A study published in 'Lipids in Health and Disease' depicted that triglyceride glucose-body mass index (TyG-BMI) is the best predictor of metabolic dysfunction-associated fatty liver disease (MAFLD) risk in Western China while triglyceride glucose-waist circumference (TyG-WC) is the most effective in the United States, highlighting the importance of tailoring approaches based on regional variations.

Investigators aimed to evaluate the diagnostic ability of 12 noninvasive scores (metabolic score for insulin resistance [METS-IR]/TyG/TyG-WC/TyG-BMI/TyG-waist-to-height ratio [WtHR]/visceral adiposity index [VAI]/hepatic steatosis index [HSI]/fatty liver index [FLI]/Zhejiang University [ZJU] index/Framingham steatosis index [FSI]/Korean National Health and Nutrition Examination Survey [KNHANES] nonalcoholic fatty liver disease [K-NAFLD]) for MAFLD.

The study enrolled eligible individuals from two different pools: one was derived from the 2017-2020. 3 cycle of the NHANES, and the other was sourced from the database of the West China Hospital Health Management Center. To evaluate the model's effectiveness, multiple metrics were employed, such as the subgroup analysis, decision curve analysis (DCA), integrated discrimination improvement (IDI), the net reclassification index (NRI), and the area under the receiver operating characteristic curve (AUC).

Overall, 4,880 subjects from the Western China cohort and 7,398 people from the NHANES cohort were encompassed. In the NHANES cohort, TyG-WC demonstrated the highest predictive accuracy for MAFLD risk, with an AUC of 0.863. On the other hand, in the Western China cohort, TyG-BMI exhibited the most effective predictive capability, with an AUC of 0.903, surpassing other models. When considering IDI, NRI, DCA, and subgroup analysis collectively, TyG-BMI maintained its superiority in the Western China cohort, while TyG-WC exhibited superiority in the NHANES cohort.

In Western China, TyG-BMI proved to be an effective diagnostic tool for the identification of volunteers at an elevated risk of MAFLD. In contrast, TyG-WC displayed superior diagnostic performance when it came to recognizing MAFLD risk in the United States population. These results emphasize the importance of choosing predictive models that are best suited to specific ethnic and regional variations.