Accuracy of continuous glucose monitoring during exercise in type 1 diabetes pregnancy

A Diabetes Pregnancy Technology Roadmap: The 2023 Norbert Freinkel Award Lecture

Norbert Freinkel emphasized the need for "more aggressive therapy with exogenous insulin" during type 1 diabetes (T1D) pregnancy. Recent advances in diabetes technology, continuous glucose monitoring (CGM), and hybrid closed-loop (HCL) insulin delivery systems allow us to revisit Freinkel's observations from a contemporary perspective. The Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) led to international recommendations that CGM be offered to all pregnant women with T1D to help them meet their pregnancy glucose targets and improve neonatal outcomes. However, despite CGM use, only 35% of trial participants reached the pregnancy glucose targets by 35 weeks' gestation, which is too late for optimal obstetric and neonatal outcomes. The constant vigilance to CGM data and insulin dose adjustment, with perpetual worry about the impact of hyperglycemia on the developing fetal structures, leave many pregnant women feeling overwhelmed. HCL systems that can adapt to marked gestational changes in insulin sensitivity and pharmacokinetics may help to bridge the gap between the nonpregnant time in range glycemic targets (70-180 mg/dL) and the substantially more stringent pregnancy-specific targets (TIRp) (63-140 mg/dL) required for optimal obstetric and neonatal outcomes. Use of HCL (CamAPS FX system) was associated with a 10.5% higher TIRp, 10.2% less hyperglycemia, and 12.3% higher overnight TIRp. Clinical benefits were accompanied by 3.7 kg (8 lb) less gestational weight gain and consistently achieved across a representative patient population of insulin pump or injection users, across trial sites, and across maternal HbA1c categories. Working collaboratively, women, HCL technology, and health care teams achieved improved glycemia with less worry, less work, and more positive pregnancy experiences.

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.

Accuracy of CGM Systems During Continuous and Interval Exercise in Adults with Type 1 Diabetes

BACKGROUND

continuous glucose monitoring systems (CGMs) play an important role in the management of T1D, but their accuracy may reduce during rapid glucose excursions. The aim of study was to assess the accuracy of recent rt-CGMs available in Italy, in subjects with T1D during 2 sessions of physical activity: moderate continuous (CON) and interval exercise (IE).

METHOD

we recruited 22 patients with T1D, on CSII associated or integrated with a CGM, to which a second different sensor was applied. Data recorded by CGMs were compared with the corresponding plasma glucose (PG) values, measured every 5 minutes with the glucose analyzer. To assess the accuracy of the CGMs, we evaluated the Sensor Bias (SB), the Mean Absolute Relative Difference (MARD) and the Clarke error grid (CEG).

RESULTS

a total of 2355 plasma-sensor glucose paired points were collected. Both average plasma and interstitial glucose concentrations did not significantly differ during CON and IE. During CON: 1. PG change at the end of exercise was greater than during IE (P = .034); 2. all sensors overestimated PG more than during IE, as shown by SB (P < .001) and MARD (P < .001) comparisons. Classifying the performance according to the CEG, significant differences were found between the 2 sessions in distribution of points in A and B zones.

CONCLUSIONS

the exercise affects the accuracy of currently available CGMs, especially during CON, suggesting, in this circumstance, the need to maintain blood glucose in a "prudent" range, above that generally recommended. Further studies are needed to investigate additional types of activities.

Concurrent Validity of a Continuous Glucose-Monitoring System at Rest and During and Following a High-Intensity Interval Training Session

Purpose: To assess the concurrent validity of a continuous blood-glucose-monitoring system (CGM) postbreakfast, preexercise, exercise, and postexercise, while assessing the impact of 2 different breakfasts on the observed level of validity. Methods: Eight nondiabetic recreational athletes (age = 30.8 [9.5] y; height = 173.6 [6.6] cm; body mass = 70.3 [8.1] kg) took part in the study. Blood glucose concentration was monitored every 10 minutes using both a CGM (FreeStyle Libre, Abbott, France) and finger-prick blood glucose measurements (FreeStyle Optimum) over 4 different periods (postbreakfast, preexercise, exercise, and postexercise). Two different breakfasts (carbohydrates [CHO] and protein oriented) over 2 days (2 × 2 d in total) were used. Statistical analyses included the Bland–Altman method, standardized mean bias (expressed in standardized units), median absolute relative difference, and the Clarke error grid analysis. Results: Overall, mean bias was trivial to small at postbreakfast (effect size ± 90% confidence limits: −0.12 ± 0.08), preexercise (−0.08 ± 0.08), and postexercise (0.25 ± 0.14), while moderate during exercise (0.66 ± 0.09). A higher median absolute relative difference was observed during exercise (13.6% vs 7%–9.5% for the other conditions). While there was no effect of the breakfast type on the median absolute relative difference results, error grid analysis revealed a higher value in zone D (ie, clinically unsafe zone) during exercise for CHO (10.5%) compared with protein (1.6%). Conclusion: The CGM device examined in this study can only be validly used at rest, after both a CHO and protein-rich breakfast. Using CGM to monitor blood glucose concentration during exercise is not recommended. Moreover, the accuracy decreased when CHO were consumed before exercise.

