ReviewJournal of perinatal medicine2018
Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses.
Review in Journal of perinatal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Racial and ethnic representation in epigenomic studies of preterm birth: a systematic review.Epigenomics · 2021Pooled it
- Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study.Journal of medical Internet research · 2025Article
- The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.Journal of aging and health · 2023Article
- Subtyping Social Determinants of Health inmedRxiv : the preprint server for health sciences · 2023Article
- A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.JMIR medical informatics · 2022Article
- Prognostic Performance of Peripheral Blood Biomarkers in Identifying Seropositive Individuals at Risk of Developing Clinically Symptomatic Chagas Cardiomyopathy.Microbiology spectrum · 2021Article
- Epigenetic biomarkers and preterm birth.Environmental epigenetics · 2020Review
- An epigenetic association of malformations, adverse reproductive outcomes, and fetal origins hypothesis related effects.Journal of assisted reproduction and genetics · 2018Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundRecent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB.
methodsThe data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24-34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised "subject-variable" bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age.
resultsThe bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length.
conclusionsThe results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.
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