TyG-BMI had the strongest association with MAFLD with OR 3.7 (95% CI 3.05–4.48) and highest predictive performance (AUC 0.81) compared to other lipid parameters in Chinese adults.
Observational (n=1,592)
No
Do non-conventional lipid parameters, particularly TyG-BMI, predict the presence of MAFLD in adults undergoing health examinations?
TyG-BMI is a strong, clinically accessible non-conventional lipid parameter for identifying individuals at high risk of MAFLD and stratifying their cardiovascular risk.
Effect estimate: OR 3.7 for TyG-BMI per 1 SD increment (95% CI 95% CI 3.05-4.48 for TyG-BMI per 1 SD increment)
p-value: p=<0.001
Objective Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a prevalent chronic hepatic condition globally, characterized by hepatic steatosis concurrent with at least one cardiometabolic risk factor, such as overweight/obesity, type 2 diabetes mellitus (T2DM), or metabolic dysregulation. This study aimed to evaluate the associations between nine non-conventional lipid parameters—BMI, NHHR, AIP, RC, GHR, CHG, LCI, TyG, TyG-BMI—and MAFLD, and to compare their predictive performance for MAFLD screening. Methods This study utilized the electronic medical record at Wuhan Union Hospital between January 2020 and November 2021, and multi-model adjustment weighted logistic regression analysis was applied to investigate the association of the nine parameters with MAFLD. Receiver operating characteristic (ROC) curves were analyzed to assess the screening ability of the nine parameters. Furthermore, the association between the most predictive parameter and MAFLD was investigated with RCS analysis, and differences in risk across populations were explored with subgroup analyses. Results A total of 1,592 participants were included in the final analysis, among whom 937 (58.86%) were diagnosed with MAFLD. Multivariable logistic regression identified NHHR, BMI, AIP, RC, GHR, LCI, TyG, and TyG-BMI as independent risk factors for MAFLD, with TyG-BMI demonstrating the strongest association (OR = 3.7, 95% CI: 3.05–4.48). The area under the ROC curve (AUC) for TyG-BMI was 0.81, and its predictive performance was significantly superior to that of the other parameters (all P 0.001 by DeLong’s test). RCS analysis revealed a nonlinear relationship between TyG-BMI and MAFLD ( P for nonlinearity0.001), with an identified inflection point at a TyG-BMI value of 222.426. Additionally, MAFLD patients in the highest TyG-BMI tertile exhibited a significantly increased risk of atherosclerotic cardiovascular disease (ASCVD) compared to those in the lowest tertile (OR = 2.55, 95% CI: 1.337–4.91) after adjustment for confounders. Conclusion The evaluated non-conventional lipid parameters, particularly TyG-BMI, are useful indicators for MAFLD identification. TyG-BMI demonstrated the strongest predictive ability for MAFLD and was independently associated with ASCVD risk in affected individuals. Elevated TyG-BMI may therefore serve as a clinically accessible marker for identifying individuals at high risk of MAFLD and for stratifying cardiovascular risk in patients with established MAFLD.
Song et al. (2026) conducted an observational in Metabolic dysfunction-associated fatty liver disease (MAFLD) (n=1,592). Nine non-conventional lipid parameters (BMI, NHHR, AIP, RC, GHR, CHG, LCI, TyG, TyG-BMI) vs. Comparison of lipid parameters against each other and conventional parameters was evaluated on Prediction and association of nine non-conventional lipid parameters with MAFLD diagnosis (OR 3.7 for TyG-BMI per 1 SD increment, 95% CI 95% CI 3.05-4.48 for TyG-BMI per 1 SD increment, p=<0.001). TyG-BMI had the strongest association with MAFLD with OR 3.7 (95% CI 3.05–4.48) and highest predictive performance (AUC 0.81) compared to other lipid parameters in Chinese adults.