Dessi completed her PhD at York University in Toronto, Canada under the supervision of Dr. Michael Riddell in 2018. Her PhD research focused on strategies to reduce dysglycemia around exercise in adults with type 1 diabetes. Dessi is currently a postdoctoral scholar at Stanford University working under the supervision of Dr. David Maahs. Her research focuses on exercise physiology and blood glucose management in type 1 diabetes.
Honors & Awards
Vanier Canada Graduate Scholarship, CIHR (2015-2018)
Doctor of Philosophy, York University (2019)
Master of Science, York University (2013)
Bachelor of Arts, Brock University (2011)
Doctor of Philosophy, York University (2018)
Master of Science, York University (2014)
Bachelor of Kinesiology, Brock University (2011)
David Maahs, Postdoctoral Faculty Sponsor
- Advances in Exercise, Physical Activity, and Diabetes. Diabetes technology & therapeutics 2020; 22 (S1): S109–S118
TITLE: CONTINUOUS GLUCOSE MONITORING VERSUS SELF-MONITORING OF BLOOD GLUCOSE TO ASSESS GLYCEMIA IN GESTATIONAL DIABETES.
Diabetes technology & therapeutics
Gestational diabetes mellitus (GDM) management using self-monitoring blood glucose (SMBG) does not normalise pregnancy outcomes.We aimed to conduct an observational study to explore if Continuous Glucose Monitoring (CGM) could identify elevated glucose levels not apparent in women with GDM managed using SMBG.A 7-day masked-CGM (iPro, Medtronic) was performed within 2 weeks of GDM diagnosis, immediately post-GDM education but prior to insulin commencement as determined by SMBG. CGM data regarding hyperglycemia (sensor glucose >126 mg/dL [06:00-00:00hrs] and > 99 mg/dL [00:00-06:00hrs] for >10% of time), time with healthcare professionals (HCP), treatment, and pregnancy outcome were collected. Comparisons (Mann-Whitney test) were performed between subjects subsequently commenced on insulin versus those continued with diet and lifestyle measures alone.Ninety women of Mean (SD) gestational age weeks 27(1) were studied. Those prescribed insulin (n=34) compared with those managed with diet and lifestyle alone (n=56) had a greater time in hyperglycemia (p=0.0001). Of those not prescribed insulin, 35/56 (61%) breached CGM cut-offs between 00:00-06:00hrs; 11/56 (20%) breached 6.00-00.00hrs CGM cut-offs for >10% of the time; and 21/45 (47%) with optimal CGM glucose levels during the daytime spent >10% time in hyperglycaemia between 00.00-06:00 hrs. In contrast, SMBG measurements exceeded the clinical targets of <120mg/dL post-dinner in 5.4% and <100mg/dL fasting in 0% of the subjects.CGM provides a more comprehensive assessment of nocturnal hyperglycemia than SMBG and could improve targeting of interventions in GDM. Larger studies to better define CGM targets are required which once established will inform studies aimed at targeting nocturnal glucose levels.
View details for DOI 10.1089/dia.2020.0073
View details for PubMedID 32324046
No Disadvantage to Insulin Pump Off vs Pump On During Intermittent High-Intensity Exercise in Adults With Type 1 Diabetes.
Canadian journal of diabetes
2020; 44 (2): 162–68
Evidence suggests that patients with type 1 diabetes (T1D) performing aerobic exercise with their insulin pump connected (pump on) vs pump disconnected (pump off) have an increased risk of hypoglycemia. It has not been examined whether this risk remains during high-intensity exercise. This study compared the effects of pump on (50% basal insulin at exercise onset) vs pump off (0% basal insulin at exercise onset) on glucose concentrations during intermittent high-intensity exercise in adults with T1D and on patients' own perspective of their glycemia.Twelve adults with T1D using insulin pump therapy completed two 40-min intermittent high-intensity exercise bouts. Insulin adjustments included: 1) pump set to 50% of usual basal rate (pump on) or 2) pump suspended (pump off) during exercise, in random order. Blood glucose was recorded every 10 min during exercise and, after providing subjects with an initial reference glucose value before exercise, participants were asked to estimate their glucose during exercise.Glucose levels were higher in pump off (8.1±1.3 mmol/L) vs pump on (7.4±2.1 mmol/L) at exercise start (p<0.05), but were similar by the end of exercise (p=0.9). During exercise, hypoglycemia incidence did not differ between conditions (1 of 12 for both). However, the percentage of time in hypoglycemia at 12 h after exercise was 5±8% vs 1±2% for pump on vs pump off, respectively (p=0.3). Participants were better able to estimate their own glucose during pump on vs pump off (r2=0.46 vs r2=0.11).Pump on vs pump off at exercise onset showed no significant differences in blood glucose concentrations during 40 min of intermittent high-intensity exercise.
