Modeling the effect of physical activity on postprandial glucose turnover

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

Quantification of postprandial glucose flux following endurance exercise training and overfeeding by the triple tracer technique

Blood glucose responses following a meal are governed by complex regulatory systems. However, past studies investigating diet, exercise and glucose metabolism commonly utilize non-physiological experimental conditions. Using physiologically-representative measurement techniques, this research demonstrates that changes to glucose metabolism in response to diet and exercise are more subtle than commonly thought

Dynamic Modeling of Free Fatty Acid, Glucose, and Insulin During Rest and Exercise in Insulin Dependent Diabetes Mellitus Patients

Malfunctioning of the beta-cells of the pancreas leads to the metabolic disease known as diabetes mellitus (DM), which is characterized by significant glucose variation due to lack of insulin secretion, lack of insulin action, or both. DM can be broadly classified into two types: type 1 diabetes mellitus (T1DM) - which is caused mainly due to lack of insulin secretion; and type 2 diabetes mellitus (T2DM) - which is caused due to lack of insulin action. The most common intensive insulin treatment for T1DM requires administration of insulin subcutaneously 3 - 4 times daily in order to maintain normoglycemia (blood glucose concentration at 70 to 120 mg/dl). Although the effectiveness of this technique is adequate, wide glucose fluctuations persist depending upon individual daily activity, such as meal intake, exercise, etc. For tighter glucose control, the current focus is on the development of automated closed-loop insulin delivery systems. In a model-based control algorithm, model quality plays a vital role in controller performance. In order to have a reliable model-based automatic insulin delivery system operating under various physiological conditions, a model must be synthesized that has glucose-predicting ability and includes all the major energy-providing substrates at rest, as well as during physical activity. Since the 1960s, mathematical models of metabolism have been proposed in the literature. The majority of these models are glucose-based and have ignored the contribution of free fatty acid (FFA) metabolism, which is an important source of energy for the body. Also, significant interactions exist among FFA, glucose, and insulin. It is important to consider these metabolic interactions in order to characterize the endogenous energy production of a healthy or diabetic patient. In addition, physiological exercise induces fundamental metabolic changes in the body; this topic has also been largely overlooked by the diabetes modeling community.This dissertation takes a more lipocentric (lipid-based) approach in metabolic modeling for diabetes by combining FFA dynamics with glucose and insulin dynamics in the existing glucocentric models. A minimal modeling technique was used to synthesize a FFA model, and this was coupled with the Bergman minimal model to yield an extended minimal model. The model predictions of FFA, glucose, and insulin were validated with experimental data obtained from the literature. A mixed meal model was developed to capture the absorption of carbohydrates (CHO), proteins, and FFA from the gut into the circulatory system. The mixed meal model served as a disturbance to the extended minimal model. In a separate study, an exercise minimal model was developed to incorporate the effects of exercise on glucose and insulin dynamics. Here, the Bergman minimal model was modified by adding equations and terms to capture the changes in glucose and insulin dynamics during and after mild-to-moderate exercise.A single composite model for predicting FFA-glucose-insulin dynamics during rest and exercise was developed by combining the extended and exercise minimal models. To make the composite model more biologically relevant, modifications were made to the original model structures. The dynamical effects of insulin on glucose and FFA were divided into three parts: (i) insulin-mediated glucose uptake by the tissues, (ii) insulin-mediated suppression of endogenous glucose production, and (iii) anti-lipolytic effects of insulin. Labeled and unlabeled intra-venous glucose tolerance test data were used to estimate the parameters of the glucose model which facilitated separation of insulin action on glucose utilization and production. The model successfully captured the FFA-glucose interactions at the systemic level. The model also successfully predicted mild-to-moderate exercise effects on glucose and FFA dynamics. A detailed physiologically-based compartmental model of FFA was synthesized and integrated with the existing physiologically-based glucose-insulin model developed by Sorensen. Distribution of FFA in the circulatory system was evaluated by developing mass balance equations across the major FFA-utilizing tissues/organs. Rates of FFA production or consumption were added to each of the physiologic compartments. In order to incorporate the FFA effects on glucose, modifications were made to the existing mass balance equations in the Sorensen model. The model successfully captured the FFA-glucose-insulin interactions at the organ/tissue levels.Finally, the loop was closed by synthesizing model predictive controllers (MPC) based on the extended minimal model and the composite model. Both linear and nonlinear MPC algorithms were formulated to maintain glucose homeostasis by rejecting disturbances from mixed meal ingestion. For comparison purposes, MPC algorithms were also synthesized based on the Bergman minimal model which does not account for the FFA dynamics. The closed-loop simulation results indicated a tighter blood glucose control in the post-prandial period with the MPC formulations based on the lipocentric (extended minimal and composite) models

