Beyond obesity: lean metabolic dysfunction-associated steatohepatitis from unveiling molecular pathogenesis to therapeutic advancement.

Abstract

Nonalcoholic fatty liver disease (NAFLD), now known by the name of metabolic dysfunction-associated fatty liver disease (MAFLD), with increased global incidence, has been recognized as a significant metabolic disorder. NAFLD includes a spectrum liver disease from hepatocellular fat accumulation (isolated steatosis) to an advanced form of liver injury known as nonalcoholic steatohepatitis (NASH), which refers to distinct histologic features, including hepatocellular steatosis and injury, necroinflammation, and eventually fibrosis. Nonobese or lean individuals associated with metabolic dysregulation usually demonstrated diverse risk factors compared to obese MAFLD. The presence of normal range body mass index (BMI) and excess visceral adiposity with increased cardiometabolic and renal comorbidities, along with sarcopenia, has been evidenced to be associated with lean MASH. Genetic predispositions accompanying lifestyle and environmental factors contribute to disease initiation and progression. The genetic influence in pathophysiology indicated the significant contributions of the following genes: PNPLA3, TM6SF2, APOB, LIPA, MBOAT7, and HSD17B13, and the impact of their disease-specific variants in the development of obesity-independent MASH. The epigenetic modifications exhibited differential DNA methylation patterns in the genes involved in lipid metabolism, particularly hypomethylation of PEMT. Diet-induced and genetic animal models of lean MASH, including Slc: Wistar/ST rats, PPAR-α, PTEN, and MAT1A knockout mice models, are indicated to be pivotal in the exploration of disease progression and observing the effect of therapeutic interventions. This comprehensive review comprises the molecular and genetic pathophysiology, molecular diagnostics, and therapeutic aspects of lean MASH to enunciate a diagnostic approach that combines detailed clinical phenotyping regarding genomic analysis.