ArticleHepatology international2026
Converging pathways: the interaction between estimated glucose disposal rate and atherogenic index of plasma in MASLD risk stratification.
Article in Hepatology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is a global health burden linked to insulin resistance and atherogenic dyslipidemia, estimated by the glucose disposal rate (eGDR) and atherogenic index of plasma (AIP), respectively. However, the nature of their combined association-whether additive or interactive-with MASLD risk, and its utility for risk stratification, remains unexplored.
methodsIn this large-scale cross-sectional analysis of 30,143 adults, we calculated eGDR and AIP from routine clinical measures. Their independent and joint associations with ultrasonography-defined MASLD were assessed using multivariable logistic regression, restricted cubic splines (RCS), and interaction analysis. Based on RCS-derived thresholds, participants were categorized into four metabolic phenotypes for combined risk assessment.
resultsBoth lower eGDR and higher AIP were independently and nonlinearly associated with greater MASLD risk (p for trend < 0.001). A significant interaction was observed (p < 0.001), revealing a convergent pattern: the protective association of high eGDR appeared attenuated as AIP increased, and the risk disparity attributable to AIP narrowed markedly at low eGDR levels. Participants with the combined low-eGDR/high-AIP phenotype had the highest MASLD odds (OR = 3.42, 95% CI 3.07-3.81). These findings were robust to alternative adjustment strategies and phenotype definitions in sensitivity analyses.
conclusionseGDR and AIP exhibit a significant interactive association with MASLD risk, characterized by a convergent pattern: the incremental risk associated with atherogenic dyslipidemia (high AIP) appeared less pronounced under conditions of severe insulin resistance (low eGDR). Integrating these two metrics identifies a distinct high-risk phenotype and may inform future risk stratification strategies. Longitudinal studies are needed to establish causality and clinical utility.
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