Benefits of Real-Time Continuous Glucose Monitoring in Pregnancy

In recent years, continuous glucose monitoring (CGM) has become increasingly available with the introduction of devices that are specifically approved for use during pregnancy. Evidence in the form of randomized-controlled trials and cohort studies continues to build support for the use of CGM during pregnancy to improve measures of maternal glycemia as well as obstetric and neonatal outcomes. Based on data from the CGM in pregnant women with type 1 diabetes (CONCEPTT) trial alongside a Swedish cohort study of real-world outcomes of pregnant women with type 1 diabetes, the UK National Institute for Health and Clinical Excellence (NICE) guidelines now recommend that real-time CGM be offered to all pregnant women with type 1 diabetes. Based on these guidelines, all pregnant individuals in the United Kingdom with type 1 diabetes will receive government-funded real-time CGM for a 12-month duration. These guidelines are a game-changer and will continue to facilitate more widespread access to CGM use in the United Kingdom and beyond. This review describes the role of CGM in the management of diabetes in pregnancy, discusses contemporary maternal glucose levels and their relationship with outcomes in diabetes pregnancies, and examines the high-quality, randomized-controlled trial and the real-world clinical data evaluating the impact of CGM use.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

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.

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.

A Perspective on the Accuracy of Blood Glucose Meters During Pregnancy

Blood glucose monitoring is fundamental for hyperglycemia management during pregnancy, but are the devices up to the job? Studies assessing the accuracy of 10 commercially available glucose meters during pregnancy showed that although >98-99% of the meter values were in the acceptable zones of the error grid for the majority of the meters, the meter performance varied, with the majority showing positive bias and a few showing minimal negative bias. The mean difference between meter and laboratory plasma values varied between -0.33 and 0.73 mmol/L. Three meters showed deviations from laboratory results with a change in maternal hematocrit levels. No meters had a total analytical error <5%, and no studies evaluated meters using recent International Organization for Standardization 15197:2013 criteria. The Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) recently showed that an antenatal continuous glucose monitoring system (CGMS), as an adjunct to capillary monitoring, was associated with a lower incidence of large-for-gestational-age babies, fewer neonatal intensive care unit admissions (>24 h), and a lower incidence of neonatal hypoglycemia. The flash glucose monitoring system shows good accuracy in pregnant women but has not been marketed widely in the U.S. We suggest that meters cannot be assumed to be sufficiently accurate during pregnancy and that manufacturers should ensure a total error <5%, with bias and imprecision <2% during pregnancy. Large studies are needed to evaluate the usefulness of CGMS among pregnant women with type 2 diabetes and gestational diabetes mellitus.

Insulin Management Strategies for Exercise in Diabetes

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

Accuracy of Continuous Glucose Monitoring before, during, and after Aerobic and Anaerobic Exercise in Patients with Type 1 Diabetes Mellitus

Continuous glucose monitoring (CGM) plays an important role in treatment decisions for patients with type 1 diabetes under conventional or closed-loop therapy. Physical activity represents a great challenge for diabetes management as well as for CGM systems. In this work, the accuracy of CGM in the context of exercise is addressed. Six adults performed aerobic and anaerobic exercise sessions and used two Medtronic Paradigm Enlite-2 sensors under closed-loop therapy. CGM readings were compared with plasma glucose during different periods: one hour before exercise, during exercise, and four hours after the end of exercise. In aerobic sessions, the median absolute relative difference (MARD) increased from 9.5% before the beginning of exercise to 16.5% during exercise (p < 0.001), and then decreased to 9.3% in the first hour after the end of exercise (p < 0.001). For the anaerobic sessions, the MARD before exercise was 15.5% and increased without statistical significance to 16.8% during exercise realisation (p = 0.993), and then decreased to 12.7% in the first hour after the cessation of anaerobic activities (p = 0.095). Results indicate that CGM might present lower accuracy during aerobic exercise, but return to regular operation a few hours after exercise cessation. No significant impact for anaerobic exercise was found.