Effect of weight gain , diet and exercise on insulin sensitivity in Thoroughbred geldings.

Insulin sensitivity (SI) in horses is affected by diet, exercise and obesity and has been implicated in metabolic disease. The objectives of this research were to assess the impact of BW gain on SI utilizing two diets known to differentially impact glucose dynamics, evaluate the contribution of light exercise to overall SI and relate changes in SI to BCS to identify the threshold at which SI declines to a level consistent with an increased risk of metabolic disease. Fifteen mature Thoroughbred geldings (BW 516 ± 13 kg, BCS 4.3 ± 0.1) were fed to gain 90.8 kg on a diet high in fat and fiber (HF, n = 6) starch and sugar (HS, n = 9). To assess SI, frequently sampled i.v. glucose tolerance tests were performed before treatment initiation (CFMM), at the start (TXMM) and end (ENDMM) of weight gain and following a period of minimal exercise. Using the minimal model of glucose dynamics, data from each test was used to estimate SI, glucose effectiveness (Sg) and the acute insulin response to glucose (AIRg). Final BW was 608 ± 12 kg and BCS was 7 ± 0.1. Diet had no effect on SI, AIRg or glucose effectiveness at CFMM. Within HF, SI, Sg and AIRg were unchanged at CFMM, TXMM and ENDMM. SI decreased at TXMM in HS (P = 0.05) and remained unchanged through ENDMM. SI in HS was lower than HF at TXMM (P = 0.01) and ENDMM (P = 0.07). At ENDMM, AIRg was higher in HS than HF (P = 0.01) and glucose effectiveness was reduced in both diets (P < 0.05). Following the minimal exercise period, SI decreased in HF (P = 0.03). These results indicate that diet may be more influential on SI than weight gain in mature Thoroughbred geldings. The higher SI in HF appears to be partially dependent on some level of physical activity. Because a BCS increase of 3 scores was not associated with a reduction in SI, the BCS where the perceived risk of metabolic disease is increased likely lies above that achieved in this study (BCS 7)

A model of beta-cell response to GLP-1 to quantify incretin effect in healthy and prediabetic subjects

