Dyslipidemia, a complex disorder characterized by systemic lipid profile abnormalities, affects more than half of adults globally and constitutes a major modifiable risk factor for atherosclerotic cardiovascular disease. Mounting evidence has established the gut microbiota (GM) as a pivotal metabolic modulator that is correlated with atherogenic lipid profiles through dietary biotransformation, immunometabolic regulation, and bioactive metabolite signaling. However, the host-microbe interactions that drive dyslipidemia pathogenesis involve complex gene-environment crosstalk spanning epigenetic modifications to circadian entrainment. Mechanistically, GM perturbations disrupt lipid homeostasislipopolysaccharide-triggered hepatic very low-density lipoprotein overproduction, short-chain fatty acid-G protein-coupled receptor 43/41-mediated adipocyte lipolysis, bile acid-farnesoid X receptor/Takeda G protein-coupled receptor 5 axis dysfunction altering cholesterol flux, microbial β-oxidation intermediates impairing mitochondrial energetics, and host-microbiota non-coding RNA crosstalk regulating lipogenic genes. This comprehensive review systematically examines three critical dimensions, including bidirectional GM-lipid axis interactions, molecular cascades bridging microbial ecology to metabolic dysfunction, and translational applications of GM modulation through precision probiotics, structure-specific prebiotics, and a metabolically optimized fecal microbiota transplantation protocol. Notwithstanding these advances, critical gaps persist in establishing causal microbial taxa-pathway relationships and optimal intervention timing. Future directions require longitudinal multi-omic studies, gnotobiotic models for mechanistic validation, and machine learning-driven personalized microbiota profiling. This synthesis provides a framework for developing microbiota-centric strategies targeting dyslipidemia pathophysiology, with implications for precision dyslipidemia management and next-generation cardiovascular disease prevention. via
