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)

Management and Medical Care for Individuals with Type 1 Diabetes Running a Marathon

Background: Limited data exist on managing type 1 diabetes mellitus (T1DM) during long-distance endurance events such as marathons. This study aimed to assess glycemic control and participant safety during a marathon. Methods: Five men with T1DM, participating in the 22nd Poznan Marathon, were recruited. They completed health questionnaires and received training on glycemic management. Their physical capacity was assessed (including maximal oxygen uptake on a cycle ergometer). Participants reduced their insulin doses and consumed breakfast 2.5-3 h before the race. During the marathon, self-monitoring blood glucose (SMBG) and ketone levels were measured at five checkpoints (start, 10 km, 19 km, 30 km, and finish). The medical team followed a pre-approved protocol, providing carbohydrate and fluid supplementation as needed. Glycemia was monitored by two continuous glucose monitoring (CGM) systems (FreeStyle Libre 2 and Dexcom G6) and SMBG. Results: The participants' median age was 44 years (34-48), with a diabetes duration of 10 years (6-14), and a BMI of 22.5 kg/m2 (22.0-23.3). All finished the marathon in an average time of 4:02:56 (±00:43:11). Mean SMBG was 125.6 (±43.5) mg/dL, while CGM readings were 149.6 (±17.9) mg/dL (FreeStyle Libre 2) and 155.4 (±12.9) mg/dL (Dexcom G6). One participant experienced prolonged hypoglycemia undetected by CGM, whereas another developed symptomatic hypoglycemia between SMBG measurements. Conclusions: Safe marathon completion in people with T1DM requires individualized insulin dose adjustments, appropriate carbohydrate supplementation, and dedicated medical support at checkpoints. Combining CGM with periodic SMBG measurements further enhances safety and helps to detect potential glycemic excursions.

The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Glycemic Variables and Participant-Reported Outcomes in Adults With Type 1 Diabetes: A Randomized Crossover Trial

AIM

Real-time continuous glucose monitoring (rtCGM) has several advantages over intermittently scanned continuous glucose monitoring (isCGM) but generally comes at a higher cost. Do-it-yourself rtCGM (DIY-rtCGM) potentially has benefits similar to those of rtCGM. This study compared outcomes in adults with type 1 diabetes using DIY-rtCGM versus isCGM.

METHODS

In this crossover trial, adults with type 1 diabetes were randomized to use isCGM or DIY-rtCGM for eight weeks before crossover to use the other device for eight weeks, after a four-week washout period where participants reverted back to isCGM. The primary endpoint was time in range (TIR; 3.9-10 mmol/L). Secondary endpoints included other glycemic control measures, psychosocial outcomes, and sleep quality.

RESULTS

Sixty participants were recruited, and 52 (87%) completed follow-up. Glucose outcomes were similar in the DIY-rtCGM and isCGM groups, including TIR (53.1% vs 51.3%; mean difference -1.7% P = .593), glycosylated hemoglobin (57.0 ± 17.8 vs 61.4 ± 12.2 mmol/L; P = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ± 3.8% vs 3.8 ± 4.0%; P = .947). Hypoglycemia Fear Survey total score (1.17 ± 0.52 vs 0.97 ± 0.54; P = .02) and fear of hypoglycemia score (1.18 ± 0.64 vs 0.97 ± 0.45; P = .02) were significantly higher during DIY-rtCGM versus isCGM. Diabetes Treatment Satisfaction Questionnaire status (DTSQS) score was also higher with DIY-rtCGM versus isCGM (28.7 ± 5.8 vs 26.0 ± 5.8; P = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ± 0.4 vs 3.32 ± 0.51; P = .045); sleep quality did not differ between the two groups.

CONCLUSION

Although the use of DIY-rtCGM did not improve glycemic outcomes compared with isCGM, it positively impacted several patient-reported psychosocial variables. DIY-rtCGM potentially provides an alternative, cost-effective rtCGM option.

The use of automated insulin delivery around physical activity and exercise in type 1 diabetes: a position statement of the European Association for the Study of Diabetes (EASD) and the International Society for Pediatric and Adolescent Diabetes (ISPAD)

Regular physical activity and exercise (PA) are cornerstones of diabetes care for individuals with type 1 diabetes. In recent years, the availability of automated insulin delivery (AID) systems has improved the ability of people with type 1 diabetes to achieve the recommended glucose target ranges. PA provide additional health benefits but can cause glucose fluctuations, which challenges current AID systems. While an increasing number of clinical trials and reviews are being published on different AID systems and PA, it seems prudent at this time to collate this information and develop a position statement on the topic. This joint European Association for the Study of Diabetes (EASD)/International Society for Pediatric and Adolescent Diabetes (ISPAD) position statement reviews current evidence on AID systems and provides detailed clinical practice points for managing PA in children, adolescents and adults with type 1 diabetes using AID technology. It discusses each commercially available AID system individually and provides guidance on their use in PA. Additionally, it addresses different glucose responses to PA and provides stratified therapy options to maintain glucose levels within the target ranges for these age groups.

Managing obesity in adults with type 1 diabetes

As the prevalence of obesity has reached epidemic proportions, its prevalence has also increased among adults living with type 1 diabetes mellitus. Unlike the pathophysiologic relationship between obesity and type 2 diabetes mellitus, the relationship between obesity and type 1 diabetes mellitus, and management of obesity in the setting of type 1 diabetes mellitus, have not been well reviewed. In this article, we discuss the comprehensive management of obesity in adults with type 1 diabetes mellitus, focusing on medical nutrition therapy and adjunct therapies such as weight loss-promoting medications and metabolic/bariatric surgery.

Strategies to reduce hyperglycemia-related anxiety in elite athletes with type 1 diabetes: A qualitative analysis

OBJECTIVE

Managing blood glucose levels is challenging for elite athletes with type 1 diabetes (T1D) as competition can cause unpredictable fluctuations. While fear of hypoglycemia during physical activity is well documented, research on hyperglycemia-related anxiety (HRA) is limited. HRA refers to the heightened fear that hyperglycemia-related symptoms will impair functioning. This study investigates current strategies employed to mitigate HRA during competition and the development of alternative approaches.

RESEARCH DESIGN AND METHODS

Elite athletes with TID, aged >14 who self-reported HRA during competition were recruited. Elite athletes were defined as individuals exercising >10 hours per week whose athletic performance has achieved the highest competition level. 60 to 90-minute virtual semi-structured interviews were analyzed using an Interpretative Phenomenological Analysis.

RESULTS

Ten elite athletes with T1D (average age 25 ± 3 years; T1D duration 12 ± 8 years; number of competitions per year 27 ± 19; training time per week 12 ± 6 hours) reported the strategies they currently use to mitigate HRA. These strategies include managing insulin and nutrition intake, embracing social support networks, using technology, practicing relaxation techniques, establishing routines, performing pre-competition aerobic exercise, and maintaining adequate sleep hygiene. Several additional approaches that could be implemented were identified including establishing targeted support networks, developing peer-reviewed resources on HRA, ensuring support teams have sufficient tools, and improving existing technology.

