Interstitial Glucose and Physical Exercise in Type 1 Diabetes: Integrative Physiology, Technology, and the Gap In-Between. Nutrients. 2018;10. [PMID: 29342932 PMCID: PMC5793321 DOI: 10.3390/nu10010093]

Laboratory medicine and sports: where are we now?

Laboratory medicine in sport and exercise has significantly developed during the last decades with the awareness that physical activity contributes to improved health status, and is present in monitoring both professional and recreational athletes. Training and competitions can modify concentrations of a variety of laboratory parameters, so the accurate laboratory data interpretation includes controlled and known preanalytical and analytical variables to prevent misleading interpretations. The paper represents a comprehensive summary of the lectures presented during the 35th Annual Symposium of the Croatian Society of Medical Biochemistry and Laboratory Medicine. It describes management of frequent sport injuries and sums up current knowledge of selected areas in laboratory medicine and sports including biological variation, changes in biochemical parameters and glycemic status. Additionally, the paper polemicizes sex hormone disorders in sports, encourages and comments research in recreational sports and laboratory medicine. In order to give the wider view, the connection of legal training protocols as well as monitoring prohibited substances in training is also considered through the eyes of laboratory medicine.

Continuous measurement of interstitial glycaemia in professional female UCI world tour cyclists undertaking a 9-day cycle training camp

Nine cyclists (age: 26 ± 5 years, height: 168 ± 5 cm and mass 58.5 ± 4.5 kg) were observed using continuous glucose monitoring devices throughout a training camp. Interstitial glucose [iG] data were captured via the Abbott libre sense biosensor (Abbott Laboratories) and paired with the Supersapiens software (TT1 Products Inc.). [iG] data were split into time ranges, that is, overall (24-hourly), day-time (06:00-23:59), night-time (00:00-05:59) and exercise. [iG] data were stratified into percentage of time, below range ([TBR] < 70 mg/dl), in range ([TIR] 70-140 mg/dl) and above range ([TAR] ≥ 141 mg/dl). Differences in diurnal and nocturnal data were analysed via repeated measures analysis of variance and paired t-tests where appropriate. p-value of ≤0.05 was accepted as significant. Riders spent an average of 3 ± 1% TAR, 93 ± 2% TIR and 8 ± 3% TBR. Mean 24 h [iG] was 93 ± 2 mg/dl with a coefficient of variation (CV) of 18 ± 1%. Mean (day: 95 ± 3 vs. night: 86 ± 3 mg/dl and p < 0.001) and CV (day: 18 ± 1 vs. night: 9 ± 1% and p < 0.001) in [iG] were higher during the day-time hours. TAR was greater during the day (day: 3 ± 1 vs. night: 0 ± 0% and p < 0.001) but TBR and TIR were similar. Glucose levels below the clinical range may have implications for those without diabetes and warrants further investigation.

Practical considerations for continuous glucose monitoring in elite athletes with type 1 diabetes mellitus: A narrative review

Type 1 diabetes mellitus (T1DM) refers to a metabolic condition where a lack of insulin impairs the usual homeostatic mechanisms to control blood glucose levels. Historically, participation in competitive sport has posed a challenge for those with T1DM, where the dynamic changes in blood glucose during exercise can result in dangerously high (hyperglycaemia) or low blood glucoses (hypoglycaemia) levels. Over the last decade, research and technological development has enhanced the methods of monitoring and managing blood glucose levels, thus reducing the chances of experiencing hyper- or hypoglycaemia during exercise. The introduction of continuous glucose monitoring (CGM) systems means that glucose can be monitored conveniently, without the need for frequent fingerpick glucose checks. CGM devices include a fine sensor inserted under the skin, measuring levels of glucose in the interstitial fluid. Readings can be synchronized to a reader or mobile phone app as often as every 1-5 min. Use of CGM devices is associated with lower HbA1c and a reduction in hypoglycaemic events, promoting overall health and athletic performance. However, there are limitations to CGM, which must be considered when being used by an athlete with T1DM. These limitations can be addressed by individualized education plans, using protective equipment to prevent sensor dislodgement, as well as further research aiming to: (i) account for disparities between CGM and true blood glucose levels during vigorous exercise; (ii) investigate the effects of temperature and altitude on CGM accuracy, and (iii) explore of the sociological impact of CGM use amongst sportspeople without diabetes on those with T1DM.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes

Background

Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting.

Materials and methods

Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day.

Results

Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6.

