Differential Impact of Hyperglycemia in Critically Ill Patients: Significance in Acute Myocardial Infarction but Not in Sepsis?

Hyperglycemia is a common condition in critically ill patients admitted to an intensive care unit (ICU). These patients represent an inhomogeneous collective and hyperglycemia might need different evaluation depending on the underlying disorder. To elucidate this, we investigated and compared associations of severe hyperglycemia (>200 mg/dL) and mortality in patients admitted to an ICU for acute myocardial infarction (AMI) or sepsis as the two most frequent admission diagnoses. From 2006 to 2009, 2551 patients 69 (58–77) years; 1544 male; 337 patients suffering from type 2 diabetes (T2DM)) who were admitted because of either AMI or sepsis to an ICU in a tertiary care hospital were investigated retrospectively. Follow-up of patients was performed between May 2013 and November 2013. In a Cox regression analysis, maximum glucose concentration at the day of admission was associated with mortality in the overall cohort (HR = 1.006, 95% CI: 1.004–1.009; p < 0.001) and in patients suffering from myocardial infarction (HR = 1.101, 95% CI: 1.075–1.127; p < 0.001) but only in trend in patients admitted to an ICU for sepsis (HR = 1.030, 95% CI: 0.998–1.062; p = 0.07). Severe hyperglycemia was associated with adverse intra-ICU mortality in the overall cohort (23% vs. 13%; p < 0.001) and patients admitted for AMI (15% vs. 5%; p < 0.001) but not for septic patients (39% vs. 40%; p = 0.48). A medical history of type 2 diabetes (n = 337; 13%) was not associated with increased intra-ICU mortality (15% vs. 15%; p = 0.93) but in patients with severe hyperglycemia and/or a known medical history of type 2 diabetes considered in combination, an increased mortality in AMI patients (intra-ICU 5% vs. 13%; p < 0.001) but not in septic patients (intra-ICU 38% vs. 41%; p = 0.53) could be evidenced. The presence of hyperglycemia in critically ill patients has differential impact within the different etiological groups. Hyperglycemia in AMI patients might identify a sicker patient collective suffering from pre-diabetes or undiagnosed diabetes with its’ known adverse consequences, especially in the long-term. Hyperglycemia in sepsis might be considered as adaptive survival mechanism to hypo-perfusion and consecutive lack of glucose in peripheral cells. AMI patients with hyperglycemic derailment during an ICU-stay should be closely followed-up and extensively screened for diabetes to improve patients’ outcome.     

Cocoa Phytochemicals: Recent Advances in Molecular Mechanisms on Health

Recent reports on cocoa are appealing in that a food commonly consumed for pure pleasure might also bring tangible benefits for human health. Cocoa consumption is correlated with reduced health risks of cardiovascular diseases, hypertension, atherosclerosis, and cancer, and the health-promoting effects of cocoa are mediated by cocoa-driven phytochemicals. Cocoa is rich in procyanidins, theobromine, (?)-epicatechin, catechins, and caffeine. Among the phytochemicals present in consumed cocoa, theobromine is most available in human plasma, followed by caffeine, (?)-epicatechin, catechin, and procyanidins. It has been reported that cocoa phytochemicals specifically modulate or interact with specific molecular targets linked to the pathogenesis of chronic human diseases, including cardiovascular diseases, cancer, neurodegenerative diseases, obesity, diabetes, and skin aging. This review summarizes comprehensive recent findings on the beneficial actions of cocoa-driven phytochemicals in molecular mechanisms of human health.     

Nonalcoholic Fatty Liver: A Possible New Target for Type 2 Diabetes Prevention and Treatment

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder worldwide. Several lines of evidence have indicated a pathogenic role of insulin resistance, and a strong association with type 2 diabetes (T2MD) and metabolic syndrome. Importantly, NAFLD appears to enhance the risk for T2MD, as well as worsen glycemic control and cardiovascular disease in diabetic patients. In turn, T2MD may promote NAFLD progression. The opportunity to take into account NAFLD in T2MD prevention and care has stimulated several clinical studies in which antidiabetic drugs, such as metformin, thiazolidinediones, GLP-1 analogues and DPP-4 inhibitors have been evaluated in NAFLD patients. In this review, we provide an overview of preclinical and clinical evidences on the possible efficacy of antidiabetic drugs in NAFLD treatment. Overall, available data suggest that metformin has beneficial effects on body weight reduction and metabolic parameters, with uncertain effects on liver histology, while pioglitazone may improve liver histology. Few data, mostly preclinical, are available on DPP4 inhibitors and GLP-1 analogues. The heterogeneity of these studies and the small number of patients do not allow for firm conclusions about treatment guidelines, and further randomized, controlled studies are needed.     

