Advancing diagnosis and early risk assessment of preeclampsia through noninvasive cell-free DNA methylation profiling

Abstract

Background: Aberrant embryo implantation and suboptimal placentation can lead to (severe) complications such as preeclampsia and fetal growth restriction later in pregnancy. Current identification of high-risk pregnancies relies on a combination of risk factors, biomarkers, and ultrasound examinations, a relatively inaccurate approach. Previously, aberrant DNA methylation due to placental hypoxia has been identified as a potential marker of placental insufficiency and, hence, potential (future) pregnancy complications. The goal of the Early Prediction of prEgnancy Complications Testing, or the ExPECT study, is to validate a genome-wide, cell-free DNA (cfDNA) methylation strategy to diagnose preeclampsia accurately. More importantly, the predictive potential of this strategy is also explored to reliably identify high-risk pregnancies early in gestation. Furthermore, a longitudinal study was conducted, including sequential blood samples from pregnant individuals experiencing both uneventful and complicated gestations, to assess the methylation dynamics of cfDNA throughout these pregnancies. A significant strength of this study is its enzymatic digest, which enriches CpG-rich regions across the genome without the need for proprietary reagents or prior selection of regions of interest. This makes it useful for the cost-effective discovery of novel markers.

Results: Investigation of methylation patterns throughout pregnancy showed different methylation trends between unaffected and affected pregnancies. We detected differentially methylated regions (DMRs) in pregnancies complicated with preeclampsia as early as 12 weeks of gestation, with distinct differences in the methylation profile between early and late pregnancy. Two classification models were developed to diagnose and predict preeclampsia, demonstrating promising results on a small set of validation samples.

Conclusions: This study offers valuable insights into methylation changes at specific genomic regions throughout pregnancy, revealing critical differences between normal and complicated pregnancies. The power of noninvasive cfDNA methylation profiling was successfully proven, suggesting the potential to integrate this noninvasive approach into routine prenatal care.

Keywords: Cell-free DNA; CfDNA methylation; DMRs; Differentially methylated regions; Epigenetics; Placental insufficiency; Prediction; Preeclampsia; Prenatal.

Fig. 1

Overview of the sample collection per analysis cohort. A stacked bar chart showing the number of samples collected at given time points during pregnancy. Each bar represents a group of samples collected during the specified week of gestation. Colors show the sample type, controls in green, and preeclampsia (PE) cases in orange. Separate charts show the numbers of samples used in the different analyses, presymptomatic (A), symptomatic (B) and longitudinal (C) study

Fig. 2

General decrease in methylation during pregnancy, with a significant genome-wide hypomethylation in preeclampsia patients near late pregnancy. Box plots showing the mean genomic methylation level of controls and preeclampsia (PE) cases. Means were compared with Wilcoxon rank-sum tests using Bonferroni multiple testing correction. Methylation levels from presymptomatic samples, sampled between 11 and 14 weeks of gestation, show no significant difference. Methylation levels between cases and controls sampled after 20 weeks of gestation show a significantly lower methylation level of cases compared to controls. Significant methylation differences between presymptomatic and symptomatic groups can be observed for both preeclampsia cases and controls

Fig. 3

Significantly differently methylated regions (DMRs) enable distinct clustering of preeclampsia cases and controls during third trimester pregnancies. Heatmap figures show a hierarchical clustering of samples (columns) from the symptomatic analysis set (A) and the presymptomatic analysis set (B). The clustering is based on the methylation level of the top 250 significant DMRs for the symptomatic set and all 42 DMRs for the presymptomatic set. The sample category, fetal sex, and aspirin treatment are included as additional annotations per sample

Fig. 4

Multiple machine learning classifiers allow successful classification and prediction of preeclampsia patients. Three different types of classification models were tested: random forest (1), support vector machine (2), and a weighted logistic regression model with elastic net regularization (3). Models in the top row (A) are trained on a symptomatic sample set (> 20 weeks gestational age, n = 57). Models in the bottom row (B) are trained on a presymptomatic sample set (between 11 and 14 weeks of gestation, n = 126). Curves are based on a symptomatic test set (n = 20) in orange and a presymptomatic test set in green (n = 43)

Fig. 5

General genome-wide methylation trends across pregnancy differ between preeclampsia cases and controls. Mean whole genome methylation follows a similar trend for preeclampsia patients and controls across pregnancy duration. At 11–13 weeks of gestation, a lower global methylation is noted for preeclampsia cases (A). The methylation pattern of two preeclampsia-related genes, sFLT-1 (B) and PlGF (C), is slightly different for controls and preeclampsia patients. Smoothing was performed with local polynomial regression fitting, and standard error (SE) is shown as a band