Multidrug-Resistant Infections and Metabolic Syndrome: An Overlooked Bidirectional Relationship

Over the past two decades, metabolic syndrome (MetS) and infections caused by multidrug-resistant (MDR) pathogens have emerged as converging global health challenges. Traditionally investigated as separate entities, accumulating evidence increasingly supports a bidirectional relationship between them, mediated by chronic inflammation, immune dysregulation, gut microbiota alterations, and antibiotic-driven expansion of the resistome. This narrative review examines the complex immunometabolic interplay linking MetS and MDR infections, focusing on molecular mechanisms, clinical implications, and prospective research directions. A systematic literature search was conducted using major databases, including PubMed and Scopus, targeting studies from the last 15 years that explore the interface between metabolic dysfunction and antimicrobial resistance. Particular attention is given to key immunometabolic pathways such as the IRS–PI3K–AKT–mTOR axis; the contribution of visceral adiposity and Toll-like receptor (TLR)-mediated inflammation; and the role of gut dysbiosis in augmenting both susceptibility to infections and metabolic derangements. Evidence is presented supporting the hypothesis that MetS increases host vulnerability to MDR pathogens, while chronic MDR infections may reciprocally induce systemic metabolic reprogramming. Viral infections with established metabolic sequelae (e.g., HIV, hepatitis C virus [HCV], and cytomegalovirus [CMV]) are also considered to broaden the conceptual framework. Although current data remain largely associative and fragmented, the emerging MetS–MDR syndemic model poses substantial challenges for translational research, antimicrobial stewardship, and personalized therapeutic strategies. Recognizing this reciprocal relationship is pivotal for refining infection risk stratification, optimizing treatment, and informing public health policies. Further investigations are warranted to elucidate the magnitude and directionality of this association and to identify predictive immunometabolic biomarkers that may guide targeted interventions in high-risk populations.