Placental epigenetic clocks derived from crowdsourcing: Implications for the study of accelerated aging in obstetrics

Abstract

Epigenetic gestational age acceleration has been implicated in obstetric syndromes including preeclampsia, yet robust conclusions require accurate and unbiased epigenetic age models. Herein, we curated 1,842 public placental methylomes and organized a DREAM challenge to develop models of gestational age. Participants were blinded to the test data that we generated from 384 placentas encompassing normal and complicated pregnancies. Models developed during and post-challenge compared favorably to existing models in terms of accuracy, yet they were better calibrated throughout gestation and indicated that reports of accelerated epigenetic aging in preterm preeclampsia were likely due to modeling artifacts. The models show that accelerated aging is associated with a decrease in birthweight percentiles in male neonates delivered at term. By contrast, preterm accelerated aging was protective against delivery of a small-for-gestational-age neonate regardless of fetal sex. This work informs our understanding of the fetal sex-dimorphic role of the placenta epigenome in obstetrics.

Keywords: Epigenetics; Pregnancy.

Graphical abstract

Figure 1

Distribution of gestational age by dataset and phenotypic group (A) Gestational age distribution across 1,842 publicly available placental samples, grouped by dataset IDs from various studies used in the Placental Clock DREAM Challenge. Each dataset is represented by a unique color, and gestational age is plotted on the x axis. (B) Gestational age distribution for 384 test samples from the test dataset, grouped by phenotypic categories: term controls, term small for gestational age (SGA), term preeclampsia (PE), preterm PE, preterm labor (PTL), preterm SGA, and preterm pre-labor rupture of membranes (PPROM).

Figure 2

Overview of the placental clock DREAM challenge The placental clock DREAM challenge aimed to develop models for predicting gestational age using DNA methylation profiles from placental samples. We curated 1,842 placenta methylomes analyzed with HumanMethylation450K (n = 930) and HumanMethylation850K (n = 912) arrays from public repositories. Of these, 1,742 samples were used as the training dataset, while 100 samples profiled on the 850K platform were specifically set aside as leaderboard data to provide real-time feedback during model development. A blinded test dataset of 384 samples was generated using the HumanMethylation850K array for final evaluation. Participants submitted dockerized models that were assessed on a virtual Linux server. Performance metrics, including root mean squared error (RMSE), mean absolute error (MAE), and Pearson correlation coefficient, were calculated to rank teams. See also STAR Methods, DREAM challenge.

Figure 3

Performance of top three teams in the placental clock DREAM challenge (A–C) Scatterplots of chronological gestational age (GA) versus predicted GA by team 1 (A), team 2 (B), and team 3 (C). Each point is colored by phenotypic group: preterm labor (PTL), preterm pre-labor rupture of membranes (PPROM), preterm preeclampsia (preterm PE), term PE, term small for gestational age (SGA), and controls. Performance metrics (correlation, root mean squared error [RMSE] and mean absolute error [MAE]) are displayed for each team based on all 384 samples shown in Table 1.

Figure 4

Robustness of team rankings in the placental clock DREAM challenge The violin plots display the distribution of team performance (root mean squared error, RMSE). across 1,000 bootstrap resampling iterations of the test set. Bayes factor evaluates the relative likelihood of a model (k) outperforming the next-ranked model (k + 1), providing a measure of ranking robustness. See also STAR Methods, DREAM challenge.

Figure 5

Performance of post-challenge ensemble models (A and B) Scatterplots show chronological gestational age (GA) on the x axis versus predicted GA by the “wisdom of crowds” placental clock (A) and the automated machine learning solution, AutoGluon (B), respectively. Points are color-coded by phenotypic groups: preterm labor (PTL), preterm pre-labor rupture of membranes (PPROM), preterm preeclampsia (preterm PE), term PE, term small for gestational age (SGA), and controls. Performance metrics (correlation, root mean squared error [RMSE] and mean absolute error [MAE]) are displayed for each model based on all 384 samples shown in Table 1.

Figure 6

Performance of post challenge placental clock Scatterplot shows chronological gestational age (GA) on the x axis versus predicted GA by the post challenge placental clock. Points are color-coded by phenotypic groups: preterm labor (PTL), preterm pre-labor rupture of membranes (PPROM), preterm preeclampsia (preterm PE), term PE, term small for gestational age (SGA), and controls. Performance metrics (correlation, root mean squared error [RMSE] and mean absolute error [MAE]) are displayed based on all 384 samples shown in Table 1.

Figure 7

Epigenetic age acceleration across clinical phenotypes (A and B) Boxplots comparing epigenetic age acceleration, defined as the difference between epigenetic age and chronological gestational age, for six clinical phenotypes using the control placental clock (A) and post challenge placental clock (B). Mean acceleration values and one-sample t test p values are noted for each of the six phenotypes: preterm labor (PTL), preterm pre-labor rupture of membranes (PPROM), preterm preeclampsia (preterm PE), term PE, term small for gestational age (SGA), and controls. See also STAR Methods, Quantification and statistical analysis.

Figure 8

Correlation between epigenetic age acceleration and birth weight percentiles for the test set (A and B) Scatterplots showing the relationship between birth weight percentiles on y axis and epigenetic age acceleration as determined by the post challenge placenta clock on the x axis in term (A) and preterm (B) samples.