A Network Analysis of Molecular Interactions to Study the Development of New-onset Diabetes and Hypertension after COVID-19 Infection Using Bioinformatics Tools

Abstract

IntroductionThe association between COVID-19 infection and the development of new-onset diabetes and hypertension is an emerging area of research. However, a comprehensive understanding of the underlying molecular mechanisms is still lacking. Network analysis using bioinformatics tools can provide valuable insights into the complex molecular interactions involved in these conditions after COVID-19 infection.ObjectiveThis study aims to use bioinformatics tools to analyze the network of molecular interactions related to new-onset diabetes and hypertension following COVID-19 infection.MethodsData from publicly available databases were utilized, including gene expression profiles and protein-protein interaction information. Differential expression analysis was performed to identify genes that were differentially expressed in individuals with new-onset diabetes and hypertension after COVID-19 infection compared to healthy controls. A protein interaction network was constructed using bioinformatics tools to explore the functional relationships among the identified differentially expressed genes.ResultsThe network analysis revealed several key proteins and pathways related to the pathogenesis of new-onset diabetes and hypertension after COVID-19 infection. Notably, proteins involved in insulin signaling, glucose metabolism, inflammation, and blood pressure regulation were found to be prominently associated. The signaling pathway and the renin-angiotensin system were identified as key pathways in this context.ConclusionThis study provides insights by showing a network-based perspective on the molecular interactions involved in the development of new-onset diabetes and hypertension after COVID-19 infection.