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      Validation of the Fatty Liver Index for Nonalcoholic Fatty Liver Disease in Middle-Aged and Elderly Chinese

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      , MD, , PhD, , MD, , MD, , MD, , MD, , MD, , MD, , PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD, , MD, PhD
      Medicine
      Wolters Kluwer Health

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          Abstract

          The fatty liver index (FLI), which is an algorithm based on waist circumference, body mass index (BMI), triglyceride, and gamma-glutamyl-transferase (GGT), was initially developed to detect fatty liver in Western countries. Our study aimed to evaluate the accuracy and optimal cut-off point of the FLI for predicting nonalcoholic fatty liver disease (NAFLD) in middle-aged and elderly Chinese.

          This cross-sectional study included 8626 Chinese adults aged 40 years or above recruited from Jiading District, Shanghai, China. Anthropometric and biochemical features were collected by a standard protocol. NAFLD was diagnosed by hepatic ultrasonography. The accuracy and cut-off point of the FLI to detect NAFLD were evaluated by area under the receiver operator characteristic curve (AUROC) and the maximum Youden index analysis, respectively.

          The AUROC of the FLI for NAFLD was 0.834 (95% confidence interval: 0.825–0.842), and larger than that of its each individual component [0.786 (0.776–0.796), 0.783 (0.773–0.793), 0.727 (0.716–0.739), and 0.707 (0.695–0.719) for waist circumference, BMI, triglyceride, and GGT, respectively] (all P <  0.001). The optimal cut-off point of the FLI for diagnosing NAFLD was 30 with the maximum Youden Index of 0.51, achieving a high sensitivity of 79.89% and a specificity of 71.51%. The FLI-diagnosed NAFLD individuals were in worse metabolic characteristics (waist circumference, BMI, blood pressure, serum lipids, and aminotransferases) than ultrasonography-diagnosed NAFLD patients (all P <  0.05).

          The FLI could accurately identify NAFLD and the optimal cut-off point was 30 in middle-aged and elderly Chinese. As FLI-diagnosed NAFLD patients were in worse metabolism, much attention should be paid to the metabolic controls and managements of NAFLD.

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          The Measurement of Observer Agreement for Categorical Data

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            Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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              The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population

              Background Fatty liver (FL) is the most frequent liver disease in Western countries. We used data from the Dionysos Nutrition & Liver Study to develop a simple algorithm for the prediction of FL in the general population. Methods 216 subjects with and 280 without suspected liver disease were studied. FL was diagnosed by ultrasonography and alcohol intake was assessed using a 7-day diary. Bootstrapped stepwise logistic regression was used to identify potential predictors of FL among 13 variables of interest [gender, age, ethanol intake, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase (GGT), body mass index (BMI), waist circumference, sum of 4 skinfolds, glucose, insulin, triglycerides, and cholesterol]. Potential predictors were entered into stepwise logistic regression models with the aim of obtaining the most simple and accurate algorithm for the prediction of FL. Results An algorithm based on BMI, waist circumference, triglycerides and GGT had an accuracy of 0.84 (95%CI 0.81–0.87) in detecting FL. We used this algorithm to develop the "fatty liver index" (FLI), which varies between 0 and 100. A FLI < 30 (negative likelihood ratio = 0.2) rules out and a FLI ≥ 60 (positive likelihood ratio = 4.3) rules in fatty liver. Conclusion FLI is simple to obtain and may help physicians select subjects for liver ultrasonography and intensified lifestyle counseling, and researchers to select patients for epidemiologic studies. Validation of FLI in external populations is needed before it can be employed for these purposes.
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                Author and article information

                Journal
                Medicine (Baltimore)
                Medicine (Baltimore)
                MEDI
                Medicine
                Wolters Kluwer Health
                0025-7974
                1536-5964
                October 2015
                09 October 2015
                : 94
                : 40
                : e1682
                Affiliations
                From the State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine and Shanghai Clinical Center for Endocrine and Metabolic Diseases (XH, MX, YC, KP, YH, PW, LD, LL, YX, YC, JL, WW, YB, GN); and Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China (XH, MX, YC, KP, YH, PW, LD, LL, YX, YC, JL, WW, YB, GN).
                Author notes
                Correspondence: Yufang Bi, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, 197 Rui-Jin 2nd Road, Shanghai 200025, China (e-mail: byf10784@ 123456rjh.com.cn ).
                Article
                01682
                10.1097/MD.0000000000001682
                4616754
                26448014
                37d3be7d-c654-4ca7-a595-f050d0035626
                Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

                This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0, where it is permissible to download, share and reproduce the work in any medium, provided it is properly cited. The work cannot be changed in any way or used commercially. http://creativecommons.org/licenses/by-nc-nd/4.0

                History
                : 24 June 2015
                : 19 August 2015
                : 1 September 2015
                Categories
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                Research Article
                Observational Study
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