Dessi P. Zaharieva

Exercise and the Development of the Artificial Pancreas One of the More Difficult Series of Hurdles

Regular physical activity (PA) promotes numerous health benefits for people living with type 1 diabetes (T1D). However, PA also complicates blood glucose control. Factors affecting blood glucose fluctuations during PA include activity type, intensity and duration as well as the amount of insulin and food in the body at the time of the activity. To maintain equilibrium with blood glucose concentrations during PA, the rate of glucose appearance (Ra) to disappearance (Rd) in the bloodstream must be balanced. In nondiabetics, there is a rise in glucagon and a reduction in insulin release at the onset of mild to moderate aerobic PA. During intense aerobic -anaerobic work, insulin release first decreases and then rises rapidly in early recovery to offset a more dramatic increase in counterregulatory hormones and metabolites. An “exercise smart” artificial pancreas (AP) must be capable of sensing glucose and perhaps other physiological responses to various types and intensities of PA. The emergence of this new technology may benefit active persons with T1D who are prone to hypo and hyperglycemia.

Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time.

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise.

Effects of Selective and Non-Selective Glucocorticoid Receptor II Antagonists on Rapid-Onset Diabetes in Young Rats

The blockade of glucocorticoid (GC) action through antagonism of the glucocorticoid receptor II (GRII) has been used to minimize the undesirable effects of chronically elevated GC levels. Mifepristone (RU486) is known to competitively block GRII action, but not exclusively, as it antagonizes the progesterone receptor. A number of new selective GRII antagonists have been developed, but limited testing has been completed in animal models of overt type 2 diabetes mellitus. Therefore, two selective GRII antagonists (C113176 and C108297) were tested to determine their effects in our model of GC-induced rapid-onset diabetes (ROD). Male Sprague-Dawley rats (∼ six weeks of age) were placed on a high-fat diet (60%), surgically implanted with pellets containing corticosterone (CORT) or wax (control) and divided into five treatment groups. Each group was treated with either a GRII antagonist or vehicle for 14 days after surgery: CORT pellets (400 mg/rat) + antagonists (80 mg/kg/day); CORT pellets + drug vehicle; and wax pellets (control) + drug vehicle. After 10 days of CORT treatment, body mass gain was increased with RU486 (by ∼20% from baseline) and maintained with C113176 administration, whereas rats given C108297 had similar body mass loss (∼15%) to ROD animals. Fasting glycemia was elevated in the ROD animals (>20 mM), normalized completely in animals treated with RU486 (6.2±0.1 mM, p<0.05) and improved in animals treated with C108297 and C113176 (14.0±1.6 and 8.8±1.6 mM, p<0.05 respectively). Glucose intolerance was normalized with RU486 treatment, whereas acute insulin response was improved with RU486 and C113176 treatment. Also, peripheral insulin resistance was attenuated with C113176 treatment along with improved levels of β-cell function while C108297 antagonism only provided modest improvements. In summary, C113176 is an effective agent that minimized some GC-induced detrimental metabolic effects and may provide an alternative to the effective, but non-selective, GRII antagonist RU486.

Effects of acute caffeine supplementation on reducing exercise-associated hypoglycaemia in individuals with Type 1 diabetes mellitus

To determine the effects of acute caffeine ingestion on glycaemia during moderate to vigorous intensity aerobic exercise and in recovery in individuals with Type 1 diabetes.

Caffeine and glucose homeostasis during rest and exercise in diabetes mellitus

Caffeine is a substance that has been used in our society for generations, primarily for its effects on the central nervous system that causes wakefulness. Caffeine supplementation has become increasingly more popular as an ergogenic aid for athletes and considerable scientific evidence supports its effectiveness. Because of their potential to alter energy metabolism, the effects of coffee and caffeine on glucose metabolism in diabetes have also been studied both epidemiologically and experimentally. Predominantly targeting the adenosine receptors, caffeine causes alterations in glucose homeostasis by decreasing glucose uptake into skeletal muscle, thereby causing elevations in blood glucose concentration. Caffeine intake has also been proposed to increase symptomatic warning signs of hypoglycemia in patients with type 1 diabetes and elevate blood glucose levels in patients with type 2 diabetes. Other effects include potential increases in glucose counterregulatory hormones such as epinephrine, which can also decrease peripheral glucose disposal. Despite these established physiological effects, increased coffee intake has been associated with reduced risk of developing type 2 diabetes in large-scale epidemiological studies. This review paper highlights the known effects of caffeine on glucose homeostasis and diabetes metabolism during rest and exercise.

