Microbial exposure and host responses to infection reshape the disease trajectory of MASLD
Yunqi Zhang1, Qingqing Xun1, Shuyu Liu1
1School of Clinical Medicine, Jining Medical University, Jining, Shandong, China.
Abstract:
Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a major driver of the growing global burden of chronic liver disease and has attracted increasing attention because of its progressive hepatic outcomes. Metabolic syndrome-related factors, such as obesity and abnormal glucose metabolism, confer on MASLD both a risk of liver disease progression and systemic metabolic consequences. Evidence from observational studies and experimental models suggests that infection and microbial exposure may be associated with MASLD progression and may modify its clinical course through alterations in the gut-liver axis, enhanced immune activation, and amplification of metabolic inflammation. Meanwhile, the metabolic disturbances associated with MASLD can remodel the intestinal ecological niche and hepatic immune environment, impairing the host capacity to clear and regulate microbial signals and thereby altering infection susceptibility and clinical outcomes. Despite growing interest in infection and microbial dysbiosis in MASLD, most studies remain centered on individual pathogens. A unifying framework linking microbial exposure, host metabolic dysfunction, and inflammatory amplification is still lacking. For most infectious exposures, causal directionality and clinical modifiability remain uncertain. Accordingly, this review organizes the available evidence on the potential bidirectional relationships between infection and MASLD. It examines how gut-liver microbial signals, viral comorbidities, chronic colonization, and acute infection may shape the course of MASLD. It also considers how MASLD-related metabolic dysfunction may, in turn, modify host responses to infection. Overall, incorporating infection and microbial dysbiosis into the conceptual framework of MASLD may help move beyond a metabolism-centered explanatory model and identify priorities for future risk-stratification and intervention studies.
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