1,721,244 research outputs found
Insulin secretion and hepatic extraction in humans by minimal modeling of C-peptide and insulin kinetics.
No full textMethods for measuring insulin secretion and hepatic insulin extraction in vivo, e.g., hepatic vein catheterization, are invasive, and can be applied during steady state only. We introduce a noninvasive method for measuring in vivo insulin secretion and its extraction by the liver during an intravenous glucose tolerance test (IVGTT). This method is based on a minimal model of C-peptide secretion and kinetics that is used for interpreting plasma C-peptide concentration data during an IVGTT in normal humans. The model allows the reconstruction of the time course of insulin secretion and, used in conjunction with a minimal model of insulin delivery and kinetics (described in a previous study), provides a noninvasive measure of the time course of hepatic insulin extraction [H(t)]. The C-peptide model also provides a direct prehepatic measure of beta-cell sensitivity to glucose, expressed by two parameters related to first (phi IC)- and second (phi IIC)-phase insulin secretion. In the 11 healthy volunteers we studied, these parameters were 61 +/- 11 pM.min-1.mg-1.dl and 0.0154 +/- 0.0034 pM.min-2.mg-1.dl, respectively. H(t) showed an initial decrement for approximately 30-50 min (from a fasting value of 63 +/- 8% to a nadir of 53 +/- 9%) after the glucose stimulus, then a steady value of approximately 62% was reestablished and maintained throughout the experiment. The validity of the C-peptide model was further assessed by comparing its estimate of the fractional plasma clearance rate (k01) with that obtained in experiments in which biosynthetic human C-peptide was administered
Estimation of B-cell secretion and insulin hepatic extraction by the minimal modelling technique.
No full textMathematical models are a necessary tool to quantify physiological processes the direct measurement of which is not possible. Pancreatic beta-cell and liver are respectively the secreting and the major degrading site of insulin. To provide a quantitative description of these processes, we have conceived a method which exploits two minimal mathematical models. By using one model the post-hepatic delivery of insulin into the systemic circulation, IDRT(t), is estimated; the other model yields CPST(t), i.e. the secretion rate of C-peptide, which is equimolarly released by the beta-cell with insulin, but is not degraded by the liver. The estimated C-peptide flow rate into plasma is thus representative of that of pre-hepatic insulin. The difference between CPST(t) and IDRT(t) gives the insulin extraction by the hepatocytes. The parameters of the models are estimated in every single subject from the analysis of glucose, insulin, and C-peptide concentration data measured after an intravenous glucose injection. As an example of its usefulness, the method has been applied in patients with liver cirrhosis and in obese non-diabetic subjects, with the purpose of elucidating which mechanism is responsible for the peripheral dynamic hyperinsulinaemia characteristic of such metabolic states. Because mechanism is responsible for the peripheral dynamic hyperinsulinaemia characteristic of such metabolic states. Because of its relative non-invasiveness compared to other techniques this model-based method should prove useful in several other clinical investigations
Perché Mario R. Capecchi, Martin J. Evans e Oliver Smithies hanno ricevuto il Premio Nobel 2007 per la fisiologia e la medicina?
No full textReduced glucose effectiveness as a feature of glucose intolerance evidence in elderly type-2 diabetic subjects.
No full textOne of the factors determining glucose tolerance is glucose disappearance independent from the dynamic insulin (glucose effectiveness); the debate on its role in the development of Type-2 diabetes is still open. The aim of the present study was to evaluate insulin delivery, insulin sensitivity (SI), and glucose effectiveness (SG) in a group of elderly Type-2 diabetic patients (D, 4/6 F/M, age 67 +/- 2 years, 64 +/- 2 kg, BMI 23.8 +/- 0.5 kg/m2), compared to young controls (C, 4/6 F/M, 25 +/- 2 years, 72 +/- 4 kg, 23.7 +/- 1.1 kg/m2) and elderly controls (E, 2/4 F/M, 73 +/- 3 years, 63 +/- 4 kg, 23.1 +/- 0.5 kg/m2). We performed oral (OGTT) and intravenous (FSIGT) glucose tolerance tests. The OGTT showed that C and E were normotolerant, while D had a markedly reduced glucose tolerance. This was also confirmed in the FSIGT where the glucose tolerance index (KG) was 0.6 +/- 0.1% min-1 in D vs 1.8 +/- 0.2 in C and 1.5 +/- 0.2 in E (p < 0.0002). Total insulin area of D and the overall insulin delivery were not different from those of the control groups. The early phase area was instead significantly reduced (0.19 +/- 0.02 mU min/mL vs 0.61 +/- 0.06 of C and 0.46 +/- 0.06 of E, p < 0.001) given the reduction in the dynamic first-phase insulin delivery (0.86 +/- 0.17 min(microU/mL)/(mg/dL) vs 3.95 +/- 0.61 in C (p < 0.005) and 2.61 +/- 0.66 (p < 0.001) in E). SI of D was 3.4 +/- 0.4 10(-4) min-1/(microU/mL), not different from that of C (4.7 +/- 0.6) and E (3.5 +/- 0.2). This study showed a marked difference between SG of D and that of both control groups [0.010 +/- 0.001 min-1 vs 0.026 +/- 0.004 (p < 0.001) of C and 0.020 +/- 0.003 (p < 0.002) of E], mostly due to the zero-insulin component GEZI which was 0.006 +/- 0.001 in D vs 0.021 +/- 0.004 in C and 0.016 +/- 0.003 in E (p < 0.003). In the elderly groups, when taken together, SG exhibited a positive correlation with the area under insulin concentration during the early phase and with KG (r = 0.69, p = 0.0032 and r = 0.90, p = 0.0001, respectively), demonstrating the importance of the first-phase insulin delivery in modulating glucose effectiveness and glucose toleranc
Generation of a Tph2/EGFP knockin mouse line for the study of the role of serotonin during the central nervous system development
No full textNon-invasive glucose monitoring: Assessment of technologies and devices according to quantitative criteria
No full textAbstract
Aim of this review was to describe the main technologies for non-invasive glucose monitoring and the corresponding most relevant devices. The review tries to overcome the limitations of previous reviews on this topic, such as the lack of objective criteria for inclusion or exclusion of technologies or devices, and the poor organization of the information, which often does not allow easy comparison between technologies and devices. In this review, the information is concise and organized into specific categories, and hence it becomes easy to compare advantages and disadvantages of the different technologies and devices. For technologies, the categories of information considered are the technology name, the underlying physical principle, the technology limitations and the measurement sites on the human body. For devices, the categories of information are the device name, its approval condition (FDA Approval and/or CE Mark), the technology on which it is based, a device general description, the tests performed on the device, the corresponding results, safety information, aspects affecting usability, current status of the device and the manufacturer, an Internet reference for the device. A total of 14 technologies and 16 devices are included. Conclusions of the review were that, despite some interesting and promising technologies and devices, a satisfactory solution to the non-invasive glucose monitoring problem still requires further efforts
On a simple model of insulin secretion.
No full textA model of the insulin secretion process is presented. The model aims to be a simple one as compared with previous models, i.e. it predicts experimental evidence on insulin secretion under a variety of glucose inputs with a mathematical structure of low complexity. The model predicts in the β-pancreas two linearly connected pools of stored and promptly releasable insulin. Insulin synthesis and release have been modelled as nonlinear glucose-controlled processes. Validation of the model is made by simulation studies both on insulin secretion data as well as on blood insulin concentration data, by aggregating the proposed model to a compartmental model of insulin kinetics
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