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Background <p>The association between visceral adiposity index (VAI) and metabolic dysfunction-associated steatotic liver disease (MASLD) remains unestablished. Our study sought to investigate the potential relationship between VAI and MASLD risk.</p> Methods <p>This study employed data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance.</p> Results <p>Higher VAI tertiles correlated with increased MASLD risk (adjusted OR for T3 vs. T1: 7.08, 95% CI: 4.35-11.5; P-trend=0.003). Machine learning models demonstrated robust predictive accuracy, with random forest (AUC=0.869) and gradient boosting machine (AUC=0.868) outperforming non-invasive scores. However, lipid accumulation product (LAP, AUC=0.834) and fatty liver index (FLI, AUC=0.833) achieved superior diagnostic performance compared to VAI (AUC=0.736), while maintaining clinical interpretability through simplicity and routine parameter availability.</p> Conclusions <p>While VAI demonstrated significant positive associations with MASLD risk, non-invasive scores like LAP and FLI emerged as superior diagnostic tools, balancing accuracy with clinical practicality.</p>

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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月1日

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  • Tuo Zhou,
  • Xiang Ding,
  • Linjie Chen,
  • Qianxiong Huang,
  • Linfang He

摘要

Background

The association between visceral adiposity index (VAI) and metabolic dysfunction-associated steatotic liver disease (MASLD) remains unestablished. Our study sought to investigate the potential relationship between VAI and MASLD risk.

Methods

This study employed data from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). Weighted multivariable regression models, subgroup analyses, and machine learning algorithms were used to evaluate associations and predictive performance.

Results

Higher VAI tertiles correlated with increased MASLD risk (adjusted OR for T3 vs. T1: 7.08, 95% CI: 4.35-11.5; P-trend=0.003). Machine learning models demonstrated robust predictive accuracy, with random forest (AUC=0.869) and gradient boosting machine (AUC=0.868) outperforming non-invasive scores. However, lipid accumulation product (LAP, AUC=0.834) and fatty liver index (FLI, AUC=0.833) achieved superior diagnostic performance compared to VAI (AUC=0.736), while maintaining clinical interpretability through simplicity and routine parameter availability.

Conclusions

While VAI demonstrated significant positive associations with MASLD risk, non-invasive scores like LAP and FLI emerged as superior diagnostic tools, balancing accuracy with clinical practicality.