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From indirect regulation to multi-layered metabolic control in MASLD: Multi-receptor agonism and
1Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200030, People's Republic of China; Shanghai Medical College of Fudan University, 130 Dongan Road, Shanghai 200030, People's Republic of China.
Abstract:
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown clinical efficacy in metabolic dysfunction-associated steatotic liver disease (MASLD), yet their therapeutic performance appears limited in several recurring settings, including advanced fibrosis, metabolically milder populations, and treatment discontinuation. These limitations suggest that treatment responses are shaped not only by pharmacological potency, but also by the regulatory systems through which GLP-1RAs act. Here, we propose a conceptual multi-layered framework in which GLP-1R-based therapies improve hepatic outcomes predominantly, although not exclusively, through indirect and systemic metabolic regulation. Within this framework, therapeutic responses may be influenced by the integrity of intrahepatic and systemic signaling networks, the magnitude of baseline metabolic perturbation, and the persistence of pharmacological input, which can be interpreted as structural, contextual, and temporal constraints. Multi-receptor agonists may partially mitigate these constraints by expanding regulatory inputs through glucose-dependent insulinotropic polypeptide receptor (GIPR) and glucagon receptor (GCGR) co-activation. However, receptor-level modulation alone may remain insufficient to ensure durable responses after treatment withdrawal. We further discuss the gut microbiota and its metabolites as a candidate system-level regulatory layer that integrates host metabolic, immune, and endocrine signaling. Across diverse interventions, convergence appears more consistent at the level of microbiota-associated metabolic functions than taxonomic composition, but its causal significance remains incompletely established. Cross-intervention convergence analysis may therefore serve as a hypothesis-generating strategy to identify recurrent microbiota-metabolite axes with potential mechanistic and therapeutic relevance.
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