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Updated: Aug 27, 2025
3D Imaging of the Liver Extracellular Matrix in a Mouse Model of Non-Alcoholic Steatohepatitis
Published on: February 25, 2022
N-terminal propeptide of type 3 collagen-based sequential algorithm can identify high-risk steatohepatitis and
Liang-Jie Tang1,2, Gang Li1, Mohammed Eslam3
1MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China.
Background And Aims:
With metabolic dysfunction-associated fatty liver disease (MAFLD) incidence and prevalence sharply increasing globally, there is an urgent need for non-invasive diagnostic tests to accurately screen high-risk MAFLD patients for liver inflammation and fibrosis. We aimed to develop a novel sequential algorithm based on N-terminal propeptide of type 3 collagen (PRO-C3) for disease risk stratification in patients with MAFLD.
Methods:
A derivation and independent validation cohort of 327 and 142 patients with biopsy-confirmed MAFLD were studied. We compared the diagnostic performances of various non-invasive scores in different disease states, and a novel sequential algorithm was constructed by combining the best performing non-invasive scores.
Results:
For patients with high-risk progressive steatohepatitis (i.e., steatohepatitis + NAFLD activity score ≥ 4 + F ≥ 2), the AUROC of FAST score was 0.801 (95% confidence interval (CI): 0.739-0.863), and the negative predictive value (NPV) was 0.951. For advanced fibrosis (≥ F3) and cirrhosis (F4), the AUROCs of ADAPT and Agile 4 were 0.879 (95%CI 0.825-0.933) and 0.943 (95%CI 0.892-0.994), and the NPV were 0.972 and 0.992. Sequential algorithm of ADAPT + Agile 4 combination was better than other combinations for risk stratification of patients with severe fibrosis (AUROC = 0.88), with similar results in the validation cohort. Meanwhile, in all subgroup analyses (stratifying by sex, age, diabetes, NAS, BMI and ALT), ADAPT + Agile 4 had a good diagnostic performance.
Conclusions:
The new sequential algorithm reliably identifies liver inflammation and fibrosis in MAFLD, making it easier to exclude low-risk patients and recommending high-risk MAFLD patients for clinical trials and emerging pharmacotherapies.
