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Background <p>The inflammation burden index (IBI) is a novel biomarker that reflects systemic inflammation. This study aimed to explore the association of IBI with hepatic steatosis and fibrosis.</p> Methods <p>This cross-sectional study analyzed the NHANES data of adult participants in the United States from 2017 to 2018. Feature importance analysis was used to select covariates. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), calibration curve, multivariate logistic regression, subgroup analysis, interaction assessment, and restricted cubic spline (RCS) analysis were performed to investigate the association of IBI with hepatic steatosis and fibrosis. Additionally, integrated discrimination improvement (IDI) were employed to evaluate the incremental value. Statistical mediation analysis was conducted to assess the potential mediating role of IBI. Finally, we further validated our findings using data from NHANES 2021–2023.</p> Results <p>A total of 3657 adult participants were included in the study. Comparative ROC analysis indicated that for hepatic steatosis, established scores like HSI (AUC = 0.773) and DSI (AUC = 0.727) demonstrated higher predictive accuracy than logIBI (AUC = 0.643). However, for liver fibrosis, logIBI (AUC = 0.640) significantly outperformed standard transaminase-based scores such as FIB-4 (AUC = 0.536) and APRI (AUC = 0.526). Adding logIBI to HSI (for steatosis) and FIB-4 (for fibrosis) significantly improved risk reclassification (IDI, all <i>P</i> &lt; 0.05). In multivariable regression, we found significant relationships between logIBI and both hepatic steatosis (OR = 1.189, 95% CI 1.009–1.402) and liver fibrosis (OR = 1.691, 95% CI 1.074–2.662), which were moderated by BMI (all <i>P</i> for interaction &lt; 0.05) but attenuated after adjusting for metabolic risk factors. These associations were successfully replicated in the validation cohort. The RCS curve revealed the linear relationships across all BMI subgroups (all <i>P</i> for nonlinear &gt; 0.05). Statistical mediation analysis suggested that logIBI partially mediated the relationship between hepatic steatosis and liver fibrosis.</p> Conclusions <p>Our findings indicated that IBI was associated with hepatic steatosis and liver fibrosis. Although its independent predictive value is limited by metabolic confounders, its superiority over FIB-4 suggests it serves as a valuable pathophysiological marker reflecting the inflammatory burden, potentially complementing existing non-invasive assessments.</p>

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Inflammation burden index as a complementary marker for the assessment of hepatic steatosis and fibrosis: evidence from NHANES 2017–2018

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Shaoguang Chen,
  • Lixiao Zhu,
  • Yulou Jiang

摘要

Background

The inflammation burden index (IBI) is a novel biomarker that reflects systemic inflammation. This study aimed to explore the association of IBI with hepatic steatosis and fibrosis.

Methods

This cross-sectional study analyzed the NHANES data of adult participants in the United States from 2017 to 2018. Feature importance analysis was used to select covariates. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), calibration curve, multivariate logistic regression, subgroup analysis, interaction assessment, and restricted cubic spline (RCS) analysis were performed to investigate the association of IBI with hepatic steatosis and fibrosis. Additionally, integrated discrimination improvement (IDI) were employed to evaluate the incremental value. Statistical mediation analysis was conducted to assess the potential mediating role of IBI. Finally, we further validated our findings using data from NHANES 2021–2023.

Results

A total of 3657 adult participants were included in the study. Comparative ROC analysis indicated that for hepatic steatosis, established scores like HSI (AUC = 0.773) and DSI (AUC = 0.727) demonstrated higher predictive accuracy than logIBI (AUC = 0.643). However, for liver fibrosis, logIBI (AUC = 0.640) significantly outperformed standard transaminase-based scores such as FIB-4 (AUC = 0.536) and APRI (AUC = 0.526). Adding logIBI to HSI (for steatosis) and FIB-4 (for fibrosis) significantly improved risk reclassification (IDI, all P < 0.05). In multivariable regression, we found significant relationships between logIBI and both hepatic steatosis (OR = 1.189, 95% CI 1.009–1.402) and liver fibrosis (OR = 1.691, 95% CI 1.074–2.662), which were moderated by BMI (all P for interaction < 0.05) but attenuated after adjusting for metabolic risk factors. These associations were successfully replicated in the validation cohort. The RCS curve revealed the linear relationships across all BMI subgroups (all P for nonlinear > 0.05). Statistical mediation analysis suggested that logIBI partially mediated the relationship between hepatic steatosis and liver fibrosis.

Conclusions

Our findings indicated that IBI was associated with hepatic steatosis and liver fibrosis. Although its independent predictive value is limited by metabolic confounders, its superiority over FIB-4 suggests it serves as a valuable pathophysiological marker reflecting the inflammatory burden, potentially complementing existing non-invasive assessments.