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
Performance · Association of TyG-BMI with MASLD (WMU cohort)Result — go to the resultOdds ratio 7.36 95% CI 3.89–13.94Authors' conclusion
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.
Study context
- Population
- T2D patients: WMU cohort (discovery, n=449) and Japanese NAGALA cohort (external validation, n=306)449 WMU + 306 NAGALA
- Comparison
- TyG-BMI tertiles
- Outcome
- MASLD
Methodological detailstwo cohorts (discovery and external validation), Boruta and LASSO feature selection
- Two cohorts (discovery and external validation)
- Boruta and LASSO feature selection
Performance
Association of TyG-BMI with MASLD (WMU cohort)
Odds ratio
7.36
95% CI 3.89–13.94
As reported in the abstract
- Significant linear dose-response relationship
- P for overall < 0.001
Further findings
Discrimination and reclassification adding TyG-BMI to baseline model
- AUC increased (baseline → with TyG-BMI)
- 0.7288 to 0.7920
- NRI
- 0.6088, P < 0.001
- DCA and calibration plots confirmed high clinical net benefit and accuracy
Interaction TyG-BMI × low-density lipoprotein cholesterol (LDL-C)
- Significant synergistic interaction between TyG-BMI and LDL-C
Original publication
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