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Background <p>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.</p> Methods <p>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.</p> Results <p>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 (<i>P</i> for overall &lt; 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, <i>P</i> &lt; 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).</p> Conclusions <p>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.</p> Graphical abstract <p></p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月28日

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

摘要

Background

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.

Methods

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.

Results

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).

Conclusions

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.

Graphical abstract