Modeling the acute effects of exercise on glucose dynamics in healthy nondiabetic subjects

Biomarker Responses to Acute Exercise and Relationship with Brain Blood Flow

BACKGROUND

There is evidence that aerobic exercise is beneficial for brain health, but these effects are variable between individuals and the underlying mechanisms that modulate these benefits remain unclear.

OBJECTIVE

We sought to characterize the acute physiological response of bioenergetic and neurotrophic blood biomarkers to exercise in cognitively healthy older adults, as well as relationships with brain blood flow.

METHODS

We measured exercise-induced changes in lactate, which has been linked to brain blood flow, as well brain-derived neurotrophic factor (BDNF), a neurotrophin related to brain health. We further quantified changes in brain blood flow using arterial spin labeling.

RESULTS

As expected, lactate and BDNF both changed with time post exercise. Intriguingly, there was a negative relationship between lactate response (area under the curve) and brain blood flow measured acutely following exercise. Finally, the BDNF response tracked strongly with change in platelet activation, providing evidence that platelet activation is an important mechanism for trophic-related exercise responses.

CONCLUSIONS

Lactate and BDNF respond acutely to exercise, and the lactate response tracks with changes in brain blood flow. Further investigation into how these factors relate to brain health-related outcomes in exercise trials is warranted.

A physical activity-intensity driven glycemic model for type 1 diabetes

BACKGROUND AND OBJECTIVE

The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered.

METHODS

Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed.

RESULTS

The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002).

CONCLUSIONS

The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.

Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.