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• W2274025732 abstract "In healthy subjects, glucose regulation relies on a complex control system that keeps blood glucose level within a narrow range around its basal value. A common element that offers a net benefit for most individuals with and without diabetes is regular physical activity, which is known to enhance insulin sensitivity, improve glycemic control and reduce the risk of cardiovascular mortality. Nowadays numerous studies have demonstrated increased rate of glucose uptake (Rd) and rate of endogenous glucose production (EGP) during physical activity in individuals with and without diabetes in the postabsorptive state, while very few have examined the effects of exercise in the postprandial state although many people, with and without diabetes, exercise a few hours after a meal. A method for the quantification of the effect and effect size of exercise on insulin sensitivity and a physiological model quantitatively describing the effect of exercise on glucose turnover in the postprandial state have never been developed. This represents a significant knowledge gap, especially in type 1 diabetes, because this information could be incorporated into currently available artificial pancreas control algorithms, thus extending their applicability to treat people with type 1 diabetes. However, such tools will need to be developed and tested in healthy subjects before validating in those with diabetes.In this work data of 12 healthy individuals who underwent a triple-tracer mixed-meal and a moderate-intensity exercise session 2 hours after meal ingestion for 75 minutes have been used. The tracer-to-tracee clamp method was used to accurately estimate postprandial glucose turnover continuously after the meal, during and after exercise by clamping tracer-to-tracee ratios in order to minimize non-steady-state errors. Since it is almost impossible to realize a perfect clamp of the plasma tracer-to-tracee ratio, the use of models, to compensate the non-steady-state errors, is needed. Use of models requires the estimation of derivatives both for tracer-to-tracee ratio and glucose signals and, due to ill-conditioning, this issue is generally solved via regularized deconvolution. However, an implicit assumption of standard regularized deconvolution is that, in a Bayesian embedding, expectations on smoothness of the unknown input can be formalized by describing it a priori as the multiple integration of a stationary white noise process but, because of physical activity, signals represented marked nonstationarity. We solved the problem by resorting to an improved stochastic deconvolution method, in order to account for nonstationarity introduced by exercise. Fluxes analysis showed that during exercise session EGP rose, which can be explained by both falling insulin and rising glucagon concentrations, while glucose concentrations fell and Rd plateaued, which can be explained by increasing muscle uptake by both insulin-independent and -dependent mechanisms. In order to quantify the effect and effect size of exercise on net insulin sensitivity (SI), i.e. the ability of insulin to stimulate glucose disposal and suppress endogenous glucose production, we developed a method able to calculate net insulin sensitivity and evaluate the relative contribution of liver and disposal insulin sensitivity based on glucose fluxes data. SI was estimated first using data of first 2 hours after the meal, i.e. in absence of physical activity, and then using the data of the whole experiment, i.e. in presence of physical activity. We found that net SI increases by almost 75% during moderate-intensity exercise and that this increase is associated to insulin-dependent glucose disposal. Furthermore, we validated these results by calculating an index of net insulin sensitivity, both in absence and presence of physical activity, based on an integral formula using glucose and insulin concentration data. We found a strong correlation between net SI indices calculated with the two methods. The results on effect size of physical activity on SI have been incorporated into the UVA/Padova T1DM simulator in order to suggest the best strategy that could be adopted during artificial pancreas clinical trials that involves a session of moderate physical activity. We showed that, in order to prevent hypoglycemia during and after exercise, any control algorithm would benefit by knowing in advance of upcoming physical activity and, if patient-specific basal insulin reduction pattern is not available, an optimal basal reduction strategy has been proposed. However, the method for the quantification of the effect size of exercise on insulin sensitivity was not able to tease out the insulin-independent effect of exercise on glucose uptake. Therefore, to discriminate the effect of exercise on insulin-dependent and -independent glucose turnover, the development of a mathematical model to assess the effect of physical activity on glucose kinetics has been approached. A set of models of increasing complexity have been developed and selection was tackled using standard criteria (e.g. ability to describe the data, precision of parameter estimates, model parsimony, residual independence). The models proposed well fitted the data and allowed the estimation of physiologically interpretable parameters quantifying the effect of physical activity on glucose turnover." @default.
• W2274025732 created "2016-06-24" @default.
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• W2274025732 date "2014-01-30" @default.
• W2274025732 modified "2023-09-27" @default.
• W2274025732 title "Modeling the effect of physical activity on postprandial glucose turnover" @default.
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