Investigating the postprandial metabolic and inflammatory phenotypes of healthy subjects and patients with metabolic dysfunction-associated steatotic liver disease and metabolic-dysfunction associated steatohepatitis to understand disease progression

Metabolic dysfunction-associated steatotic liver disease (MASLD), considered as the hepatic component of the metabolic syndrome, is the leading cause of chronic liver disease around the world (1) . The MASLD disease spectrum begins with isolated steatosis. A subset of patients will develop metabolic dysfunction-associated steatohepatitis (MASH) which is characterized by steatosis and inflammation, resulting in hepatocyte injury with or without fibrosis. MASH is regarded as a progressive phenotype, as it can advance to cirrhosis and subsequently to hepatocellular carcinoma (2) . Compared with fasting measurements alone, postprandial responses to nutrient challenges offer a greater insight into the phenotypic heterogeneity that occurs across health and disease (3) . To better understand the phenotypes that occur across the spectrum of MASLD, we have characterized the postprandial metabolic and inflammatory responses to different dietary challenges in healthy individuals and patients with MASLD and MASH. 10 healthy individuals, 18 patients with MASLD, and 15 with MASH were recruited. Body composition was assessed using bioelectrical impedance analysis (Tanita). Liver fat and stiffness were measured by FibroScan in patients only. All participants completed a high fat mixed meal challenge containing 655kcals, 46g fat, 33g saturated fat, 50g carbohydrates, 45g sugar, and 10g protein. Patients with MASLD and MASH also completed a high-fructose challenge containing 400kcals, 75g fructose and 25g glucose. Fasting and postprandial blood samples were drawn at each challenge at 0, 30, 60, 90, 120, 180, 240 and 360-minutes. Glucose was measured in whole blood using a HemoCue Glucose 201+ analyzer. Serum insulin and plasma IL-6 and CRP will be measured by ELISA. Plasma triglycerides, non-esterified fatty acids (NEFA), and glycerol will be measured by colorimetric assay. Bodyweight (p = 0.0247), BMI (p = 0.0448), and hip circumference (p = 0.0237) were greater in MASH vs MASLD. Healthy participants had significantly lower bodyweight, BMI, and waist and hip circumferences vs both MASLD and MASH (p<0.0001 for each variable). Liver fat was similar between MASLD and MASH, whereas liver stiffness was greater in MASH (p<0.0001). Alanine transaminase (ALT) was increased in MASH vs MASLD (p = 0.0408), while concentrations of other liver enzymes were similar between groups. Total cholesterol (p = 0.0492) and LDL-cholesterol (p = 0.0197) were also higher in MASH vs MASLD, but there was no difference in triglyceride concentrations or HDL-cholesterol. There was a significant impact of group on glucose response to the HFMM (p = 0.0321), and glucose responses varied between the 3 groups across different time points (p<0.0001). The HFMM, which is more representative of a typical meal, revealed glucose intolerance in MASLD and MASH which was also greater in MASLD. Analysis of the full cohort will enable greater characterization of the phenotypes that occur across the spectrum of MASLD.