Conclusion

Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

The effects of acute hyperglycaemia on sports and exercise performance in type 1 diabetes: A systematic review and meta-analysis

OBJECTIVES

People with type 1 diabetes (T1D) are advised by health care professionals to target mild hyperglycaemia before and during exercise, to reduce the risk of hypoglycaemia. This review aimed to summarise the available evidence on the effects of acute hyperglycaemia on sports and exercise performance in T1D.

DESIGN

Systematic review and meta-analysis.

METHODS

Medline, EMBASE, CENTRAL, and Web of Science were searched until 29th May 2023 for studies investigating the effects of acute hyperglycaemia on any sports or exercise performance outcome in T1D. Random-effects meta-analysis was performed using standardised mean differences (SMD) when more than one study reported data for similar outcomes. Certainty of evidence for each outcome was assessed using GRADE.

RESULTS

Seven studies were included in the review, comprising data from 119 people with T1D. Meta-analysis provided moderate-certainty evidence that acute hyperglycaemia does not significantly affect aerobic exercise performance (SMD -0.17; 95 % CI -0.59, 0.26; p = 0.44). There is low- or very-low certainty evidence that acute hyperglycaemia has no effect on anaerobic (two outcomes), neuromuscular (seven outcomes) or neurocognitive performance (three outcomes), except impaired isometric knee extension strength. One study provided low-certainty evidence that the performance effects of hyperglycaemia may depend on circulating insulin levels.

CONCLUSIONS

Acute hyperglycaemia before or during exercise appears unlikely to affect aerobic performance to an extent that is relevant to most people with T1D, based on limited evidence. Future research in this field should focus on anaerobic, neuromuscular and neurocognitive performance, and examine the relevance of circulating insulin levels.

Factory-Calibrated Continuous Glucose Monitoring Systems in Type 1 Diabetes: Accuracy during In-Clinic Exercise and Home Use

Continuous glucose monitors (CGMs) are valuable tools for improving glycemic control, yet their accuracy might be influenced by physical activity. This study sought to assess the accuracy of the three latest factory-calibrated CGM systems available in Europe at the time the study was conducted, both during aerobic exercise and in typical daily scenarios. The accuracy evaluation, based on metrics such as the median absolute relative difference (MARD) and point and rate error-grid analyses (PEGA and REGA), involved 13 adults with type 1 diabetes. Participants wore all sensors during a 1 h in-clinic exercise session followed by a subsequent 3-day home period, with blood glucose measurements serving as reference values in both contexts. During exercise, no statistically significant differences in MARD were observed (Dexcom G6: 12.6%, Guardian 4: 10.7%, and Freestyle Libre 2: 17.2%; p = 0.31), and similarly, no significant differences emerged in PEGA-zone-AB (100%, 100%, 96.8%; p = 0.37). Nevertheless, Freestyle Libre 2 showed comparatively diminished accuracy in estimating glucose trends during exercise (REGA-zone-AB: 100%, 93.0%, 73.3%; p = 0.0003). In the home environment, Freestyle Libre 2 exhibited a significantly higher MARD when compared to the other systems (10.2%, 11.9%, 16.7%, p = 0.02). Overall, Dexcom G6 and Guardian 4 demonstrated superior accuracy in both exercise and daily life scenarios compared to Freestyle Libre 2.

Perceptions of Continuous Glucose Monitoring Systems in the T1D Exchange Diabetes Registry: Satisfaction, Concerns, and Areas for Future Improvement

Manufacturers continue to improve performance and usability of continuous glucose monitoring (CGM) systems. As CGM becomes a standard of care, especially for people on insulin therapy, it is important to routinely gauge how satisfied people with diabetes are with this technology. This article describes survey feedback from a large cohort of people with diabetes using older and current CGM systems and highlights areas of current satisfaction, concern, and future system improvement.

Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

Exercise and Self-Management in Adults with Type 1 Diabetes

PURPOSE OF REVIEW

The purpose of this review paper is to examine the most recent evidence of exercise-related self-management in adults with type 1 diabetes (T1D).

RECENT FINDINGS

This paper reviews the benefits and barriers to exercise, diabetes self-management education, the role of the healthcare provider in assessment and counseling, the use of technology, and concerns for special populations with T1D. Adults with T1D may not exercise at sufficient levels. Assessing current levels of exercise, counseling during a clinical visit, and the use of technology may improve exercise in this population.

