Investigating the Role of T-Cell Avidity and Killing Efficacy in Relation to Type 1 Diabetes Prediction

During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset

A local glucose-and oxygen concentration-based insulin secretion model for pancreatic islets

<p>Abstract</p> <p>Background</p> <p>Because insulin is the main regulator of glucose homeostasis, quantitative models describing the dynamics of glucose-induced insulin secretion are of obvious interest. Here, a computational model is introduced that focuses not on organism-level concentrations, but on the quantitative modeling of local, cellular-level glucose-insulin dynamics by incorporating the detailed spatial distribution of the concentrations of interest within isolated avascular pancreatic islets.</p> <p>Methods</p> <p>All nutrient consumption and hormone release rates were assumed to follow Hill-type sigmoid dependences on local concentrations. Insulin secretion rates depend on both the glucose concentration and its time-gradient, resulting in second-and first-phase responses, respectively. Since hypoxia may also be an important limiting factor in avascular islets, oxygen and cell viability considerations were also built in by incorporating and extending our previous islet cell oxygen consumption model. A finite element method (FEM) framework is used to combine reactive rates with mass transport by convection and diffusion as well as fluid-mechanics.</p> <p>Results</p> <p>The model was calibrated using experimental results from dynamic glucose-stimulated insulin release (GSIR) perifusion studies with isolated islets. Further optimization is still needed, but calculated insulin responses to stepwise increments in the incoming glucose concentration are in good agreement with existing experimental insulin release data characterizing glucose and oxygen dependence. The model makes possible the detailed description of the intraislet spatial distributions of insulin, glucose, and oxygen levels. In agreement with recent observations, modeling also suggests that smaller islets perform better when transplanted and/or encapsulated.</p> <p>Conclusions</p> <p>An insulin secretion model was implemented by coupling local consumption and release rates to calculations of the spatial distributions of all species of interest. The resulting glucose-insulin control system fits in the general framework of a sigmoid proportional-integral-derivative controller, a generalized PID controller, more suitable for biological systems, which are always nonlinear due to the maximum response being limited. Because of the general framework of the implementation, simulations can be carried out for arbitrary geometries including cultured, perifused, transplanted, and encapsulated islets.</p

Applicazione medico-legale dell'indice CDT. Studio casistico sulla popolazione bresciana.

considerata un marcatore biochimico con un’alta sensibilità e specificità diagnostica per l’abuso alcoolico cronico. Nel presente lavoro si è effettuata una comparazione tra l’indice di CDT valutato nella popolazione di sesso maschile e l’indice di CDT nella popolazione di sesso femminile. Metodi: Il siero proveniente da 110 donatori volontari di sangue con differente consumo di alcool, dichiarato in un questionario in forma anonima, è stato analizzato con metodo HPLC/UV utilizzando reattivi ClinRep® Recipe per il dosaggio di CDT. Risultati e conclusioni: le variabili analizzate sono state poste in correlazione e rappresentate mediante grafici; il valore medio di CDT su tutta la popolazione è risultato essere 0,68%, con D.S. 0,291, varianza 0,08, moda 0,74, mediana 0,65, CV 42,78; il valore medio di CDT è risultato nei maschi 0,65% e nelle femmine 0,79%. Per gli 88 maschi e le 22 femmine partecipanti sono state registrate e stratificate le abitudini di assunzione alcoolica suddividendo in tre categorie le bevande secondo la gradazione alcoolica. Le donne partecipanti allo studio presentano CDT maggiore rispetto agli uomini; le correlazioni effettuate tra CDT rispetto ad ALT, età, peso corporeo, unite alla conoscenza del reale introito di alcool, confermano l’utilità dell’indice CDT nella discriminazione tra abuso ed uso moderato di alcool

Assessement of 1th and snd phase of insulin secretion during OGTT and IVGTT

We recently assessed 1st and 2nd phase f-cell insulin secretion by applying the same model of glucose-induced insulin secretion to plasma glucose and C-peptide curves during both IVGTTs and hyperglicemic clamps. In the present study we have extended the same modeling strategy to standard OGTT (time 0\u2019-120\u2019). We performed in 31 subjects (18 with normal glucose regulation [NGR], 7 with impared glucose regulation [IGR], and 6 with newly diagnosed type 2 diabetes [T2DM]) a standard OGTT (blood samples for plasma glucose/C-peptide were collected every 5\u2019-20\u2019 from 0\u2019 to 120\u2019), and an IVGTT (12 g per m2 of BSA; blood samples collected every 1\u2019-20\u2019 from 0\u2019 to 180\u2019-240\u2019) on 2 separate day. We have applied the same modeling strategy to both tests and obtained a fairly good fit of the data in both the IVGTT and the OGTT. We thus estimated first (\uf0731st) and second (\uf0732nd) phase insulin secretion during both tests. Results are normalized per m2 of BSA. In the pooled data, OGTT \uf0731st and \uf0732nd (2996\ub1299 e 96.1\ub17.37, respectively) were significantly higher (p<0.01) than IVGTT \uf0731st and \uf0732nd (467\ub167 e 43.8\ub14.3), reflecting the well known potentiating effect of oral glucose on \uf062-cell response. Moreover, OGTT \uf0731st and \uf0732nd were positively and significantly correlated to IVGTT \uf0731st and \uf0732nd (r=0.50 e r=0.52, respectively; p<0.01 for both). Finally, in NGR, IGR and T2DM subjects OGTT \uf0731st (3609\ub1430, 2439\ub1437 e 1807\ub1220) and \uf0732nd (112\ub19.5, 80.8\ub113 e 66.2\ub1 11.5, respectively) showed a similar declining pattern as the one observed with the IVGTT (624\ub183, 427\ub1112 and 42.8\ub127.8 for IVGTT \uf0731st; 44.8\ub16.5, 48.3\ub19.1 and 35.5\ub15 for IVGTT \uf0732nd, respectively). These data demonstrate that is feasible to assess 1st and the 2nd insulin secretion phase during a standard OGTT and provide a physiological tool to measure f-cell function in states of normal and/or altered glucose regulation