Background and objective: Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity. Methods: The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects. Results: Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects. Conclusions: The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise.

Personalized medicine strategies are gaining momentum nowadays, enabling the introduction of targeted treatments based on individual differences that can lead to greater therapeutic efficacy by reducing adverse effects. Despite its crucial role, studying the contribution of the immune system (IS) in this context is difficult because of the intricate interplay between host, pathogen, therapy, and other external stimuli. To address this problem, a multidisciplinary approach involving in silico models can be of great help. In this perspective, we will discuss the use of a well-established agent-based model of the immune response, C-ImmSim, to study the relationship between long-lasting diseases and the combined effects of IS, drug therapies and exogenous factors such as physical activity and dietary habits.

Early detection of prediabetes is crucial to preventing its progression to diabetes. Providing individuals with a personalized sense of their risk could improve prevention efforts. While complex mathematical models that simulate metabolic and inflammatory processes offer detailed and patient-specific insights, their computational cost usually makes them impractical for real-time prediction on mobile platforms. This work introduces a long short-term memory (LSTM) surrogate for the MT2D model, that simulates the main metabolic and inflammatory processes undergoing the transition to prediabetes. The model is developed using a dataset of 43 669 simulated subjects, each with lifestyle inputs and biomarker outputs over six months. Using 8 time series inputs, the surrogate predicts the dynamics of 11 key metabolic and inflammatory outputs, closely replicating the behaviour of the MT2D model. After training, the proposed LSTM model reduces computational time from an average of 8.4 hours to 0.1 seconds per simulation, making it suitable for mobile device deployment. The model achieves root mean squared errors on the order of 10-2 on scaled data, and shows promise for prediabetes risk assessment by capturing trends in inflammatory biomarkers. This surrogate model can provide real-time and patient-specific insights into the metabolic health, potentially improving the understanding of prediabetes risk.

This contribution outlines current research aimed at developing models for personalized type 2 diabetes mellitus (T2D) prevention in the framework of the European project PRAESIIDIUM (Physics Informed Machine Learn-ing-Based Prediction and Reversion of Impaired Fasting Glucose Management) aimed at building a digital twin for preventing T2D in patients at risk. Specifically, the modelling approaches include both a multiscale, hybrid computational model of the human metaflammatory (metabolic and inflammatory) status, and data-driven models of the risk of developing T2D able to generate personalized recommendations for mitigating the individ-ual risk. The prediction algorithm will draw on a rich set of information for training, derived from prior clinical data, the individual's family history, and prospective clinical trials including clinical variables, wearable sensors, and a tracking mobile app (for diet, physical activity, and lifestyle). The models developed within the project will be the basis for building a platform for healthcare professionals and patients to estimate and monitor the indi-vidual risk of T2D in real time, thus potentially supporting personalized prevention and patient engagement.

Type 2 diabetes mellitus (T2D), the most common form of diabetes, is a chronic metabolic disorder characterized by hyperglycemia, insulin resistance, and insulin deficiency. Although genetic predisposition determines in part the susceptibility to T2D, an unhealthy diet and a sedentary lifestyle are commonly recognised as essential drivers for the onset of the disease. Indeed, considerable evidence suggests that regular exercise and appropriate nutrition bring undeniable health benefits by reducing the risk of developing T2D or delaying its onset. The literature dealing with mathematical modelling for diabetes is abundant and in the view of a growing more personalized medicine the benefits of having tools to represent different virtual patient populations are clear. In this study, we describe a multi-scale computational model of the human metabolic and inflammatory status that is determined by individual dietary and activity habits. It includes a description of the immune activation and inflammation, a model for the food intake, stomach emptying and gut absorption of all three macronutrients (proteins, carbohydrates, fats), a component to account for the effects of physical activity on the hormones' regulation and the inflammatory state of the individual, and finally, a characterization of energy intake-expenditure balance. All these pieces are merged into a single integrated simulation tool to provide a helpful aid that can be used proactively to prevent the onset of the disease. Moreover, this model turns out to help design virtual cohorts of patients to conduct in silico studies.