Accuracy of Real Time Continuous Glucose Monitoring during Different Liquid Solution Challenges in Healthy Adults: A Randomized Controlled Cross-Over Trial

Continuous glucose monitoring (CGM) represents an integral of modern diabetes management, however, there is still a lack of sensor performance data when rapidly consuming different liquids and thus changing total body water. 18 healthy adults (ten females, age: 23.1 ± 1.8 years, BMI 22.2 ± 2.1 kg·m−2) performed four trial visits consisting of oral ingestion (12 mL per kg body mass) of either a 0.9% sodium chloride, 5% glucose or Ringer’s solution and a control visit, in which no liquid was administered (control). Sensor glucose levels (Dexcom G6, Dexcom Inc., San Diego, CA, USA) were obtained at rest and in 10-min intervals for a period of 120 min after solution consumption and compared against reference capillary blood glucose measurements. The overall MedARD [IQR] was 7.1% [3.3−10.8]; during control 5.9% [2.7−10.8], sodium chloride 5.0% [2.7−10.2], 5% glucose 11.0% [5.3−21.6] and Ringer’s 7.5% [3.1−13.2] (p < 0.0001). The overall bias [95% LoA] was 4.3 mg·dL−1 [−19 to 28]; during control 3.9 mg·dL−1 [−11 to 18], sodium chloride 4.8 mg·dL−1 [−9 to 19], 5% glucose 3.6 mg·dL−1 [−33 to 41] and Ringer’s solution 4.9 mg·dL−1 [−13 to 23]. The Dexcom G6 CGM system detects glucose with very good accuracy during liquid solution challenges in normoglycemic individuals, however, our data suggest that in people without diabetes, sensor performance is influenced by different solutions.

A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future

One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.

Accuracy of Flash Glucose Monitoring During Postprandial Rest and Different Walking Conditions in Overweight or Obese Young Adults

Aims: To investigate the accuracy of FreeStyle Libre^TM flash glucose monitoring (FGM) relevant to plasma glucose (PG) measurements during postprandial rest and different walking conditions in overweight/obese young adults. Methods: Data of 40 overweight/obese participants from two randomized crossover studies were pooled into four trials: (1) sitting (SIT, n = 40); (2) walking continuously for 30 min initiated 20 min before individual postprandial glucose peak (PPGP) (20iP + CONT, n = 40); (3) walking continuously for 30 min initiated at PPGP (iP + CONT, n = 20); and (4) accumulated walking for 30 min initiated 20 min before PPGP (20iP + ACCU, n = 20). Paired FGM and PG were measured 4 h following breakfast. Results: The overall mean absolute relative difference (MARD) between PG and FGM readings was 16.4 ± 8.6% for SIT, 16.2 ± 4.7% for 20iP + CONT, 16.7 ± 12.2% for iP + CONT, and 19.1 ± 6.8% for 20iP + ACCU. The Bland-Altman analysis showed a bias of -1.03 mmol⋅L^-1 in SIT, -0.89 mmol⋅L^-1 in 20iP + CONT, -0.82 mmol⋅L^-1 in iP + CONT, and -1.23 mmol⋅L^-1 in 20iP + ACCU. The Clarke error grid analysis showed that 99.6-100% of the values in all trials fell within zones A and B. Conclusion: Although FGM readings underestimated PG, the FGM accuracy was overall clinically acceptable during postprandial rest and walking in overweight/obese young adults.

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

Time in Range for Closed-Loop Systems versus Standard of Care during Physical Exercise in People with Type 1 Diabetes: A Systematic Review and Meta-Analysis

The aim of this systematic review and meta-analysis was to compare time in range (TIR) (70–180 mg/dL (3.9–10.0 mmol/L)) between fully closed-loop systems (CLS) and standard of care (including hybrid systems) during physical exercise in people with type 1 diabetes (T1D). A systematic literature search was conducted in EMBASE, PubMed, Cochrane Central Register of Controlled Trials, and ISI Web of Science from January 1950 until January 2020. Randomized controlled trials including studies with different CLS were compared against standard of care in people with T1D. The meta-analysis was performed using the random effects model and restricted maximum likelihood estimation method. Six randomized controlled trials involving 153 participants with T1D of all age groups were included. Due to crossover test designs, studies were included repeatedly (a–d) if CLS or physical exercise interventions were different. Applying this methodology increased the comparisons to a total number of 266 participants. TIR was higher with an absolute mean difference (AMD) of 6.18%, 95% CI: 1.99 to 10.38% in favor of CLS. In a subgroup analysis, the AMD was 9.46%, 95% CI: 2.48% to 16.45% in children and adolescents while the AMD for adults was 1.07% 95% CI: −0.81% to 2.96% in favor of CLS. In this systematic review and meta-analysis CLS moderately improved TIR in comparison to standard of care during physical exercise in people with T1D. This effect was particularly pronounced for children and adolescents showing that the use of CLS improved TIR significantly compared to standard of care.