Glucose regulation, in healthy subjects, relies on a complex control system that keeps blood glucose level within a narrow range around its basal value. Impairment of the glucose regulatory system is the cause of several metabolic derangements, including diabetes, which is characterized by chronic hyperglycemia which leads to severe micro and macro-vascular complications. Diabetes is generally classified into two categories, type 1 and type 2 diabetes. Both arise from complex interactions between genes and the environment, and are characterized by an absolute deficiency of insulin production (type 1) or a relative deficiency of the pancreas to produce insulin in amounts sufficient to meet the body needs (type 2). The prevalence of diabetes is increasing dramatically in populations of the world, and its global incidence has been increasing steadily in the past several years. Traditional medications for type 2 diabetes, including insulin, sulfonylureas, glitinides, acarbose, metformin, and thiazolidinediones, lower blood glucose through diverse mechanisms of action. However, many of the oral hypoglycemic agents lose their efficacy over time, resulting in progressive deterioration in β-cell function and loss of glycemic control due to progressive loss of β-cell mass. Consequently, there is an increasing interest in developing therapeutic agents that preserve or restore functional β-cells mass such as the incretin hormone Glucagon-Like Peptide-1 (GLP-1). It not only acutely lowers blood glucose by promoting insulin secretion and inhibiting glucagon release, but also engages signaling pathways in the islet β-cells that leads to stimulation of β-cells proliferation and neo-genesis and inhibition of β-cell apoptosis. Impairment of insulin secretion and glucagon suppression suggests that decreased β-cells responsiveness to GLP-1 is part of the pathogenesis of type 2 diabetes. Thus the ability to measure the effect of GLP-1 on insulin secretion can be useful to understand the pathogenesis of type 2 diabetes. Moreover it can be employed to optimized GLP-1 based therapy by determining those individuals who may benefit more from such therapy. However, a mechanistic model enabling direct quantitation of pancreatic response to GLP-1 has never been developed. In this contribution a mathematical model which describes the mechanism of GLP-1 action on insulin secretion is proposed. It provides a direct measure of the β-cells responsivity indexes to glucose and GLP-1. Three databases were used to develop, test and validate the model. Data of 88 healthy individuals, who underwent a hyperglycemic clamp with a concomitant GLP-1 intravenous infusion, were used for model formulation. A set of models of increasing complexity describing GLP-1 action on insulin secretion were tested. All models share the common assumption that insulin secretion is made up of two components, one proportional to glucose rate of change through dynamic responsivity, Φd, and one proportional to glucose through static responsivity, Φs, but differ in the modality of GLP-1 control on β-cells. For each model potentiation index П was derived representing the percent increase in secretion due to 1 pmol/l of circulating GLP-1. All the models fit the data well, as confirmed by the run test, which supported randomness of residuals in 70% of the subjects and provide precise estimate of model parameters. Model selection was tackled using standard criteria (e.g. ability to describe the data, precision of parameter estimates, model parsimony, residual independence). The most parsimonious model in most subjects assumes that above-basal insulin secretion depends linearly on GLP-1 concentration and its rate of change. However, the hyperglycemic clamp with concomitant intravenous infusion of GLP-1, is not physiological and easy to perfume in large scale studies. Thus data of 22 impairing fasting glucose (IFG) subjects, studied twice with a mixed meal, were used to test the model performance in a more physiological condition. We found that during an oral test, a simpler model is sufficient to describe the data. Validation of the model was performed using both simulations and real data of 10 healthy subjects studied with an OGTT and matched intravenous glucose challenge (I-IVG). The protocol allows to calculate a model-independent index (PI) from the comparison of insulin secretion rate estimated in these two occasions. The comparison between model-derived Π and incretin potentiation index PI shows that they are very similar (П = 6.55, CV = 65%; PI = 6.15 % per pmol/l). In addition in silico validation proved the ability of the model to single out the effect of GLP-1 on insulin secretion since it correctly estimated П in the 93 ± 1% of the simulations

Dynamical hybrid modeling of human metabolism

Human metabolism plays a key role in disease pathogenesis and drug action. Half a century of biochemical literature leveraged by the advent of genomics allowed the emergence of computational modeling techniques and the in silico analysis of complex biological systems. In particular, Constraint-Based Reconstruction and Analysis (COBRA) methods address the complexity of metabolism through building tissue-specific networks in their steady state. It is known that biological systems respond to perturbations induced by pathogens, drugs or malignant processes by shifting their activity to safeguard key metabolic functions. Extending the modeling framework to consider the dynamics of these complex systems will bring simulations closer to observable human phenotypes. In this thesis, I combined physiologically-based pharmacokinetic (PBPK) models with genome-scale metabolic models (GSMMs) to form hybrid genome-scale dynamical models that provide a hypothesis-free framework to study the perturbations induced by one or more perturbagen on human tissues. On a first stage, these methodologies were applied to decipher the absorption of levodopa and amino acids by the intestinal epithelium and allowed to derive a model-based diet for Parkinson's Disease patients. In the next phase, we extended the study to 605 drugs in order to predict the occurrence of gastrointestinal side effects through a machine learning classifier, using a combination of gene expression and metabolic reactions set as features. Finally, the approach upscaled to several tissues, specifically to investigate the genesis of metabolic symptoms in type 1 diabetes and to suggest key metabolic players underlying within and between-individual variability to insulin action. Taken as whole, the integration of two modeling techniques constrained by expert biological knowledge and heterogeneous data types will be a step forward in achieving convergence in human biology

