Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

N. Pouladi; l. Achour; H. Li; J. Berghout; C. Kenost; M.L. Gonzalez-Garay; Y.A. Lussier

doi:10.15265/IY-2016-040

Yearbook of Medical Informatics, Table of Contents

Yearb Med Inform 2016; 25(01): 194-206
DOI: 10.15265/IY-2016-040

IMIA and Schattauer GmbH

Georg Thieme Verlag KG Stuttgart

Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records

N. Pouladi^*

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

l. Achour^*

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

H. Li

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

J. Berghout

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

C. Kenost

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

M.L. Gonzalez-Garay

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

,

Y.A. Lussier

¹BIO5 Institute, The University of Arizona, Tucson, AZ, USA

²Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA

³Department of Medicine, The University of Arizona, Tucson, AZ, USA

⁴University of Arizona Cancer Center, The University of Arizona, Tucson, AZ, USA

› Author Affiliations

Abstract

Summary

Objectives: Disease comorbidity is a pervasive phenomenon impacting patients’ health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity.

Methods: We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present.

Results: Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins. Conclusion: This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.

Keywords

Comorbidity - disease similarity - electronic health records - phenome - biomolecules - function - omics - modularity - integrative data analysis

Full Text

References

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