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      Exenatide and dapagliflozin combination improves markers of liver steatosis and fibrosis in patients with type 2 diabetes

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          Abstract

          Aim

          To assess the efficacy of exenatide (EXE) once weekly + dapagliflozin once daily (DAPA) versus each drug alone in reducing biomarkers of fatty liver/steatosis and fibrosis in a post hoc analysis of DURATION‐8, a 104‐week study in 695 patients with type 2 diabetes uncontrolled by metformin monotherapy.

          Materials and methods

          We evaluated the impact of the study treatments on non‐invasive markers of hepatic steatosis (fatty liver index [FLI] and non‐alcoholic fatty liver disease [NAFLD] liver fat score), fibrosis (fibrosis‐4 index [FIB‐4]) and severe fibrosis (NAFLD fibrosis score), along with liver enzymes and insulin resistance, at weeks 28 and 52. All outcomes in this analysis were exploratory, with nominal P values reported.

          Results

          At week 28, biomarkers of fatty liver/steatosis and fibrosis were reduced from baseline in all treatment groups. At week 28, EXE once weekly + DAPA effects for decrease in FLI were stronger than those of EXE once weekly + placebo (PLB; −2.92, 95% confidence interval [CI] −5.11, −0.73; P = 0.0092) or DAPA+PLB (−2.77 [95% CI −4.93, −0.62]; P = 0.0119), and stronger than those of EXE once weekly + PLB at week 52 (−3.23 [95% CI −5.79, −0.68]; P = 0.0134). FIB‐4 showed reduction versus baseline only in the EXE once weekly + DAPA group at both week 28 (−0.06 [95% CI −0.11, −0.01]; P = 0.0135) and week 52 (−0.05 [95% CI −0.09, −0.004]; P = 0.0308).

          Conclusions

          The EXE once weekly + DAPA combination showed stronger effects than EXE once weekly + PLB or DAPA + PLB in ameliorating markers of hepatic steatosis and fibrosis in patients with type 2 diabetes. Prospective trials are needed to validate these findings.

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          Most cited references 33

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          EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease.

          (2016)
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            The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD.

            Patients with nonalcoholic fatty liver disease (NAFLD) and advanced liver fibrosis are at the highest risk for progressing to end-stage liver disease. We constructed and validated a scoring system consisting of routinely measured and readily available clinical and laboratory data to separate NAFLD patients with and without advanced fibrosis. A total of 733 patients with NAFLD confirmed by liver biopsy were divided into 2 groups to construct (n = 480) and validate (n = 253) a scoring system. Routine demographic, clinical, and laboratory variables were analyzed by multivariate modeling to predict presence or absence of advanced fibrosis. Age, hyperglycemia, body mass index, platelet count, albumin, and AST/ALT ratio were independent indicators of advanced liver fibrosis. A scoring system with these 6 variables had an area under the receiver operating characteristic curve of 0.88 and 0.82 in the estimation and validation groups, respectively. By applying the low cutoff score (-1.455), advanced fibrosis could be excluded with high accuracy (negative predictive value of 93% and 88% in the estimation and validation groups, respectively). By applying the high cutoff score (0.676), the presence of advanced fibrosis could be diagnosed with high accuracy (positive predictive value of 90% and 82% in the estimation and validation groups, respectively). By applying this model, a liver biopsy would have been avoided in 549 (75%) of the 733 patients, with correct prediction in 496 (90%). a simple scoring system accurately separates patients with NAFLD with and without advanced fibrosis, rendering liver biopsy for identification of advanced fibrosis unnecessary in a substantial proportion of patients.
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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

                Contributors
                amalia@ifc.cnr.it
                Journal
                Diabetes Obes Metab
                Diabetes Obes Metab
                10.1111/(ISSN)1463-1326
                DOM
                Diabetes, Obesity & Metabolism
                Blackwell Publishing Ltd (Oxford, UK )
                1462-8902
                1463-1326
                14 December 2019
                March 2020
                : 22
                : 3 ( doiID: 10.1111/dom.v22.3 )
                : 393-403
                Affiliations
                [ 1 ] CNR Institute of Clinical Physiology Pisa Italy
                [ 2 ] Global Medical CVRM, BioPharmaceuticals Medical, AstraZeneca Gaithersburg Maryland United States
                [ 3 ] Department of Diabetes, Nutrition and Metabolic Diseases Carol Davila University of Medicine and Pharmacy Bucharest Romania
                [ 4 ] Late CVRM, BioPharmaceuticals R&D Gaithersburg Maryland United States
                [ 5 ] Pharmapace Inc., San Diego California United States
                [ 6 ] Division of Endocrinology, Diabetes and Metabolic Diseases Sidney Kimmel Medical College of Thomas Jefferson University Philadelphia Pennsylvania United States
                Author notes
                [* ] Correspondence

                Amalia Gastaldelli, Cardiometabolic Risk Laboratory, Institute of Clinical Physiology, CNR, Via Moruzzi 1, 56124 Pisa, Italy.

                Email: amalia@ 123456ifc.cnr.it

                Article
                DOM13907
                10.1111/dom.13907
                7064910
                31692226
                © 2019 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                Page count
                Figures: 2, Tables: 3, Pages: 11, Words: 7541
                Product
                Funding
                Funded by: AstraZeneca , open-funder-registry 10.13039/100004325;
                Award ID: GPP3
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                March 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.7 mode:remove_FC converted:11.03.2020

                Endocrinology & Diabetes

                dapagliflozin, exenatide, liver, type 2 diabetes

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