PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis
Year: 2024
Type: article
Abstract: Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data availability statement: The data underlying this article are available in the article and in its online web application or supplementary material.
Authors: Siwei Zhang, Nick Strayer, Tess Vessels, Karmel Choi, Geoffrey W Wang, Yajing Li, Cosmin A Bejan, Ryan S Hsi, Alexander G Bick, Digna R Velez Edwards, Michael R Savona, Elizabeth J Phillips, Jill M Pulley, Wesley H Self, Wilkins Consuelo Hopkins, Dan M Roden, Jordan W Smoller, Douglas M Ruderfer, Yaomin Xu
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,549
Funders: Vanderbilt University, Vanderbilt University Medical Center, Georgia Clinical and Translational Science Alliance, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, National Center for Research Resources, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Pharmacogenomics of Arrhythmia Therapy, G1531600799, G1766209448, G2103760183, Understanding the genetic risk underlying racial disparities in uterine fibroids, G2615776415, G2683268942, Improving Prediction of Drug Action, G3395468139, G3619590958, G364208108, G3791753988, G3938985443, G4049315607, Automated Storage and Retrieval of Biological Systems, G4395870004, G4410787187, Leveraging Electronic Health Records and Genomic Biobanks for Kidney Stone Disease, G560889077, G5679716143, G5717004637, Genetic Epidemiology of Multiple Sclerosis, G6589054092, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, VESPA: Vanderbilt Electronic Systems for Pharmacogenomic Assessment, G7163342664, The Vanderbilt Institute for Clinical and Translational Research (VICTR), Vanderbilt Genome Electronic Records Project, PsycheMERGE: Leveraging electronic health records and genomics for mental health research, G8098630230, Epidemiologic Architecture for Genes Linked to Environment (EAGLE), G8332127685, Vanderbilt Institute for Clinical and Translational Research (VICTR)
OpenAlex ID: W4401478355