Obesity is highly correlated with insulin resistance and the development of type 2 diabetes. Insulin resistance will result in a decrease in insulin's ability to stimulate glucose uptake into peripheral tissue and will suppress glucose production by the liver. However, the development of peripheral and hepatic insulin resistance relative to one another in the context of obesity-associated insulin resistance is not well understood. To examine this phenomena, we used the moderate fat-fed dog model, which has been shown to develop both subcutaneous and visceral adiposity and severe insulin resistance. Six normal dogs were fed an isocaloric diet with a modest increase in fat content for 12 weeks, and they were assessed at weeks 0, 6, and 12 for changes in insulin sensitivity and glucose turnover. By week 12 of the diet, there was a more than twofold increase in trunk adiposity as assessed by magnetic resonance imaging because of an accumulation in both subcutaneous and visceral fat depots with very little change in body weight. Fasting plasma insulin had increased by week 6 (150% of week 0) and remained increased up to week 12 of the study (170% of week 0). Surprisingly, there appeared to be no change in the rates of insulin-stimulated glucose uptake as measured by euglycemic-hyperinsulinemic clamps throughout the course of fat feeding. However, there was an increase in steady-state plasma insulin levels at weeks 6 and 12, indicating a moderate degree of peripheral insulin resistance. In contrast to the moderate defect seen in the periphery, there was a marked impairment in insulin's ability to suppress endogenous glucose production during the clamp such that by week 12 of the study, there was a complete inability of insulin to suppress glucose production. Our results indicate that a diet enriched with a moderate amount of fat results in the development of both subcutaneous and visceral adiposity, hyperinsulinemia, and a modest degree of peripheral insulin resistance. However, there is a complete inability of insulin to suppress hepatic glucose production during the clamp, suggesting that insulin resistance of the liver may be the primary defect in the development of insulin resistance associated with obesity.
Insulin resistance is associated with a plethora of chronic illnesses, including Type 2 diabetes, dyslipidemia, clotting dysfunction, and colon cancer. The relationship between obesity and insulin resistance is well established, and an increase in obesity in Western countries is implicated in increased incidence of diabetes and other diseases. Central, or visceral, adiposity has been particularly associated with insulin resistance; however, the mechanisms responsible for this association are unclear. Our laboratory has been studying the physiological mechanisms relating visceral adiposity and insulin resistance. Moderate fat feeding of the dog yields a model reminiscent of the metabolic syndrome, including visceral adiposity, hyperinsulinemia, and insulin resistance. We propose that insulin resistance of the liver derives from a relative increase in the delivery of free fatty acids (FFA) from the omental fat depot to the liver (via the portal vein). Increased delivery results from 1) more stored lipids in omental depot, 2) severe insulin resistance of the central fat depot, and 3) possible regulation of visceral lipolysis by the central nervous system. The significance of portal FFA delivery results from the importance of FFA in the control of liver glucose production. Insulin regulates liver glucose output primarily via control of adipocyte lipolysis. Thus, because FFA regulate the liver, it is expected that visceral adiposity will enhance delivery of FFA to the liver and make the liver relatively insulin resistant. It is of interest how the intact organism compensates for insulin resistance secondary to visceral fat deposition. While part of the compensation is enhanced B-cell sensitivity to glucose, an equally important component is reduced liver insulin clearance, which allows for a greater fraction of B-cell insulin secretion to bypass liver degradation, to enter the systemic circulation, and to result in hyperinsulinemic compensation. The signal(s) resulting in B-cell up-regulation and reduced liver insulin clearance with visceral adiposity is (are) unknown, but it appears that the glucagon-like peptide (GLP-1) hormone plays an important role. The integrated response of the organism to central adiposity is complex, involving several organs and tissue beds. An investigation into the integrated response may help to explain the features of the metabolic syndrome.
