OBJECTIVE: To evaluate whether the homeostasis model assessment (HOMA) is a reliable surrogate measure of in vivo insulin sensitivity in humans. RESEARCH DESIGN AND METHODS: In the present study, we compared insulin sensitivity as assessed by a 4-h euglycemic (approximately 5 mmol/l) hyperinsulinemic (approximately 300 pmol/l) clamp with HOMA in 115 subjects with various degrees of glucose tolerance and insulin sensitivity. RESULTS: We found a strong correlation between clamp-measured total glucose disposal and HOMA-estimated insulin sensitivity (r = -0.820, P

https://care.diabetesjournals.org/content/diacare/23/1/57.full.pdf

Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity.

An analyte monitor includes a sensor, a sensor control unit, and a display unit. The sensor has, for example, a substrate, a recessed channel formed in the substrate, and conductive material disposed in the recessed channel to form a working electrode. The sensor control unit typically has a housing adapted for placement on skin and is adapted to receive a portion of an electrochemical sensor. The sensor control unit also includes two or more conductive contacts disposed on the housing and configured for coupling to two or more contact pads on the sensor. A transmitter is disposed in the housing and coupled to the plurality of conductive contacts for transmitting data obtained using the sensor. The display unit has a receiver for receiving data transmitted by the transmitter of the sensor control unit and a display coupled to the receiver for displaying an indication of a level of an analyte. The analyte monitor may also be part of a drug delivery system to alter the level of the analyte based on the data obtained using the sensor.

Analyte Monitoring Device And Methods Of Use

The PPARs: from orphan receptors to drug discovery.

Evidence is reviewed that free fatty acids (FFAs) are one important link between obesity and insulin resistance and NIDDM. First, plasma FFA levels are elevated in most obese subjects. Second, physiological elevations in plasma FFA concentrations inhibit insulin stimulated peripheral glucose uptake in a dose-dependent manner in normal controls and in patients with NIDDM. Two possible mechanisms are identified: 1) a fat-related inhibition of glucose transport or phosphorylation, which appears after 3-4 h of fat infusion, and 2) a decrease in muscle glycogen synthase activity, which appears after 4-6 h of fat infusion. Third, FFAs stimulate insulin secretion in nondiabetic individuals. Some of this insulin is transmitted in the peripheral circulation and is able to compensate for FFA-mediated peripheral insulin resistance. FFA-mediated portal hyperinsulinemia counteracts the stimulation of FFAs on hepatic glucose production (HGP) and thus prevents hepatic glucose overproduction. We speculate that, in obese individuals who are genetically predisposed to develop NIDDM, FFAs will eventually fail to promote insulin secretion. The stimulatory effect of FFAs on HGP would then become unchecked, resulting in hyperglycemia. Hence, continuously elevated levels of plasma FFAs may play a key role in the pathogenesis of NIDDM in predisposed individuals by impairing peripheral glucose utilization and by promoting hepatic glucose overproduction.

Role of Fatty Acids in the Pathogenesis of Insulin Resistance and NIDDM

Type 1 diabetes: new perspectives on disease pathogenesis and treatment

A closed loop infusion system controls the rate that fluid is infused into the body of a user. The closed loop infusion system includes a sensor system, a controller, and a delivery system. The sensor system includes a sensor for monitoring a condition of the user. The sensor produces a sensor signal, which is representative of the condition of the user. The sensor signal is used to generate a controller input. The controller uses the controller input to generate commands to operate the delivery system. The delivery system infuses a liquid into the user at a rate dictated by the commands from the controller. Preferably, the sensor system monitors the glucose concentration in the body of the user, and the liquid infused by the delivery system into the body of the user includes insulin. The sensor system uses the sensor signal to generate a message that is sent to the delivery system. The message includes the information used to generate the controller input. The sensor may be a subcutaneous sensor in contact with interstitial fluid. Also, two or more sensors may be used by the sensor system.

Closed loop system for controlling insulin infusion

Apparatuses and methods for medical monitoring physiological characteristics values such as blood glucose levels for the treatment of diabetes, are presented. The apparatuses and methods provide dynamic glucose monitoring functions that perform predictive analysis to anticipate harmful conditions, such as glucose crash and hyperglycemic incidents for a patient. The dynamic functions can also be used to maximize athletic performance and warn of inadequate nocturnal basal rate. Other aspects include advanced alarm and reminder functions, as well as advanced data presentation tools to further facilitate convenient and efficient management of various physiological conditions.

System for monitoring physiological characteristics

An automated closed-loop insulin delivery system based on subcutaneous glucose sensing and subcutaneous insulin delivery was evaluated in 10 subjects with type 1 diabetes (2 men, 8 women, mean [±SD] age 43.4 ± 11.4 years, duration of diabetes 18.2 ± 13.5 years). Closed-loop control was assessed over ∼30 h and compared with open-loop control assessed over 3 days. Closed-loop insulin delivery was calculated using a model of the β-cell’s multiphasic insulin response to glucose. Plasma glucose was 160 ± 66 mg/dl at the start of closed loop and was thereafter reduced to 71 ± 19 by 1:00 p.m. (preprandial lunch). Fasting glucose the subsequent morning on closed loop was not different from target (124 ± 25 vs. 120 mg/dl, respectively; P > 0.05). Mean glucose levels were not different between the open and closed loop (133 ± 63 vs. 133 ± 52 mg/dl, respectively; P > 0.65). However, glucose was within the range 70–180 mg/dl 75% of the time under closed loop versus 63% for open loop. Incidence of biochemical hypoglycemia (blood glucose

/pdf/feasibility-of-automating-insulin-delivery-for-the-treatment-2o93gldvjn.pdf

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

The present study investigated the relationship between blood and subcutaneous interstitial fluid (ISF) glucose by employing an amperometric glucose sensor specifically developed for 3-day continuo...

Kerstin Rebrin

Papers

Closed loop system for controlling insulin infusion

System for monitoring physiological characteristics

Feasibility of Automating Insulin Delivery for the Treatment of Type 1 Diabetes

Subcutaneous glucose predicts plasma glucose independent of insulin: implications for continuous monitoring

Closed-loop insulin delivery—the path to physiological glucose control