Accuracy, User Acceptability, and Safety Evaluation for the FreeStyle Libre Flash Glucose Monitoring System When Used by Pregnant Women with Diabetes

BACKGROUND

Accuracy of the FreeStyle Libre™ Flash Glucose Monitoring System has not been evaluated in pregnant women with diabetes. The aim of this study was to determine accuracy (compared to self-monitoring of blood glucose [SMBG]), clinical safety, and acceptability of the FreeStyle Libre System when used at home by this population.

MATERIALS AND METHODS

Seventy-four participants, with type 1 (T1D, n = 24), type 2 (T2D, n = 11), or gestational (n = 39) diabetes, were enrolled across 13 sites (9 in United Kingdom, 4 in Austria). Average gestation was 26.6 ± 6.8 weeks (mean ± standard deviation), age was 30.5 ± 5.1 years, diabetes duration was 13.1 ± 7.3 years for T1D and 3.2 ± 2.5 years for T2D, and 49/74 (66.2%) used insulin to manage their diabetes. Sensors were worn for up to 14 days. Sensor glucose values (masked) were compared with capillary SMBG values (made at least 4 times/day).

RESULTS

Clinical accuracy of sensor results versus SMBG results was demonstrated, with 88.1% and 99.8% of results within Zone A and Zones A and B of the Consensus Error Grid, respectively. Overall mean absolute relative difference was 11.8%. Sensor accuracy was unaffected by the type of diabetes, the stage of pregnancy, whether insulin was used, age or body mass index. User questionnaires indicated high levels of satisfaction with sensor wear, system use, and comparison to SMBG. There were no unanticipated device-related adverse events.

CONCLUSIONS

Good agreement was demonstrated between the FreeStyle Libre System and SMBG. Accuracy of the system was unaffected by patient characteristics, indicating that the system is safe and accurate to use by pregnant women with diabetes.

A comparison of adipose tissue interstitial glucose and venous blood glucose during postprandial resistance exercise in patients with type 2 diabetes

Resistance exercise during the postprandial period lowers venous glucose concentrations in individuals with type 2 diabetes, but the impact of resistance exercise on interstitial glucose concentrations is not well understood. The objective of this study was to compare subcutaneous adipose tissue interstitial glucose and venous blood glucose concentrations during postprandial resistance exercise in patients with type 2 diabetes. Eleven individuals completed two trials in a random order including a no-exercise (NoEx) and a postprandial resistance exercise trial (M-Ex). During the trials, the individuals consumed a meal and either remained sedentary (NoEx) or performed a session of resistance training beginning 45 min after the meal (M-Ex) while interstitial and venous glucose concentrations were simultaneously measured. Venous glucose during exercise was ~11% lower ( P = 0.05) during M-Ex (8.0 ± 0.5 mmol/l) compared with NoEx (9.0 ± 0.5 mmol/l) whereas interstitial glucose during M-Ex (10.4 ± 0.7 mmol/l) was not different compared with interstitial glucose during NoEx (10.1 ± 0.7 mmol/l). Bland-Altman plots revealed that the difference (bias) between interstitial and venous glucose during exercise was more than twofold greater during M-Ex (2.36 ± 2.07 mmol/l) compared with NoEx (1.11 ± 1.69 mmol/l). The mean (33.8 ± 6.2 mmol/l) and median (34.7 ± 6.3 mmol/l) absolute relative difference during exercise were 73% and 78% greater compared with the mean (19.5 ± 4.1 mmol/l) and median (19.5 ± 4.1 mmol/l) absolute relative difference during NoEx ( P = 0.04). Resistance exercise has unequal effects on glucose concentrations within different bodily compartments as exercise reduced venous glucose concentrations but not adipose tissue interstitial glucose concentrations in the abdominal region in individuals with type 2 diabetes. NEW & NOTEWORTHY This is the first study to compare subcutaneous adipose tissue interstitial glucose concentrations and venous blood glucose concentrations during postprandial resistance exercise in individuals with type 2 diabetes. We find that resistance exercise effectively reduces systemic venous blood glucose concentrations but not subcutaneous adipose tissue interstitial glucose concentrations in the abdominal region. Resistance exercise has differential effects on glucose concentrations depending on its compartmentalization within the body.

