Comparative efficacy of glucose-lowering drugs on liver steatosis as assessed by means of magnetic resonance imaging in patients with type 2 diabetes mellitus: systematic review and network meta-analysis

Burra P, Becchetti C, Germani G (2020) NAFLD and liver transplantation: Disease burden, current management and future challenges. JHEP Rep 2:100192. https://doi.org/10.1016/j.jhepr.2020.100192

Article  PubMed  PubMed Central  Google Scholar 

Riazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE et al (2022) The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 7:851–861. https://doi.org/10.1016/s2468-1253(22)00165-0

Article  PubMed  Google Scholar 

EASL-EASD-EASO (2016) Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. J Hepatol. 64:1388–402. https://doi.org/10.1016/j.jhep.2015.11.004

Article  Google Scholar 

Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M et al (2018) The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 67:328–357. https://doi.org/10.1002/hep.29367

Article  PubMed  Google Scholar 

Cusi K, Isaacs S, Barb D, Basu R, Caprio S, Garvey WT et al (2022) American Association of Clinical Endocrinology Clinical Practice Guideline for the Diagnosis and Management of Nonalcoholic Fatty Liver Disease in Primary Care and Endocrinology Clinical Settings: Co-Sponsored by the American Association for the Study of Liver Diseases (AASLD). Endocr Pract 28:528–562. https://doi.org/10.1016/j.eprac.2022.03.010

Article  PubMed  Google Scholar 

Harrison SA, Allen AM, Dubourg J, Noureddin M, Alkhouri N (2023) Challenges and opportunities in NASH drug development. Nat Med 29:562–573. https://doi.org/10.1038/s41591-023-02242-6

Article  CAS  PubMed  Google Scholar 

Caussy C, Alquiraish MH, Nguyen P, Hernandez C, Cepin S, Fortney LE et al (2018) Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis. Hepatology 67:1348–1359. https://doi.org/10.1002/hep.29639

Article  CAS  PubMed  Google Scholar 

Flint A, Andersen G, Hockings P, Johansson L, Morsing A, Sundby Palle M et al (2021) Randomised clinical trial: semaglutide versus placebo reduced liver steatosis but not liver stiffness in subjects with non-alcoholic fatty liver disease assessed by magnetic resonance imaging. Aliment Pharmacol Ther 54:1150–1161. https://doi.org/10.1111/apt.16608

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tamaki N, Munaganuru N, Jung J, Yonan AQ, Loomba RR, Bettencourt R et al (2022) Clinical utility of 30% relative decline in MRI-PDFF in predicting fibrosis regression in non-alcoholic fatty liver disease. Gut 71:983–990. https://doi.org/10.1136/gutjnl-2021-324264

Article  CAS  PubMed  Google Scholar 

Stine JG, Munaganuru N, Barnard A, Wang JL, Kaulback K, Argo CK et al (2021) Change in MRI-PDFF and Histologic Response in Patients With Nonalcoholic Steatohepatitis: A Systematic Review and Meta-Analysis. Clin Gastroenterol Hepatol 19:2274–83.e5. https://doi.org/10.1016/j.cgh.2020.08.061

Article  PubMed  Google Scholar 

Ghosal S, Datta D, Sinha B (2021) A meta-analysis of the effects of glucagon-like-peptide 1 receptor agonist (GLP1-RA) in nonalcoholic fatty liver disease (NAFLD) with type 2 diabetes (T2D). Sci Rep 11:22063. https://doi.org/10.1038/s41598-021-01663-y

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mantovani A, Byrne CD, Targher G (2022) Efficacy of peroxisome proliferator-activated receptor agonists, glucagon-like peptide-1 receptor agonists, or sodium-glucose cotransporter-2 inhibitors for treatment of non-alcoholic fatty liver disease: a systematic review. Lancet Gastroenterol Hepatol 7:367–378. https://doi.org/10.1016/s2468-1253(21)00261-2

Article  PubMed  Google Scholar 

Shao SC, Kuo LT, Chien RN, Hung MJ, Lai EC (2020) SGLT2 inhibitors in patients with type 2 diabetes with non-alcoholic fatty liver diseases: an umbrella review of systematic reviews. BMJ Open Diabetes Res Care. 8. https://doi.org/10.1136/bmjdrc-2020-001956

Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C et al (2015) The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 162:777–784. https://doi.org/10.7326/m14-2385

Article  PubMed  Google Scholar 

Rücker G, Cates CJ, Schwarzer G (2017) Methods for including information from multi-arm trials in pairwise meta-analysis. Res Synth Methods 8:392–403. https://doi.org/10.1002/jrsm.1058

Article  PubMed  Google Scholar 

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors) (2022) Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane. Available from. www.training.cochrane.org/handbook.

