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CN 22-1108/R
Volume 41 Issue 10
Oct.  2025
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Article Navigation >  Journal of Clinical Hepatology  > 2025  >  41(10): 2054-2061

Value of body roundness index in predicting the risk of metabolic dysfunction-associated fatty liver disease

DOI: 10.12449/JCH251015
  • 1.

    Department of Spleen，Stomach，Liver and Gallbladder，The First Affiliated Hospital of Henan University of Chinese Medicine，Zhengzhou 450000，China

  • 2.

    The First Clinical Medical College of Henan University of Chinese Medicine，Zhengzhou 450000，China

  • 3.

    Collaborative Innovation Center of Prevention and Treatment of Major Diseases by Chinese and Western Medicine，Kaifeng，Henan 475399，China

  • 4.

    Collaborative Innovation Center of Research and Development on the Whole Industry Chain of Yu-Yao，Zhengzhou 450007，China

Research funding:

National Natural Science Foundation of China (81904154);

National Natural Science Foundation of China (82205086);

Henan Province Science and Technology Research Program (242102310500);

Special Scientific Research Project on Traditional Chinese Medicine under the “Double First-Class” Initiative in Henan Province (HSRP-DFCTCM-2023-1-10);

Joint Fund of the Henan Provincial Science and Technology Research Program （Project of Advantageous Discipline Cultivation） (242301420096);

Joint Fund of the Henan Provincial Science and Technology Research Program （Project of Advantageous Discipline Cultivation） (242301420021);

Traditional Chinese Medicine Inheritance and Innovation Project of Henan Provincial Health Commission (2023ZXZX1162)

More Information
  • Corresponding author: LIU Minghao， liumh015@163.com （ORCID： 0009-0001-7712-4605）
  • Received Date: 2025-04-23
  • Accepted Date: 2025-07-21
  • Published Date: 2025-10-25
    Abstract
  •   Objective  To investigate the association between body roundness index （BRI） and the risk of metabolic dysfunction-associated fatty liver disease （MAFLD） based on the National Health and Nutrition Examination Survey （NHANES） database， as well as the clinical value of BRI as a noninvasive tool for risk prediction.  Methods  Based on the NHANES data in 2015—2020， the 4 573 individuals were divided into MAFLD group with 2 508 individuals and non-MAFLD group with 2 065 individuals， and BRI was calculated for each individual. In order to ensure data quality and reduce the impact of abnormal values on analytical results， the boxplot method was used to remove abnormal levels of BRI and improve the robustness of data. The Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The multivariate Logistic regression model was established to investigate the association between BRI and MAFLD. BRI was divided into four groups based on quantiles， and with the first quantile （Q1） as reference， odds ratio （OR） and 95% confidence interval （CI） were calculated for the other three models. Restricted cubic spline was used to investigate the dose-effect relationship between BRI and MAFLD. The receiver operating characteristic （ROC） curve was plotted and the area under the ROC curve （AUC） was calculated to assess the efficacy of BRI in the diagnosis of MAFLD. The decision curve analysis was used to investigate the potential clinical value of the model in clinical practice. The interaction analysis and the subgroup analysis were performed to investigate the difference in the association between BRI and MAFLD between different populations. The Lasso regression analysis was conducted for the screening and analysis of characteristic variables.  Results  Compared with the non-MAFLD group， the MAFLD group had a significantly higher BRI （Z=36.29， P<0.001）. After adjustment for the variables including age， sex， ethnicity， educational level， the proportion of individuals with poor income， marital status， smoking， hypertension， diabetes， alanine aminotransferase， aspartate aminotransferase， gamma-glutamyl transpeptidase， and high-density lipoprotein cholesterol， the fully adjusted Logistic regression model showed that BRI was significantly positively associated with the risk of MAFLD （OR=2.53， 95%CI： 2.28 — 2.80， P<0.001）. In addition， the highest BRI quartile （Q4） group had a significantly higher risk of MAFLD than the lowest quartile （Q1） group （OR=83.45， 95%CI： 51.87 — 134.26， P<0.001）. The restricted cubic spline analysis further confirmed the significant nonlinear association between BRI and MAFLD （P for nonlinear<0.001）. The interaction analysis and the subgroup analysis showed that the interaction between hypertension and BRI had statistical significance （P for interaction=0.003）， and compared with the individuals without hypertension， the individuals with hypertension had a stronger association between BRI and MAFLD （OR=1.60， 95%CI： 1.23 — 2.08， P<0.001）. The ROC curve analysis showed that the fully adjusted model based on BRI had a strong discriminatory ability in differentiating MAFLD from non-MAFLD， with an AUC of 0.887 （95%CI： 0.877 — 0.896）. The decision curve analysis showed that the fully adjusted model had good net benefits within the risk threshold of 0.10 — 0.75， which was commonly used in clinical practice. The model based on the key variables identified by the Lasso regression analysis had an AUC of 0.882 （95%CI： 0.872 — 0.892）， which confirmed the robustness of the prediction results.  Conclusion  There is a significant positive correlation between BRI and the risk of MAFLD， with a stronger association observed in the hypertensive population. As a body index reflecting abdominal obesity and visceral fat accumulation， BRI shows promising application prospects in the risk assessment of MAFLD.

     

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