Regular physical exercise and appropriate nutrition affect metabolic and hormonal responses and may reduce the risk of developing chronic non-communicable diseases such as high blood pressure, ischemic stroke, coronary heart disease, some types of cancer, and type 2 diabetes mellitus. Computational models describing the metabolic and hormonal changes due to the synergistic action of exercise and meal intake are, to date, scarce and mostly focussed on glucose absorption, ignoring the contribution of the other macronutrients. We here describe a model of nutrient intake, stomach emptying, and absorption of macronutrients in the gastrointestinal tract during and after the ingestion of a mixed meal, including the contribution of proteins and fats. We integrated this effort to our previous work in which we modeled the effects of a bout of physical exercise on metabolic homeostasis. We validated the computational model with reliable data from the literature. The simulations are overall physiologically consistent and helpful in describing the metabolic changes due to everyday life stimuli such as multiple mixed meals and variable periods of physical exercise over prolonged periods of time. This computational model may be used to design virtual cohorts of subjects differing in sex, age, height, weight, and fitness status, for specialized in silico challenge studies aimed at designing exercise and nutrition schemes to support health.

Amino acids (AAs) are well known to be involved in the regulation of glucose metabolism and, in particular, of insulin secretion. However, the effects of different AAs on insulin release and kinetics have not been completely elucidated. The aim of this study was to propose a mathematical model that includes the effect of AAs on insulin kinetics during a mixed meal tolerance test. To this aim, five different models were proposed and compared. Validation was performed using average data, derived from the scientific literature, regarding subjects with normal glucose tolerance (CNT) and with type 2 diabetes (T2D). From the average data of the CNT and T2D people, data for two virtual populations (100 for each group) were generated for further model validation. Among the five proposed models, a simple model including one first-order differential equation showed the best results in terms of model performance (best compromise between model structure parsimony, estimated parameters plausibility, and data fit accuracy). With regard to the contribution of AAs to insulin appearance/disappearance (k model parameter), model analysis of the average data from the literature yielded 0.0247 (confidence interval, CI: 0.0168 - 0.0325) and -0.0048 (CI: -0.0281 - 0.0185) ?U·ml/(?mol·l·min), for CNT and T2D, respectively. This suggests a positive effect of AAs on insulin secretion in CNT, and negligible effect in T2D. In conclusion, a simple model, including single first-order differential equation, may help to describe the possible AAs effects on insulin kinetics during a physiological metabolic test, and provide parameters that can be assessed in the single individuals.

Background: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. Conclusions: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM.

In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients.

Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients. In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statis- tical model able to recapitulate the simulated dynamics. The resulting machine learning model adequately predicts the synthetic data and can therefore be used as a computationally- cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self assessment by informed and aware individuals.

Background. Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim. In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results. The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

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The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise. Built upon an existing model, it allows to detail better both subjects' characteristics and physical exercise, thus determining to a greater extent the dynamics of the hormones and the metabolites considered.

Interleukin-6 (IL-6) has been recently shown to play a central role in glucose homeostasis, since it stimulates the production and secretion of Glucagon-like Peptide-1 (GLP-1) from intestinal L-cells and pancreas, leading to an enhanced insulin response. In resting conditions, IL-6 is mainly produced by the adipose tissue whereas, during exercise, skeletal muscle contractions stimulate a marked IL-6 secretion as well. Available mathematical models describing the effects of exercise on glucose homeostasis, however, do not account for this IL-6 contribution. This study aimed at developing and validating a system model of exercise's effects on plasma IL-6 dynamics in healthy humans, combining the contributions of both adipose tissue and skeletal muscle. A two-compartment description was adopted to model plasma IL-6 changes in response to oxygen uptake's variation during an exercise bout. The free parameters of the model were estimated by means of a cross-validation procedure performed on four different datasets. A low coefficient of variation (< 10%) was found for each parameter and the physiologically meaningful parameters were all consistent with literature data. Moreover, plasma IL-6 dynamics during exercise and post-exercise were consistent with literature data from exercise protocols differing in intensity, duration and modality. The model successfully emulated the physiological effects of exercise on plasma IL-6 levels and provided a reliable description of the role of skeletal muscle and adipose tissue on the dynamics of plasma IL-6. The system model here proposed is suitable to simulate IL-6 response to different exercise modalities. Its future integration with existing models of GLP-1-induced insulin secretion might provide a more reliable description of exercise's effects on glucose homeostasis and hence support the definition of more tailored interventions for the treatment of type 2 diabetes.