Evaluation of subcutaneous glucose monitoring systems under routine environmental conditions in patients with type 1 diabetes

Continuous and flash glucose monitoring (GM) systems have been established in diabetes care. We compared the sensor performance of 3 commercially available GM systems. A total of 12 patients with type 1 diabetes were included in a single-centre, open-label study in which the sensor performance of the Abbott FreeStyle libre (Abbott), Dexcom G4 Platinum (Dexcom) and Medtronic MiniMed 640G (Medtronic) systems over 12 hours was compared during mimicked real-life conditions (meals, exercise, hypo- and hyperglycaemia). Sensor performance was determined by fulfilment of ISO 15197:2013 criteria, calculating mean absolute relative difference (MARD), and was also illustrated using Parkes error grid and Bland-Altman plots. Sensor performance during changes in metabolic variables (lactate, betahydroxybutyrate, glucagon, non-esterified-fatty-acids) was determined by Spearman's rank correlation coefficient testing. The systems fulfilled ISO 15197:2013 criteria by 73.2% (Abbott), 56.1% (Dexcom) and 52.0% (Medtronic). The MARDs ± standard deviation in the entire glycaemic range were 13.2% ± 10.9% (Abbott), 16.8% ± 12.3% (Dexcom) and 21.4% ± 17.6% (Medtronic), respectively. All sensors performed less accurately during hypoglycaemia and best during hyperglycaemia. We did not observe an influence of metabolic variables on sensor performance.

CGM, Pregnancy, and Remote Monitoring

The glycemic goals of pregnancy are very narrow to reduce excess risks for numerous maternal and fetal complications. Continuous glucose monitors (CGMs) may help women achieve glucose goals and reduce hypoglycemia. CGM use has been found to be safe and effective in pregnancies associated with diabetes. CGM use can accurately identify glycemic patterns among women with and without diabetes in pregnancy. The data on the effects of CGM use on maternal and fetal outcomes are conflicting. Using CGMs in conjunction with continuous subcutaneous insulin infusion therapy in pregnancies complicated by diabetes may improve outcomes. There are limitations of CGM use that affect patients in and outside of pregnancy, as well as specific barriers that only affect pregnant women. Of importance, CGM use does not replace standard clinical care, but may be used an adjunctive tool in pregnancy. CGM remote monitoring in pregnancy is an understudied field. In this study, we review the studies on CGM use in pregnancy.

Different insulin types and regimens for pregnant women with pre-existing diabetes

BACKGROUND

Insulin requirements may change during pregnancy, and the optimal treatment for pre-existing diabetes is unclear. There are several insulin regimens (e.g. via syringe, pen) and types of insulin (e.g. fast-acting insulin, human insulin).

OBJECTIVES

To assess the effects of different insulin types and different insulin regimens in pregnant women with pre-existing type 1 or type 2 diabetes.

SEARCH METHODS

We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (30 October 2016), ClinicalTrials.gov (17 October 2016), the WHO International Clinical Trials Registry Platform (ICTRP; 17 October 2016), and the reference lists of retrieved studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) that compared different insulin types and regimens in pregnant women with pre-existing diabetes.We had planned to include cluster-RCTs, but none were identified. We excluded quasi-randomised controlled trials and cross-over trials. We included studies published in abstract form and contacted the authors for further details when applicable. Conference abstracts were superseded by full publications.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed trials for inclusion, conducted data extraction, assessed risk of bias, and checked for accuracy. We assessed the quality of the evidence using the GRADE approach.