A Pilot Study Validating Select Research-Grade and Consumer-Based Wearables Throughout a Range of Dynamic Exercise Intensities in Persons With and Without Type 1 Diabetes: A Novel Approach:

Background: The increasing popularity of wearable technology necessitates the evaluation of their accuracy to differentiate physical activity (PA) intensities. These devices may play an integral role in customizing PA interventions for primary prevention and secondary management of chronic diseases. For example, in persons with type 1 diabetes (T1D), PA greatly affects glucose concentrations depending on the intensity, mode (ie, aerobic, anaerobic, mixed), and duration. This variability in glucose responses underscores the importance of implementing dependable wearable technology in emerging avenues such as artificial pancreas systems. Methods: Participants completed three 40-minute, dynamic non-steady-state exercise sessions, while outfitted with multiple research (Fitmate, Metria, Bioharness) and consumer (Garmin, Fitbit) grade wearables. The data were extracted according to the devices’ maximum sensitivity (eg, breath by breath, beat to beat, or minute time stamps) and averaged into minute-by-minute data. The variables of interest, heart rate (HR), breathing frequency, and energy expenditure (EE), were compared to validated criterion measures. Results: Compared to deriving EE by laboratory indirect calorimetry standard, the Metria activity patch overestimates EE during light-to-moderate PA intensities (L-MI) and moderate-to-vigorous PA intensities (M-VI) (mean ± SD) (0.28 ± 1.62 kilocalories· minute-1, P < .001, 0.64 ± 1.65 kilocalories· minute-1, P < .001, respectively). The Metria underestimates EE during vigorous-to-maximal PA intensity (V-MI) (–1.78 ± 2.77 kilocalories · minute-1, P < .001). Similarly, compared to Polar HR monitor, the Bioharness underestimates HR at L-MI (–1 ± 8 bpm, P < .001) and M-VI (5 ± 11 bpm, P < .001), respectively. A significant difference in EE was observed for the Garmin device, compared to the Fitmate (P < .001) during continuous L-MI activity. Conclusions: Overall, our study demonstrates that current research-grade wearable technologies operate within a ~10% error for both HR and EE during a wide range of dynamic exercise intensities. This level of accuracy for emerging research-grade instruments is considered both clinically and practically acceptable for research-based or consumer use. In conclusion, research-grade wearable technology that uses EE kilocalories · minute-1 and HR reliably differentiates PA intensities.

Insulin Management Strategies for Exercise in Diabetes

There is no question that regular exercise can be beneficial and lead to improvements in overall cardiovascular health. However, for patients with diabetes, exercise can also lead to challenges in maintaining blood glucose balance, particularly if patients are prescribed insulin or certain oral hypoglycemic agents. Hypoglycemia is the most common adverse event associated with exercise and insulin therapy, and the fear of hypoglycemia is also the greatest barrier to exercise for many patients. With the appropriate insulin dose adjustments and, in some cases, carbohydrate supplementation, blood glucose levels can be better managed during exercise and in recovery. In general, insulin strategies that help facilitate weight loss with regular exercise and recommendations around exercise adjustments to prevent hypoglycemia and hyperglycemia are often not discussed with patients because the recommendations can be complex and may differ from one individual to the next. This is a review of the current published literature on insulin dose adjustments and starting-point strategies for patients with diabetes in preparation for safe exercise.

Accuracy of wrist-worn activity monitors during common daily physical activities and types of structured exercise (Preprint)

Background Wrist-worn activity monitors are often used to monitor heart rate (HR) and energy expenditure (EE) in a variety of settings including more recently in medical applications. The use of real-time physiological signals to inform medical systems including drug delivery systems and decision support systems will depend on the accuracy of the signals being measured, including accuracy of HR and EE. Prior studies assessed accuracy of wearables only during steady-state aerobic exercise. Objective The objective of this study was to validate the accuracy of both HR and EE for 2 common wrist-worn devices during a variety of dynamic activities that represent various physical activities associated with daily living including structured exercise. Methods We assessed the accuracy of both HR and EE for two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) during dynamic activities. Over a 2-day period, 20 healthy adults (age: mean 27.5 [SD 6.0] years; body mass index: mean 22.5 [SD 2.3] kg/m2; 11 females) performed a maximal oxygen uptake test, free-weight resistance circuit, interval training session, and activities of daily living. Validity was assessed using an HR chest strap (Polar) and portable indirect calorimetry (Cosmed). Accuracy of the commercial wearables versus research-grade standards was determined using Bland-Altman analysis, correlational analysis, and error bias. Results Fitbit and Garmin were reasonably accurate at measuring HR but with an overall negative bias. There was more error observed during high-intensity activities when there was a lack of repetitive wrist motion and when the exercise mode indicator was not used. The Garmin estimated HR with a mean relative error (RE, %) of −3.3% (SD 16.7), whereas Fitbit estimated HR with an RE of −4.7% (SD 19.6) across all activities. The highest error was observed during high-intensity intervals on bike (Fitbit: −11.4% [SD 35.7]; Garmin: −14.3% [SD 20.5]) and lowest error during high-intensity intervals on treadmill (Fitbit: −1.7% [SD 11.5]; Garmin: −0.5% [SD 9.4]). Fitbit and Garmin EE estimates differed significantly, with Garmin having less negative bias (Fitbit: −19.3% [SD 28.9], Garmin: −1.6% [SD 30.6], P<.001) across all activities, and with both correlating poorly with indirect calorimetry measures. Conclusions Two common wrist-worn devices (Fitbit Charge 2 and Garmin vívosmart HR+) show good HR accuracy, with a small negative bias, and reasonable EE estimates during low to moderate-intensity exercise and during a variety of common daily activities and exercise. Accuracy was compromised markedly when the activity indicator was not used on the watch or when activities involving less wrist motion such as cycle ergometry were done.