Assessment of the clinical value of five noninvasive predictors of metabolic dysfunction-associated steatotic liver disease in Han Chinese adults.

BACKGROUND: Fatty Liver Index (FLI), Triglyceride-Glucose Index (TyG), Lipid Accumulation Product (LAP), Zhejiang University Index (ZJU), and Visceral Adiposity Index (VAI) are five classical predictive models for fatty liver disease. Our cross-sectional study aimed to identify the optimal predictors by comparing the predictive value of five models for metabolic dysfunction-associated steatotic liver disease (MASLD) risk. METHODS: Data on 2687 participants were collected from West China Hospital of Sichuan University. Controlled attenuation parameters assessed by transient elastography were used to effectively diagnose MASLD. Logistic regression analysis was used to estimate the odd ratios and 95% confidence intervals between indices and MASLD risk. Receiver operating characteristic curves were plotted to evaluate the predictive value of indices. RESULTS: This study included 1337 normal and 1350 MASLD samples. The average age of MASLD patients is 47 years old, and the prevalence was higher in males (39.3%) than in females (10.9%). Five indices were positively correlated with MASLD risk, with the strongest correlation for TyG. Overall, the area under the curve of the indicators was: ZJU 0.988, FLI 0.987, LAP 0.982, TyG 0.942, and VAI 0.941. In the gender stratification, ZJU (0.989) performed best in males. FLI (0.988) and ZJU (0.987) had similar predictive ability in females. In the age stratification, FLI performed better in predicting the middle-aged group aged 30-40 years (0.991). CONCLUSION: For Chinese Han adults, ZJU is the best predictive index for initial screening of MASLD. FLI can serve as an alternative tool for ZJU to predict females.

Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease.

BACKGROUND: The absence of distinct symptoms in the majority of individuals with metabolic dysfunction-associated fatty liver disease (MAFLD) poses challenges in identifying those at high risk, so we need simple, efficient and cost-effective noninvasive scores to aid healthcare professionals in patient identification. While most noninvasive scores were developed for the diagnosis of nonalcoholic fatty liver disease (NAFLD), consequently, the objective of this study was to systematically assess the diagnostic ability of 12 noninvasive scores (METS-IR/TyG/TyG-WC/TyG-BMI/TyG-WtHR/VAI/HSI/FLI/ZJU/FSI/K-NAFLD) for MAFLD. METHODS: The study recruited eligible participants from two sources: the National Health and Nutrition Examination Survey (NHANES) 2017-2020.3 cycle and the database of the West China Hospital Health Management Center. The performance of the model was assessed using various metrics, including area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and subgroup analysis. RESULTS: A total of 7398 participants from the NHANES cohort and 4880 patients from the Western China cohort were included. TyG-WC had the best predictive power for MAFLD risk in the NHANES cohort (AUC 0.863, 95% CI 0.855-0.871), while TyG-BMI had the best predictive ability in the Western China cohort (AUC 0.903, 95% CI 0.895-0.911), outperforming other models, and in terms of IDI, NRI, DCA, and subgroup analysis combined, TyG-WC remained superior in the NAHANES cohort and TyG-BMI in the Western China cohort. CONCLUSIONS: TyG-BMI demonstrated satisfactory diagnostic efficacy in identifying individuals at a heightened risk of MAFLD in Western China. Conversely, TyG-WC exhibited the best diagnostic performance for MAFLD risk recognition in the United States population. These findings suggest the necessity of selecting the most suitable predictive models based on regional and ethnic variations.

Development and validation of a new nomogram to screen for MAFLD.

BACKGROUND AND AIM: Metabolic dysfunction-associated fatty liver disease (MAFLD) poses significant health and economic burdens on all nations. Thus, identifying patients at risk early and managing them appropriately is essential. This study's goal was to develop a new predictive model for MAFLD. Additionally, to improve the new model's clinical utility, researchers limited the variables to readily available simple clinical and laboratory measures. METHODS: Based on the National Health and Nutrition Examination Survey (NHANES) cycle 2017-2020.3, the study was a retrospective cross-sectional study involving 7300 participants. By least absolute shrinkage and selection operator (LASSO) regression, significant indicators independently associated with MAFLD were identified, and a predictive model called the MAFLD prediction nomogram (MPN) was developed. The study then compared the MPN with six existing predictive models for MAFLD. The model was evaluated by measuring the area under receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) curve. RESULTS: In this study, researchers identified nine predictors from 33 variables, including age, race, arm circumference (AC), waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT)-to-aspartate aminotransferase (AST) ratio, triglyceride-glucose index (TyG), hypertension, and diabetes. The diagnostic accuracy of the MPN for MAFLD was significantly better than that of the other six existing models in both the training and validation cohorts (AUC 0.868, 95% confidence interval (CI) 0.858-0.877, and AUC 0.863, 95% CI 0.848-0.878, respectively). The MPN showed a higher net benefit than the other existing models. CONCLUSIONS: This nonimaging-assisted nomogram based on demographics, laboratory factors, anthropometrics, and comorbidities better predicted MAFLD than the other six existing predictive models. Using this model, the general population with MAFLD can be assessed rapidly.