Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics

The gut microbiota (GM) is a pivotal regulator of host metabolism and a contributor to the pathophysiology of obesity, type 2 diabetes (T2D), and metabolic syndrome (MS). Disruptions in GM composition and function are collectively termed dysbiosis. This review synthesizes current evidence on GM dysbiosis, moving beyond simple taxonomic associations, to examine functional drivers of metabolic dysfunction. Dysbiosis impairs metabolic health through several interconnected pathways: enhanced dietary energy extraction, compromised intestinal barrier integrity leading to metabolic endotoxemia, chronic low-grade “meta-inflammation,” and the disruption of circadian rhythms and neuro-immune signaling. Beyond bacteria, dysbiosis of the gut virome and mycobiota may further modulate metabolic risk. Animal and emerging human studies indicate that reduced virome diversity and altered phage–bacteria interactions can amplify dysbiosis, promote inflammatory signaling, and impair metabolic homeostasis. Recognition of GM dysbiosis as a contributor to metabolic disease has prompted development of therapeutic strategies aimed at restoring microbial balance and function. These interventions span a spectrum from established clinical approaches with indirect microbiota effects to experimental therapies designed to directly manipulate microbial composition or activity. We evaluate the clinical readiness of GM-targeted therapies, including dietary patterns, prebiotics, probiotics, and fecal microbiota transplantation. While established metabolic treatments such as glucagon-like peptide-1 (GLP-1) receptor agonists and bariatric surgery significantly reshape the GM, direct microbial manipulations often yield variable results in human trials. We conclude that the future of metabolic management lies in personalized microbiomics, utilizing artificial intelligence and precision-based interventions to restore specific functional microbial deficits tailored to the individual host profile.