CONCLUSIONS

Elite athletes with T1D use physiological and psychological strategies to mitigate HRA during competition. This finding highlights the need for increased support and education for these athletes, and advancements in technology. A multidisciplinary approach involving healthcare professionals, athletic staff, and peer mentors could help integrate personalized anxiety management and diabetes care strategies into training regimens, enhancing both mental resilience and performance outcomes for athletes with T1D.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

14. Children and Adolescents: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The Use of Automated Insulin Delivery around Physical Activity and Exercise in Type 1 Diabetes: A Position Statement of the European Association for the Study of Diabetes (EASD) and the International Society for Pediatric and Adolescent Diabetes (ISPAD)

Regular physical activity and exercise (PA) are cornerstones of diabetes care for individuals with type 1 diabetes. In recent years, the availability of automated insulin delivery (AID) systems has improved the ability of people with type 1 diabetes to achieve the recommended glucose target ranges. PA provides additional health benefits but can cause glucose fluctuations, which challenges current AID systems. While an increasing number of clinical trials and reviews are being published on different AID systems and PA, it seems prudent at this time to collate this information and develop a position statement on the topic. This joint European Association for the Study of Diabetes (EASD)/International Society for Pediatric and Adolescent Diabetes (ISPAD) position statement reviews current evidence on AID systems and provides detailed clinical practice points for managing PA in children, adolescents and adults with type 1 diabetes using AID technology. It discusses each commercially available AID system individually and provides guidance on its use in PA. Additionally, it addresses different glucose responses to PA and provides stratified therapy options to maintain glucose levels within the target ranges for these age groups.

Accuracy of a continuous glucose monitoring system applied before, during, and after an intense leg-squat session with low- and high-carbohydrate availability in young adults without diabetes

PURPOSE

The aim was to assess the accuracy of a continuous blood glucose monitoring (CGM) device (Abbott FreeStyle Libre 3) against capillary blood glucose measurement (BGM) before, during, and after an intense lower body strength training session in connection with high- versus low-carbohydrate breakfasts.

METHODS

Nine adults (22 ± 2 years) completed a strength training session (10 × 10 at 60% 1RM) twice after high-carbohydrate and twice after low-carbohydrate breakfasts. CGM accuracy versus BGM was assessed across four phases: post-breakfast, pre-exercise, exercise, and post-exercise.

RESULTS

Overall fed state mean BGM levels were 84.4 ± 20.6 mg/dL. Group-level Bland-Altman analysis showed acceptable agreement between CGM and BGM across all phases, with mean biases between - 7.95 and - 17.83 mg/dL; the largest discrepancy was in the post-exercise phase. Mean absolute relative difference was significantly higher post-exercise compared to pre-exercise and exercise phases, for overall data and after the high-carbohydrate breakfast (all p ≤ 0.02). Clark Error Grid analysis showed 50.5-64.3% in Zone A and 31.7-44.6% in Zone B, with an increase in treatment errors during and after exercise.

CONCLUSION

In this group of healthy participants undergoing strength training, CGM showed satisfactory accuracy in glucose monitoring but varied substantially between individuals compared to BGM and fails in meeting clinical criteria for diabetic monitoring. CGM could aid non-diabetic athletes by tracking glucose fluctuations due to diet and exercise. Although utilization of CGM shows potential in gathering, analyzing, and interpreting interstitial glucose for improving performance, the application in sports nutrition is not yet validated, and challenges in data interpretation could limit its adoption.

Sleep Disturbances in Type 1 Diabetes and Mitigating Cardiovascular Risk

Cardiovascular disease (CVD) is a major cause of morbidity and mortality in persons with type 1 diabetes (T1D). Despite control of known cardiovascular (CV) risk factors and better glycemic management, persons with T1D still face heightened CVD risk, suggesting additional contributing factors. Sleep has recently been recognized as a CV risk factor; however; the role of sleep in CVD specifically in T1D population has only started to emerge. Extensive evidence suggests that persons with T1D often encounter sleep disturbances. This review aims to comprehensively explore the relationship between sleep disturbances and CVD in T1D, proposed possible mediators including glycemic control, which has been studied more extensively, and less studied factors such as blood pressure, lipid metabolism, and weight management. Stress and self-care behaviors likely also play a role in the relationship between sleep disturbances and CVD. The evidence regarding sleep interventions in the context of T1D in mitigating these CV risk factors has recently been shown in early, small-scale studies. Sleep assessments should be a part of the standard of care in persons with T1D. Further research should focus on understanding the impact and mechanistic pathways of sleep disturbances on CV risk and developing T1D-specific sleep interventions to reduce CVD burden in this population.

Assessing the influence of insulin type (ultra-rapid vs rapid insulin) and exercise timing on postprandial exercise-induced hypoglycaemia risk in individuals with type 1 diabetes: a randomised controlled trial

AIMS/HYPOTHESIS

The relationship between pre-meal insulin type, exercise timing and the risk of postprandial exercise-induced hypoglycaemia in people living with type 1 diabetes is unknown. We aimed to evaluate the effects of exercise timing (60 vs 120 min post meal) and different insulin types (aspart vs ultra-rapid aspart) on hypoglycaemic risk.

METHODS

This was a four-way crossover randomised trial including 40 individuals with type 1 diabetes using multiple daily injections (mean HbA1c 56 mmol/mol [7.4%]). Participants, who were recruited from the Montreal Clinical Research Institute, undertook 60 min cycling sessions (60% ofV ˙ O 2 peak ) after breakfast (60 min [EX60min] or 120 min [EX120min] post meal) with 50% of their usual insulin dose (aspart or ultra-rapid aspart). Eligibility criteria included age ≥18 years old, clinical diagnosis of type 1 diabetes for at least 1 year and HbA1c ≤80 mmol/mol (9.5%). Participants were allocated using sequentially numbered, opaque sealed envelopes. Participants were masked to their group assignment, and each participant was allocated a unique identification number to ensure anonymisation. The primary outcome was change in blood glucose levels between exercise onset and nadir.

RESULTS

Prior to exercise onset, time spent in hyperglycaemia was lower for EX60min vs EX120min (time >10.0 mmol/l: 56.6% [1.2-100%] vs 78.0% [52.7-97.9%]; p<0.001). The glucose reduction between exercise onset and nadir was less pronounced with EX60min vs EX120min (-3.8±2.7 vs -4.7±2.5 mmol/l; p<0.001). A similar number of hypoglycaemic events occurred during both exercise timings. Blood glucose between exercise onset and nadir decreased less with ultra-rapid aspart compared with aspart (-4.1±2.3 vs -4.4±2.8 mmol/l; p=0.037). While a similar number of hypoglycaemic events during exercise were observed, less post-exercise hypoglycaemia occurred with ultra-rapid aspart (n=0, 0%, vs n=15, 38%; p=0.003). No interactions between insulin types and exercise timings were found.

CONCLUSIONS/INTERPRETATION

EX60min blunted the pre-exercise glucose increase following breakfast and was associated with a smaller glucose reduction during exercise. Ultra-rapid aspart led to a smaller blood glucose reduction during exercise and might be associated with diminished post-exercise hypoglycaemia.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03659799 FUNDING: This study was funded by Novo Nordisk Canada.

