Integrating lipometabolic and adiposity indices to enhance risk stratification for metabolic dysfunction-associated steatotic liver disease in type 2 diabetes: clinical utility and interplay between triglyceride-glucose body mass index and low-density lipoprotein cholesterol.

Authors

Yanxiu Yang, Xiaoxiao Qu, Mengran Shi, Jialu Huang, Guanghong Li, Xingyu Lu, Enxin You, Jingyun Qian, Jing Xu, Minghua Jiang, Guosong Jiang, Qipeng Xie

Published in

Cardiovascular diabetology. Jun 28, 2026. Epub Jun 28, 2026.

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) significantly exacerbates the prognosis of patients with type 2 diabetes mellitus (T2DM). We aimed to compare metabolic and adiposity-related surrogates for MASLD using data-driven feature selection and to validate a parsimonious risk model across distinct populations.
We included 449 T2DM patients from the WMU cohort (discovery) and 306 from the Japanese NAGALA cohort (external validation). A two-stage data-driven feature-selection framework (Boruta and LASSO) was implemented to identify a parsimonious two-variable signature (TyG-BMI and SGLT2i). Based on these results, TyG-BMI was prioritized for systematic evaluation. Association and dose-response relationships were assessed via multivariate logistic regression and restricted cubic splines. Multiplicative and additive interactions between TyG-BMI and LDL-C were further explored. Clinical utility was evaluated via AUC, NRI, IDI, and decision curve analysis.
The Boruta algorithm ranked TyG-BMI as the feature with the highest importance score for MASLD classification. Subsequently, LASSO regression (utilizing the 1-standard-error criterion λ1se) identified a parsimonious two-variable signature comprising TyG-BMI and SGLT2i. In the WMU cohort, TyG-BMI exhibited a potent association with MASLD (T3 vs. T1: OR = 7.36, 95% CI 3.89-13.94) and a significant linear dose-response relationship (P for overall < 0.001). Incorporation of TyG-BMI into the baseline model improved discriminative performance (AUC increased from 0.7288 to 0.7920) and was associated with improved continuous reclassification (NRI: 0.6088, P < 0.001). DCA and calibration plots confirmed high clinical net benefit and accuracy. Furthermore, a significant synergistic interaction was observed between TyG-BMI and low-density lipoprotein cholesterol (LDL-C).
TyG-BMI, selected through data-driven feature selection, may serve as a practical candidate predictor of MASLD in patients with T2DM. The observed interaction between TyG-BMI and LDL-C suggests that their joint assessment may further refine MASLD risk stratification. The derived parsimonious model offers a high-performing, non-invasive tool for early MASLD risk stratification across Asian populations.

PMID:
42365329
Bibliographic data and abstract were imported from PubMed on 28 Jun 2026.

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