Severe COVID-19 disproportionately impacts patients with comorbidities such as type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD), affecting 10–30% of cases. This study elucidates shared molecular mechanisms by identifying common hub genes and genetic variants across these conditions using an integrative bioinformatics approach. We curated 5463 COVID-19-related genes from DisGeNET, GeneCards, T-HOD, and other databases, comparing them with gene sets for T1D (324 genes), T2D (497), OBCD (835), CVD (1756), HTN (837), and CeVD (1421). Functional similarity analysis via ToppGene, hub gene prediction with cytoHubba, and Cytoscape-based protein–protein interaction networks identified four hub genes—CCL2, IL6, IL10, and TLR4—consistently shared across all conditions (p < 1.0 × 10−5). Enrichr-based gene ontology and KEGG analyses revealed cytokine signaling and inflammation as key drivers of COVID-19 cytokine storms. Polymorphisms like IL6 rs1800795 and TLR4 rs4986790 contribute to immune dysregulation, consistent with previous genomic studies. These genes suggest therapeutic targets, such as tocilizumab for IL6-driven inflammation. While computational, requiring biochemical validation, this study illuminates shared pathways, advancing prospects for precision medicine and multi-omics research in high-risk COVID-19 populations.
On 21 December 2019, the virus responsible for COVID-19, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in China. Since then, the virus has spread to more than 180 countries and continues to evolve, with emerging variants and the persistence of long COVID contributing to ongoing public health challenges. As of early 2025, over 900 million confirmed infections have been reported worldwide, with official death counts of approximately 7 million—although modeling studies suggest that the true global toll may exceed 20 million. They were identified to be a zoonotic virus, which are viruses that are transferred from a non-human animal to a human. Studies show that the bats were the likely reservoirs, due to it being genetically similar to the SARS bat viruses [1]. The single-stranded positive-sense RNA was encapsulated by its nucleocapsid, with it surrounded by spike proteins, which help with receptor recognition and the cell membrane fusion process [2]. Computational studies of SARS-CoV-2 variants, such as Delta and Omicron, have further elucidated differences in spike protein structure and function, potentially contributing to variations in infectivity and immune evasion [3]. Clinically, infection typically presents with respiratory symptoms such as dry cough, sore throat, difficulty breathing, and fever, which generally appear within two or more days after exposure [4].
Despite the unprecedented global vaccination campaign—with more than 14 billion doses administered worldwide, including periodic booster shots updated to counter new mutations—and advances in therapeutic approaches such as drug repurposing [5] and vaccine booster development [6], COVID-19 remains a significant health crisis. For instance, computational assessments of Omicron sub-variants (BA.1, BA.1.1, BA.2, and BA.3) have highlighted increased spike protein infectivity, which may exacerbate disease outcomes in patients with comorbidities [7]. Additionally, long COVID, characterized by persistent symptoms, shares molecular pathways with chronic comorbidities, as revealed by computational analyses of SARS-CoV-2 gene expression, highlighting the need for targeted therapies in high-risk groups [8].
Comorbidities refer to one or more conditions that are present along with a main disease. These conditions are typically chronic, which can affect an individual’s ability to function. This also means that comorbidities can affect the individual in terms of being more vulnerable to getting a disease. COVID-19 is no exception, as there have been reports of comorbidities present in people with the disease [4,9,10,11]. Among the most prevalent comorbidities, type 1 diabetes (T1D), type 2 diabetes (T2D), obesity (OBCD), cardiovascular disease (CVD), hypertension (HTN), and cerebrovascular disease (CeVD) affect 10–30% of COVID-19 patients, with T2D (20–30%) and CVD (10–15%) being particularly common [3,6,7,8]. These conditions elevate severe outcome risks, with odds ratios of 1.5–3.0 for mortality and 30–50% ICU admission rates for T2D patients [6,7]. These comorbidities were selected due to their high prevalence, strong association with severe COVID-19, and robust gene set availability in databases like DisGeNET, T-HOD, and GeneCards, unlike chronic obstructive pulmonary disease (COPD) or chronic kidney disease (CKD), which lack sufficient curated genetic data [3,6]. The World Health Organization emphasizes that all age groups are susceptible, but the elderly with these comorbidities face the highest risk due to age-related immune and physiological declines [3].
Genomic and biochemical studies have identified several susceptibility genes associated with COVID-19 severity and immune response. For instance, genome-wide association studies (GWAS) have pinpointed variants in genes such as ACE2, TMPRSS2, and HLA loci that influence SARS-CoV-2 entry and immune regulation [12,13]. Additionally, genes involved in interferon signaling, such as IFITM3 and IRF7, have been linked to differential disease outcomes [14]. Studies have also highlighted the role of inflammatory and immune response genes, including CCL2, IL6, IL10, and TLR4, in modulating COVID-19 severity [15,16]. These findings underscore the importance of genetic factors in determining susceptibility and disease progression, providing a foundation for our bioinformatics analysis of shared hub genes between COVID-19 and its comorbidities.
While prior studies have identified susceptibility genes for COVID-19. There are currently not enough studies explaining why and how the common comorbidities are linked to COVID-19 in terms of genetics. With how prevalent COVID-19 is in people with comorbidities, there could be potential answers that lie in genetics that may potentially play a role. Shared genetic factors likely contribute to the increased severity of COVID-19 in patients with comorbidities, potentially through common molecular pathways that influence disease susceptibility and progression. Knowledge of gene similarities among COVID-19 and the comorbidities can prove to be useful for future prospects, such as in drug design, where it can inhibit the actions of these particular genes common among all the comorbidities. Despite advances in identifying COVID-19 susceptibility genes, the genetic links between COVID-19 and its common comorbidities remain underexplored. Shared genetic factors likely underlie the heightened severity in patients with T1D, T2D, OBCD, CVD, HTN, and CeVD, potentially through pathways like cytokine signaling and inflammation that exacerbate SARS-CoV-2 pathogenesis [9]. Understanding these shared mechanisms could inform targeted therapies, such as drugs inhibiting pro-inflammatory genes, to mitigate severe outcomes in high-risk populations [14]. This study employs an integrative bioinformatics approach, leveraging databases (DisGeNET, T-HOD, GeneCards, CTD) and tools (ToppGene, Cytoscape 3.8.0, cytoHubba 0.1, ToppGene Suite, Cincinnati, OH, USA) to identify common hub genes and genetic variants across COVID-19 and these comorbidities. By predicting shared hub genes via interactome analysis, we aim to uncover novel molecular insights, offering potential biomarkers and therapeutic targets to address the compounded effects of COVID-19 in patients with chronic conditions.
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