An evaluation of how exercise position statement guidelines are being used in the real world in type 1 diabetes: Findings from the type 1 diabetes exercise initiative (T1DEXI)

AIMS

Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise.

METHODS

In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy.

RESULTS

Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (-2.2 %, P=0.02) for OL but not significant for AID (-1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (-1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia.

CONCLUSIONS

Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range.

Exercise as a therapeutic approach to alleviate diabetic kidney disease: mechanisms, clinical evidence and potential exercise prescriptions

Diabetic kidney disease (DKD) is a global and severe complication that imposes a significant burden on individual health, families, and society. Currently, the main treatment approaches for DKD include medication, blood glucose control, protein-restricted diet, and blood pressure management, all of which have certain limitations. Exercise, as a non-pharmacological intervention, has attracted increasing attention. This review introduces the mechanisms and clinical evidence of exercise on DKD, and proposes potential exercise prescriptions. Exercise can improve blood glucose stability related to DKD and the renin-angiotensin-aldosterone system (RAAS), reduce renal oxidative stress and inflammation, enhance the crosstalk between muscle and kidneys, and improve endothelial cell function. These mechanisms contribute to the comprehensive improvement of DKD. Compared to traditional treatment methods, exercise has several advantages, including safety, effectiveness, and no significant side effects. It can be used as an adjunct therapy to medication, blood glucose control, protein-restricted diet, and blood pressure management. Despite the evident benefits of exercise in DKD management, there is still a lack of large-scale, long-term randomized controlled trials to provide more evidence and develop exercise guidelines for DKD. Healthcare professionals should actively encourage exercise in DKD patients and develop personalized exercise plans based on individual circumstances.

Novel Protocol for the Use of Advanced Hybrid Closed-Loop System in Adolescents Engaged in Contact Sports

INTRODUCTION

Managing exercise remains challenging for adolescent athletes with type 1 diabetes (T1D), especially in contact sports. Even the use of hybrid closed loops can cause problems due to the need to disconnect the pump during some training or competitions. This study evaluated the efficacy of a novel protocol for the use of an advanced hybrid closed-loop system in adolescent football players with T1D during a sports camp.

METHODS

Eleven boys aged 14.9 years (25-75th percentile: 14-15.5), with a diabetes duration of 5.7 years (5.2-7) and regular training schedules in junior football leagues, participated in the study. They started AHCL (MiniMed780G, Medtronic) therapy a month before a week-long sports camp and were observed during the sports camp and the preceding week. Daily camp activities included two 1.5-h training sessions. Protocol included a 90-min temporary target of 150 mg/dL before and insulin pump disconnection during training. Physical activity was tracked using wGT3X-BT Actigraph monitors.

RESULTS

The camp provided conditions of demanding physical activity (6.6 [6-6.9] h/day of moderate-to-vigorous intensity). After starting AHCL, the average participant time spent in the target glucose range (70-180 mg/dL) was 79.34 ± 8.46%, and no significant change was observed during the camp (mean difference +0.79 ± 8.24%, p = 0.7581). Median glucose levels dropped by 10.91 ± 12.08 mg/dL (p = 0.0134), and time in the tight target range increased by 11.41 ± 11.60% (p = 0.0008) without increasing the time below range (<70 mg/dL) or glycemic variability. During the camp, daily insulin dose and basal/bolus ratio remained comparable with baseline, but the relative amount of automated bolus insulin decreased by 14.24 ± 4.65% (p < 0.0001).

CONCLUSION

The predefined regimen, including a temporary target before and disconnection of AHCL during football training, was safe and may provide satisfactory glucose control in active adolescents with T1D. This protocol could be adapted for use in other intensive contact sports.

Applying technologies to simplify strategies for exercise in type 1 diabetes

Challenges and fears related to managing glucose levels around planned and spontaneous exercise affect outcomes and quality of life in people living with type 1 diabetes. Advances in technology, including continuous glucose monitoring, open-loop insulin pump therapy and hybrid closed-loop (HCL) systems for exercise management in type 1 diabetes, address some of these challenges. In this review, three research or clinical experts, each living with type 1 diabetes, leverage published literature and clinical and personal experiences to translate research findings into simplified, patient-centred strategies. With an understanding of limitations in insulin pharmacokinetics, variable intra-individual responses to aerobic and anaerobic exercise, and the features of the technologies, six steps are proposed to guide clinicians in efficiently communicating simplified actions more effectively to individuals with type 1 diabetes. Fundamentally, the six steps centre on two aspects. First, regardless of insulin therapy type, and especially needed for spontaneous exercise, we provide an estimate of glucose disposal into active muscle meant to be consumed as extra carbohydrates for exercise ('ExCarbs'; a common example is 0.5 g/kg body mass per hour for adults and 1.0 g/kg body mass per hour for youth). Second, for planned exercise using open-loop pump therapy or HCL systems, we additionally recommend pre-emptive basal insulin reduction or using HCL exercise modes initiated 90 min (1-2 h) before the start of exercise until the end of exercise. Modifications for aerobic- and anaerobic-type exercise are discussed. The burden of pre-emptive basal insulin reductions and consumption of ExCarbs are the limitations of HCL systems, which may be overcome by future innovations but are unquestionably required for currently available systems.

Yoga as an alternative to cycling in type 1 diabetes: A preliminary study of acute effects on glucose levels

We evaluated the acute effects of yoga compared to cycling on glucose change and variability, and the occurrence of hypoglycemia in adults with type 1 diabetes. Fifteen participants performed 50 min of cycling or yoga. Glucose values were collected before and after exercise. Coefficient of variation (CV) and hypoglycemic episodes were evaluated from the start up to 12 h after exercise. Cycling and yoga significantly reduced glucose values during exercise, and CV was lower after yoga. One hypoglycemic episode occurred with yoga and seven with cycling. Yoga is a safe exercise that acutely reduces glucose values, but with lower risk of hypoglycemia compared to cycling.

Diabetes Technology Meeting 2023

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Carbohydrate Ingestion Increases Interstitial Glucose and Mitigates Neuromuscular Fatigue during Single-Leg Knee Extensions

INTRODUCTION

We aimed to investigate the neuromuscular contributions to enhanced fatigue resistance with carbohydrate (CHO) ingestion and to identify whether fatigue is associated with changes in interstitial glucose levels assessed using a continuous glucose monitor (CGM).

METHODS

Twelve healthy participants (six males, six females) performed isokinetic single-leg knee extensions (90°·s -1 ) at 20% of the maximal voluntary contraction (MVC) torque until MVC torque reached 60% of its initial value (i.e., task failure). Central and peripheral fatigue were evaluated every 15 min during the fatigue task using the interpolated twitch technique and electrically evoked torque. Using a single-blinded crossover design, participants ingested CHO (85 g sucrose per hour), or a placebo (PLA), at regular intervals during the fatigue task. Minute-by-minute interstitial glucose levels measured via CGM and whole blood glucose readings were obtained intermittently during the fatiguing task.

