Metabolic dysfunction-associated steatotic liver disease (MASLD). Assessing metabolic dysfunction, cardiovascular risk factors, and lifestyle habits
Hala Abdallah, Mohamad Khalil, Ewa Awada, Elisa Lanza, Agostino Di Ciaula, Piero Portincasa
Abstract
BACKGROUND AND AIMS: Steatotic liver disease (SLD), a hallmark of metabolic dysfunction, is closely associated with atherosclerotic cardiovascular disease (ASCVD). This study investigated gender differences, metabolic risk factors, and lifestyle habits in a large cohort of dysmetabolic patients with SLD, reclassified under the novel MASLD framework. METHODS: A total of 400 patients (mean age: 58.6 ± 0.9 years; BMI: 29.0 ± 0.3 kg/m²; 178 females) were enrolled from a cardiometabolic outpatient clinic. Clinical evaluations included physical examinations, anthropometry, and laboratory analyses. Lifestyle factors including Mediterranean diet (MD) adherence and physical activity were assessed using validated questionnaires. Hepatic steatosis and carotid atherosclerotic plaques were evaluated via ultrasonography. Patients were stratified into MASLD or MetALD (MASLD with high alcohol intake). Metabolic indices and 10-year ASCVD risk scores were calculated. RESULTS: SLD was present in 65.5 % of patients, predominantly as MASLD (93.9 %). Men had higher SLD prevalence and reported greater smoking, alcohol intake, and CVD history. MetALD patients exhibited a higher prevalence of family history of CVD, atherosclerotic plaques, and elevated ASCVD risk, despite similar dysmetabolic profiles compared to MASLD. Among SLD patients, waist circumference was significantly associated with ASCVD risk, whereas MD adherence showed no independent association. The TyG index outperformed FLI, HSI, and MAF-5 in predicting ASCVD risk in patients with low fibrosis risk and no prior major cardiovascular events. CONCLUSION: Accurate cardiovascular risk stratification in MASLD requires comprehensive phenotyping. Waist circumference is a key risk indicator, and the TyG index may serve as a valuable tool for ASCVD risk prediction in select SLD populations.