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Background <p>Metabolic dysfunction-associated steatohepatitis (MASH) is a chronic metabolic liver disease. This study sought to investigate risk factors and correlation between degree of hepatic steatosis and fibrosis in patients with MASH.</p> Materials and methods <p>Total 175 MASH patients were divided into 240 ~ 264 dB/m group, 265 ~ 294 dB/m group and ≥ 295 dB/m group according to controlled attenuation parameters (CAP) to evaluate hepatic steatosis, &lt; 7.3&#xa0;kPa and ≥ 7.3&#xa0;kPa groups according to liver stiffness value (LSM) to assess liver fibrosis. Multivariate logistic regression analysis analyzed influencing factors of hepatic steatosis and liver fibrosis. Correlation between hepatic steatosis and fibrosis and non-invasive prediction model for liver fibrosis was explored.</p> Results <p>Compared with 240-264dB/m group, body mass index (BMI), alanine aminotransferase (GPT), aspartate aminotransferase (GOT), uric acid (UA), and LSM levels increased in 265-294dB/m and ≥ 295dB/m group, which were higher in ≥ 295dB/m group (<i>P</i> &lt; 0.05). Relative to &lt; 7.3&#xa0;kPa group, BMI, GPT, GOT, and CAP levels increased and neutrophil/lymphocyte (NLR) decreased in ≥ 7.3&#xa0;kPa group (<i>P</i> &lt; 0.001). BMI, GPT, UA and LSM were risk factors affecting hepatic steatosis; GOT and CAP were risk factors affecting liver fibrosis, while NLR was a protective factor (<i>P</i> &lt; 0.05). There was a positive correlation (<i>r</i> = 0.572, <i>P</i> &lt; 0.001) between LSM and degree of hepatic steatosis. The optimal cutoff value of established a non-invasive liver fibrosis predictive model was 0.492 with AUC of 0.819 (95% CI: 0.735–0.903). The internal validation by Bootstrap method showed good discrimination of the predictive model with the corrected AUC of 0.801.</p> Conclusions <p>There is a positive correlation between liver steatosis and fibrosis. Establishing non-invasive prediction model for liver fibrosis has a good predictive value for liver fibrosis in MASH.</p>

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Analysis of risk factors and correlation between the degree of hepatic steatosis and fibrosis in patients with metabolic dysfunction-associated steatohepatitis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Xiaojin Yang,
  • Desheng Xie,
  • Yao Du,
  • DongHong Zhang,
  • Houji Chen,
  • Yinghua Yuan

摘要

Background

Metabolic dysfunction-associated steatohepatitis (MASH) is a chronic metabolic liver disease. This study sought to investigate risk factors and correlation between degree of hepatic steatosis and fibrosis in patients with MASH.

Materials and methods

Total 175 MASH patients were divided into 240 ~ 264 dB/m group, 265 ~ 294 dB/m group and ≥ 295 dB/m group according to controlled attenuation parameters (CAP) to evaluate hepatic steatosis, < 7.3 kPa and ≥ 7.3 kPa groups according to liver stiffness value (LSM) to assess liver fibrosis. Multivariate logistic regression analysis analyzed influencing factors of hepatic steatosis and liver fibrosis. Correlation between hepatic steatosis and fibrosis and non-invasive prediction model for liver fibrosis was explored.

Results

Compared with 240-264dB/m group, body mass index (BMI), alanine aminotransferase (GPT), aspartate aminotransferase (GOT), uric acid (UA), and LSM levels increased in 265-294dB/m and ≥ 295dB/m group, which were higher in ≥ 295dB/m group (P < 0.05). Relative to < 7.3 kPa group, BMI, GPT, GOT, and CAP levels increased and neutrophil/lymphocyte (NLR) decreased in ≥ 7.3 kPa group (P < 0.001). BMI, GPT, UA and LSM were risk factors affecting hepatic steatosis; GOT and CAP were risk factors affecting liver fibrosis, while NLR was a protective factor (P < 0.05). There was a positive correlation (r = 0.572, P < 0.001) between LSM and degree of hepatic steatosis. The optimal cutoff value of established a non-invasive liver fibrosis predictive model was 0.492 with AUC of 0.819 (95% CI: 0.735–0.903). The internal validation by Bootstrap method showed good discrimination of the predictive model with the corrected AUC of 0.801.

Conclusions

There is a positive correlation between liver steatosis and fibrosis. Establishing non-invasive prediction model for liver fibrosis has a good predictive value for liver fibrosis in MASH.