View details for DOI 10.1016/j.jcjd.2019.05.015
View details for PubMedID 31416695
- Advances in Exercise, Physical Activity, and Diabetes Mellitus. Diabetes technology & therapeutics 2019; 21 (S1): S112–S122
Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes.
Diabetes technology & therapeutics
2019; 21 (6): 313–21
Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG). Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager (PDM; Insulet, Billerica, MA) with built-in glucose meter (FreeStyle; Abbott Laboratories, Abbott Park, IL), while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM. Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of -7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6-22)% [median (interquartile range)] during exercise and 8 (5-14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%-99% of the time for SMBG. Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6-22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement.
View details for DOI 10.1089/dia.2018.0364
View details for PubMedID 31059282
View details for PubMedCentralID PMC6551983
Improved Open-Loop Glucose Control With Basal Insulin Reduction 90 Minutes Before Aerobic Exercise in Patients With Type 1 Diabetes on Continuous Subcutaneous Insulin Infusion.
2019; 42 (5): 824–31
To reduce exercise-associated hypoglycemia, individuals with type 1 diabetes on continuous subcutaneous insulin infusion typically perform basal rate reductions (BRRs) and/or carbohydrate feeding, although the timing and amount of BRRs necessary to prevent hypoglycemia are unclear. The goal of this study was to determine if BRRs set 90 min pre-exercise better attenuate hypoglycemia versus pump suspension (PS) at exercise onset.Seventeen individuals completed three 60-min treadmill exercise (∼50% of VO2peak) visits in a randomized crossover design. The insulin strategies included 1) PS at exercise onset, 2) 80% BRR set 90 min pre-exercise, and 3) 50% BRR set 90 min pre-exercise.Blood glucose level at exercise onset was higher with 50% BRR (191 ± 49 mg/dL) vs. 80% BRR (164 ± 41 mg/dL; P < 0.001) and PS (164 ± 45 mg/dL; P < 0.001). By exercise end, 80% BRR showed the smallest drop (-31 ± 58 mg/dL) vs. 50% BRR (-47 ± 50 mg/dL; P = 0.04) and PS (-67 ± 41 mg/dL; P < 0.001). With PS, 7 out of 17 participants developed hypoglycemia versus 1 out of 17 in both BRR conditions (P < 0.05). Following a standardized meal postexercise, glucose rose with PS and 50% BRR (both P < 0.05), but failed to rise with 80% BRR (P = 0.16). Based on interstitial glucose, overnight mean percent time in range was 83%, 83%, and 78%, and time in hypoglycemia was 2%, 1%, and 5% with 80% BRR, 50% BRR, and PS, respectively (all P > 0.05).Overall, a 50-80% BRR set 90 min pre-exercise improves glucose control and decreases hypoglycemia risk during exercise better than PS at exercise onset, while not compromising the postexercise meal glucose control.
View details for DOI 10.2337/dc18-2204
View details for PubMedID 30796112
Individual glucose responses to prolonged moderate intensity aerobic exercise in adolescents with type 1 diabetes: The higher they start, the harder they fall.
