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PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗
PubMed2026

Incidence of Cirrhosis in Fibrotic Metabolic Dysfunction-Associated Steatohepatitis: A Meta-Analysis of Placebo Arms from Randomized Clinical Trials.

BACKGROUNDS AND AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) with stage F2-F3 fibrosis represents the main target population for emerging pharmacotherapies. However, data on short-term progression to cirrhosis (F4) in this group remain limited. We aimed to evaluate the incidence of cirrhosis in placebo-treated patients with fibrotic MASH in randomized controlled trials (RCTs). METHODS: In this single-arm meta-analysis, we systematically searched PubMed and Cochrane Library from inception to December 13, 2024, for pharmacological Phase ≥ 2 RCTs reporting cirrhosis events (detected in liver biopsy or clinical signs) among patients with fibrotic MASH receiving placebo. Incidence rates were pooled using generalized linear mixed models with Clopper-Pearson confidence intervals (CIs). RESULTS: We identified a total of 11 RCTs, including 586 patients with fibrotic MASH. Total follow-up was 657.23 person-years (PYs), with 83 cirrhosis events reported. The pooled incidence rate was 13.09 per 100 PYs (95% CI 7.81 to 21.12, I2 = 75.6%, τ2 = 0.682). In subgroup analysis, the incidence of cirrhosis was 3.40 per 100 PYs in MASH F2 (95% CI 1.10 to 10.02, I2 = 0%, τ2 = 0) and 17.90 per 100 PYs (95% CI 10.63 to 28.55, I2 = 70.2%, τ2 = 0.561) in MASH F3, with significant differences between stages (p = 0.006). Sensitivity analyses showed consistent estimates. Most RCTs were judged to have a low risk of bias. CONCLUSIONS: This study provides stage-specific data on cirrhosis incidence in fibrotic MASH, highlighting the high short-term risk associated with MASH F3 in trial settings. These data may inform benchmarks to guide event expectations, enrichment strategies, sample size assumptions, and the interpretation of future MASH clinical trials.

Humans↗