RESULTS

CHO ingestion increased time to task failure over PLA (113 ± 69 vs 81 ± 49 min, mean ± SD; P < 0.001) and was associated with higher glycemia as measured by CGM (106 ± 18 vs 88 ± 10 mg·dL -1 , P < 0.001) and whole blood glucose sampling (104 ± 17 vs 89 ± 10 mg·dL -1 , P < 0.001). When assessing the values in the CHO condition at a similar time point to those at task failure in the PLA condition (i.e., ~81 min), MVC torque, percentage voluntary activation, and 10 Hz torque were all better preserved in the CHO versus PLA condition ( P < 0.05).

CONCLUSIONS

Exogenous CHO intake mitigates neuromuscular fatigue at both the central and peripheral levels by raising glucose concentrations rather than by preventing hypoglycemia.

Glycemic Management Around Postprandial Exercise in People With Type 1 Diabetes: Challenge Accepted

CONTEXT

The precise glycemic impact and clinical relevance of postprandial exercise in type 1 diabetes (T1D) has not been clarified yet.

OBJECTIVE

This work aimed to examine acute, subacute, and late effects of postprandial exercise on blood glucose (BG).

METHODS

A randomized, controlled trial comprised 4 laboratory visits, with 24-hour follow-up at home. Participants included adults with T1D (n = 8), aged 44 ± 13 years, with body mass index of 24 ± 2.1. Intervention included 30 minutes of rest (CONTROL), walking (WALK), moderate-intensity (MOD), or intermittent high-intensity (IHE) exercise performed 60 minutes after a standardized meal. Main outcome measures included BG change during exercise/control (acute), and secondary outcomes included the subacute (≤2 h after) and late glycemic effects (≤24 h after).

RESULTS

Exercise reduced postprandial glucose (PPG) excursion compared to CONTROL, with a consistent BG decline in all patients for all modalities (mean declines -45 ± 24, -71 ± 39, and -35 ± 21 mg/dL, during WALK, MOD, and IHE, respectively (P < .001). For this decline, clinical superiority was demonstrated separately for each exercise modality vs CONTROL. Noninferiority of WALK vs MOD was not demonstrated, noninferiority of WALK vs IHE was demonstrated, and equivalence of IHE vs MOD was not demonstrated. Hypoglycemia did not occur during exercise. BG increased in the hour after exercise (more than after CONTROL; P < .001). More than half of participants showed hyperglycemia after exercise necessitating insulin correction. There were more nocturnal hypoglycemic events after exercise vs CONTROL (P < .05).

CONCLUSION

Postprandial exercise of all modalities is effective, safe, and feasible if necessary precautions are taken (ie, prandial insulin reductions), as exercise lowered maximal PPG excursion and caused a consistent and clinically relevant BG decline during exercise while there was no hypoglycemia during or shortly after exercise. However, there seem to be 2 remaining challenges: subacute postexercise hyperglycemia and nocturnal hypoglycemia.

Comparison between a tubeless, on-body automated insulin delivery system and a tubeless, on-body sensor-augmented pump in type 1 diabetes: a multicentre randomised controlled trial

AIMS/HYPOTHESIS

This study compares the efficacy and safety of a tubeless, on-body automated insulin delivery (AID) system with that of a tubeless, on-body sensor-augmented pump (SAP).

METHODS

This multicentre, parallel-group, RCT was conducted at 13 tertiary medical centres in South Korea. Adults aged 19-69 years with type 1 diabetes who had HbA1c levels of <85.8 mmol/mol (<10.0%) were eligible. The participants were assigned at a 1:1 ratio to receive a tubeless, on-body AID system (intervention group) or a tubeless, on-body SAP (control group) for 12 weeks. Stratified block randomisation was conducted by an independent statistician. Blinding was not possible due to the nature of the intervention. The primary outcome was the percentage of time in range (TIR), blood glucose between 3.9 and 10.0 mmol/l, as measured by continuous glucose monitoring. ANCOVAs were conducted with baseline values and study centres as covariates.

RESULTS

A total of 104 participants underwent randomisation, with 53 in the intervention group and 51 in the control group. The mean (±SD) age of the participants was 40±11 years. The mean (±SD) TIR increased from 62.1±17.1% at baseline to 71.5±10.7% over the 12 week trial period in the intervention group and from 64.7±17.0% to 66.9±15.0% in the control group (difference between the adjusted means: 6.5% [95% CI 3.6%, 9.4%], p<0.001). Time below range, time above range, CV and mean glucose levels were also significantly better in the intervention group compared with the control group. HbA1c decreased from 50.9±9.9 mmol/mol (6.8±0.9%) at baseline to 45.9±7.4 mmol/mol (6.4±0.7%) after 12 weeks in the intervention group and from 48.7±9.1 mmol/mol (6.6±0.8%) to 45.7±7.5 mmol/mol (6.3±0.7%) in the control group (difference between the adjusted means: -0.7 mmol/mol [95% CI -2.0, 0.8 mmol/mol] (-0.1% [95% CI -0.2%, 0.1%]), p=0.366). No diabetic ketoacidosis or severe hypoglycaemia events occurred in either group.

CONCLUSIONS/INTERPRETATION

The use of a tubeless, on-body AID system was safe and associated with superior glycaemic profiles, including TIR, time below range, time above range and CV, than the use of a tubeless, on-body SAP.

TRIAL REGISTRATION

Clinical Research Information Service (CRIS) KCT0008398 FUNDING: The study was funded by a grant from the Korea Medical Device Development Fund supported by the Ministry of Science and ICT; the Ministry of Trade, Industry and Energy; the Ministry of Health and Welfare; and the Ministry of Food and Drug Safety (grant number: RS-2020-KD000056).

The Glucose Management Indicator: Time to Change Course?

Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.

Associations between daily step count classifications and continuous glucose monitoring metrics in adults with type 1 diabetes: analysis of the Type 1 Diabetes Exercise Initiative (T1DEXI) cohort

AIMS/HYPOTHESIS

Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics.

METHODS

In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups-below (<7000), meeting (7000-10,000) or exceeding (>10,000) the daily step count goal-to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9-10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l).

RESULTS

A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7%]; 73% female; 45% hybrid closed-loop system, 38% standard insulin pump, 17% multiple daily insulin injections) were included in the study. Between-participant analyses showed that individuals who exceeded the mean daily step count goal over the 4 week period had a similar TIR (75±14%) to those meeting (74±14%) or below (75±16%) the step count goal (p>0.05). In the within-participant comparisons, TIR was higher on days when the step count goal was exceeded or met (both 75±15%) than on days below the step count goal (73±16%; both p<0.001). The TBR was also higher when individuals exceeded the step count goals (3.1%±3.2%) than on days when they met or were below step count goals (difference in means -0.3% [p=0.006] and -0.4% [p=0.001], respectively). The total daily insulin dose was lower on days when step count goals were exceeded (0.52±0.18 U/kg; p<0.001) or were met (0.53±0.18 U/kg; p<0.001) than on days when step counts were below the current recommendation (0.55±0.18 U/kg). Step count had a larger effect on CGM-based metrics in participants with a baseline HbA1c ≥53 mmol/mol (≥7.0%).