Mathematical Models for Optimisation of Drug Administration in Intensive Care Units

Clinical status of critically ill patients is often extreme and rapidly evolving. Hence, pharmacological therapies must be tailored to patients' characteristics and adapted according to the evolution of their clinical pictures. To identify optimal personalized treatments, possible scenarios produced by different therapeutic choices must be predicted and compared. This process requires complex analyses involving the development of appropriate mathematical models. In this Thesis, I focused on two important aspects of the pharmacological treatment of critically ill patients: the administration of antimicrobial drugs and the control of their glycaemic level. Although these problems are clinically very different, the modelling of their pathophysiological mechanisms can be addressed with similar tools. I performed analyses based on retrospective clinical data collected with MargheritaTre, an electronic health record developed by GiViTI. The software to synchronize databases from hospitals to our laboratory and to preprocess data for analyses was written for the purpose of this Thesis. Starting from the study of the physiological mechanisms at the basis of vancomycin pharmacokinetics I constructed a model to describe the evolution of the plasma concentration of this drug in critically ill patients. Compartment models were fitted on a sample of 141 patients, testing about 30 patient covariates and several functional dependencies for each variable. Glucose dynamics were described through a system of delay differential equations reproducing intake, uptake and endogenous production of glucose, and organ-organ interactions mediated by hormones. Existing models, describing only the dynamics of glucose and insulin, fail to reproduce the correct evolution when glucose concentrations vary too rapidly. I improved these models, by introducing an equation describing glucagon dynamics and taking into account its effect on glucose metabolism. I investigated the dynamical properties of my model with analytical analyses, numerical simulations and fitting it to observed data

Metabolic effects of duodenal mucosal resurfacing on insulin resistant women with polycystic ovary syndrome

Background Insulin resistant conditions such as T2DM, obesity and PCOS are significant contributors of morbidity and mortality worldwide. At present, the principal treatment modalities are lifestyle measures, pharmacotherapy and metabolic surgery. Although metabolic surgery is a highly-effective option, there remains no ideal remedy. This has resulted in the development of endoluminal procedures such as duodenal mucosal resurfacing (DMR) to fill the treatment gap. Initial DMR results suggests efficacy in patients with T2DM. The DOMINO trial aimed to investigate the insulin-sensitising effect of DMR in women with PCOS, as a model of insulin resistance, as it additionally allowed assessment of reproductive function. Methods This was a mechanistic study conducted using a multi-centre prospective double-blinded sham-controlled RCT design. Thirty women of reproductive potential with PCOS, insulin resistance and oligomenorrhoea were randomised to receive either DMR or a sham endoscopic procedure with 6 months follow-up. All participants were investigated with OGTTs and hyperinsulinaemic-euglycaemic clamps pre- and post-procedure. Participants were also investigated with weekly reproductive blood tests and pelvic ultrasound scans from 3-months post-procedure to completion of the trial. Results Thirty women (mean age 31.1years, mean BMI 42.5kg/m2, mean HOMA-IR 6.2) were recruited. The rate of glucose appearance (Ra) and disappearance (Rd)– to quantify insulin sensitivity– were not significantly different between the DMR and sham groups. Ovulatory events from pelvic ultrasound scans and reproductive blood tests did not demonstrate a difference between the two groups. Conclusion DMR use did not result in significant improvement in insulin sensitivity or reproductive function in women with PCOS, insulin resistance and oligomenorrhoea. This suggests that the improvement in glycaemia and insulin resistance seen in patients with Type 2 diabetes melitus post-DMR is likely secondary to a pathophysiological difference that is not evident in a cohort of patients without T2DM. However, further evidence is needed to substantiate this hypothesis.Open Acces

