The Implications of Convergence and Divergence in Disease Comorbidities

Abstract

Comorbidity is a medical term defined by the concurrent presence of two or more medical disorders within an individual. Comorbidities are likely to exhibit progressive exacerbation over time, complicate therapy, and often worsen disease outcomes. Research in comorbidity studies has traditionally focused on phenotypic characterizations of comorbid diseases to enhance prevention and management, while recent efforts have increasingly explored the biological commonalities convergently underlying these complex conditions. Convergence in network biology helps inform how shared genetic and biological factors can contribute to multiple diseases by affecting various levels of biology (from genes to phenotypes). Statistical models and computational methods can reveal divergence within subgroups that may have unique factors leading to different disease outcomes, thus creating distinct pathways in comorbid disease progression. Both directions benefit from the construction of extensive reservoirs of biological data amassed through the initiatives of biobanks, medical investigations, and electronic health records. Comorbidity research still faces gaps, especially when it comes to understanding how underlying interactions between diseases can converge or diverge to shape progression among subgroups. This composition addresses these gaps by investigating phenotypic divergences in populations, such as disparities in chronic pain progression for traditionally underrepresented populations in medical research, while also enhancing the understanding of convergent biological underpinnings between comorbid diseases (e.g., multiple myeloma and IgA nephropathy). The research also reviews how data fusion methods can be used in biomedical research to investigate comorbid diseases from multi-omics perspectives. Research Project 1 (Appendix A) investigates the divergence of chronic pains and disparities based on sociodemographic factors. Results showed instances of traditionally underrepresented groups in research such as females, blacks, and low-income earners, having significantly higher incidence rates of subsequent chronic pain or mental conditions when considering an initial condition. This finding is critical because it suggests that these groups may experience disproportionate burdens of disease, potentially due to differences in access to care, or treatment. By identifying disparities, the research emphasizes the value of studies that encompass diverse populations to deepen our understanding of diseases and their progression. Research Project 2 (Appendix B) shows a unique comorbidity of multiple myeloma and IgA nephropathy. Through multipartite network analysis and curation, evidence shows biological connections between two genes THOC5 and CBFA2T3. Network analysis associating them with the same gene ontology term “myeloid leukocyte differentiation” implies shared functional roles. This convergence shows a potential link for the genes to influence similar stem cell pathways, leading to dysregulation in hematopoietic cell development. There is considerable research on similar blood disorders like leukemias and monoclonal gammopathies, but knowledge gaps remain for myeloma, which suggests the need for further biological verification. Research Project 3 (Appendix C) offers a synthesis of multi-omics data fusion methods, emphasizing their application in studying disease comorbidities. The investigation highlights how integrative approaches can reveal shared molecular mechanisms across conditions by evaluating diverse computational frameworks. This positions data fusion as a foundational tool for identifying biological convergence in comorbidity research and advancing systems-level models of disease. Research Projects 1 and 2 tackle significant gaps in health research by exploring shared mechanisms in comorbidities and variability in population health, contributing to a more comprehensive understanding of comorbidities. Research Project 3 shows how multi-omics data fusion techniques equipped with advanced computational methods can uncover latent biological relationships across diseases. Together, these studies demonstrate the value of investigating comorbidities from multi-scale frameworks, enabling a more nuanced understanding of the intersecting biological and social complexities that shape the progression of co-occurring diseases.