Fear of hypoglycemia, a game changer during physical activity in type 1 diabetes mellitus patients

Hypoglycemia limits optimal glycemic management of patients with type 1 diabetes mellitus (T1DM). Fear of hypoglycemia (FoH) is a significant psychosocial consequence that negatively impacts the willingness of T1DM patients to engage in and profit from the health benefits of regular physical activity (e.g., cardiometabolic health, improved body composition, cardiovascular fitness, quality of life). Technological advances, improved insulin regimens, and a better understanding of the physiology of various types of exercise could help ameliorate FoH. This narrative review summarizes the available literature on FoH in children and adults and tools to avoid it.

Review: Glucose-sensitive insulin

BACKGROUND

Hypoglycemia, the condition of low blood sugar, is a common occurance in people with diabetes using insulin therapy. Protecting against hypoglycaemia by engineering an insulin preparation that can auto-adjust its biological activity to fluctuating blood glucose levels has been pursued since the 1970s, but despite numerous publications, no system that works well enough for practical use has reached clinical practise.

SCOPE OF REVIEW

This review will summarise and scrutinise known approaches for producing glucose-sensitive insulin therapies. Notably, systems described in patent applications will be extensively covered, which has not been the case for earlier reviews of this area.

MAJOR CONCLUSIONS

The vast majority of published systems are not suitable for product development, but a few glucose-sensitive insulin concepts have recently reached clinical trials, and there is hope that glucose-sensitive insulin will become available to people with diabetes in the near future.

Performance of the Intermittently Scanned Continuous Glucose Monitoring (isCGM) System during a High Oral Glucose Challenge in Adults with Type 1 Diabetes-A Prospective Secondary Outcome Analysis

To assess intermittently scanned continuous glucose monitoring (isCGM) performance for different rates of change in plasma glucose (RCPG) during glycemic challenges in type 1 diabetes (T1D). Nineteen people with T1D (7 females; age 35 ± 11 years; HbA_1c 7.3 ± 0.6% (56 ± 7 mmol/mol)) performing two glycemic challenges (OGTT) were included. During OGTTs, plasma glucose was compared against sensor glucose for timepoints 0 min (pre-OGTT), +15 min, +30 min, +60 min, +120 min, +180 min, and +240 min by means of median absolute (relative) difference (MARD and MAD) and Clarke Error Grid (CEG), then was stratified for RCPG and glycemic ranges. Overall, MARD was 8.3% (4.0–14.8) during hypoglycemia level 1 18.8% (15.8–22.0), euglycemia 9.5% (4.3–15.1), hyperglycemia level 1 9.4% (4.0–17.2), and hyperglycemia level 2 7.1% (3.3–11.9). The MARD was associated with the RCPG (p < 0.0001), detailing significant differences in comparison of low, moderate, high, and very high RCPG (p = 0.014). Overall, CEG resulted in 88% (212 values) of comparison points in zone A, 12% (29 values) in zone B, and 0.4% (1 value) in zone D. The isCGM system was accurate during OGTTs. Its performance was dependent on the RCPG and showed an overestimation of the actual reference glucose during hypoglycemia.

Improved Nocturnal Glycaemia and Reduced Insulin Use Following Clinical Exercise Trial Participation in Individuals With Type 1 Diabetes

Aim: To explore the influence of clinical exercise trial participation on glycaemia and insulin therapy use in adults with type 1 diabetes (T1D).

Research Design and Methods: This study involved a secondary analysis of data collected from 16 individuals with T1D who completed a randomized clinical trial consisting of 23-h in-patient phases with a 45-min evening bout of moderate intensity continuous exercise. Participants were switched from their usual basal-bolus therapy to ultra-long acting insulin degludec and rapid-acting insulin aspart as well as provided with unblinded interstitial flash-glucose monitoring systems. To assess the impact of clinical trial participation, weekly data obtained at the screening visit (pre-study involvement) were compared against those collated on the last experimental visit (post-study involvement). Interstitial glucose [iG] data were split into distinct glycaemic ranges and stratified into day (06:00–23:59) and night (00:00–05:59) time periods. A p-value of ≤ 0.05 was accepted for significance.

Results: Following study completion, there were significant decreases in both the mean nocturnal iG concentration (Δ-0.9 ± 4.5 mmol.L⁻¹, p < 0.001) and the time spent in severe hyperglycaemia (Δ-7.2 ± 9.8%, p = 0.028) during the night-time period. The total daily (Δ-7.3 ± 8.4 IU, p = 0.003) and basal only (Δ-2.3 ± 3.8 IU, p = 0.033) insulin dose requirements were reduced over the course of study involvement.

Conclusions: Participation in clinical research may foster improved nocturnal glycaemia and reduced insulin therapy use in people with T1D. Recognition of these outcomes may help encourage volunteers to partake in clinical research opportunities for improved diabetes-related health outcomes.

Clinical Trial Registration:DRKS.de; DRKS00013509.

Extent and prevalence of post-exercise and nocturnal hypoglycemia following peri-exercise bolus insulin adjustments in individuals with type 1 diabetes

AIM

To detail the extent and prevalence of post-exercise and nocturnal hypoglycemia following peri-exercise bolus insulin dose adjustments in individuals with type 1 diabetes (T1D) using multiple daily injections of insulins aspart (IAsp) and degludec (IDeg).

METHODS AND RESULTS

Sixteen individuals with T1D, completed a single-centred, randomised, four-period crossover trial consisting of 23-h inpatient phases. Participants administered either a regular (100%) or reduced (50%) dose (100%; 5.1 ± 2.4, 50%; 2.6 ± 1.2 IU, p < 0.001) of individualised IAsp 1 h before and after 45-min of evening exercise at 60 ± 6% V̇O_2max. An unaltered dose of IDeg was administered in the morning. Metabolic, physiological and hormonal responses during exercise, recovery and nocturnal periods were characterised. The primary outcome was the number of trial day occurrences of hypoglycemia (venous blood glucose ≤ 3.9 mmol L ^-1). Inclusion of a 50% IAsp dose reduction strategy prior to evening exercise reduced the occurrence of in-exercise hypoglycemia (p = 0.023). Mimicking this reductive strategy in the post-exercise period decreased risk of nocturnal hypoglycemia (p = 0.045). Combining this strategy to reflect reductions either side of exercise resulted in higher glucose concentrations in the acute post-exercise (p = 0.034), nocturnal (p = 0.001), and overall (p < 0.001) periods. Depth of hypoglycemia (p = 0.302), as well as ketonic and counter-regulatory hormonal profiles were similar.

CONCLUSIONS

These findings demonstrate the glycemic safety of peri-exercise bolus dose reduction strategies in minimising the prevalence of acute and nocturnal hypoglycemia following evening exercise in people with T1D on MDI. Use of newer background insulins with current bolus insulins demonstrates efficacy and advances current recommendations for safe performance of exercise.

CLINICAL TRIALS REGISTER

DRKS00013509.

People with type 1 diabetes and impaired awareness of hypoglycaemia have a delayed reaction to performing a glucose scan during hypoglycaemia: a prospective observational study

AIMS

Considering that people with type 1 diabetes and impaired awareness of hypoglycaemia (IAH) have a delayed perception of hypoglycaemia, the question arises whether they perform scans later in case of hypoglycaemia than people without IAH. We assessed whether time to performing a scan after reaching hypoglycaemia while using a flash glucose monitoring (flash GM) system is different in people with IAH compared with people without IAH.

METHODS

Ninety-two people with type 1 diabetes [mean (± sd) age 42 ± 14 years, HbA1c 57 ± 9 mmol/mol] using a flash GM system for 3 months were included. Flash GM data were assessed for time until scan after reaching hypoglycaemia level 1 (< 3.9 mmol/l) and level 2 (< 3.0 mmol/l) and compared for type 1 diabetes with vs. without IAH via unpaired t-test/Mann-Whitney U test (P < 0.05).

RESULTS

Significant differences were found only for the delay between reaching hypoglycaemia and scan between people with and without IAH for Gold score [hypoglycaemia level 1: IAH 78 (51-105) min vs. without IAH 63 (42-89) min, P = 0.03; night-time hypoglycaemia level 2: IAH 140 (107-227) min vs. without IAH 96 (41-155) min, P = 0.004] and Pedersen-Bjergaard score [hypoglycaemia level 1: IAH 76 (52-97) min vs. without IAH 54 (38-71) min, P = 0.011; night-time hypoglycaemia level 1: IAH 132 (79-209) min vs. without IAH 89 (59-143) min, P = 0.011; night-time hypoglycaemia level 2: IAH 134 (66-212) min vs. without IAH 80 (37-131) min, P = 0.002). Data are shown as median (i.q.r.).

CONCLUSIONS

Time until scan after reaching hypoglycaemia might be an objective assessment tool for IAH, but needs to be investigated comprehensively in future studies.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. Graphical abstract.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA)

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6–9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: −6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: −8.3 to −2.9%, HIIT MARD: 12.1 to 16.8% and MRD: −14.3 to −9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

The competitive athlete with type 1 diabetes

Regular exercise is important for health, fitness and longevity in people living with type 1 diabetes, and many individuals seek to train and compete while living with the condition. Muscle, liver and glycogen metabolism can be normal in athletes with diabetes with good overall glucose management, and exercise performance can be facilitated by modifications to insulin dose and nutrition. However, maintaining normal glucose levels during training, travel and competition can be a major challenge for athletes living with type 1 diabetes. Some athletes have low-to-moderate levels of carbohydrate intake during training and rest days but tend to benefit, from both a glucose and performance perspective, from high rates of carbohydrate feeding during long-distance events. This review highlights the unique metabolic responses to various types of exercise in athletes living with type 1 diabetes. Graphical abstract.

