ArticleEndocrine connections2025
Transferrin combined with alanine aminotransferase and body mass index improves non-invasive diagnosis of metabolic dysfunction-associated steatohepatitis.
Article in Endocrine connections, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease globally, with over 20% of patients progressing to metabolic dysfunction-associated steatohepatitis (MASH), which carries a high risk of cirrhosis and hepatocellular carcinoma. While liver biopsy remains the gold standard for MASH diagnosis, its invasiveness and limitations necessitate reliable noninvasive alternatives. This study aimed to develop a cost-effective biomarker panel using routine laboratory tests to distinguish MASLD severity stages. Methods: We conducted a single-center, retrospective study of 209 biopsy-proven MASLD patients, stratified by NAS: simple steatosis (NAS <3, n = 40), borderline (NAS 3-4, n = 120), and definitive MASH (NAS ≥5, n = 49). Clinical, biochemical, hematologic parameters and metabolic markers were analyzed. Logistic regression and ROC curves assessed diagnostic performance. Results: Key findings revealed progressive increases in BMI, ALT, AST, and transferrin (Tf) levels with disease severity. Multivariate logistic regression identified ALT and Tf as independent predictors for borderline and MASH. Notably, ALT showed superior diagnostic performance for distinguishing simple MASLD with borderline (AUC 0.763), while Tf was most effective for MASH detection (AUC 0.723). A combined model integrating BMI, ALT, and Tf demonstrated excellent diagnostic accuracy for borderline (AUC 0.840) and MASH (AUC 0.805). Conclusion: Our study highlights that a simple, cost-effective panel of routinely available biomarkers (BMI, ALT, and Tf) can effectively stratify MASLD progression, offering a practical alternative to invasive diagnostics. This approach enhances early MASH detection, facilitating timely clinical intervention.
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