Grape powder attenuates the negative effects of GLP-1 receptor antagonism by exendin-3 (9-39) in a normoglycemic mouse model.

Prediabetes is a condition affecting 35% of US adults and about 50% of US adults age 65+. Foods rich in polyphenols, including flavanols and other flavonoids, have been studied for their putative beneficial effects on many different health conditions including type 2 diabetes mellitus and prediabetes. Studies have shown that some flavanols increase glucagon-like peptide 1 (GLP-1) secretion. GLP-1 is a feeding hormone that increases insulin secretion after carbohydrate consumption, and increased GLP-1 secretion may be responsible for some of the beneficial effects on glycemic control after flavanol consumption. The present study explored the effects of grape powder consumption on metrics of glycemic health in normoglycemic and prediabetic C57BL/6J mice; additionally, the mechanism of action of grape powder polyphenols was investigated. Grape powder significantly reduced (p < 0.01) blood glucose levels following oral glucose gavage after GLP-1 receptor antagonism by exendin-3 (9-39) compared to sugar-matched control, indicating that it was able to attenuate the hyperglycemic effects of GLP-1 receptor antagonism. Grape powder was employed in acute (1.6 g grape powder per kg bodyweight) and long-term high fat diet (grape powder incorporated into treatment diets at 5% w/w) feeding studies in normoglycemic and prediabetic (diet-induced obesity) mice; grape powder did not impove glycemic control in these studies versus sugar-matched control. The mechanisms by which grape powder ameliorates the deleterious effects of GLP-1 receptor antagonism warrant further study.

Computerized identification of patients at high risk for hospital-acquired infection.

Surveillance for hospital-acquired infections is required in U.S. hospitals, and statistical methods have been used to predict the risk of infection. We used the HELP (Health Evaluation through Logical Processing) Hospital Information System at LDS Hospital to develop computerized methods to identify and verify hospital-acquired infections. The criteria for hospital-acquired infection are standardized and based on the guidelines of the Study of the Efficacy of Nosocomial Infection Control and the Centers for Disease Control. The computer algorithms are automatically activated when key items of information, such as microbiology results, are reported. Computer surveillance identified more hospital-acquired infections than did traditional methods and has replaced manual surveillance in our 520-bed hospital. Data on verified hospital-acquired infections are electronically transferred to a microcomputer to facilitate outbreak investigation and the generation of reports on infection rates. Recently, we used the HELP system to employ statistical methods to automatically identify high-risk patients. Patient data from more than 6000 patients were used to develop a high-risk equation. Stepwise logistic regression identified 10 risk factors for nosocomial infection. The HELP system now uses this logistic-regression equation to monitor and determine the risk status for all hospitalized patients each day. The computer notifies infection control practitioners each morning of patients who are newly classified as being at high risk. Of 605 hospital-acquired infections during a 6-month period, 472 (78%) occurred in high-risk patients, and 380 (63%) were predicted before the onset of infection. Computerized regression equations to identify patients at risk of having hospital-acquired infections can help focus prevention efforts.