CONCLUSIONS/INTERPRETATION

Our results suggest that, compared with days with low step counts, days with higher step counts are associated with slight increases in both TIR and TBR, along with small reductions in total daily insulin requirements, in adults living with type 1 diabetes.

DATA AVAILABILITY

The data that support the findings reported here are available on the Vivli Platform (ID: T1-DEXI; https://doi.org/10.25934/PR00008428 ).

Factory-Calibrated Continuous Glucose Monitoring System Accuracy During Exercise in Adolescents With Type 1 Diabetes Mellitus

BACKGROUND

Continuous glucose monitors (CGMs) are widely used for individuals with diabetes mellitus, particularly those with type 1 diabetes (T1D). Advancements in CGM technology allow for glycemic assessment without capillary glucose measurements as many come factory calibrated. However, exercise, an essential component of diabetes care, has been reported to alter accuracy of earlier generation CGM. Considering the importance of physical activity for individuals with T1D and the progression of CGM technology, we aimed to investigate the accuracy of the Dexcom G6 during physical activity.

METHODS

Adolescents (ages 13-20 years) exercised on a treadmill for 40 minutes, with a 10-minute break at minute 20. We obtained paired CGM and glucometer measurements before and every 10 minutes during and after exercise. Accuracy analysis was determined by mean absolute relative difference (MARD), mean absolute difference (MAD), and Clarke Error Grid Analyses.

RESULTS

Mean absolute relative difference and MAD increased during exercise (14%-33% and 24.3-34 mg/dL) but improved after exercise. We noted certain CGM locations produced greater changes in accuracy as MARD and MAD increased markedly when the CGM was on the buttocks (18%-46% and 30-41 mg/dL). We also noted decreased odds of Zone A in the Clarke error grid when the CGM was on the buttocks compared to the abdomen (odds ratio [OR]: 0.146; P = 0.0003; 95% CI = 0.052-0.415).

CONCLUSIONS

This CGM system showed alterations in accuracy during exercise. Our findings additionally suggest interstitial fluid changes in muscles during exercise alter accuracy of CGM; however, additional research is required.

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.

Exploring Technology's Influence on Health Behaviours and Well-being in Type 1 Diabetes: a Review

PURPOSE OF REVIEW

Maintaining positive health behaviours promotes better health outcomes for people with type 1 diabetes (T1D). However, implementing these behaviours may also lead to additional management burdens and challenges. Diabetes technologies, including continuous glucose monitoring systems, automated insulin delivery systems, and digital platforms, are being rapidly developed and widely used to reduce these burdens. Our aim was to review recent evidence to explore the influence of these technologies on health behaviours and well-being among adults with T1D and discuss future directions.

RECENT FINDINGS

Current evidence, albeit limited, suggests that technologies applied in diabetes self-management education and support (DSME/S), nutrition, physical activity (PA), and psychosocial care areas improved glucose outcomes. They may also increase flexibility in insulin adjustment and eating behaviours, reduce carb counting burden, increase confidence in PA, and reduce mental burden. Technologies have the potential to promote health behaviours changes and well-being for people with T1D. More confirmative studies on their effectiveness and safety are needed to ensure optimal integration in standard care practices.

Effect of combined aerobic-resistance training and subsequent detraining on brain-derived neurotrophic factor (BDNF) and depression in women with type 2 diabetes mellitus: A randomized controlled trial

AIMS

In this study, we assessed the effects of a 12-week combined aerobic-resistance training and subsequent detraining on Beck Depression Inventory (BDI) score and mediating role of BDNF and also investigated whether exercise-induced alterations are maintained following a short period of detraining in women with type 2 diabetes (T2D).

MATERIALS AND METHODS

Thirty-four women with T2D were randomly assigned to experimental or control group (age: 60.6 ± 6.3, body mass index (BMI): 30.2 ± 1.3 kg/m2 , HbA1c: 8.09 ± 0.73%). The exercise training comprised of combined aerobic-resistance programme (50%-70% heart rate reserve for aerobic exercise, and 50%-70% 1 repetition maximum for resistance exercise, respectively) performed three sessions per week over 12 weeks. The intervention period was followed by an 8-week detraining period. Data were collected at baseline and also following exercise intervention and detraining. Data were analysed by linear mixed model at p < 0.05.

RESULTS

After 12 weeks of combined exercise training and 8 weeks of detraining, there was a significant difference in BDNF (0.08; 95% confidence interval [CI] = 0.07-0.10; p = 0.001), fasting blood glucose (FBG) (-45.41; CI = -50.83, -39.98; p = 0.001), insulin (-6.47; CI = -7.04, -5.9; p = 0.001), HOMA-IR (-3.76; CI = -4.07, -3.45; p = 0.001) and BDI score (-17.17; CI = -20.29, -14.05; p = 0.001) between the experimental and control group. Multiple mediation analysis indicated that BDNF seems to have a mediating role in exercise-induced improvement of depression (p = 0.04). After the detraining period, BDI score remained unchanged and it showed a significant increase compared to before the start of training (p = 0.001).

CONCLUSIONS

It may be concluded that exercise training improves depression that is likely to be explained by increased BDNF concentration in TD2. In spite of decreased BDNF concentration following an 8-week detraining, depression score was maintained.

The Need to Prioritize Education and Resources to Support Exercise in Type 1 Diabetes: Results of an Australian Survey of Adults With Type 1 Diabetes and Health Providers

OBJECTIVES

Regular exercise is recommended for people with type 1 diabetes (PWD) to improve their health, but many do not meet recommended exercise targets. Educational resources supporting PWD to exercise exist, but their value is unclear. To determine the need for improved exercise resources in Australia, we surveyed adult PWD and health providers (HPs) about their confidence in managing type 1 diabetes (T1D) around exercise, barriers to exercise, and the adequacy of current resources.

METHODS

Australian adult PWD and HPs completed surveys to rate the importance of exercise in T1D management, confidence in managing T1D around exercise, barriers to giving and receiving education, resources used, and what form new resources should take.

RESULTS

Responses were received from 128 PWD and 122 HPs. Both groups considered exercise to be important for diabetes management. PWD cited time constraints (57%) and concern about dysglycemia (43%) as barriers to exercise, and many lacked confidence in managing T1D around exercise. HPs were more confident, but experienced barriers to providing advice, and PWD did not tend to rely on this advice. Instead, 72% of PWD found continuous glucose monitoring most helpful. Both groups desired better resources to support exercise in T1D, with PWD preferring to obtain information through a structured education program and HPs through eLearning.

CONCLUSIONS

Australian HPs and PWD appreciate the importance of exercise in T1D management and express a clear desire for improved educational resources. Our findings provide a basis for developing a comprehensive package of resources for both adult PWD and HPs, to support exercise in PWD.

