Modeling the effect of blood glucose and physical exercise on plasma adrenaline in people with type 1 diabetes

A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis

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.

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

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 .

Personalizing physical exercise in a computational model of fuel homeostasis

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.

Hormonal response during physical exercise of different intensities in adolescents with type 1 diabetes and healthy controls

BACKGROUND

Physical activity is a critical component in the care of diabetes. Although it offers health benefits it presents challenges.

OBJECTIVE

To investigate differences between adolescent boys and girls with type 1 diabetes and healthy controls in terms of maximal work capacity (VO(2) max) and hormonal response to physical exercise of different intensities.

SUBJECTS

Twelve individuals (six boys and six girls; age 14-19 yr, pubertal stage 4-5) with type 1 diabetes (duration, 6.3 ± 4.4 yr; hemoglobin A1c, 63 ± 10 mmol/mol) were compared with 12 healthy controls matched for age, sex, pubertal stage, body mass index standard deviation score, and amount of regular physical activity.

METHODS

During consecutive days, three different workloads; maximal, endurance, and interval, were performed on an Ergometer cycle. During the tests, levels of lactate, glucose, insulin, and regulatory hormones [glucagon, cortisol, growth hormone (GH), adrenaline, and noradrenaline] were measured in blood. Subcutaneous glucose was measured continuously.

RESULTS

VO(2) max did not differ between the groups, diabetes 49.8 ± 9.9 vs. control 50.7 ± 12.0 mL/min/kg. Hormonal responses did not differ between the groups except for mean peak GH level during the interval test, diabetes 63.2 ± 27.0 vs. control 33.8 ± 20.9 mU/L, p < 0.05.

CONCLUSIONS

Physical capacity and hormonal regulation of blood glucose in connection with physical exercise of different intensities did not differ between adolescents with diabetes and healthy controls. Thus, adolescents with type 1 diabetes can participate in physical activity on the same terms as healthy peers.