Korean J Med.  2004 Sep;67(3):231-240.

Predictive factors for the diagnosis of nonalcoholic steatohepatitis and the degree of liver fibrosis

• Lee DS
• Park MJ
• Chung IK
• Kwon KT
• Kim SH
• Cho CM
• Tak WY
• Kweon YO
• Kim SK
• Choi YH
• Bae HI

Abstract

BACKGROUND: Nonalcoholic steatohepatitis (NASH), which is the most severe form of nonalcoholic fatty liver disease (NAFLD), is a disease of emerging identity and importance with increasing epidemic of obesity in Korea. Since NASH may progress to fibrosis, cirrhosis, and even hepatocellular carcinoma, the identification of patients who may progress from steatosis to NASH is important. The aim of this study was to find clinical factors discriminating NASH from steatosis, and predictive factors for the degree of liver fibrosis in NASH.
METHODS
Between January 2000 and August 2003, ultrasono-guided liver biopsies were performed on 50 patients in whom NAFLD was suspected clinically. We analyzed clinical factors, laboratory variables, and histologic findings.
RESULTS
All of liver biopsies showed compatible histologic findings with NAFLD. Steatosis was observed in 10 patients (20%) and NASH in 40 patients (80%). In patients with high body mass index (BMI) (p=0.027), NASH was more frequently observed than steatosis. The diagnostic specificity of NASH was high (90%) in patients with BMI over 27.2. BMI and AST/ALT ratio were significant risk factors for severe liver fibrosis in multivariate analysis (p=0.014 and 0.04, regression coefficient 0.43 and 4.484, respectively).
CONCLUSION
BMI and AST/ALT ratio correlated the degree of liver fibrosis in NASH. So, high BMI (over 27.2) may discriminate NASH from steatosis, and high BMI and high AST/ALT ratio may be useful as predictive factors for severe liver fibrosis in NASH.

Keyword

Nonalcoholic fatty liver disease; Nonalcoholic steatohepatitis; Body mass index; Liver fibrosis; Predictive factor

MeSH Terms

Biopsy
Body Mass Index
Carcinoma, Hepatocellular
Diagnosis*
Fatty Liver*
Fibrosis
Humans
Korea
Liver Cirrhosis*
Liver*
Multivariate Analysis
Obesity
Risk Factors
Sensitivity and Specificity