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Updated: Feb 6, 2026

Mouse Model of Metabolic Dysfunction-Associated Steatotic Liver Disease with Fibrosis
Published on: July 18, 2025
Blood markers vs transient elastography for liver stiffness and steatosis in metabolic dysfunction-associated
Amal Joseph1, Sunil Mathew1, Harikumar R Nair2
1Department of Gastroenterology and Hepatology, Ernakulam Medical Centre, Kochi 682028, Kerala, India.
Background:
Liver biopsy, once the gold standard for evaluating liver fibrosis and steatosis, has been largely replaced in routine clinical practice by non-invasive tools like Fibroscan®, which evaluate liver stiffness measurement (LSM) and controlled attenuation parameter (CAP). While Fibroscan® is well-validated, cost and accessibility challenges limit its use for regular follow-up, especially in primary care.
Aim:
To investigate the diagnostic accuracy and correlation of blood-based parameters fibrosis 4 (FIB-4) score, aspartate transaminase to platelet ratio index (APRI), neutrophil-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil percentage-to-albumin ratio (NPAR) with LSM and CAP values in metabolic dysfunction-associated steatotic liver disease (MASLD) patients.
Methods:
In a cross-sectional study of 300 MASLD patients we compared FIB-4, APRI, NLR, PLR, and NPAR with LSM and CAP values. Patients were categorized based on LSM into less fibro-progressed (F0-F2) and advanced fibro-progressed (F3-F4) groups, and by CAP into S1, S2 and S3 categories. Sensitivity, specificity, positive predictive value, and negative predictive value of the markers were analyzed, and receiver operating characteristic curves were plotted.
Results:
FIB-4 [r = 0.537, P < 0.001; area under curve (AUC) = 0.806; diagnostic accuracy = 75.63%] and APRI (r = 0.513, P < 0.001; AUC = 0.772) showed strong correlations with LSM, confirming their reliability for LSM. APRI and FIB-4 are validated against fibrosis in liver biopsy, our results demonstrate comparable performance between these scores and LSM by Fibroscan®. PLR exhibited high specificity (98.0%) but showed negative correlation with LSM (r = -0.317, P < 0.01). For CAP, NPAR demonstrated the highest specificity (97.67%) and positive predictive value (91.31%), followed by NLR (specificity 92.77%, positive predictive value 91.58%), though AUC values were modest (0.562 and 0.540, respectively).
Conclusion:
FIB-4 and APRI which are robust non-invasive markers for fibrosis, correlates well with LSM as well. NPAR shows potential for steatosis assessment using CAP, warranting further validation. Negative correlation of PLR might suggest its role in liver stiffness evaluation. These markers both conventional and novel, can be used for repeated measurements during follow-up in primary care settings.
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