Effect of Once-Weekly Exenatide on Clinical Outcomes According to Baseline Risk in Patients With Type 2 Diabetes Mellitus: Insights From the EXSCEL Trial.


Background In the EXSCEL (Exenatide Study of Cardiovascular Event Lowering), exenatide once-weekly resulted in a nonsignificant reduction in major adverse cardiovascular events ( MACEs ) and a nominal 14% reduction in all-cause mortality in 14 752 patients with type 2 diabetes mellitus (T2 DM ) with and without cardiovascular disease. Whether patients at increased risk for events experienced a comparatively greater treatment benefit with exenatide is unknown. Methods and Results In the EXSCEL population, we created risk scores for MACEs and all-cause mortality using step-wise selection of baseline characteristics. A risk score was calculated for each patient, and a time-to-event model for each end point was developed including the risk score, treatment assignment, and risk-treatment interaction. Interaction P values evaluating for a differential treatment effect by baseline risk were reported. Over a median follow-up of 3.2 years (interquartile range, 2.2, 4.4), 1091 (7.4%) patients died and 1744 (11.8%) experienced a MACE . Independent predictors of MACEs and all-cause mortality included age, sex, comorbidities (eg, previous cardiovascular event), body mass index, blood pressure, hemoglobin A1c, and estimated glomerular filtration rate. The all-cause mortality and MACE risk models had modest discrimination with optimism-corrected c-indices of 0.73 and 0.71, respectively. No interaction was observed between treatment effect and risk profile for either end point (both interactions, P>0.1). Conclusions Baseline characteristics (eg, age, previous cardiovascular events) and routine laboratory values (eg, hemoglobin A1c, estimated glomerular filtration rate) provided modest prognostic value for mortality and MACEs in a broad population of patients with type 2 diabetes mellitus. Exenatide's effects on mortality and MACEs were consistent across the spectrum of baseline risk. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT 01144338.





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Publication Info

Mentz, Robert J, M Angelyn Bethel, Peter Merrill, Yuliya Lokhnygina, John B Buse, Juliana C Chan, João S Felício, Shaun G Goodman, et al. (2018). Effect of Once-Weekly Exenatide on Clinical Outcomes According to Baseline Risk in Patients With Type 2 Diabetes Mellitus: Insights From the EXSCEL Trial. Journal of the American Heart Association, 7(19). p. e009304. 10.1161/JAHA.118.009304 Retrieved from https://hdl.handle.net/10161/19763.

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Robert John Mentz

Associate Professor of Medicine

I am a cardiologist with a clinical and research interest in heart failure (going from Failure to Function), including advanced therapies such as cardiac transplantation and mechanical assist devices or “heart pumps."

I serve our group as Chief of the Heart Failure Section.

I became a heart failure cardiologist in order to help patients manage their chronic disease over many months and years. I consider myself strongly committed to compassionate patient care with a focus on quality of life and patient preference.

I am the Editor in Chief of the Journal of Cardiac Failure - The official journal of the Heart Failure Society of America.

My research interests are focused on treating co-morbid diseases in heart failure patients and improving outcomes across the cardiovascular spectrum through clinical trials and outcomes research. Below, you will find my specific research interests:

  •     Cardiometabolic disease
  •     Co-morbidity characterization (diabetes, sleep apnea, renal failure) in heart failure
  •     Phenotypic characterization and risk prognostication of patients with heart failure
  •     Role of surrogate and nonfatal endpoints in clinical heart failure trials
  •     Biomarkers in heart failure
  •     Novel pharmacological and non-pharmacological approaches to heart failure
  •     Improving site-based heart failure research

Yuliya Vladimirovna Lokhnygina

Associate Professor of Biostatistics & Bioinformatics

Statistical methods in clinical trials, survival analysis, adaptive designs, adaptive treatment strategies, causal inference in observational studies, semiparametric inference

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