OBJECTIVEOxyntomodulin (OXM) is a glucagon-like peptide 1 (GLP-1) receptor (GLP1R)/glucagon receptor (GCGR) dual agonist peptide that reduces body weight in obese subjects through increased energy expenditure and decreased energy intake. The metabolic effects of OXM have been attributed primarily to GLP1R agonism. We examined whether a long acting GLP1R/GCGR dual agonist peptide exerts metabolic effects in diet-induced obese mice that are distinct from those obtained with a GLP1R-selective agonist.RESEARCH DESIGN AND METHODSWe developed a protease-resistant dual GLP1R/GCGR agonist, DualAG, and a corresponding GLP1R-selective agonist, GLPAG, matched for GLP1R agonist potency and pharmacokinetics. The metabolic effects of these two peptides with respect to weight loss, caloric reduction, glucose control, and lipid lowering, were compared upon chronic dosing in diet-induced obese (DIO) mice. Acute studies in DIO mice revealed metabolic pathways that were modulated independent of weight loss. Studies in Glp1r−/− and Gcgr−/− mice enabled delineation of the contribution of GLP1R versus GCGR activation to the pharmacology of DualAG.RESULTSPeptide DualAG exhibits superior weight loss, lipid-lowering activity, and antihyperglycemic efficacy comparable to GLPAG. Improvements in plasma metabolic parameters including insulin, leptin, and adiponectin were more pronounced upon chronic treatment with DualAG than with GLPAG. Dual receptor agonism also increased fatty acid oxidation and reduced hepatic steatosis in DIO mice. The antiobesity effects of DualAG require activation of both GLP1R and GCGR.CONCLUSIONSSustained GLP1R/GCGR dual agonism reverses obesity in DIO mice and is a novel therapeutic approach to the treatment of obesity.

When we encounter an unexpected critical health problem, a hospital’s emergency department (ED) becomes our vital medical resource. Improving an ED’s timeliness of care, quality of care, and operational efficiency while reducing avoidable readmissions, is fraught with difficulties, which arise from complexity and uncertainty. In this paper, we describe an ED decision support system that couples machine learning, simulation, and optimization to address these improvement goals. The system allows healthcare administrators to globally optimize workflow, taking into account the uncertainties of incoming patient injuries and diseases and their associated care, thereby significantly reducing patient length of stay. This is achieved without changing physical layout, focusing instead on process consolidation, operations tracking, and staffing. First implemented at Grady Memorial Hospital in Atlanta, Georgia, the system helped reduce length of stay at Grady by roughly 33 percent. By repurposing existing resources, the hospital established a clinical decision unit that resulted in a 28 percent reduction in ED readmissions. Insights gained from the implementation also led to an investment in a walk-in center that eliminated more than 32 percent of the nonurgent-care cases from the ED. As a result of these improvements, the hospital enhanced its financial standing and achieved its target goal of an average ED length of stay of close to seven hours. ED and trauma efficiencies improved throughput by over 16 percent and reduced the number of patients who left without being seen by more than 30 percent. The annual revenue realized plus savings generated are approximately $190 million, a large amount relative to the hospital’s $1.5 billion annual economic impact. The underlying model, which we generalized, has been tested and implemented successfully at 10 other EDs and in other hospital units. The system offers significant advantages in that it permits a comprehensive analysis of the entire patient flow from registration to discharge, enables a decision maker to understand the complexities and interdependencies of individual steps in the process sequence, and ultimately allows the users to perform system optimization.

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