To determine the relationship between insulin sensitivity and beta-cell function, we quantified the insulin sensitivity index using the minimal model in 93 relatively young, apparently healthy human subjects of varying degrees of obesity (55 male, 38 female; 18-44 yr of age; body mass index 19.5-52.2 kg/m2) and with fasting glucose levels < 6.4 mM. SI was compared with measures of body adiposity and beta-cell function. Although lean individuals showed a wide range of SI, body mass index and SI were related in a curvilinear manner (P < 0.0001) so that on average, an increase in body mass index was associated generally with a lower value for SI. The relationship between the SI and the beta-cell measures was more clearly curvilinear and reciprocal for fasting insulin (P < 0.0001), first-phase insulin response (AIRglucose; P < 0.0001), glucose potentiation slope (n = 56; P < 0.005), and beta-cell secretory capacity (AIRmax; n = 43; P < 0.0001). The curvilinear relationship between SI and the beta-cell measures could not be distinguished from a hyperbola, i.e., SI x beta-cell function = constant. This hyperbolic relationship described the data significantly better than a linear function (P < 0.05). The nature of this relationship is consistent with a regulated feedback loop control system such that for any difference in SI, a proportionate reciprocal difference occurs in insulin levels and responses in subjects with similar carbohydrate tolerance. We conclude that in human subjects with normal glucose tolerance and varying degrees of obesity, beta-cell function varies quantitatively with differences in insulin sensitivity.(ABSTRACT TRUNCATED AT 250 WORDS)

Abstract. In order to assess whether patients with noninsulin-dependent diabetes mellitus (NIDDM) possess normal insulin secretory capacity, maximal B cell responsiveness to the potentiating effects of glucose was estimated in eight untreated patients with NIDDM and in eight nondiabetic controls. The acute insulin response to 5 g intravenous arginine was measured at five matched plasma glucose levels that ranged from 100-615 mg/dl. The upper asymptote approached by acute insulin responses (AIRma.) and the plasma glucose concentration at half-maximal responsiveness (PG50) were estimated using nonlinear regression to fit a modification of the Michaelis-Menten equation. In addition, glucagon responses to arginine were measured at these same glucose levels to compare maximal A cell suppression by hyperglycemia in diabetics and controls.Insulin responses to arginine were lower in diabetics than in controls at all matched glucose levels (P < 0.001 at all levels). In addition, estimated AIRma. was much lower in diabetics than in controls (83±21 vs. 450±93 MU/ml, P < 0.01). In contrast, PG50 was similar in diabetics and controls (234±28 vs. 197±20 mg/dl, P equals NS) and insulin responses in both groups approached or attained maxima at a glucose level of -460 mg/dl. Acute glucagon responses to arginine in patients with NIDDM were significantly higher than responses in controls at all glucose levels. In addition, although glucagon responses in control subjects reached a minimum at a glucose level of -460 mg/dl, responses

The biological response to implanted biomaterials in mammals is a complex series of events that involves many biochemical pathways. Shortly after implantation, fibrinogen and other proteins bind to the device surface, a process known as biofouling. Macrophages then bind to receptors on the proteins, join into multinucleated giant cells, and release transforming growth factor beta and other inflammatory cytokines. In response to these signals, quiescent fibroblasts are transformed into myofibroblasts, which synthesize procollagen via activation of Smad mediators. The procollagen becomes crosslinked after secretion into the extracellular space. Mature crosslinked collagen and other extracellular matrix proteins gradually contribute to formation of a hypocellular dense fibrous capsule that becomes impermeable or hypopermeable to many compounds. Porous substrates and angiogenic growth factors can stimulate formation of microvessels, which to some extent can maintain analyte delivery to implanted sensors. However, stimulation by vascular endothelial growth factor alone may lead to formation of leaky, thin-walled, immature vessels. Other growth factors are most probably needed to act upon these immature structures to create more robust vessels.During implantation of foreign bodies, the foreign-body response is difficult to overcome, and thousands of biomaterials have been tested. Biomimicry (i.e., creating membranes whose chemical structure mimics natural cellular compounds) may diminish the response, but as of this writing, it has not been possible to create a stealth material that circumvents the ability of the mammalian surveillance systems to distinguish foreign from self.

