Extensive research on tuberculosis (TB) and HIV co-infection reveals the diverse prevalence and co-epidemic patterns across populations, necessitating tailored public health strategies. Co-infection is bidirectional; individuals with HIV are more susceptible to TB, and vice versa. Antiretroviral therapy (ART) and antituberculosis treatment (ATT) are critical for managing these conditions, but pose risks due to drug–pathogen and drug–drug interactions, potentially leading to immune reconstitution inflammatory syndrome (IRIS) in patients with HIV/AIDS. IRIS, often triggered by highly active antiretroviral therapy (HAART), can exacerbate HIV progression, increase drug resistance, and deteriorate patients’ quality of life. Approximately one-third of the global population with HIV is also infected with TB, with extensive drug-resistant (XDR) and multidrug-resistant (MDR) strains posing significant challenges. Latent TB infection (LTBI) further complicates the scenario, as it can progress to active TB, particularly in individuals with both conditions. The global and Indian mortality rates for TB-HIV co-infection remain high, emphasizing the need for new strategies. Additionally, unreported cases and inadequate post-treatment monitoring contribute to the high mortality rate, particularly among patients with LTBI. The complexity of managing HIV-TB co-infection, especially with LTBI, underscores the urgency of addressing these challenges to improve the outcomes for the affected populations.

Metabolic syndrome (MeS) is a major risk factor for cardiovascular disease and is characterized by chronic low-grade inflammation, immune dysregulation, and metabolic abnormalities. However, the molecular mechanisms linking MeS to diabetic coronary artery disease (DMCAD) remain incompletely understood. Publicly available peripheral blood mononuclear cell (PBMC) transcriptomic datasets of MeS and DMCAD were analyzed using an integrative bioinformatics approach. Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment, protein–protein interaction (PPI) network construction, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and miRNA regulatory network analysis. Candidate genes were further evaluated using an independent type 2 diabetes mellitus (T2DM) dataset for external transcriptomic validation. Integrated analyses identified immune-inflammatory and immuno-metabolic pathways as central features of both MeS and DMCAD. Enrichment analyses highlighted cytokine signaling, leukocyte activation, chemotaxis, complement activation, oxidative stress, and vascular inflammatory responses. Network analyses identified CD86, CD33, CCR1, C5AR1, FPR1, CXCL16, and LILRA5 as key hub genes associated with immune regulation and cardiometabolic dysfunction. External transcriptomic validation supported the relevance of CD33, CD86, and LILRA5. miRNA network analysis identified members of the miR-17/92 family and miR-146a-5p as potential upstream regulators. TAM 2.0 enrichment analysis further linked these miRNAs to metabolic syndrome, diabetes mellitus, atherosclerosis, coronary heart disease, immune response, inflammation, and angiogenesis. Our findings suggest that coordinated immune-inflammatory and metabolic signaling networks contribute to the progression from MeS to DMCAD. The identified hub genes and miRNAs may serve as potential biomarkers and therapeutic targets for inflammation-driven cardiometabolic disease.

Background: Metabolic syndrome (MeS) is a multifactorial metabolic disorder characterized by obesity, dyslipidemia, insulin resistance, and hypertension, and is strongly associated with an increased risk of cardiovascular diseases (CVDs) and type-2 diabetes mellitus (T2DM)-related complications. Chronic low-grade inflammation and immune dysregulation are increasingly recognized as central contributors to metabolic and cardiovascular pathogenesis. However, the molecular mechanisms linking MeS-related etiologies to coronary artery disease (CAD) remain incompletely understood. The present study aimed to identify shared inflammatory and immuno-metabolic transcriptional signatures associated with MeS and its cardiovascular complications using an integrative bioinformatics approach. Methods: Three publicly available peripheral blood mononuclear cell (PBMC) microarray datasets from the Gene Expression Omnibus (GEO) database, including metabolic syndrome (GSE98895), recent-onset type 1 diabetes (GSE193273), and diabetes mellitus with coronary artery disease (GSE250283), were analyzed using R-based bioinformatics pipelines. Differentially expressed genes (DEGs) were identified using the limma package, followed by Gene Set Enrichment Analysis (GSEA) using MSigDB Gene Ontology Biological Process C5 gene sets. Shared DEGs were subjected to protein-protein interaction (PPI) network construction using STRING and Cytoscape http://www.cytoscape.org/. Hub genes were identified using the CytoHubba MCC algorithm, and associated miRNAs were predicted using miRNet. Functional enrichment analysis of candidate miRNAs was performed using TAM 2.0. Results: GSEA demonstrated prominent enrichment of inflammatory, immune-regulatory, cytokine-mediated, oxidative stress, and metabolic pathways across the datasets. T1D samples exhibited enrichment of interferon-mediated signaling, cytokine responses, and proliferative cellular programs. MeS samples showed activation of adaptive immune responses, leukocyte-mediated immunity, and inflammatory signaling pathways. DMCAD samples demonstrated strong enrichment of inflammatory cytokine production, oxidative phosphorylation, mitochondrial metabolism, reactive oxygen species pathways, and leukocyte activation. Comparative analysis identified 19 shared DEGs between MeS and DMCAD, including CXCL16, CCR1, FPR1, C5AR1, CD86, and TNFRSF21, which formed a significantly interconnected inflammatory interaction network. Functional enrichment analyses revealed enrichment of chemotaxis, complement signaling, immune receptor activity, interferon gamma response, inflammatory response, and IL6-JAK-STAT3 signaling pathways. miRNA interaction analysis identified candidate regulatory miRNAs, including miR-146a-5p, miR-21-5p, miR-155-5p, and members of the miR-17-92 cluster, which were enriched in inflammatory, cardiovascular, and metabolic disease-associated pathways. Conclusions: The present integrative bioinformatics analysis demonstrates that MeS and DMCAD share common inflammatory and immuno-metabolic transcriptional programs characterized by immune activation, cytokine signaling, oxidative stress, and vascular-inflammatory regulatory networks. The identified hub genes and candidate miRNAs may represent potential biomarkers and therapeutic targets associated with inflammation-driven cardiovascular complications in metabolic disorders.