Performance of the Eversense versus the Free Style Libre Flash glucose monitor during exercise and normal daily activities in subjects with type 1 diabetes mellitus

INTRODUCTION

Accurate blood glucose measurements are important in persons with diabetes during normal daily activities (NDA), even more so during exercise. We aimed to investigate the performance of fluorescence sensor-based and glucose oxidase-based interstitial glucose measurement during (intensive) exercise and NDA.

RESEARCH DESIGN AND METHODS

Prospective, observational study in 23 persons with type 1 diabetes when mountain biking for 6 days, followed by 6 days of NDA. Readings of the Eversense (fluorescence-based continuous glucose monitoring (CGM); subcutaneously implanted) and of the Free Style Libre (FSL; glucose oxidase-based flash glucose monitoring (FGM); transcutaneously placed) were compared with capillary glucose levels (Free Style Libre Precision NeoPro strip (FSLCstrip)).

RESULTS

Mean average differences (MAD) and mean average relative differences (MARD) were significantly different when comparing exercise with NDA (reference FSLCstrip); Eversense MAD 25±19 vs 17±6 mg/dL (p<0.001); MARD 17±6 vs 13%±6% (p<0.01) and FSL MAD 32±17 vs 18±8 mg/dL (p<0.01); MARD 20±7 vs 12%±5% (p<0.001).When analyzing the data according to the Integrated Continuous Glucose Monitoring Approvals (class II-510(K) guidelines), the overall performance of interstitial glucose readings within 20% of the FSLCstrip during exercise compared with NDA was 69% vs 81% for the Eversense and 59% vs 83% for the FSL, respectively. Within 15% of the FSLCstrip was 59% vs 70% for the Eversense and 46% vs 71% for the FSL.

CONCLUSIONS

During exercise, both fluorescence and glucose oxidase-based interstitial glucose measurements (using Eversense and FSL sensors) were less accurate compared with measurements during NDA. Even when acknowledging the beneficial effects of CGM or FGM, users should be aware of the risk of diminished accuracy of interstitial glucose readings during (intensive) exercise.

Surface Enhanced Raman Spectroscopy Based Biosensor with a Microneedle Array for Minimally Invasive In Vivo Glucose Measurements

To monitor blood glucose levels reliably, diabetic patients usually have to undergo frequent fingerstick tests to draw out fresh blood, which is painful and inconvenient with the potential risk of cross contamination especially when the lancet is reused or not properly sterilized. This work reports a novel surface-enhanced Raman spectroscopy (SERS) sensor for the in situ intradermal detection of glucose based on a low-cost poly(methyl methacrylate) microneedle (PMMA MN) array. After incorporating 1-decanethiol (1-DT) onto the silver-coated array surface, the sensor was calibrated in the range of 0-20 mM in skin phantoms then tested for the in vivo quantification of glucose in a mouse model of streptozocin (STZ)-induced type I diabetes. The results showed that the functional poly(methyl methacrylate) microneedle (F-PMMA MN) array was able to directly measure glucose in the interstitial fluid (ISF) in a few minutes and retain its structural integrity without swelling. The Clarke error grid analysis of measured data indicated that 93% of the data points lie in zones A and B. Moreover, the MN array exhibited minimal invasiveness to the skin as the skin recovered well without any noticeable adverse reaction in 10 min after measurements. With further improvement and proper validation, this polymeric MN array-based SERS biosensor has the potential to be used in painless glucose monitoring of diabetic patients in the future.

The Athlete with Type 1 Diabetes: Transition from Case Reports to General Therapy Recommendations

Fear of hypoglycemia is a common barrier to exercise and physical activity for individuals with type 1 diabetes. While some of the earliest studies in this area involved only one or two participants, the link between exercise, exogenous insulin, and hypoglycemia was already clear, with the only suggested management strategies being to decrease insulin dosage and/or consume carbohydrates before and after exercise. Over the past 50 years, a great deal of knowledge has been developed around the impact of different types and intensities of exercise on blood glucose levels in this population. Recent decades have also seen the development of technologies such as continuous glucose monitors, faster-acting insulins and commercially available insulin pumps to allow for the real-time observation of interstitial glucose levels, and more precise adjustments to insulin dosage before, during and after activity. As such, there are now evidence-based exercise and physical activity guidelines for individuals with type 1 diabetes. While the risk of hypoglycemia has not been completely eliminated, therapy recommendations have evolved considerably. This review discusses the evolution of the knowledge and the technology related to type 1 diabetes and exercise that have allowed this evolution to take place.