Weir CJ, Butcher I, Assi V, Lewis SC, Murray GD, Langhorne P et al (2018) Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review. BMC Med Res Methodol 18:25. https://doi.org/10.1186/s12874-018-0483-0

Article  PubMed  PubMed Central  Google Scholar 

Caussy C, Johansson L (2020) Magnetic resonance-based biomarkers in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Endocrinol Diabetes Metab 3:e00134. https://doi.org/10.1002/edm2.134

Article  PubMed  PubMed Central  Google Scholar 

Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I et al (2019) RoB 2: a revised tool for assessing risk of bias in randomised trials. Bmj. 366:l4898. https://doi.org/10.1136/bmj.l4898

Article  PubMed  Google Scholar 

Cipriani A, Higgins JP, Geddes JR, Salanti G (2013) Conceptual and technical challenges in network meta-analysis. Ann Intern Med 159:130–137. https://doi.org/10.7326/0003-4819-159-2-201307160-00008

Article  PubMed  Google Scholar 

Rücker G, Schwarzer G (2014) Reduce dimension or reduce weights? Comparing two approaches to multi-arm studies in network meta-analysis. Stat Med 33:4353–4369. https://doi.org/10.1002/sim.6236

Article  PubMed  Google Scholar 

Rücker G (2012) Network meta-analysis, electrical networks and graph theory. Res Synth Methods 3:312–324. https://doi.org/10.1002/jrsm.1058

Article  PubMed  Google Scholar 

Rücker G, Schwarzer G (2015) Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol 15:58. https://doi.org/10.1186/s12874-015-0060-8

Article  PubMed  PubMed Central  Google Scholar 

Turner RM, Davey J, Clarke MJ, Thompson SG, Higgins JP (2012) Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews. Int J Epidemiol 41:818–827. https://doi.org/10.1093/ije/dys041

Article  PubMed  PubMed Central  Google Scholar 

Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR (2012) Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods 3:98–110. https://doi.org/10.1002/jrsm.1044

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dias S, Welton NJ, Caldwell DM, Ades AE (2010) Checking consistency in mixed treatment comparison meta-analysis. Stat Med 29:932–944. https://doi.org/10.1002/sim.3767

Article  CAS  PubMed  Google Scholar 

Nikolakopoulou A, Higgins JPT, Papakonstantinou T, Chaimani A, Del Giovane C, Egger M et al (2020) CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med 17:e1003082. https://doi.org/10.1371/journal.pmed.1003082

Article  PubMed  PubMed Central  Google Scholar 

Papakonstantinou T, Nikolakopoulou A, Higgins JPT, Egger M, Salanti G (2020) CINeMA: Software for semiautomated assessment of the confidence in the results of network meta-analysis. Campbell Syst Rev 16:e1080

Article  PubMed  PubMed Central  Google Scholar 

Bi Y, Zhang B, Xu W, Yang H, Feng W, Li C et al (2014) Effects of exenatide, insulin, and pioglitazone on liver fat content and body fat distributions in drug-naive subjects with type 2 diabetes. Acta Diabetol 51:865–873. https://doi.org/10.1007/s00592-014-0638-3

Article  CAS  PubMed  Google Scholar 

Bizino MB, Jazet IM, de Heer P, van Eyk HJ, Dekkers IA, Rensen PCN et al (2020) Placebo-controlled randomised trial with liraglutide on magnetic resonance endpoints in individuals with type 2 diabetes: a pre-specified secondary study on ectopic fat accumulation. Diabetologia 63:65–74. https://doi.org/10.1007/s00125-019-05021-6

Article  CAS  PubMed  Google Scholar 

Bolinder J, Ljunggren Ö, Kullberg J, Johansson L, Wilding J, Langkilde AM et al (2012) Effects of dapagliflozin on body weight, total fat mass, and regional adipose tissue distribution in patients with type 2 diabetes mellitus with inadequate glycemic control on metformin. J Clin Endocrinol Metab 97:1020–1031. https://doi.org/10.1210/jc.2011-2260

Article  CAS  PubMed  Google Scholar 

Cusi K, Bril F, Barb D, Polidori D, Sha S, Ghosh A et al (2019) Effect of canagliflozin treatment on hepatic triglyceride content and glucose metabolism in patients with type 2 diabetes. Diabetes Obes Metab 21:812–821. https://doi.org/10.1111/dom.13584

Article  CAS  PubMed  Google Scholar 

Eriksson JW, Lundkvist P, Jansson PA, Johansson L, Kvarnström M, Moris L et al (2018) Effects of dapagliflozin and n-3 carboxylic acids on non-alcoholic fatty liver disease in people with type 2 diabetes: a double-blind randomised placebo-controlled study. Diabetologia 61:1923–1934. https://doi.org/10.1007/s00125-018-4675-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gaborit B, Ancel P, Abdullah AE, Maurice F, Abdesselam I, Calen A et al (2021) Effect of empagliflozin on ectopic fat stores and myocardial energetics in type 2 diabetes: the EMPACEF study. Cardiovasc Diabetol 20:57. https://doi.org/10.1186/s12933-021-01237-2

Article  CAS  PubMed  PubMed Central 

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