Interindividual glucose dynamics in different frequency bands for online prediction of subcutaneous glucose concentration in type 1 diabetic subjects

This article investigates the interindividual variability of underlying glucose dynamics and the relative predictive power of exogenous inputs in different frequency bands (FBs) for online subcutaneous glucose prediction for subjects with type 1 diabetes mellitus. Auto‐regressive (AR) models and AR models with exogenous inputs (ARX) are developed based on two groups of ambulatory subjects and two groups of in silico subjects using different combinations of FBs. Some important modeling parameters are studied with respect to their influences on glucose prediction. Four issues are of particular interest and discussed based on the illustration results, suggesting that: (i) In different frequency bands, the underlying glucose dynamics act differently across subjects for online glucose prediction; (ii) A global AR model can be developed for one subject in some FB and then used to make online glucose predictions for other subjects in the same FB, revealing little interindividual variability; (iii) The exogenous inputs have different influences in different FBs for prediction of future subcutaneous glucose concentration; (iv) The exogenous inputs may not excite the glucose signals in some FBs, that is, the inclusion of the exogenous inputs may not result in more accurate models in comparison with standard AR model in some FBs. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4228–4240, 2013     

Structural Basis for the Improved Potency of Peroxisome Proliferator‐Activated Receptor (PPAR) Agonists

The need to develop safer and more effective antidiabetic drugs is essential owing to the growth worldwide of the diabetic population. Targeting the PPAR receptor is one strategy for the treatment of diabetes; the PPAR agonists rosiglitazone and pioglitazone are already on the market. Here we report the identification of a potent PPAR agonist, 15 , whose PPARγ activation was more than 20 times better than that of rosiglitazone. Compound 15 was designed to incorporate an indole head with a carboxylic acid group, and 4‐phenylbenzophenone tail to achieve a PPARγ EC₅₀ of 10 nM . Compound 15 showed the most potent PPARγ agonist activity among the compounds we investigated. To gain molecular insight into the improved potency of 15 , a structural biology study and binding energy calculations were carried out. Superimposition of the X‐ray structures of 15 and agonist 10 revealed that, even though they have the same indole head part, they adopt different conformations. The head part of 15 showed stronger interactions toward PPARγ; this could be due to the presence of the novel tail part 4‐phenylbenzophenone, which could enhance the binding efficiency of 15 to PPARγ.     

Stress, race, and body weight.

Objective: Stress has been identified as a significant factor in health and in racial/ethnic health disparities. A potential mediator in these relationships is body weight. Design: Cross-sectional and longitudinal relationships between stress, race, and body weight were examined in an ethnically diverse sample of overweight and obese women with Type 2 diabetes (n = 217) enrolled in a behavioral weight loss program. Main Outcome Measures: Stress (Perceived Stress Scale) was assessed at baseline only and body weight (body mass index) was assessed at baseline and 6 months. Results: Stress was not related to baseline body weight. With every 1 unit lower scored on the baseline stress measure, women lost 0.10 kg ± .04 more at 6 months (p     

Whole grains and resistant starch rich,reduced‐calorie biscuit diet as a hypoglycaemic,hypolipidaemic and insulin stimulator in streptozotocin‐induced diabetic rats

A fibre‐enriched, reduced‐calorie biscuit [formulated biscuit (FB)] was evaluated for its antidiabetic and antilipidaemic potential in streptozotocin‐induced diabetes in Wistar rats against control biscuit (CB) having high calorie and low fibre content. Animals were allocated into five groups: a control group fed with synthetic diet, two diabetes‐induced groups (CBD and FBD) and two nondiabetic groups (CBND and FBND). CBD and CBND were fed with synthetic diet + CB while FBD and FBND were fed with synthetic diet + FB. After 6 weeks of feeding, the change in weight of nondiabetic group fed with FB (+16.20%) was nonsignificantly lower compared to control (+33.01%) and CB‐fed (+34.55%) group. Nonsignificant loss in body weight was observed among diabetic group fed with FB (?11.76%) or CB (?36.88%). Feeding of FB led to a significant decrease in fasting and postprandial blood glucose levels, total cholesterol, triglycerides, LDL‐C, VLDL‐C and atherosclerotic index and increase the HDL‐C. Histological examination showed that feeding of FB was able to partially recover the destroyed β‐cell in STZ‐induced diabetic rats.     

医学领域中的新型生物传感器

生物传感器作为一项新技术已广泛地应用于人体生化指标和各种病原体的检测，糖尿病和缺血一再灌注损伤病人的监测以及空间生命科学的在线监视等医学领域，它以准确，灵敏、高效，快速，设备简便和成本低等优点，逐渐成为近年来医学诊断监测技术的热点之一，对近年来国内外应用中医学领域中的新型生物传感器进行综述。     

Inductive Learning with a Computational Network

This paper introduces a computational network which combines heterogeneous rule-extraction algorithms for intelligent data analysis. Combining induction programs may alleviate the possible negative effects of data set representation and individual program's influences, such as inductive bias. The application of the computational network to a diabetes data set shows that, when combining the various programs, an increase in rule set accuracy and comprehensibility are obtained.