MAIN RESULTS

The findings in this review were based on very low-quality evidence, from single, small sample sized trial estimates, with wide confidence intervals (CI), some of which crossed the line of no effect; many of the prespecified outcomes were not reported. Therefore, they should be interpreted with caution. We included five trials that included 554 women and babies (four open-label, multi-centre, two-arm trials; one single centre, four-arm RCT). All five trials were at a high or unclear risk of bias due to lack of blinding, unclear methods of randomisation, and selective reporting of outcomes. Pooling of data from the trials was not possible, as each trial looked at a different comparison.1. One trial (N = 33 women) compared Lispro insulin with regular insulin and provided very low-quality evidence for the outcomes. There were seven episodes of pre-eclampsia in the Lispro group and nine in the regular insulin group, with no clear difference between the two groups (risk ratio (RR) 0.68, 95% CI 0.35 to 1.30). There were five caesarean sections in the Lispro group and nine in the regular insulin group, with no clear difference between the two groups (RR 0.59, 95% CI 0.25 to 1.39). There were no cases of fetal anomaly in the Lispro group and one in the regular insulin group, with no clear difference between the groups (RR 0.35, 95% CI 0.02 to 8.08). Macrosomia, perinatal deaths, episodes of birth trauma including shoulder dystocia, nerve palsy, and fracture, and the composite outcome measure of neonatal morbidity were not reported.2. One trial (N = 42 women) compared human insulin to animal insulin, and provided very low-quality evidence for the outcomes. There were no cases of macrosomia in the human insulin group and two in the animal insulin group, with no clear difference between the groups (RR 0.22, 95% CI 0.01 to 4.30). Perinatal death, pre-eclampsia, caesarean section, fetal anomaly, birth trauma including shoulder dystocia, nerve palsy and fracture and the composite outcome measure of neonatal morbidity were not reported.3. One trial (N = 93 women) compared pre-mixed insulin (70 NPH/30 REG) to self-mixed, split-dose insulin and provided very low-quality evidence to support the outcomes. Two cases of macrosomia were reported in the pre-mixed insulin group and four in the self-mixed insulin group, with no clear difference between the two groups (RR 0.49, 95% CI 0.09 to 2.54). There were seven cases of caesarean section (for cephalo-pelvic disproportion) in the pre-mixed insulin group and 12 in the self-mixed insulin group, with no clear difference between groups (RR 0.57, 95% CI 0.25 to 1.32). Perinatal death, pre-eclampsia, fetal anomaly, birth trauma including shoulder dystocia, nerve palsy, or fracture and the composite outcome measure of neonatal morbidity were not reported.4. In the same trial (N = 93 women), insulin injected with a Novolin pen was compared to insulin injected with a conventional needle (syringe), which provided very low-quality evidence to support the outcomes. There was one case of macrosomia in the pen group and five in the needle group, with no clear difference between the different insulin regimens (RR 0.21, 95% CI 0.03 to 1.76). There were five deliveries by caesarean section in the pen group compared with 14 in the needle group; women were less likely to deliver via caesarean section when insulin was injected with a pen compared to a conventional needle (RR 0.38, 95% CI 0.15 to 0.97). Perinatal death, pre-eclampsia, fetal anomaly, birth trauma including shoulder dystocia, nerve palsy, or fracture, and the composite outcome measure of neonatal morbidity were not reported.5. One trial (N = 223 women) comparing insulin Aspart with human insulin reported none of the review's primary outcomes: macrosomia, perinatal death, pre-eclampsia, caesarean section, fetal anomaly, birth trauma including shoulder dystocia. nerve palsy, or fracture, or the composite outcome measure of neonatal morbidity.6. One trial (N = 162 women) compared insulin Detemir with NPH insulin, and supported the outcomes with very low-quality evidence. There were three cases of major fetal anomalies in the insulin Detemir group and one in the NPH insulin group, with no clear difference between the groups (RR 3.15, 95% CI 0.33 to 29.67). Macrosomia, perinatal death, pre-eclampsia, caesarean section, birth trauma including shoulder dystocia, nerve palsy, or fracture and the composite outcome of neonatal morbidity were not reported.

AUTHORS' CONCLUSIONS

With limited evidence and no meta-analyses, as each trial looked at a different comparison, no firm conclusions could be made about different insulin types and regimens in pregnant women with pre-existing type 1 or 2 diabetes. Further research is warranted to determine who has an increased risk of adverse pregnancy outcome. This would include larger trials, incorporating adequate randomisation and blinding, and key outcomes that include macrosomia, pregnancy loss, pre-eclampsia, caesarean section, fetal anomalies, and birth trauma.

Comparison of Two Continuous Glucose Monitoring Systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at Rest and During Exercise

BACKGROUND

Despite technological advances, the accuracy of continuous glucose monitoring (CGM) systems may not always be satisfactory with rapidly changing glucose levels, as is notable during exercise. We compare the performance of two current and widely used CGM systems, Dexcom G4 Platinum (Dexcom) and Medtronic Paradigm Veo Enlite system (Enlite), during both rest and exercise in adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS

Paired sensor and plasma glucose (PG) values (total of 431 data pairs for Dexcom and 425 for Enlite) were collected from 17 adults (37.3 ± 13.6 years) with T1D. To evaluate and compare the accuracy of sensor readings, criteria involving sensor bias (sensor minus PG levels), absolute relative difference (ARD), and percentage of readings meeting International Organization for Standardization (ISO) criteria were considered.