In this study, we found that the ratio of proinsulin to total immunoreactive insulin was much higher in 22 patients with Type 2 (non-insulin-dependent) diabetes mellitus than in 28 non-diabetic control subjects of similar age and adiposity (32 +/- 3 vs 15 +/- 1%, p less than 0.001). In addition, the arginine-induced acute proinsulin response to total immunoreactive insulin response ratio was greater in diabetic patients (n = 10) than in control subjects (n = 9) (8 +/- 2 vs 2 +/- 0.5%, p = 0.009), suggesting that increased islet secretion per se accounted for the increased ratio of proinsulin to immunoreactive insulin. One explanation for these findings is that increased demand for insulin in the presence of islet dysfunction leads to a greater proportion of proinsulin secreted from the B cell. We tested this hypothesis by comparing proinsulin secretion before and during dexamethasone-induced insulin resistance in diabetic patients and control subjects. Dexamethasone treatment (6 mg/day for 3 days) raised the proinsulin to immunoreactive insulin ratio in control subjects from 13 +/- 2 to 21 +/- 2% (p less than 0.0001) and in diabetic patients from 29 +/- 5 to 52 +/- 7% (p less than 0.001). Dexamethasone also raised the ratio of the acute proinsulin response to the acute immunoreactive insulin response in control subjects from 2 +/- 0.5 to 5 +/- 2% (p = 0.01) and in diabetic patients from 8 +/- 2 to 14 +/- 4% (p = NS), suggesting that the dexamethasone-induced increment in the basal ratio of proinsulin to immunoreactive insulin was also due to increased secretion.(ABSTRACT TRUNCATED AT 250 WORDS)

OBJECTIVETo minimize hypoglycemia in subjects with type 1 diabetes by automated glucagon delivery in a closed-loop insulin delivery system.RESEARCH DESIGN AND METHODSAdult subjects with type 1 diabetes underwent one closed-loop study with insulin plus placebo and one study with insulin plus glucagon, given at times of impending hypoglycemia. Seven subjects received glucagon using high-gain parameters, and six subjects received glucagon in a more prolonged manner using low-gain parameters. Blood glucose levels were measured every 10 min and insulin and glucagon infusions were adjusted every 5 min. All subjects received a portion of their usual premeal insulin after meal announcement.RESULTSAutomated glucagon plus insulin delivery, compared with placebo plus insulin, significantly reduced time spent in the hypoglycemic range (15 ± 6 vs. 40 ± 10 min/day, P = 0.04). Compared with placebo, high-gain glucagon delivery reduced the frequency of hypoglycemic events (1.0 ± 0.6 vs. 2.1 ± 0.6 events/day, P = 0.01) and the need for carbohydrate treatment (1.4 ± 0.8 vs. 4.0 ± 1.4 treatments/day, P = 0.01). Glucagon given with low-gain parameters did not significantly reduce hypoglycemic event frequency (P = NS) but did reduce frequency of carbohydrate treatment (P = 0.05).CONCLUSIONSDuring closed-loop treatment in subjects with type 1 diabetes, high-gain pulses of glucagon decreased the frequency of hypoglycemia. Larger and longer-term studies will be required to assess the effect of ongoing glucagon treatment on overall glycemic control.

We hypothesized that plasma insulin crosses the blood-cerebrospinal fluid (CSF) barrier and, as people gain weight, provides a physiological feedback signal to the central nervous system to inhibit food intake and further weight gain. However, it has not been demonstrated in man that insulin can enter the CSF from peripheral blood. To test whether increases in plasma insulin result in elevated CSF immunoreactive insulin (IRI) levels, we infused insulin iv in varying amounts approximating postprandial levels in eight normal subjects for 4.5 h. Euglycemia was maintained [88 +/- 3 (+/- SEM) mg/dl] by means of a variable glucose infusion. Samples were obtained every 30 min for measurements of insulin in peripheral plasma and insulin in lumbar CSF. Plasma IRI increased from a mean basal level of 12 +/- 1.2 microU/ml to a mean (during the 180- to 270-minute period) of 268 +/- 35 microU/ml. CSF IRI increased in all subjects during the infusion from a mean basal level of 0.9 +/- 0.1 microU/ml to a mean (during the 180- to 270-min period) of 2.8 +/- 0.4 microU/ml (P less than 0.006). By contrast, CSF IRI in two subjects who received an infusion of 0.9% saline did not increase. In summary, CSF insulin concentrations increased during peripheral infusions of insulin. This is the first demonstration in man that plasma insulin gains access to CSF and indicates a mechanism whereby peripheral insulin could provide a feedback signal to the central nervous system.

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Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, "sensor error") is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology.