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Glucocorticoids (GCs) are hormones that aid the body under stress by regulating glucose and free fatty acids. GCs maintain energy homeostasis in multiple tissues, including those in the liver and skeletal muscle, white adipose tissue (WAT), and brown adipose tissue (BAT). WAT stores energy as triglycerides, while BAT uses fatty acids for heat generation. The multiple genomic and non-genomic pathways in GC signaling vary with exposure duration, location (adipose tissue depot), and species. Genomic effects occur directly through the cytosolic GC receptor (GR), regulating the expression of proteins related to lipid metabolism, such as ATGL and HSL. Non-genomic effects act through mechanisms often independent of the cytosolic GR and happen shortly after GC exposure. Studying the effects of GCs on adipose tissue breakdown and generation (lipolysis and adipogenesis) leads to insights for treatment of adipose-related diseases, such as obesity, coronary disease, and cancer, but has led to controversy among researchers, largely due to the complexity of the process. This paper reviews the recent literature on the genomic and non-genomic effects of GCs on WAT and BAT lipolysis and proposes research to address the many gaps in knowledge related to GC activity and its effects on disease.

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h—men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70–140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Glucose data regarding extreme elite performances in athletes without diabetes remains limited. The purpose is to characterize continuous glucose monitoring (CGM) responses in elite athletes across distinct high-performance contexts. This descriptive case series includes three separate elite athletes who used a CGM during their respective sporting events. The first is an ultra-endurance relay cycling world-record performance (Race Across the West, RAW), the second is a continuous high-intensity Everesting Challenge cycling record attempt, and the third is a maximal constant-weight no-fins breath-hold depth dive performed in international competition. Glycemic outcomes, as measured by CGM, included mean, maximum, and minimum glucose, glucose standard deviation (SD), and the percentage of time in tight glucose range (TITR: 70–140 mg/dL; 3.9–7.8 mmol/L), time below range (TBR: <70 mg/dL; <3.9 mmol/L), and time above range (TAR140: >140 mg/dL; >7.8 mmol/L). Other performance data, including peak power, heart rate, and lactate, are also provided where available. During the RAW challenge lasting 44 h and 20 min, mean glucose was 91 ± 23.2 mg/dL (mean ± SD) with 9.15% TBR and 35.58% TITR during cycling and 115 ± 24.7 mg/dL with 9.11% TBR and 43.16% TITR during resting periods. In contrast, the Everesting Challenge cycling record attempt demonstrated a persistently elevated glucose profile (160 ± 5.7 mg/dL), minimal variability (CV 3.5%), and 100% TAR140. Following the maximal breath-hold depth dive, interstitial glucose was 100% TAR140 during recovery (187 ± 18.5 mg/dL), alongside marked elevations in blood lactate concentrations (peak 13.4 mmol/L). The series of case studies demonstrate that substantial deviations from traditional euglycemic ranges are common during elite performance in athletes without diabetes. Interpretation of CGM data in athletic settings should therefore be performance- and context-specific rather than based on clinical glycemic thresholds.

The impact of age, sex and body mass index on interstitial glucose levels as measured via continuous glucose monitoring (CGM) during exercise in the healthy population is largely unexplored. We conducted a multivariable generalized estimating equation (GEE) analysis on CGM data (Dexcom G6, 10 days) collected from 119 healthy exercising individuals using CGM with the following specified covariates: age; sex; BMI; exercise type and duration. Females had lower postexercise glycemia as compared with males (92 ± 18 vs. 100 ± 20 mg/dL, p = 0.04) and a greater change in glycemia during exercise from pre- to postexercise (p = 0.001) or from pre-exercise to glucose nadir during exercise (p = 0.009). Younger individuals (i.e., <20 yrs) had higher glucose during exercise as compared with all other age groups (all p < 0.05) and less CGM data in the hypoglycemic range (<70 mg/dL) as compared with those aged 20–39 yrs (p < 0.05). Those who were underweight, based on body mass index (BMI: <18.5 kg/m2), had higher pre-exercise glycemia than the healthy BMI group (104 ± 20 vs. 97 ± 17 mg/dL, p = 0.02) but similar glucose levels after exercise. Resistance exercise was associated with less of a drop in glycemia as compared with aerobic or mixed forms of exercise (p = 0.008) and resulted in a lower percent of time in the hypoglycemic (p = 0.04) or hyperglycemic (glucose > 140 mg/dL) (p = 0.03) ranges. In summary, various factors such as age, sex and exercise type appear to have subtle but potentially important influence on CGM measurements during exercise in healthy individuals.