Performance of the Freestyle Libre flash glucose monitoring (flash GM) system in individuals with type 1 diabetes: A secondary outcome analysis of a randomized crossover trial

AIMS

The efficacy of flash glucose monitoring (flash GM) systems has been demonstrated by improvements in glycaemia; however, during high rates of glucose flux, the performance of continuous glucose monitoring systems was impaired, as detailed in previous studies. This study aimed to determine the performance of the flash GM system during daily-life glycaemic challenges such as carbohydrate-rich meals, bolus insulin-induced glycaemic disturbances and acute physical exercise in individuals with type 1 diabetes.

MATERIALS AND METHODS

This study comprised four randomized trial visits with alternating pre- and post-exercise bolus insulin doses. Throughout the four 14-hour inpatient phases, 19 participants received three carbohydrate-rich meals and performed moderate-intensity exercise. Venous blood glucose and capillary blood glucose during exercise was compared to interstitial glucose concentrations. Flash GM accuracy was assessed by median absolute relative difference (MARD) (interquartile range [IQR]) using the Bland-Altman method and Clark error grid, as well as according to guidelines for integrated CGM approvals (Class II-510(K)).

RESULTS

The overall MARD (IQR) during inpatient phases was 14.3% (6.9%-22.8%), during hypoglycaemia (≤3.9 mmol/L) was 31.6% (16.2%-46.8%), during euglycaemia (4.0 mmol/L - 9.9 mmol/L) was 16.0% (8.5%-24.0%) and during hyperglycaemia (≥10 mmol/L) was 9.4% (5.1%-15.7%). Overall Bland-Altman analysis showed a bias (95% LoA) of 1.26 mmol/L (-1.67 to 4.19 mmol/L). The overall MARD during acute exercise was 29.8% (17.5%-39.8%), during hypoglycaemia was 45.1% (35.2%-51.1%), during euglycaemia was 30.7% (18.7%-39.2%) and during hyperglycaemia was 16.3% (10.0%-22.8%).

CONCLUSION

Flash GM interstitial glucose readings were not sufficiently accurate within the hypoglycaemic range and during acute exercise and require confirmatory blood glucose measurements.

Acute glycemic responses along 10-week high-intensity training protocols in type 1 diabetes patients

Glycemic fluctuations were compared throughout 10-week high-intensity training protocols in T1DM patients. Differences were compared using the rate of change in glycaemia during exercise (RoC_E). HIIT sessions led to lower RoC_E in most weeks than other training protocols. The occurrence of level 1 hypoglycemia along sessions were similar among interventions.

Pre-Exercise Blood Glucose Levels Determine the Amount of Orally Administered Carbohydrates during Physical Exercise in Individuals with Type 1 Diabetes-A Randomized Cross-Over Trial

The aim of the study was to assess the amount of orally administered carbohydrates needed to maintain euglycemia during moderate-intensity exercise in individuals with type 1 diabetes. Nine participants with type 1 diabetes (four women, age 32.1 ± 9.0 years, BMI 25.5 ± 3.9 kg/m², HbA_1c 55 ± 7 mmol/mol (7.2 ± 0.6%)) on insulin Degludec were randomized to cycle for 55 min at moderate intensity (63 ± 7% VO_2peak) for five consecutive days on either 75% or 100% of their regular basal insulin dose. The impact of pre-exercise blood glucose concentration on the carbohydrate requirement was analyzed by one-way ANOVA stratified for pre-exercise blood glucose quartiles. The effect of the basal insulin dose on the amount of orally administered carbohydrates was evaluated by Wilcoxon matched-pairs signed-rank test. The amount of orally administered carbohydrates during the continuous exercise sessions was similar for both trial arms (75% or 100% basal insulin) with median [IQR] of 36 g (9–62 g) and 36 g (9–66 g) (p = 0.78). The amount of orally administered carbohydrates was determined by pre-exercise blood glucose concentration for both trial arms (p = 0.03). Our study elucidated the importance of pre-exercise glucose concentration related orally administered carbohydrates to maintain euglycemia during exercise in individuals with type 1 diabetes.

Impact of physical exercise on sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system in people with Type 1 diabetes: a randomized crossover trial

AIMS

To evaluate the sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system using reference blood glucose levels during moderate-intensity exercise while on either full or reduced basal insulin dose in people with Type 1 diabetes.

