A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank.

Vivek Sriram,Yonghyun Nam,Dokyoon Kim,Sang-Hyuk Jung,Seung Mi Lee,Anurag Verma,Manu Shivakumar

doi:10.3390/jpm11121382

Abstract

Background: Recent 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. Methods: We 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. Results: A 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. Conclusion: Egocentric DDNs hold promise as a clinical tool for the network-based identification of potential disease complications for a variety of phenotypes.

Highlights

Licensee MDPI, Basel, Switzerland.With pregnancy-related complication disorders afflicting 8% of the American population, much literature exists regarding acute phenotypes during pregnancy, including preeclampsia, placenta previa, and gestational diabetes [1]
The node for preeclampsia and eclampsia was connected to nine other pregnancy complications, including miscarriage/stillbirth (634), known or suspected fetal abnormality affecting the management of the mother (655), complications of labor and delivery (669), hemorrhage during pregnancy, childbirth, and postpartum complications (635), placenta previa and abruptio placenta (635.3), other complications of pregnancy (646), hypertension complicating pregnancy, childbirth, and the puerperium
Through our disease–disease network (DDN), we can see that preeclampsia and eclampsia have potential genetic associations with 61 alter-diseases belonging to 13 different disease groups outside of pregnancy complications

Summary

Introduction

Licensee MDPI, Basel, Switzerland.With pregnancy-related complication disorders afflicting 8% of the American population, much literature exists regarding acute phenotypes during pregnancy, including preeclampsia, placenta previa, and gestational diabetes [1]. Recent evidence suggests that women with pregnancy-related disorders are at risk for long-term medical complications, such as gestational diabetes with type. With the lack of research into long-term associations of pregnancy-related diseases, the chronic effects of obstetric disorders remain severely understudied. Recent studies have found that women with obstetric disorders are at increased risk for a variety of long-term complications. 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. We constructed egocentric DDNs, where each network focuses on a pregnancy-related disorder and its neighboring diseases. Results: A 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. Our proposed method achieved an increase in average area under the receiver operating characteristic curve (AUC) by a factor of

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