Fever plays a role in activating innate immunity while its relevance in activating adaptive immunity is less clear. Even brief exposure to elevated temperatures significantly impacts on the immunostimulatory capacity of dendritic cells (DCs), but the consequences on immune response remain unclear. To address this issue, we analyzed the gene expression profiles of normal human monocyte-derived DCs from nine healthy adults subjected either to fever-like thermal conditions (39 degrees C) or to normal temperature (37 degrees C) for 180 minutes. Exposure of DCs to 39 degrees C caused upregulation of 43 genes and downregulation of 24 genes. Functionally, the up/downregulated genes are involved in post-translational modification, protein folding, cell death and survival, and cellular movement. Notably, when compared to monocytes, DCs differentially upregulated transcription of the secreted protein IGFBP-6, not previously known to be specifically linked to hyperthermia. Exposure of DCs to 39 degrees C induced apoptosis/necrosis and resulted in accumulation of IGFBP-6 in the conditioned medium at 48 h. IGFBP-6 may have a functional role in the hyperthermic response as it induced chemotaxis of monocytes and T lymphocytes, but not of B lymphocytes. Thus, temperature regulates complex biological DC functions that most likely contribute to their ability to induce an efficient adaptive immune response.

Broader comprehension of gene expression regulatory mechanisms can be gained from a global analysis of how transcription and degradation are coordinated to orchestrate complex cell responses. The role of messenger RNA (mRNA) turnover modulation in gene expression levels has become increasingly recognized. From such perspective, in this review we briefly illustrate how a simple but effective mathematical model of mRNA turnover and some experimental findings, may together shed light on the molecular mechanisms underpinning the major role of mRNA decay rates in shaping the kinetics of gene activation and repression

Fever plays a role in activating innate immunity while its relevance in activating adaptive immunity is less clear. Even brief exposure to elevated temperatures significantly impacts on the immunostimulatory capacity of dendritic cells (DCs), but the consequences on immune response remain unclear. To address this issue, we analyzed the gene expression profiles of normal human monocyte-derived DCs from nine healthy adults subjected either to fever-like thermal conditions (39°C) or to normal temperature (37°C) for 180 minutes. Exposure of DCs to 39°C caused upregulation of 43 genes and downregulation of 24 genes. Functionally, the up/ downregulated genes are involved in post-translational modification, protein folding, cell death and survival, and cellular movement. Notably, when compared to monocytes, DCs differentially upregulated transcription of the secreted protein IGFBP-6, not previously known to be specifically linked to hyperthermia. Exposure of DCs to 39°C induced apoptosis/necrosis and resulted in accumulation of IGFBP-6 in the conditioned medium at 48 h. IGFBP-6 may have a functional role in the hyperthermic response as it induced chemotaxis of monocytes and T lymphocytes, but not of B lymphocytes. Thus, temperature regulates complex biological DC functions that most likely contribute to their ability to induce an efficient adaptive immune response.

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.

Within the complex pathological picture associated to diabetes, high glucose (HG) has ". per se" effects on cells and tissues that involve epigenetic reprogramming of gene expression. In fetal tissues, epigenetic changes occur genome-wide and are believed to induce specific long term effects. Human umbilical vein endothelial cells (HUVEC) obtained at delivery from gestational diabetic women were used to study the transcriptomic effects of chronic hyperglycemia in fetal vascular cells using Affymetrix microarrays. In spite of the small number of samples analyzed (n=6), genes related to insulin sensing and extracellular matrix reorganization were found significantly affected by HG. Quantitative PCR analysis of gene promoters identified a significant differential DNA methylation in TGFB2. Use of Ea.hy926 endothelial cells confirms data on HUVEC. Our study corroborates recent evidences suggesting that epigenetic reprogramming of gene expression occurs with persistent HG and provides a background for future investigations addressing genomic consequences of chronic HG. © 2014 Elsevier Inc.

We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named “fight-club hubs” characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named “switch genes” was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.