Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease - PubMed (original) (raw)

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

Haoxuan Zou et al. Lipids Health Dis. 2023.

Abstract

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.

Keywords: Decision curve analysis; External validation of prediction models; Integrated discrimination improvement; Metabolic dysfunction-associated fatty liver disease; Net reclassification index; Receiver operating characteristic curve.

© 2023. BioMed Central Ltd., part of Springer Nature.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Fig. 1

Flow diagram of study design

Fig. 2

Fig. 2

ROC curves for predicting MAFLD in the NHANES cohort (A) and Western China cohort (B). The x-axis is the specificity; the y-axis is the sensitivity

Fig. 3

Fig. 3

AUC and 95% CI for noninvasive scores to detect MAFLD risk in different subgroups of the NHANES cohort. A for METS-IR, (B) for TyG, (C) for TyG-BMI, (D) for TyG-WC, (E) for TyG-WtHR, (F) for VAI, (G) for HSI, (H) for FLI, (I) for LAP, (J) for ZJU , (K) for FSI, and (L) for K-NAFLD

Fig. 4

Fig. 4

The Clinical utility of the indices were evaluated by decision curves in the NHANES cohort (A) and Western China cohort (B). The x-axis measures the threshold probability. The y-axis represents net benefits , calculated by subtracting the relative harms (false positive) from the benefits (true positives)

Fig. 5

Fig. 5

AUC and 95% CI for noninvasive scores to detect MAFLD risk in different subgroups of the Western China cohort. A for METS-IR, (B) for TyG, (C) for TyG-BMI, (D) for TyG-WC, (E) for TyG-WtHR, (F) for VAI, (G) for HSI, (H) for FLI, (I) for LAP, (J) for ZJU, (K) for FSI, and (L) for K-NAFLD

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