2019; 20 (1): 99–106
To evaluate the pattern of change in blood glucose concentrations and hypoglycemia risk in response to prolonged aerobic exercise in adolescents with type 1 diabetes (T1D) that had a wide range in pre-exercise blood glucose concentrations.Individual blood glucose responses to prolonged (~60 minutes) moderate-intensity exercise were profiled in 120 youth with T1D.The mean pre-exercise blood glucose concentration was 178 ± 66 mg/dL, ranging from 69 to 396 mg/dL, while the mean change in glucose during exercise was -76 ± 55 mg/dL (mean ± SD), ranging from +83 to -257 mg/dL. Only 4 of 120 youth (3%) had stable glucose levels during exercise (ie, ± ≤10 mg/dL), while 4 (3%) had a rise in glucose >10 mg/dL, and the remaining (93%) had a clinically significant drop (ie, >10 mg/dL). A total of 53 youth (44%) developed hypoglycemia (≤70 mg/dL) during exercise. The change in glucose was negatively correlated with the pre-exercise glucose concentration (R2 = 0.44, P < 0.001), and tended to be greater in those on multiple daily insulin injections (MDI) vs continuous subcutaneous insulin infusion (CSII) (-98 ± 15 vs -65 ± 7 mg/dL, P = 0.05). No other collected variables appeared to predict the change in glucose including age, weight, height, body mass index, disease duration, daily insulin dose, HbA1c , or sex.Youth with T1D have variable glycemic responses to prolonged aerobic exercise, but this variability is partially explained by their pre-exercise blood glucose levels. When no implementation strategies are in place to limit the drop in glycemia, the incidence of exercise-associated hypoglycemia is ~44% and having a high pre-exercise blood glucose concentration is only marginally protective.
View details for DOI 10.1111/pedi.12799
View details for PubMedID 30467929
- The Accuracy of Continuous Glucose Monitoring and Flash Glucose Monitoring During Aerobic Exercise in Type 1 Diabetes. Journal of diabetes science and technology 2019; 13 (1): 140–41
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.
Journal of diabetes science and technology
2018; 12 (3): 569–76
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.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.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.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.
View details for DOI 10.1177/1932296817750401
View details for PubMedID 29320885
View details for PubMedCentralID PMC6154246
Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study.
JMIR mHealth and uHealth
2018; 6 (12): e10338
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.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.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.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.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.
View details for DOI 10.2196/10338
View details for PubMedID 30530451
View details for PubMedCentralID PMC6305876
The Effects of Basal Insulin Suspension at the Start of Exercise on Blood Glucose Levels During Continuous Versus Circuit-Based Exercise in Individuals with Type 1 Diabetes on Continuous Subcutaneous Insulin Infusion.
Diabetes technology & therapeutics
2017; 19 (6): 370–78
Exercise causes glycemic disturbances in individuals with type 1 diabetes (T1D). Continuous moderate-intensity aerobic exercise (CON) generally lowers blood glucose (BG) levels and often leads to hypoglycemia. In comparison, circuit-based exercise (CIRC) may attenuate the drop in BG. The goal of this study is to contrast the effects of basal insulin suspension at the onset of two different forms of exercise (CON vs. CIRC).Twelve individuals (six men and six women) with T1D on insulin pump therapy were recruited for the study. All participants completed a maximal aerobic fitness test and two 40-min exercise sessions, consisting of either continuous treadmill walking or a circuit workout. Basal insulin infusion was stopped at the onset of exercise and resumed in recovery. After providing an initial reference value, volunteers were blinded to their [BG] and were asked to estimate their levels during exercise.Oxygen consumption (47.5 ± 7.5 vs. 54.5 ± 13.5 mL·kg-1·min-1, P = 0.03) and heart rate (122 ± 20 vs. 144 ± 20 bpm, P = 0.003) were lower in CON vs. CIRC. Despite the lower workload, BG levels dropped more with CON vs. CIRC (delta BG = -3.8 ± 1.5 vs. -0.5 ± 3.0 mmol/L for CON vs. CIRC, respectively, P = 0.001). Participants were able to estimate their BG more accurately during CON (r = 0.83) vs. CIRC (r = 0.33) based on a regression analysis.Despite a lower intensity of exercise, with full basal insulin suspension at the start of exercise, CON results in a larger drop in BG vs. CIRC. These findings have implications for single hormone-based artificial pancreas development for exercise. While this study does not negate the importance of frequent capillary BG monitoring during exercise, it does suggest that if persons are knowledgeable about their pre-exercise BG levels, they can accurately perceive the changes in BG during CON, but not during CIRC.
View details for DOI 10.1089/dia.2017.0010
View details for PubMedID 28613947
View details for PubMedCentralID PMC5510047
Insulin Management Strategies for Exercise in Diabetes.
Canadian journal of diabetes
2017; 41 (5): 507–16
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.
View details for DOI 10.1016/j.jcjd.2017.07.004
View details for PubMedID 28942788
Effects of acute caffeine supplementation on reducing exercise-associated hypoglycaemia in individuals with Type 1 diabetes mellitus.
