To determine the effect of tirzepatide, a dual agonist of glucose-dependent insulinotropic polypeptide and glucagon-like peptide 1 receptors, on biomarkers of nonalcoholic steatohepatitis (NASH) and fibrosis in patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODSPatients with T2DM received either once weekly tirzepatide (1, 5, 10, or 15 mg), dulaglutide (1.5 mg), or placebo for 26 weeks. Changes from baseline in alanine aminotransferase (ALT), aspartate aminotransferase (AST), keratin-18 (K-18), procollagen III (Pro-C3), and adiponectin were analyzed in a modified intentionto-treat population. RESULTSSignificant (P < 0.05) reductions from baseline in ALT (all groups), AST (all groups except tirzepatide 10 mg), K-18 (tirzepatide 5, 10, 15 mg), and Pro-C3 (tirzepatide 15 mg) were observed at 26 weeks. Decreases with tirzepatide were significant compared with placebo for K-18 (10 mg) and Pro-C3 (15 mg) and with dulaglutide for ALT (10, 15 mg). Adiponectin significantly increased from baseline with tirzepatide compared with placebo (10, 15 mg). CONCLUSIONSIn post hoc analyses, higher tirzepatide doses significantly decreased NASH-related biomarkers and increased adiponectin in patients with T2DM.The prevalence of nonalcoholic fatty liver disease (NAFLD) is ;25% globally and ;60-75% in patients with type 2 diabetes mellitus (T2DM) (1,2). Nonalcoholic steatohepatitis (NASH) (NAFLD with inflammation and hepatocyte injury, with or without fibrosis) can progress to cirrhosis, liver failure, hepatocellular carcinoma, and increased cardiovascular risk (3,4). T2DM increases the risk of NASH twofold (5). Weight loss through lifestyle modification reduces liver fat; weight reductions $10% can induce NASH resolution in most patients (6).Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) promote weight loss and may have efficacy in NASH (7). Tirzepatide, a 39-amino acid synthetic peptide, has agonist activity at both glucose-dependent insulinotropic polypeptide (GIP) and GLP-1

Context Novel dual GIP and GLP-1 receptor agonist (RA) tirzepatide demonstrated substantially greater glucose control and weight loss (WL) compared with selective GLP-1RA dulaglutide. Objective Explore mechanisms of glucose control by tirzepatide. Design Post-hoc analyses of fasting biomarkers and multiple linear regression analysis. Setting 47 sites in 4 countries. Patients or other Participants: 316 subjects with Type 2 diabetes. Interventions Tirzepatide (1, 5, 10, 15 mg), dulaglutide (1.5 mg), placebo. Main Outcome Measures Analyze biomarkers of beta-cell function and insulin resistance (IR) and evaluate WL contributions to IR improvements at 26 weeks. Results HOMA2-B significantly increased with dulaglutide and tirzepatide 5, 10, and 15 mg compared with placebo (p&lt;0.02). Proinsulin/insulin and proinsulin/C-peptide ratios significantly decreased with tirzepatide 10 and 15 mg compared with placebo and dulaglutide (p&lt;0.007). Tirzepatide 10 and 15 mg significantly decreased fasting insulin (p&lt;0.033) and tirzepatide 10 mg significantly decreased HOMA2-IR (p=0.004) compared with placebo and dulaglutide. Markers of improved insulin sensitivity (IS) adiponectin, IGFBP-1, and IGFBP-2 significantly increased by one or more doses of tirzepatide (p&lt;0.05). To determine whether improvements in IR were directly attributable to WL, multiple linear regression analysis with potential confounding variables age, sex, metformin, triglycerides, and HbA1c was conducted. WL significantly (p&lt;0.028) explained only 13% and 21% of improvement in HOMA2-IR with tirzepatide 10 and 15 mg, respectively. Conclusions Tirzepatide improved markers of IS and beta-cell function to a greater extent than dulaglutide. IS effects of tirzepatide were only partly attributable to WL, suggesting dual receptor agonism confers distinct mechanisms of glycemic control.

Aim: To better understand the marked decrease in serum triglycerides observed with tirzepatide in patients with type 2 diabetes, additional lipoprotein-related biomarkers were measured post hoc in available samples from the same study. Materials and Methods: Patients were randomized to receive once-weekly subcutaneous tirzepatide (1, 5, 10 or 15 mg), dulaglutide (1.5 mg) or placebo. Serum lipoprotein profile, apolipoprotein (apo) A-I, B and C-III and preheparin lipoprotein lipase (LPL) were measured at baseline and at 4, 12 and 26 weeks. Lipoprotein particle profile by nuclear magnetic resonance was assessed at baseline and 26 weeks. The lipoprotein insulin resistance (LPIR) score was calculated. Results: At 26 weeks, tirzepatide dose-dependently decreased apoB and apoC-III levels, and increased serum preheparin LPL compared with placebo. Tirzepatide 10 and 15 mg decreased large triglyceride-rich lipoprotein particles (TRLP), small lowdensity lipoprotein particles (LDLP) and LPIR score compared with both placebo and dulaglutide. Treatment with dulaglutide also reduced apoB and apoC-III levels but had no effect on either serum LPL or large TRLP, small LDLP and LPIR score. The number of total LDLP was also decreased with tirzepatide 10 and 15 mg compared with placebo. A greater reduction in apoC-III with tirzepatide was observed in patients with high compared with normal baseline triglycerides. At 26 weeks, change in apoC-III, but not body weight, was the best predictor of changes in triglycerides with tirzepatide, explaining up to 22.9% of their variability.

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Motivation: The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. Although this problem has recently been addressed using penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses on the binary regression coefficient vectors.Results: The resulting method, which we call iMOMLogit, provides improved performance in identifying true models and reducing estimation and prediction error in a number of simulation studies. More importantly, its application to several genomic datasets produces predictions that have high accuracy using far fewer explanatory variables than competing methods. We also describe a novel approach for setting prior hyperparameters by examining the total variation distance between the prior distributions on the regression parameters and the distribution of the maximum likelihood estimator under the null distribution. Finally, we describe a computational algorithm that can be used to implement iMOMLogit in ultrahigh-dimensional settings (p>>n) and provide diagnostics to assess the probability that this algorithm has identified the highest posterior probability model.Availability and implementation: Software to implement this method can be downloaded at: http://www.stat.tamu.edu/∼amir/code.html.Contact: wwang7@mdanderson.org or vjohnson@stat.tamu.eduSupplementary information: Supplementary data are available at Bioinformatics online.