To determine the effects of exercise order on acute glycemic responses in individuals with type 1 diabetes performing both aerobic and resistance exercise in the same session. Twelve physically active individuals with type 1 diabetes (HbA(1c) 7.1 ± 1.0%) performed aerobic exercise (45 min of running at 60% V(O(2peak))) before 45 min of resistance training (three sets of eight, seven different exercises) (AR) or performed the resistance exercise before aerobic exercise (RA). Plasma glucose was measured during exercise and for 60 min after exercise. Interstitial glucose was measured by continuous glucose monitoring 24 h before, during, and 24 h after exercise. Significant declines in blood glucose levels were seen in AR but not in RA throughout the first exercise modality, resulting in higher glucose levels in RA (AR = 5.5 ± 0.7, RA = 9.2 ± 1.2 mmol/L, P = 0.006 after 45 min of exercise). Glucose subsequently decreased in RA and increased in AR over the course of the second 45-min exercise bout, resulting in levels that were not significantly different by the end of exercise (AR = 7.5 ± 0.8, RA = 6.9 ± 1.0 mmol/L, P = 0.436). Although there were no differences in frequency of postexercise hypoglycemia, the duration (105 vs. 48 min) and severity (area under the curve 112 vs. 59 units ⋅ min) of hypoglycemia were nonsignificantly greater after AR compared with RA. Performing resistance exercise before aerobic exercise improves glycemic stability throughout exercise and reduces the duration and severity of postexercise hypoglycemia for individuals with type 1 diabetes.

Vigorous intermittent exercise can improve indices of glycemia in the 24 h postexercise period in apparently healthy individuals. We examined the effect of a single session of bodyweight exercise (BWE) on glycemic responses using continuous glucose monitoring (CGM) under controlled dietary conditions. Healthy inactive adults (n = 27; 8 males, 19 females; age: 23 ± 3 years) completed 2 virtually supervised trials spaced ~ 1 week apart in a randomized, crossover manner. The trials involved an 11-min BWE protocol that consisted of 5 × 1-min bouts performed at a self-selected pace interspersed with 1-min active recovery periods or a non-exercise sitting control period (CON). Mean heart rate during the BWE protocol was 147 ± 14 beats per min (75% of age-predicted maximum). Mean 24 h glucose after BWE and CON was not different (5.0 ± 0.4 vs 5.0 ± 0.5 mM respectively; p = 0.39). There were also no differences between conditions for measures of glycemic variability or the postprandial glucose responses after ingestion of a 75 g glucose drink or lunch, dinner, and breakfast meals. This study demonstrates the feasibility of conducting a remotely supervised BWE intervention using CGM under free-living conditions. Future studies should investigate the effect of repeated sessions of BWE training or responses in people with impaired glycemic control.

Smartphone technologies, and the applications (apps) that they host, are developing rapidly mainly with regard to communication, information processing, design, features and connectivity with other devices. Technologies used in modern treatment modalities and monitoring of type 1 diabetes are also rapidly evolving and can communicate with smartphones and apps. Therefore, numerous web-based and smartphone apps aim to provide information and various patient data metrics (e.g. caloric intake, activity levels, glucose monitoring) that can be accessed and processed for decision support by smartphone apps. In this narrative review, we highlight current information about the effectiveness of interventions through smartphone apps with a focus on apps designed to give guidance to patients with type 1 diabetes on physical activity monitoring and glucose control during and after structured exercise sessions, as these patients are experiencing huge therapeutic challenges during exercise. Furthermore, we propose a number of critical elements for future apps designed for people with type 1 diabetes.

Glucose homeostasis is primarily maintained by pancreatic hormones, insulin and glucagon, with an emerging role for a third islet hormone, somatostatin, in regulating insulin and glucagon responses. Under healthy conditions, somatostatin secreted from pancreatic islet δ-cells inhibits both insulin and glucagon release through somatostatin receptor- induced cAMP-mediated downregulation and paracrine inhibition of β- and α-cells, respectively. Since glucagon is the body’s most important anti-hypoglycemic hormone, and because glucagon counterregulation to hypoglycemia is lost in diabetes, the study of somatostatin biology has led to new investigational medications now in development that may help to restore glucagon counterregulation in type 1 diabetes. This review highlights the normal regulatory role of pancreatic somatostatin signaling in healthy islet function and how the inhibition of somatostatin receptor signaling in pancreatic α-cells may restore normal glucagon counterregulation in diabetes mellitus.

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/m ; 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.

Regular physical activity improves cardiometabolic and musculoskeletal health, helps with weight management, improves cognitive and psychosocial functioning, and is associated with reduced mortality related to cancer and diabetes mellitus. However, turnover rates of glucose in the blood increase dramatically during exercise, which often results in either hypoglycaemia or hyperglycaemia as well as increased glycaemic variability in individuals with type 1 diabetes mellitus (T1DM). A complex neuroendocrine response to an acute exercise session helps to maintain circulating levels of glucose in a fairly tight range in healthy individuals, while several abnormal physiological processes and limitations of insulin therapy limit the capacity of people with T1DM to exercise in a normoglycaemic state. Knowledge of the acute and chronic effects of exercise and regular physical activity is critical for the formulation of clinical strategies for the management of insulin and nutrition for active patients with T1DM. Emerging diabetes-related technologies, such as continuous glucose monitors, automated insulin delivery systems and the administration of solubilized glucagon, are demonstrating efficacy for preserving glucose homeostasis during and after exercise in this population of patients. This Review highlights the beneficial effects of regular exercise and details the complex endocrine and metabolic responses to different types of exercise for adults with T1DM. An overview of basic clinical strategies for the preservation of glucose homeostasis using emerging technologies is also provided.Exercise has many beneficial effects; however, glycaemia needs to be carefully managed in patients with type 1 diabetes mellitus undertaking exercise. This Review characterizes the exercise response in people with type 1 diabetes mellitus and provides clinical management strategies to address glucose control around exercise.