Modulation of carotid body activity as a therapeutic intervention in metabolic diseases

A diabetes tipo 2 é uma das doenças crónicas mais comuns no mundo, cuja prevalência continua a aumentar. Em 2045 estima-se que esta doença afete cerca de 629 milhões de pessoas no mundo. A diabetes tipo 2 é caracterizada pela resistência periférica à insulina, o anormal metabolismo hepático de glucose e a falha progressiva das células beta do pâncreas. Existem vários fármacos disponíveis para o tratamento da diabetes tipo 2 contudo, com o tempo, o controlo da glucose deteriora-se progressivamente e, mesmo com ajustes na medicação, uma proporção considerável de indivíduos continua pouco controlado. Deste modo, é essencial o desenvolvimento de novas estratégicas terapêuticas para o controlo da diabetes tipo 2. Nos últimos anos, o corpo carotídeo (CB), um quimoreceptor periférico que deteta alterações de O2, CO2 e pH no sangue, tem também sido descrito como um sensor metabólico envolvido no controlo da homeostasia energética. De facto, foi descrito que o CB está envolvido na génesis da resistência à insulina e hipertensão induzidos pelas dietas hipercalóricas. Este trabalho teve como objetivo avaliar o papel do CB no controlo da homeostasia da glucose e investigar uma possível metodologia para modular a atividade do CB com o intuito de encontrar um novo tratamento para a diabetes tipo 2. O capítulo I introduz conceitos gerais na diabetes tipo 2, tais como, a sinalização da insulina, a homeostasia da glucose e as opções terapêuticas para o tratamento da diabetes tipo 2. Para além disso, conceitos relacionados com a função do CB e o papel do ATP e da adenosina na neurotransmissão do CB, bem como, o seu papel como sensor metabólico serão também abordados. No capítulo II estão descritos os objetivos gerais e específicos do presente trabalho. No capítulo III demonstrou-se que a ressecção do nervo do seio carotídeo (CSN), o nervo sensitivo do CB, restaura a sensibilidade à insulina em dois modelos animais de prediabetes, um efeito que se mantém com a continuação da administração das dietas hipercalóricas. Para além disso, foi observado que a ressecção do CSN normaliza a atividade do sistema nervo simpático, a pressão arterial, a função endotelial, o perfil lipídico e os níveis plasmáticos de glucose e insulina. Foi também descrito que o mecanismo pelo qual é restaurada a homeostasia da glucose envolve uma melhoria na captação de glucose no fígado e no tecido adiposo perientérico bem como, o restauro das vias de sinalização da insulina no músculo esquelético e no tecido adiposo. No capítulo IV foi descrito que a modulação bioelectrónica do CSN através da aplicação de uma corrente alternada de alta frequência (KHFAC) restaura a sensibilidade à insulina e a tolerância à glucose num modelo animal de diabetes tipo 2. Para além disso, observou-se que estes efeitos são reversíveis após o término do estímulo elétrico de alta frequência. Assim, este trabalho suporta o potencial terapêutico da medicina bioelectrónica na diabetes tipo 2. Uma abordagem farmacológica poderá também ser utilizada para modular a atividade do CSN. Desta forma, no capítulo V foi estudado o papel do ATP e da adenosina na atividade do CSN no basal e em resposta à hipóxia. Observou-se que a frequência de descarga do CSN está aumentada num modelo animal de prediabetes, sendo este efeito modulado pelo ATP e pela adenosina. Tendo em conta que a adenosina contribui mais do que o ATP para gerar atividade do CSN na hipóxia moderada, enquanto que o ATP tem um papel preponderante durante a hipóxia intensa e, sabendo que hipóxias intensas são menos frequentes em situações fisiológicas, é sugerido que a modulação da sinalização do ATP no CB poderá ser um alvo terapêutico para o tratamento da diabetes tipo 2. Finalmente, no capítulo VI, é apresentada uma discussão geral e integrada da presente tese de Doutoramento. Em conclusão, os resultados apresentados neste trabalho contribuem para reforçar que a modulação do CB/CSN representa uma nova