Diabetic medicine : a journal of the British Diabetic Association
2016; 33 (4): 488–96
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.A total of 13 patients with Type 1 diabetes [eight women, five men: mean ± sd age 25.9 ± 8.8 years, BMI 71.9 ± 11.0 kg, maximal oxygen consumption 46.6 ± 12.7 ml/kg/min, body fat 19.9 ± 7.2%, duration of diabetes 14.4 ± 10.1 years and HbA1c 55 ± 8 mmol/mol (7.4 ± 0.8%)] were recruited. Participants ingested capsules that contained gelatin or pure caffeine (6.0 mg/kg body mass) and performed afternoon exercise for 45 min at 60% maximal oxygen consumption on two separate visits with only circulating basal insulin levels.The main finding was that a single caffeine dose attenuates the drop in glycaemia by 1.8 ± 2.8 mmol/l compared with placebo intake during exercise (P=0.056). Continuous glucose monitoring data, however, showed that caffeine was associated with elevated glycaemia at bedtime after exercise, compared with placebo, but lower glucose concentrations in the early morning the next day.Caffeine intake should be considered as another strategy that may modestly attenuate hypoglycaemia in individuals with Type 1 diabetes during exercise, but should be taken with precautionary measures as it may increase the risk of late-onset hypoglycaemia.
View details for DOI 10.1111/dme.12857
View details for PubMedID 26173655
The "ups" and "downs" of a bike race in people with type 1 diabetes: dramatic differences in strategies and blood glucose responses in the Paris-to-Ancaster Spring Classic.
Canadian journal of diabetes
2015; 39 (2): 105–10
Recommendations for insulin adjustments and carbohydrate intake exist for individuals with type 1 diabetes who are undertaking moderate exercise. Very few guidelines exist for athletes with type 1 diabetes who are competing in events of higher intensity or longer duration. This observational study reports the strategies adopted by 6 habitually active men with type 1 diabetes (glycated hemoglobin = 8.3%±2.0%) undertaking a relatively intense endurance cycling event.Participants wore continuous glucose monitoring (CGM) sensors for 24 hours before competition, while racing and overnight postrace. They were asked to eat their regular meals and snacks and make their usual insulin adjustments before, during and after competition. All food intake and insulin adjustments were recorded in detail.Participants used a variety of adjustments for exercise. Of 6 participants, 4 decreased their insulin dosages and all participants consumed carbohydrates during the race (mean = 87±57 g). In spite of these strategies, 3 of the 6 participants experienced mild to moderate hypoglycemia (not requiring assistance) during the event. Hyperglycemia was seen in all participants 3 hours postexercise. There were no incidents of nocturnal hypoglycemia.Individuals with type 1 diabetes can compete in intensive long-distance athletic events using a variety of nutrition- and insulin-adjustment strategies. In addition to finely tuned insulin adjustments and increased carbohydrate intake, vigilance will always be required to maintain some semblance of glycemic control during events of extended duration.
View details for DOI 10.1016/j.jcjd.2014.09.003
View details for PubMedID 25492557
Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time.
Journal of diabetes science and technology
2015; 9 (6): 1200–1207
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.
View details for DOI 10.1177/1932296815609369
View details for PubMedID 26443291
View details for PubMedCentralID PMC4667299
Exercise and the Development of the Artificial Pancreas: One of the More Difficult Series of Hurdles.
Journal of diabetes science and technology
2015; 9 (6): 1217–26
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.
View details for DOI 10.1177/1932296815609370
View details for PubMedID 26428933
View details for PubMedCentralID PMC4667314
- Prevention of exercise-associated dysglycemia: a case study-based approach. Diabetes spectrum : a publication of the American Diabetes Association 2015; 28 (1): 55–62
Effects of selective and non-selective glucocorticoid receptor II antagonists on rapid-onset diabetes in young rats.
2014; 9 (3): e91248
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.
View details for DOI 10.1371/journal.pone.0091248
View details for PubMedID 24642683
View details for PubMedCentralID PMC3958344
Caffeine and glucose homeostasis during rest and exercise in diabetes mellitus.
Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme
2013; 38 (8): 813–22
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.
View details for DOI 10.1139/apnm-2012-0471
View details for PubMedID 23855268