Design and In Silico Evaluation of an Exercise Decision Support System Using Digital Twin Models

BACKGROUND

Managing glucose levels during exercise is challenging for individuals with type 1 diabetes (T1D) since multiple factors including activity type, duration, intensity and other factors must be considered. Current decision support tools lack personalized recommendations and fail to distinguish between aerobic and resistance exercise. We propose an exercise-aware decision support system (exDSS) that uses digital twins to deliver personalized recommendations to help people with T1D maintain safe glucose levels (70-180 mg/dL) and avoid low glucose (<70 mg/dL) during and after exercise.

METHODS

We evaluated exDSS using various exercise and meal scenarios recorded from a large, free-living study of aerobic and resistance exercise. The model inputs were heart rate, insulin, and meal data. Glucose responses were simulated during and after 30-minute exercise sessions (676 aerobic, 631 resistance) from 247 participants. Glucose outcomes were compared when participants followed exDSS recommendations, clinical guidelines, or did not modify behavior (no intervention).

RESULTS

exDSS significantly improved mean time in range for aerobic (80.2% to 92.3%, P < .0001) and resistance (72.3% to 87.3%, P < .0001) exercises compared with no intervention, and versus clinical guidelines (aerobic: 82.2%, P < .0001; resistance: 80.3%, P < .0001). exDSS reduced time spent in low glucose for both exercise types compared with no intervention (aerobic: 15.1% to 5.1%, P < .0001; resistance: 18.2% to 6.6%, P < .0001) and was comparable with following clinical guidelines (aerobic: 4.5%, resistance: 8.1%, P = N.S.).

CONCLUSIONS

The exDSS tool significantly improved glucose outcomes during and after exercise versus following clinical guidelines and no intervention providing motivation for clinical evaluation of the exDSS system.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry

BACKGROUND

Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes.

METHODS

In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model.

RESULTS

The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone.

CONCLUSIONS

Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

An overview of diabetes mellitus in pregnant women with obesity

Rates of obesity are increasing world-wide with an estimated 1billion people projected to be obese by 2030 if current trends remain unchanged. Obesity currently considered one of the most significant associated factors of non-communicable diseases poses the greatest threat to health. Diabetes mellitus is an important metabolic disorder closely associated with obesity. It is therefore expected that with the increasing rates of obesity, the rates of diabetes in pregnancy will also be rising. This disorder may pre-date pregnancy (diagnosed or undiagnosed and diagnosed for the first time in pregnancy) or may be of onset in pregnancy. Irrespective of the timing of onset, diabetes in pregnancy is associated with both fetal and maternal complications. Outcomes are much better if control is maximised. Early diagnosis, multidisciplinary care and tailored management with optimum glycaemic control is associated with a significant reduction in not only pregnancy complications but long-term consequences on both the mother and offspring. This review brings together the current understanding of the pathogenesis of the endocrine derangements that are associated with diabetes in pregnancy how screening should be offered and management including pre-pregnancy care and the role of newer agents in management.

Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women

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.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Evaluation of Effects of Continuous Glucose Monitoring on Physical Activity Habits and Blood Lipid Levels in Persons With Type 1 Diabetes Managed With Multiple Daily Insulin Injections: An Analysis Based on the GOLD Randomized Trial (GOLD 8)

BACKGROUND

People with type 1 diabetes generally view it easier to exercise when having continuous information of the glucose levels. We evaluated whether patients with type 1 diabetes managed with multiple daily insulin injections (MDI) exercised more after initiating continuous glucose monitoring (CGM) and whether the improved glycemic control and well-being associated with CGM translates into improved blood lipids and markers of inflammation.

METHOD

The GOLD trial was a randomized cross-over trial over 16 months where patients used either CGM or capillary self-monitoring of blood glucose (SMBG) over six months, with a four-month wash-out period between the two treatment periods. We compared grade of physical activity, blood lipids, apolipoproteins, and high-sensitivity C-reactive protein (hsCRP) levels during CGM and SMBG.

RESULTS

There were 116 patients with information of physical activity estimated by the International Physical Activity Questionnaire (IPAQ) during both CGM and SMBG. No changes were found during CGM or SMBG, IPAQ scores 3305 versus 3878 (P = .16). In 136 participants with information of blood lipid levels with no change in lipid-lowering medication during the two treatment periods, HbA1c differed by 4.2 mmol/mol (NGSP 0.39%) between SMBG and CGM treatment (P < .001). No significant changes existed in low-density lipoprotein, high-density lipoprotein, triglycerides, total cholesterol, apolipoprotein A1, apolipoprotein B1, or hsCRP, during CGM and SMBG.

CONCLUSION

Although many patients experience it easier to perform physical activity when monitoring glucose levels with CGM, it does not influence the amount of physical activity in persons with type 1 diabetes. Blood lipids, apolipoprotein, and hsCRP levels were similar during CGM and SMBG.

5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

14. Children and Adolescents: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Effects of a Low-Carbohydrate-High-Protein Pre-Exercise Meal in Type 1 Diabetes-a Randomized Crossover Trial

CONTEXT

Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise.

OBJECTIVE

To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump-treated type 1 diabetes.

METHODS

Fourteen adults (11 women, 3 men) with insulin pump-treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise.

RESULTS

Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P = .001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ-1.49 ± 1.89 vs HCLP: Δ-3.78 ± 1.95 mmol/L, P = .001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P < .001).

CONCLUSION

Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise.

Road map for personalized exercise medicine in T2DM

The number of patients with type 2 diabetes mellitus (T2DM) is rising at an alarming rate. Regular physical activity and exercise are cornerstones in the therapy of T2DM. While a one-size-fits-all approach fails to account for many between-subject differences, the use of personalized exercise medicine has the potential of optimizing health outcomes. Here, a road map for personalized exercise therapy targeted at patients with T2DM is presented. It considers secondary complications, glucose management, response heterogeneity, and other relevant factors that might influence the effectiveness of exercise as medicine, taking exercise-medication-diet interactions, as well as feasibility and acceptance into account. Furthermore, the potential of artificial intelligence and machine learning-based applications in assisting sports therapists to find appropriate exercise programs is outlined.

A Nutritional Approach to Optimizing Pump Therapy in Type 1 Diabetes Mellitus

Achieving optimal glucose control in individuals with type 1 diabetes (T1DM) continues to pose a significant challenge. While continuous insulin infusion systems have shown promise as an alternative to conventional insulin therapy, there remains a crucial need for greater awareness regarding the necessary adaptations for various special circumstances. Nutritional choices play an essential role in the efficacy of diabetes management and overall health status for patients with T1DM. Factors such as effective carbohydrate counting, assessment of the macronutrient composition of meals, and comprehending the concept of the glycemic index of foods are paramount in making informed pre-meal adjustments when utilizing insulin pumps. Furthermore, the ability to handle such situations as physical exercise, illness, pregnancy, and lactation by making appropriate adjustments in nutrition and pump settings should be cultivated within the patient-practitioner relationship. This review aims to provide healthcare practitioners with practical guidance on optimizing care for individuals living with T1DM. It includes recommendations on carbohydrate counting, managing mixed meals and the glycemic index, addressing exercise-related challenges, coping with illness, and managing nutritional needs during pregnancy and lactation. Additionally, considerations relating to closed-loop systems with regard to nutrition are addressed. By implementing these strategies, healthcare providers can better equip themselves to support individuals with T1DM in achieving improved diabetes management and enhanced quality of life.

