ArticleJournal of personalized medicine2021
A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Phenome-wide comorbidity network analysis reveals clinical risk patterns in enthesopathy and enthesitis.medRxiv : the preprint server for health sciences · 2025Article
- The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank.Human genomics · 2025Article
- Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.Bioinformatics (Oxford, England) · 2024Article
- Phenome-wide association network demonstrates close connection with individual disease trajectories from the HUNT study.PloS one · 2024Article
- Comorbidity network analysis using graphical models for electronic health records.Frontiers in big data · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundRecent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. However, the underlying pathophysiology of these connections remains undetermined. A network-based view incorporating knowledge of other diseases and genetic associations will aid our understanding of the role of genetics in pregnancy-related disease complications.
methodsWe built a disease-disease network (DDN) using UK Biobank (UKBB) summary data from a phenome-wide association study (PheWAS) to elaborate multiple disease associations. We also constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. We then applied graph-based semi-supervised learning (GSSL) to translate the connections in the egocentric DDNs to pathologic knowledge.
resultsA total of 26 egocentric DDNs were constructed for each pregnancy-related phenotype in the UKBB. Applying GSSL to each DDN, we obtained complication risk scores for additional phenotypes given the pregnancy-related disease of interest. Predictions were validated using co-occurrences derived from UKBB electronic health records. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of 1.35 from 55.0% to 74.4% compared to the use of the full DDN.
conclusionEgocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.