The absolute concentration of albumin was measured in the interstitial fluid of subcutaneous adipose tissue and skeletal muscle in six healthy volunteers by combining the method of open-flow microperfusion and the no-net-flux calibration technique. By use of open-flow microperfusion, four macroscopically perforated double lumen catheters were inserted into the tissue regions of interest and constantly perfused. Across the macroscopic perforations of the catheters interstitial fluid was partially recovered in the perfusion fluid. Catheters were perfused with five solutions, each containing different concentrations of albumin. Absolute interstitial albumin concentrations were calculated by applying linear regression analysis to perfusate vs. sampled albumin concentration (no-net-flux calibration technique). Interstitial albumin concentrations were significantly lower (P < 0.0001) in adipose tissue (7.36 g/l; r = 0.99, P < 0.0003; range: 4.3-10.7 g/l) and in skeletal muscle (13.25 g/l; r = 0.99, P < 0.0012; range: 9.7 to 15.7 g/l) compared with the serum concentration (48.9 +/- 0.7 g/l, mean +/- SE, n = 6; range: 46.4-50.4 g/l). Furthermore, interstitial albumin concentrations were significantly higher in skeletal muscle compared with adipose tissue (P < 0.01). The study indicates that open-flow microperfusion allows stable sampling of macromolecules from the interstitial space of peripheral tissue compartments. Moreover, the present data report for the first time in healthy humans in vivo the true albumin concentrations of interstitial fluid of adipose tissue and skeletal muscle.
OBJECTIVE -To evaluate a fully automated algorithm for the establishment of tight glycemic control in critically ill patients and to compare the results with different routine glucose management protocols of three intensive care units (ICUs) across Europe (Graz, Prague, and London).RESEARCH DESIGN AND METHODS -Sixty patients undergoing cardiac surgery (age 67 Ϯ 9 years, BMI 27.7 Ϯ 4.9 kg/m 2 , 17 women) with postsurgery blood glucose levels Ͼ120 mg/dl (6.7 mmol/l) were investigated in three different ICUs (20 per center). Patients were randomized to either blood glucose management (target range 80 -110 mg/dl [4.4 -6.1 mmol/l]) by the fully automated model predictive control (MPC) algorithm (n ϭ 30, 10 per center) or implemented routine glucose management protocols (n ϭ 30, 10 per center). In all patients, arterial glucose was measured hourly to describe the glucose profile until the end of the ICU stay but for a maximum period of 48 h. CONCLUSIONS -The data suggest that the MPC algorithm is safe and effective in controlling glycemia in critically ill postsurgery patients. RESULTS Diabetes Care 29:271-276, 2006E pidemiological studies have revealed a significant relationship between impaired glycemic control and poor outcome in patients with acute cardiovascular events (1-3), postoperative wound infections (4,5), and trauma (6). Patients with diabetes are affected, but patients with stress hyperglycemia with no previous diagnosis of diabetes also have a poor prognosis (1,2,7,8). Critical illness and trauma induce counterregulatory hormone release and alterations in carbohydrate metabolism such as enhanced hepatic gluconeogenesis, insulin resistance, and relative insulin deficiency (9,10).A growing body of evidence indicates that treatment of hyperglycemia improves clinical outcome (11). In a prospective randomized trial in Leuven, postoperative patients were treated with an intensive insulin protocol (12). Strict glycemic control (80 -110 mg/dl) resulted in a reduction of in-hospital mortality and a decrease in organ system dysfunction compared with moderate hyperglycemia (180 -200 mg/dl). In another study performed on a mixed medical-surgical population, the implementation of an intensive glucose management protocol led to decreased mortality, morbidity, and length of intensive care unit (ICU) stay of critically ill adult patients (13).Based on this clinical evidence, efforts have to be made to maintain strict glycemic control in critically ill patients. To achieve this goal, the implementation of complex intensive insulin infusion protocols based on frequent bedside glucose monitoring is required. Numerous guidelines have been developed and tested to implement tight glycemic control in ICUs (13-18). However, most of these guidelines still require user interventions or intuitive decisions of ICU staff.The development of a closed-loop control system that automatically regulates the dose of insulin based on glucose measurements could permit tight glycemic control without increasing the work-
OBJECTIVEReliability of continuous glucose monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events.RESEARCH DESIGN AND METHODSThe smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature, and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes.RESULTSThe denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.CONCLUSIONSWe have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.
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