An updated algorithm for an effective choice of continuous glucose monitoring for people with insulin-treated diabetes

PURPOSE

Continuous Glucose Monitoring (CGM) is a key tool for insulin-treated people with diabetes (PwD). CGM devices include both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), which are associated with an improvement of glucose control and less hypoglycemia in clinical trials of people with type 1 and type 2 diabetes.

METHODS

This is an expert position to update a previous algorithm on the most suitable choice of CGM for insulin-treated PwD in light of the recent evidence and clinical practice.

RESULTS

We identified six different clinical scenarios, including type 1 diabetes, type 2 diabetes, pregnancy on intensive insulin therapy, regular physical exercise, new onset of diabetes, and frailty. The use of rtCGM or isCGM is suggested, on the basis of the predominant clinical issue, as suboptimal glucose control or disabling hypoglycemia, regardless of baseline HbA1c or individualized HbA1c target.

CONCLUSION

The present algorithm may help to select the best CGM device based on patients' clinical characteristics, needs and clinical context, offering a further opportunity of a "tailored" therapy for people with insulin-treated diabetes.

Safety and performance of a hybrid closed-loop insulin delivery system with carbohydrate suggestion in adults with type 1 diabetes prone to hypoglycemia

AIMS

To evaluate the safety and performance of a hybrid closed-loop (HCL) system with automatic carbohydrate suggestion in adults with type 1 diabetes (T1D) prone to hypoglycemia.

METHODS

A 32-hour in-hospital pilot study, including a night period, 4 meals and 2 vigorous unannounced 45-minute aerobic sessions, was conducted in 11 adults with T1D prone to hypoglycemia. The primary outcome was the percentage of time in range 70-180 mg/dL (TIR). Main secondary outcomes were time below range < 70 mg/dL (TBR < 70) and < 54 (TBR < 54). Data are presented as median (10th-90th percentile ranges).

RESULTS

The participants, 6 (54.5%) men, were 24 (22-48) years old, and had 22 (9-32) years of T1D duration. All of them regularly used an insulin pump and a continuous glucose monitoring system. The median TIR was 78.7% (75.6-91.2): 92.7% (68.2-100.0) during exercise and recovery period, 79.3% (34.9-100.0) during postprandial period, and 95.4% (66.4-100.0) during overnight period. The TBR < 70 and TBR < 54 were 0.0% (0.0-6.6) and 0.0% (0.0-1.2), respectively. A total of 4 (3-9) 15-g carbohydrate suggestions were administered per person. No severe acute complications occurred during the study.

CONCLUSIONS

The HCL system with automatic carbohydrate suggestion performed well and was safe in this population during challenging conditions in a hospital setting.

A Prospective Study of the Effect of Gastroduodenal Artery Reconstruction on Duodenal Oxygenation and Enzyme Content After Pancreas Transplantation

BACKGROUND

Whole pancreas transplantation provides durable glycemic control and can improve survival rate; however, it can carry an increased risk of surgical complications. One devastating complication is a duodenal leak at the site of enteroenteric anastomosis. The gastroduodenal artery (GDA) supplies blood to the donor duodenum and pancreas but is commonly ligated during procurement. Since we have not had expressive changes in pancreatic back table surgical techniques in the recent decades, we hypothesized whether back table GDA reconstruction, improving perfusion of the donor duodenum and head of the pancreas, could lead to fewer surgical complications in simultaneous pancreas-kidney (SPK) transplants.

MATERIAL AND METHODS

Between 2017 and 2021, we evaluated demographic information, postoperative complications, intraoperative donor duodenum, recipient bowel O2 tissue saturation, and patient morbidity through the Comprehensive Complication Index (CCI®).

RESULTS

A total of 26 patients were included: 13 underwent GDA reconstruction (GDA-R), and 13 had GDA ligation (GDA-L). There were no pancreatic leaks in the GR group compared to 38% (5/13) in the GDA-L group (p = 0.03913). Intraoperative tissue oxygen saturation was higher in the GDA-R group than in the GDA-L (95.18 vs.76.88%, p < 0,001). We observed an increase in transfusion rate in GDA-R (p < 0.05), which did not result in a higher rate of exploration (p = 0.38). CCI® patient morbidity was also significantly lower in the GDA-R group (s < 0.05).

CONCLUSIONS

This study identified improved intraoperative duodenal tissue oxygen saturation in the GDA-R group with an associated reduction in pancreatic leaks and CCI® morbidity risk. A larger prospective multicenter study comparing the two methods is warranted.

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models

BACKGROUND

New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities.

METHODS

The aim of this study was to investigate the efficacy of three different predictive models for the glucose level classification: (1) an autoregressive integrated moving average model (ARIMA), (2) logistic regression, and (3) long short-term memory networks (LSTM). The performance of these models was evaluated in predicting hypoglycemia (<70 mg/dL), euglycemia (70-180 mg/dL), and hyperglycemia (>180 mg/dL) classes 15 min and 1 h ahead. More specifically, the confusion matrices were obtained and metrics such as precision, recall, and accuracy were computed for each model at each predictive horizon.

RESULTS

As expected, ARIMA underperformed the other models in predicting hyper- and hypoglycemia classes for both the 15 min and 1 h horizons. For the 15 min forecast horizon, the performance of logistic regression was the highest of all the models for all glycemia classes, with recall rates of 96% for hyper, 91% for norm, and 98% for hypoglycemia. For the 1 h forecast horizon, the LSTM model turned out to be the best for hyper- and hypoglycemia classes, achieving recall values of 85% and 87% respectively.

CONCLUSIONS

Our findings suggest that different models may have varying strengths and weaknesses in predicting glucose level classes, and the choice of model should be carefully considered based on the specific requirements and context of the clinical application. The logistic regression model proved to be more accurate for the next 15 min, particularly in predicting hypoglycemia. However, the LSTM model outperformed logistic regression in predicting glucose level class for the next hour. Future research could explore hybrid models or ensemble approaches that combine the strengths of multiple models to further enhance the accuracy and reliability of glucose predictions.

