Predictors of ≥15% Weight Reduction and Associated Changes in Cardiometabolic Risk Factors With Tirzepatide in Adults With Type 2 Diabetes in SURPASS 1-4.

OBJECTIVE: To identify predictors of body weight (BW) reduction of ≥15% with tirzepatide treatment and to describe associated clinical parameters of participants with type 2 diabetes (T2D) who achieved different categorical measures of BW reduction (<5%, ≥5 to <10%, ≥10 to <15%, and ≥15%) across four studies from the phase 3 SURPASS clinical trial program for T2D. RESEARCH DESIGN AND METHODS: The multivariate model for predictor of a BW reduction of ≥15% included age, sex, race, BW, HbA1c, tirzepatide dose and baseline metformin use, fasting serum glucose, and non-HDL cholesterol. Baseline characteristics and change from baseline to week 40/42 for efficacy parameters were described and analyzed in treatment-adherent participants (≥75% doses administered and on treatment at week 40/42) receiving once weekly tirzepatide (5 mg, 10 mg, or 15 mg) (N = 3,188). RESULTS: Factors significantly associated with achieving a BW reduction of ≥15% with tirzepatide were higher tirzepatide doses, female sex, White or Asian race, younger age, metformin background therapy, and lower HbA1c, fasting serum glucose, and non-HDL cholesterol at baseline. With higher categorical BW reduction, there were greater reductions in HbA1c, triglycerides, ALT, waist circumference, and blood pressure. CONCLUSIONS: Baseline factors associated with a higher likelihood of achieving a BW reduction of ≥15% with tirzepatide were higher tirzepatide doses, female sex, White or Asian race, younger age, metformin background therapy, better glycemic status, and lower non-HDL cholesterol. With greater BW reduction, participants with T2D achieved larger improvements in glycemia and cardiometabolic risk parameters. These findings help inform which people with T2D are most likely to achieve greater BW reduction with improved cardiometabolic risk factors with tirzepatide.

A Decomposition Framework for Image Denoising Algorithms.

In this paper, we consider an image decomposition model that provides a novel framework for image denoising. The model computes the components of the image to be processed in a moving frame that encodes its local geometry (directions of gradients and level lines). Then, the strategy we develop is to denoise the components of the image in the moving frame in order to preserve its local geometry, which would have been more affected if processing the image directly. Experiments on a whole image database tested with several denoising methods show that this framework can provide better results than denoising the image directly, both in terms of Peak signal-to-noise ratio and Structural similarity index metrics.