METHODS

Ten participants with Type 1 diabetes [four women, mean ± sd age 31.4 ± 9.0 years, BMI 25.5±3.8 kg/m² , HbA_1c 55±7 mmol/mol (7.2±0.6%)] exercised on a cycle ergometer for 55 min at a moderate intensity for 5 consecutive days at the clinical research facility, while receiving either their usual or a 75% basal insulin dose. After a 4-week washout period, participants performed the second exercise period having switched to the alternative basal insulin dose. During exercise, reference capillary blood glucose values were analysed using the fully enzymatic-amperometric method and compared with the interstitial glucose values obtained. Intermittently viewed continuous glucose monitoring accuracy was analysed according to median (interquartile range) absolute relative difference, and Clarke error grid and Bland-Altman analysis for overall glucose levels during exercise, stratified by glycaemic range and basal insulin dosing scheme (P<0.05).

RESULTS

A total of 845 glucose values were available during exercise to evaluate intermittently viewed continuous glucose monitoring sensor performance. The median (interquartile range) absolute relative difference between the reference values and those obtained by the sensor across the glycaemic range overall was 22 (13.9-29.7)%, and was 36.3 (24.2-45.2)% during hypoglycaemia, 22.8 (14.6-30.6)% during euglycaemia and 15.4 (9-21)% during hyperglycaemia. Usual basal insulin dose was associated with a worse sensor performance during exercise compared with the reduced (75%) basal insulin dose [median (interquartile range) absolute relative difference: 23.7 (17.2-30.7)% vs 20.5 (12-28.1)%; P<0.001).

CONCLUSIONS

The intermittently viewed continuous glucose monitoring sensor showed diminished accuracy during exercise. Absolute glucose readings derived from the sensor should be used cautiously and need confirmation by additional finger-prick blood glucose measurements.

Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes

Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) (P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL (P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages (P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.

Performance of the Flash Glucose Monitoring System during exercise in youth with Type 1 diabetes

AIM

Metabolic changes during exercise may affect the accuracy of glucose sensors impacting on Type 1 diabetes (T1D) management. The present study aimed at assessing the performance of the Flash Glucose Monitoring system (isCGM) during exercise and in free-living condition in youth with T1D.

METHODS

Seventeen youth (53% male), aged 13.7 ± 3.8 years, with T1D for 5.4 ± 3.8 years, HbA1c 7.4 ± 1.0% (57 ± 11 mmol/mol), were enrolled. Paired isCGM, plasma (PG) and capillary (CG) glucose values (total of 136) were collected during an interval exercise (45 min at 55% VO_2max load with 20 s sprints at 80% VO_2max every 10 min). Paired isCGM and CG (total of 832) were collected during free-living condition.

RESULTS

During exercise, isCGM absolute relative difference (ARDs) means/medians were 12.5/9.4% versus PG and 15.4/10.8% versus CG. During rest, ARDs means/medians were 16.6/12.0%. The Consensus Error Grid analysis showed 98.4% of readings during exercise and 97.24% during rest in zones A + B. Percentage of readings meeting the ISO criteria for CG levels <5.55 mmol/L was 62.5% during exercise, 53.4% during rest; for CG levels ≥5.55 mmol/L was 64.0% during exercise, 60.4% during rest.

CONCLUSIONS

isCGM demonstrated similar clinical safety and performance during exercise and in everyday life; further studies are needed to confirm its accuracy during exercise.

Continuous Glucose Monitoring and Exercise in Type 1 Diabetes: Past, Present and Future

Prior to the widespread use of continuous glucose monitoring (CGM), knowledge of the effects of exercise in type 1 diabetes (T1D) was limited to the exercise period, with few studies having the budget or capacity to monitor participants overnight. Recently, CGM has become a staple of many exercise studies, allowing researchers to observe the otherwise elusive late post-exercise period. We performed a strategic search using PubMed and Academic Search Complete. Studies were included if they involved adults with T1D performing exercise or physical activity, had a sample size greater than 5, and involved the use of CGM. Upon completion of the search protocol, 26 articles were reviewed for inclusion. While outcomes have been variable, CGM use in exercise studies has allowed the assessment of post-exercise (especially nocturnal) trends for different exercise modalities in individuals with T1D. Sensor accuracy is currently considered adequate for exercise, which has been crucial to developing closed-loop and artificial pancreas systems. Until these systems are perfected, CGM continues to provide information about late post-exercise responses, to assist T1D patients in managing their glucose, and to be useful as a tool for teaching individuals with T1D about exercise.