Investigating sensor location on the effectiveness of continuous glucose monitoring during exercise in a non-diabetic population

The purpose of this investigation was to evaluate whether continuous glucose monitoring (CGM) sensors worn on the active muscle may provide enhanced insight into glucose control in non-diabetic participants during cycling exercise compared to traditional sensor placement on the arm. Data from 9 healthy participants (F:3) was recorded using CGM sensors on the arm (triceps brachii) and leg (vastus medialis) following 100 g glucose ingestion during 30 min experimental visits of: resting control, graded cycling, electrically stimulated quadriceps contractions, and passive whole-body heating. Finger capillary glucose was used to assess sensor accuracy. Under control conditions, the traditional arm sensor better reflected capillary glucose, with a mean absolute relative difference (MARD) of 12.4 ± 9.3% versus 18.3 ± 11.4% in the leg (P = 0.02). For the intended use during exercise, the sensor-site difference was attenuated, with similar MARDs during cycling (arm:15.5 ± 12% versus leg:16.7 ± 10.8%, P = 0.96) and quadriceps stimulation (arm:15.5 ± 14.8% versus leg:13.9 ± 9.5%, P = 0.9). At rest, glucose at the leg was consistently lower than the arm (P = 0.01); whereas, during graded cycling, the leg-glucose was lower only after maximal intensity exercise (P = 0.02). There was no difference between sensors during quadriceps stimulation (P = 0.8). Passive heating caused leg-skin temperature to increase by 3.1 ± 1.8°C versus 1.1 ± 0.72°C at the arm (P = 0.002), elevating MARD in the leg (23.5 ± 16.2%) and lowering glucose in the leg (P < 0.001). At rest, traditional placement of CGM sensors on the arm may best reflect blood glucose; however, during cycling, placement on the leg may offer greater insight to working muscle glucose concentrations, and this is likely due to greater blood-flow rather than muscle contractions.HighlightsWearing a continuous glucose monitoring (CGM) sensor on the arm may better reflect capillary glucose concentrations compared to wearing a sensor on the inner thigh at rest.With passive or active leg-muscle contractions, site-specific differences compared to capillary samples are attenuated; therefore, wearing a CGM sensor on the active-muscle during exercise may provide greater information to non-diabetic athletes regarding glucose flux at the active muscle.Discrepancies in CGM sensors worn at different sites likely primarily reflects differences in blood flow, as passive skin heating caused the largest magnitude difference between arm and leg sensor readings compared to the other experimental conditions (control, electric muscle stimulation, and cycling exercise).

Relationship between acute glucose variability and cognitive decline in type 2 diabetes: A systematic review and meta-analysis

BACKGROUND

Cognitive decline is one of the most widespread chronic complications of diabetes, which occurs in more than half of the patients with type 2 diabetes (T2DM). Emerging evidences have suggested that glucose variability (GV) is associated with the pathogenesis of diabetic complications. However, the influence of acute GV on cognitive dysfunction in T2DM is still controversial. The aim of the study was to evaluate the association between acute GV and cognitive defect in T2DM, and provide a most recent and comprehensive summary of the evidences in this research field.

METHODS

PubMed, Cochrane library, EMBASE, Web of science, Sinomed, China National Knowledge Infrastructure (CNKI), and Wanfang were searched for articles that reported on the association between acute GV and cognitive impairment in T2DM.

RESULTS

9 eligible studies were included, with a total of 1263 patients with T2DM involved. Results showed that summary Fisher's z value was -0.23 [95%CI (-0.39, -0.06)], suggesting statistical significance (P = 0.006). Summary r value was -0.22 [95%CI (-0.37, -0.06)]. A lower cognitive performance was found in the subjects with greater glucose variation, which has statistical significance. Mean amplitude of glycemic excursions (MAGE) was associated with a higher risk of poor functional outcomes. Fisher's z value was -0.35 [95%CI (-0.43, -0.25)], indicating statistical significance (P = 0.011). Sensitivity analyses by omitting individual studies showed stability of the results.

CONCLUSIONS

Overall, higher acute GV is associated with an increased risk of cognitive impairment in patients with T2DM. Further studies should be required to determine whether targeted intervention of reducing acute GV could prevent cognitive decline.

Quantifying insulin-mediated and noninsulin-mediated changes in glucose dynamics during resistance exercise in type 1 diabetes

Exercise can cause dangerous fluctuations in blood glucose in people living with type 1 diabetes (T1D). Aerobic exercise, for example, can cause acute hypoglycemia secondary to increased insulin-mediated and noninsulin-mediated glucose utilization. Less is known about how resistance exercise (RE) impacts glucose dynamics. Twenty-five people with T1D underwent three sessions of either moderate or high-intensity RE at three insulin infusion rates during a glucose tracer clamp. We calculated time-varying rates of endogenous glucose production (EGP) and glucose disposal (Rd) across all sessions and used linear regression and extrapolation to estimate insulin- and noninsulin-mediated components of glucose utilization. Blood glucose did not change on average during exercise. The area under the curve (AUC) for EGP increased by 1.04 mM during RE (95% CI: 0.65-1.43, P < 0.001) and decreased proportionally to insulin infusion rate (0.003 mM per percent above basal rate, 95% CI: 0.001-0.006, P = 0.003). The AUC for Rd rose by 1.26 mM during RE (95% CI: 0.41-2.10, P = 0.004) and increased proportionally with insulin infusion rate (0.04 mM per percent above basal rate, CI: 0.03-0.04, P < 0.001). No differences were observed between the moderate and high resistance groups. Noninsulin-mediated glucose utilization rose significantly during exercise before returning to baseline roughly 30-min postexercise. Insulin-mediated glucose utilization remained unchanged during exercise sessions. Circulating catecholamines and lactate rose during exercise despite relatively small changes observed in Rd. Results provide an explanation of why RE may pose a lower overall risk for hypoglycemia.NEW & NOTEWORTHY Aerobic exercise is known to cause decreases in blood glucose secondary to increased glucose utilization in people living with type 1 diabetes (T1D). However, less is known about how resistance-type exercise impacts glucose dynamics. Twenty-five participants with T1D performed in-clinic weight-bearing exercises under a glucose clamp. Mathematical modeling of infused glucose tracer allowed for quantification of the rate of hepatic glucose production as well as rates of insulin-mediated and noninsulin-mediated glucose uptake experienced during resistance exercise.

The Type 1 Diabetes and EXercise Initiative: Predicting Hypoglycemia Risk During Exercise for Participants with Type 1 Diabetes Using Repeated Measures Random Forest

Objective: Exercise is known to increase the risk for hypoglycemia in type 1 diabetes (T1D) but predicting when it may occur remains a major challenge. The objective of this study was to develop a hypoglycemia prediction model based on a large real-world study of exercise in T1D. Research Design and Methods: Structured study-specified exercise (aerobic, interval, and resistance training videos) and free-living exercise sessions from the T1D Exercise Initiative study were used to build a model for predicting hypoglycemia, a continuous glucose monitoring value <70 mg/dL, during exercise. Repeated measures random forest (RMRF) and repeated measures logistic regression (RMLR) models were constructed to predict hypoglycemia using predictors at the start of exercise and baseline characteristics. Models were evaluated with area under the receiver operating characteristic curve (AUC) and balanced accuracy. Results: RMRF and RMLR had similar AUC (0.833 vs. 0.825, respectively) and both models had a balanced accuracy of 77%. The probability of hypoglycemia was higher for exercise sessions with lower pre-exercise glucose levels, negative pre-exercise glucose rates of change, greater percent time <70 mg/dL in the 24 h before exercise, and greater pre-exercise bolus insulin-on-board (IOB). Free-living aerobic exercises, walking/hiking, and physical labor had the highest probability of hypoglycemia, while structured exercises had the lowest probability of hypoglycemia. Conclusions: RMRF and RMLR accurately predict hypoglycemia during exercise and identify factors that increase the risk of hypoglycemia. Lower glucose, decreasing levels of glucose before exercise, and greater pre-exercise IOB largely predict hypoglycemia risk in adults with T1D.