Taken together, these data indicate that endogenous GLP-1 system is activated during sepsis. Patients with T2D display an enhanced and prolonged activation as compared to nondiabetic patients. Extreme early increased GLP-1 levels during sepsis indicate poor prognosis.
Background: Culture-positive gram-negative sepsis induces greater magnitude of early innate immunity / inflammatory response compared with culture-negative sepsis. We previously demonstrated increased activation of antiinflammatory Glucagon Like Peptide-1 (GLP-1) hormone in initial phase of sepsis more pronounced in diabetes patients. However, whether GLP-1 system is hyperactivated during the early innate immune response to gram-negative sepsis and modulated by diabetes remains unknown. Objectives: Total and active GLP-1, soluble Dipeptidyl peptidase 4 (sDPP-4) enzyme, and innate immunity markers presepsin (sCD14) and procalcitonin (PCT) in plasma were determined by ELISA on admission and after 2 to 4 days in 37 adult patients with and without type 2 diabetes and gram-negative or culture-negative sepsis of different severity. Results: Severe but not non-severe sepsis was associated with markedly increased GLP-1 system response, which correlated with PCT and the organ dysfunction marker lactate. Culture-positive gram-negative bacteria but not culture-negative sepsis induced hyper-activation of GLP-1 system, which correlated with increased innate immune markers sCD14, PCT, and lactate. GLP-1 inhibitory enzyme sDPP-4 was down regulated by sepsis and correlated negatively with sCD14 in gram-negative sepsis. Diabetic patients demonstrated increased GLP-1 response but significantly weaker innate immune response to severe and gram-negative sepsis. Conclusions: Early stage of gram-negative sepsis is characterized by endogenous GLP-1 system hyperactivity associated with over activation of innate immune response and organ dysfunction, which are modulated by diabetes. Total GLP-1 may be novel marker for rapid diagnosis of gram-negative sepsis and its severity.
Early detection of left ventricular systolic dysfunction (LVSD) may prompt early care and improve outcomes for asymptomatic patients. Standard 12-lead ECG may be used to predict LVSD. We aimed to compare the performance of Machine Learning Algorithms (MLA) and physicians in predicting LVSD from a standard 12-lead ECG. By utilizing a dataset of 13,820 pairs of ECGs and echocardiography, a deep residual convolutional neural network was trained for predicting LVSD (ejection fraction (EF) < 50%) from ECG. The ECGs of the test set (n = 850) were assessed for LVSD by the MLA and six physicians. The performance was compared using sensitivity, specificity, and C-statistics. The interobserver agreement between the physicians for the prediction of LVSD was moderate (κ = 0.50), with average sensitivity and specificity of 70%. The C-statistic of the MLA was 0.85. Repeating this analysis with LVSD defined as EF < 35% resulted in an improvement in physicians’ average sensitivity to 84% but their specificity decreased to 57%. The MLA C-statistic was 0.88 with this threshold. We conclude that although MLA outperformed physicians in predicting LVSD from standard ECG, prior to robust implementation of MLA in ECG machines, physicians should be encouraged to use this approach as a simple and readily available aid for LVSD screening.
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