estratégia terapêutica na diabetes tipo 2.Type 2 diabetes (T2D) is one of the most common chronic diseases, whose prevalence continues to increase, being expected to affect 629 million people in the world in 2045. The principal defects in T2D are peripheral insulin resistance, abnormal hepatic glucose metabolism and progressive pancreatic beta cell failure. Despite the several different drugs available for T2D treatment, over time, glucose control deteriorates progressively and even with the rearrange of medication, a sizeable proportion of individuals remain poorly control. Therefore, is crucial the development of new therapeutic strategies to control this epidemic. In the last years, the carotid body (CB), a peripheral chemoreceptor that sense changes in blood O2, CO2 and pH, have also been described as a metabolic sensor implicated in the control of energy homeostasis. In fact, it was described that CB overactivity is involved in the genesis of insulin resistance and hypertension induced by the hypercaloric diets. The aims of the present work were to investigate the role of CB in the control of glucose homeostasis and to search a method/approach to modulate CB activity aiming to treat T2D. Chapter I introduces general concepts in T2D field, as the insulin signaling, glucose homeostasis and the therapeutic options for T2D treatment. Additionally, fundamental concepts of CB function and the role of ATP and adenosine in the CB neurotransmission, as well as, the role of CB as a metabolic sensor are also addressed. In chapter II are described the general and specific aims of the present work. In chapter III it was demonstrated that the carotid sinus nerve (CSN), the CB sensitive nerve, resection restores the insulin sensitivity in two prediabetes animal models, an effect that was maintained even when the animals were continuously fed hypercaloric diets. Moreover, it was also demonstrated that CSN resection normalized systemic sympathetic nervous system activity, blood pressure, endothelial function, lipid profile and plasma glucose and insulin levels. Additionally, the mechanism behind the repair of glucohomeostasis involves an improvement in glucose uptake in the liver and perienteric adipose tissue and a restored insulin signaling pathways in skeletal muscle and adipose tissue. In chapter IV it was demonstrated that the bioelectronic modulation of the CSN by using the kilohertz frequency alternating current (KHFAC) is capable to restore the insulin sensitivity and the glucose tolerance in a diet-induced early stage T2D animal model. Furthermore, it was also described that these effects were reversed after discontinuation of the electrical stimulus. This work support a potential role for bioelectronic medicines in the treatment of T2D. Another approach that could be used to modulate the CSN activity is a pharmacological approach. In chapter V, it was explored the role of ATP and adenosine on the basal and CSN chemosensory activity evoked by hypoxia. It was shown that the CSN frequency of discharge is overactivated in a prediabetes animal model, being this effect modulated by ATP and adenosine. Since adenosine contributes more than ATP to generate CSN activity in moderate hypoxia, while ATP shows a more preponderant role during intense hypoxia and knowing that intense hypoxias are less prone to occur, it is suggested that the modulation of ATP signaling in the CB could be a therapeutic target to treat T2D. Finally, in chapter VI, it is presented a general and integrated discussion of this PhD thesis. In conclusion, the data present in this work contribute to strengthen that the modulation of CB/CSN activity represents a novel therapeutic approach for T2D.Fundação para a Ciência e Tecnologia (FCT – Portugal)Fundo Social Europeu (FSE)Programa Operacional Capital Humano (POCH)Galvani Bioelectronics (former GlaxoSmithKline Bioelectronics R&D)Ministry of Economy and Competitiveness and the European Fund for Regional Development (MINECO/FEDER)Institute of Health Carlos III (Espanha