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Serum Extracellular Vesicles as Pathogenetic Signals in Obese and Lean Patients with Metabolic Dysfunction-Associated
Chi-Yi Chen1, Che-Yu Hsu2, Wei-Pang Chung3,4
1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia Yi City 600, Taiwan.
Abstract:
Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent worldwide and represents a growing healthcare challenge due to its risk of progression and association with metabolic comorbidities. Extracellular vesicles (EVs), nanosized membrane-bound particles mediating intercellular communication, have emerged as candidate biomarkers in multiple diseases. This study aimed to characterize serum EV profiles in MASLD patients, stratified into obese and lean groups using a body mass index cutoff of 23 for Asians. Methods: We enrolled 170 MASLD patients, 83 obese (median age 50, range 20-80) and 87 lean (median age 50, range 20-87), along with 57 non-MASLD controls (median age 44, range 21-86). Serum EV concentrations and particle sizes were quantified using nanoparticle tracking analysis and correlated with clinical and laboratory parameters. EV cargo proteins, including tetraspanins (CD9, CD63) and lipid droplet-associated perilipins (PLIN2, PLIN3), were assessed by Western blotting. Results: Obese MASLD patients displayed marked biochemical abnormalities, whereas lean MASLD patients showed levels comparable to non-MASLD controls. Nevertheless, serum EV concentrations were elevated in both the obese and lean MASLD groups. Importantly, in lean MASLD, EV levels correlated strongly with disruptions in lipid and glycemic homeostasis. Furthermore, a reduction in the PLIN3/CD63 ratio was observed in EVs isolated from lean MASLD patients. Conclusions: Circulating EVs are elevated in both obese and lean MASLD, but lean patients demonstrate a distinctive decrease in the EV PLIN3/CD63 ratio. These findings highlight the potential of EV profiling to uncover disease heterogeneity and to inform risk stratification in MASLD.
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