td></tr><tr><td>cg25627055</td><td>TRIM41</td><td>5:180650368</td><td>5'UTR; 1stExon</td><td>Island</td><td>0.027</td><td>3.4E-08</td><td>4.2E-04</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg03712991</td><td>SNX18</td><td>5:53813394</td><td>TSS200</td><td>Island</td><td>0.025</td><td>2.4E-10</td><td>4.6E-04</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg06570818</td><td>AGPAT1; RNF5; RNF5P1</td><td>6:32146466</td><td>Nearest gene</td><td>OpenSea</td><td>-0.006</td><td>1.3E-08</td><td>1.2E-03</td><td>28-40 wk</td><td>DBP</td></tr><tr><td>cg24195524</td><td>RNF5; RNF5P1</td><td>6:32147809</td><td>Nearest gene</td><td>OpenSea</td><td>0.002</td><td>1.2E-07</td><td>2.5E-02</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg06880028</td><td>COL12A1; FILIP1</td><td>6:75918165</td><td>Nearest gene</td><td>S_Shore</td><td>0.004</td><td>3.2E-09</td><td>2.2E-03</td><td>0-13 wk</td><td>SBP</td></tr><tr><td>cg13534424</td><td>CAMK2B</td><td>7:44365292</td><td>TSS200</td><td>Island</td><td>0.006</td><td>1.1E-07</td><td>3.7E-03</td><td>0-13 wk</td><td>DBP</td></tr><tr><td>cg14203118</td><td>EEF1D</td><td>8:144680893</td><td>TSS1500</td><td>Island</td><td>0.01</td><td>2.2E-08</td><td>6.8E-04</td><td>0-13 wk</td><td>SBP</td></tr><tr><td>cg17813879</td><td>SH2D4A</td><td>8:19252188</td><td>Nearest gene</td><td>OpenSea</td><td>0.009</td><td>2.9E-08</td><td>1.1E-02</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg13392293</td><td>CDK5RAP2</td><td>9:123342137</td><td>Body</td><td>Island</td><td>0.026</td><td>2.2E-10</td><td>6.0E-08</td><td>14-27 wk</td><td>SBP</td></tr><tr><td>cg13392293</td><td>CDK5RAP2</td><td>9:123342137</td><td>Body</td><td>Island</td><td>0.031</td><td>1.3E-13</td><td>3.2E-14</td><td>28-40 wk</td><td>SBP</td></tr><tr><td>cg13392293</td><td>CDK5RAP2</td><td>9:123342137</td><td>Body</td><td>Island</td><td>0.011</td><td>6.6E-15</td><td>9.1E-04</td><td>14-27 wk</td><td>DBP</td></tr><tr><td>cg13392293</td><td>CDK5RAP2</td><td>9:123342137</td><td>Body</td><td>Island</td><td>0.027</td><td>5.4E-14</td><td>2.0E-02</td><td>14-27 wk</td><td>SBP</td></tr></tbody></table>

* Build 37 chromosome position

† Methylation fold change/delta beta per 1 mmHg increase. Estimates that are statistically significant after FDR correction are shown in bold

‡ Empirical P-value

§ Bacon-corrected FDR P-value

‖ 0-13 wk, 1st trimester; 14-27 wk, 2nd trimester; 28-40 wk, 3rd trimester