Cargando…

Diagnostic ability using fatty liver and metabolic markers for metabolic‐associated fatty liver disease stratified by metabolic/glycemic abnormalities

AIMS/INTRODUCTION: Although several noninvasive predictive markers for fatty liver and metabolic markers have been used for fatty liver prediction, whether such markers can also predict metabolic‐associated fatty liver disease (MAFLD) remains unclear. We aimed to examine the ability of existing fatt...

Descripción completa

Detalles Bibliográficos
Autores principales: Okada, Akira, Yamada, Gen, Kimura, Takeshi, Hagiwara, Yasuhiro, Yamaguchi, Satoko, Kurakawa, Kayo Ikeda, Nangaku, Masaomi, Yamauchi, Toshimasa, Matsuyama, Yutaka, Kadowaki, Takashi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951571/
https://www.ncbi.nlm.nih.gov/pubmed/36566480
http://dx.doi.org/10.1111/jdi.13966
Descripción
Sumario:AIMS/INTRODUCTION: Although several noninvasive predictive markers for fatty liver and metabolic markers have been used for fatty liver prediction, whether such markers can also predict metabolic‐associated fatty liver disease (MAFLD) remains unclear. We aimed to examine the ability of existing fatty liver or metabolic markers to predict MAFLD. MATERIALS AND METHODS: Participants in a high‐volume center in Tokyo were classified into groups with and without MAFLD, based on the presence of metabolic abnormalities and fatty liver diagnosed through abdominal ultrasonography, between 2008 and 2018. The diagnostic abilities of three fatty liver markers: fatty liver index (FLI), hepatic steatosis index (HSI), and lipid accumulation product (LAP), and three common metabolic markers: waist‐to‐height ratio (WHR), body mass index (BMI), and waist circumference (WC), for predicting MAFLD, were evaluated. Analyses stratified by MAFLD subtypes were performed. RESULTS: Of 92,374 individuals, 19,392 (36.1%) had MAFLD. The diagnostic performances for MAFLD prediction, measured as c‐statistics, for FLI, HSI, LAP, WHR, BMI, and WC were 0.906, 0.892, 0.878, 0.844, 0.877, and 0.878, respectively. Optimal cutoff values for diagnosing MAFLD for FLI, HSI, LAP, WHR, BMI, and WC were 20.3, 32.7, 20.0, 0.49, 22.9, and 82.1, respectively. Analyses stratified by MAFLD subtypes, based on BMI and metabolic/glycemic abnormalities, suggested that FLI and HSI had acceptable (c‐statistics >0.700) diagnostic abilities throughout all the analyses. CONCLUSIONS: All six markers were excellent predictors of MAFLD in diagnosing among the general population, with FLI and HSI particularly useful among all sub‐populations.