Evaluation of Metabolic Adipose Liver Diseases and Liver Fibrosis Confirms the Effectiveness of the Second Stage Model
Feb 18, 2025
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A research team led by Kim Seung-up and Lee Hye-won of the Department of Gastroenterology at Severance Hospital recently announced that they have confirmed the clinical effect of the American Society of Gastroenterology recommendation model in predicting disease risk through severe fibrosis evaluation of MASLD patients with a research team from Hong Kong Chinese University.
The findings were published in the recent issue of the Journal of Hepaticology (IF 26.8), a prestigious journal of liver disease research.
MASLD is a disease in which fat accumulates in the liver and is diagnosed in about 30% of the Korean population. It is closely related to metabolic diseases such as obesity, diabetes, and dyslipidemia, and can develop into cirrhosis or liver cancer, so active management through accurate diagnosis is necessary. In the case of MASLD patients, the incidence of hepatocellular carcinoma is known to be more than 10 times higher than that of the general population. The risk of developing liver cancer is further increased in the presence of advanced liver fibrosis.
Several international guidelines emphasize that imaging tests such as ultrasound, CT, and MRI are primarily performed for the diagnosis of MASLD, and additional evaluation of liver fibrosis is necessary. This is because the prognosis varies depending on the degree of liver fibrosis, and patients who need specialized treatment can be selected.
The American Society of Gastroenterology has evaluated FIB-4 as a first-stage evaluation for the evaluation of liver fibrosis in patients with MASLD, and has since recommended a second-stage evaluation of LSM through liver fibrosis scanning.
Professor Kim Seung-up and Professor Lee Hye-won's research team confirmed the clinical effectiveness of the two-stage prognosis prediction model of FIB-4 and LSM presented by the American Society of Gastroenterology through an international multicenter cohort analysis.
The research team investigated the incidence of liver-related diseases such as liver failure and hepatocellular carcinoma in 12,950 MASLD patients who underwent liver fibrosis scans at 16 tertiary medical institutions in the United States, Europe and Asia from February 2004 to January 2023.
First, according to the FIB-4 outcome value of the patients, those below 1.3 were classified as the low-risk group (8582 patients), 1.3 to 2.67 were classified as the medium-risk group (3096 patients), and 2.67 were classified as the high-risk group (1272). Among them, for precise prognosis prediction for the intermediate risk group, LSM 8 kPa was subdivided into the low risk group (1971), 8-12 kPa was subdivided into the intermediate risk group (595), and 12.0 kPa was further subdivided into the high risk group (530).
Finally, out of 12,950 people, about 15,553 (81.5%) were classified as low-risk groups, about 595 (4.6%) as medium-risk groups, and about 1,802 (13.9%) as high-risk groups.
The research team identified the five-year cumulative incidence of liver-related diseases in three groups.
As a result, liver-related diseases were identified in 0.5% (about 53) of the low-risk group, 1% (about 6) of the medium-risk group, and 10.8% (about 195) of the high-risk group. The higher the risk group, the more liver-related diseases such as liver failure and hepatocellular carcinoma occurred.
Among other non-invasive tests to evaluate liver fibrosis, there was no significant difference in risk group classification even when Agile 3+, Agile 4, FAST, etc., which included liver fibrosis scans, were applied.
Professor Kim Seung-up said "FIB-4 and LSM-based two-stage risk classification approaches in patients with MASLD are effective in predicting the risk of liver disease more accurately and screening patients who need more active management and treatment"This study once again suggests the importance of patient-specific prognosis prediction using non-invasive diagnostic methods and patient management through them"
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This article was translated by Naver AI translator.
