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

To shed light on how acute exercise affects blood glucose (BG) concentrations in nondiabetic subjects, we develop a physiological pharmacokinetic/pharmacodynamic model of postprandial glucose dynamics during exercise. We unify several concepts of exercise physiology to derive a multiscale model that includes three important effects of exercise on glucose dynamics: increased endogenous glucose production (EGP), increased glucose uptake in skeletal muscle (SM), and increased glucose delivery to SM by capillary recruitment (i.e. an increase in surface area and blood flow in capillary beds). We compare simulations to experimental observations taken in two cohorts of healthy nondiabetic subjects (resting subjects (n = 12) and exercising subjects (n = 12)) who were each given a mixed-meal tolerance test. Metabolic tracers were used to quantify the glucose flux. Simulations reasonably agree with postprandial measurements of BG concentration and EGP during exercise. Exercise-induced capillary recruitment is predicted to increase glucose transport to SM by 100%, causing hypoglycemia. When recruitment is blunted, as in those with capillary dysfunction, the opposite occurs and higher than expected BG levels are predicted. Model simulations show how three important exercise-induced phenomena interact, impacting BG concentrations. This model describes nondiabetic subjects, but it is a first step to a model that describes glucose dynamics during exercise in those with type 1 diabetes (T1D). Clinicians and engineers can use the insights gained from the model simulations to better understand the connection between exercise and glucose dynamics and ultimately help patients with T1D make more informed insulin dosing decisions around exercise.

Modeling the acute effects of exercise on insulin kinetics in type 1 diabetes.

Our objective is to develop a physiology-based model of insulin kinetics to understand how exercise alters insulin concentrations in those with type 1 diabetes (T1D). We reveal the relationship between the insulin absorption rate ([Formula: see text]) from subcutaneous tissue, the insulin delivery rate ([Formula: see text]) to skeletal muscle, and two physiological parameters that characterize the tissue: the perfusion rate (Q) and the capillary permeability surface area (PS), both of which increase during exercise because of capillary recruitment. We compare model predictions to experimental observations from two pump-wearing T1D cohorts [resting subjects ([Formula: see text]) and exercising subjects ([Formula: see text])] who were each given a mixed-meal tolerance test and a bolus of insulin. Using independently measured values of Q and PS from literature, the model predicts that during exercise insulin concentration increases by 30% in plasma and by 60% in skeletal muscle. Predictions reasonably agree with experimental observations from the two cohorts, without the need for parameter estimation by curve fitting. The insulin kinetics model suggests that the increase in surface area associated with exercise-induced capillary recruitment significantly increases [Formula: see text] and [Formula: see text], which explains why insulin concentrations in plasma and skeletal muscle increase during exercise, ultimately enhancing insulin-dependent glucose uptake. Preventing hypoglycemia is of paramount importance in determining the proper insulin dose during exercise. The presented model provides mechanistic insight into how exercise affects insulin kinetics, which could be useful in guiding the design of decision support systems and artificial pancreas control algorithms.

Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes.

Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.