RESULTS

Both Dexcom and Enlite performed equally well during the rest period, with respective mean/median biases of -0.12/-0.02 mmol/L versus -0.18/-0.40 (P = 0.78, P = 0.66) mmol/L and ARDs of 13.77/13.34% versus 12.38/11.95% (P = 0.53, P = 0.70). During exercise, sensor bias means/medians were -0.40/-0.21 mmol versus -0.26/-0.24 mmol/L (P = 0.67, P = 0.62) and ARDs were 22.53/15.13% versus 20.44/14.11% (P = 0.58, P = 0.68) for Dexcom and Enlite, respectively. Both sensors demonstrated significantly lower performance during exercise; median ARD comparison at rest versus exercise for both Dexcom and Enlite showed a P = 0.02. More data pairs met the ISO criteria for Dexcom and Enlite at rest, 73.6% and 76.9% compared with exercise 48.2% and 53.9%.

CONCLUSION

Dexcom and Enlite demonstrated comparable overall performances during rest and physical activity. However, a lower accuracy was observed during exercise for both sensors, necessitating a fine-tuning of their performance with physical activity.

Accuracy of continuous glucose monitoring during differing exercise conditions

AIM

Depending on intensity, exercise may induce a strong hormonal and metabolic response, including acid-base imbalances and changes in microcirculation, potentially interfering with the accuracy of continuous glucose monitoring (CGM). The present study aimed at comparing the accuracy of the Dexcom G4 Platinum (DG4P) CGM during continuous moderate and intermittent high-intensity exercise (IHE) in adults with type 1 diabetes (T1DM).

METHODS

Ten male individuals with well-controlled T1DM (HbA1c 7.0 ± 0.6% [54 ± 6 mmol/mol]) inserted the DG4P sensor 2 days prior to a 90 min cycling session (50% VO2peak) either with (IHE) or without (CONT) a 10s all-out sprint every 10 min. Venous blood samples for reference glucose measurement were drawn every 10 min and euglycemia (target 7 mmol/l) was maintained using an oral glucose solution. Additionally, lactate and venous blood gas variables were determined.

RESULTS

Mean reference blood glucose was 7.6 ± 0.2 mmol/l during IHE and 6.7 ± 0.2 mmol/l during CONT (p<0.001). IHE resulted in significantly higher levels of lactate (7.3 ± 0.5 mmol/l vs. 2.6 ± 0.3 mmol/l, p<0.001), while pH values were significantly lower in the IHE group (7.27 vs. 7.38, p=0.001). Mean absolute relative difference (MARD) was 13.3 ± 2.2% for IHE and 13.6 ± 2.8% for CONT suggesting comparable accuracy (p=0.90). Using Clarke Error Grid Analysis, 100% of CGM values during both IHE and CONT were in zones A and B (IHE: 77% and 23%; CONT: 78% and 22%).

CONCLUSIONS

The present study revealed good and comparable accuracy of the DG4P CGM system during intermittent high intensity and continuous moderate intensity exercise, despite marked differences in metabolic conditions. This corroborates the clinical robustness of CGM under differing exercise conditions.

CLINICAL TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT02068638.

Adding heart rate signal to a control-to-range artificial pancreas system improves the protection against hypoglycemia during exercise in type 1 diabetes

BACKGROUND

We present a clinical trial establishing the feasibility of a control-to-range (CTR) closed-loop system informed by heart rate (HR) and assess the effect of HR information added to CTR on the risk for hypoglycemia during and after exercise.

SUBJECTS AND METHODS

Twelve subjects with type 1 diabetes (five men, seven women; weight, 68.9 ± 3.1 kg; age, 38 ± 3.3 years; glycated hemoglobin, 6.9 ± 0.2%) participated in a randomized crossover clinical trial comparing CTR versus CTR+HR in two 26-h admissions, each including 30 min of mild exercise. The CTR algorithm was implemented in the DiAs portable artificial pancreas platform based on an Android(®) (Google, Mountainview, CA) smartphone. We assessed blood glucose (BG) decline during exercise, the Low BG Index (LBGI) (a measure of hypoglycemic risk), number of hypoglycemic episodes (BG <70 mg/dL) and overall glucose control (percentage time within the target range 70 mg/dL ≤ BG ≤ 180 mg/dL).

RESULTS

Using HR to inform the CTR algorithm reduced significantly the BG decline during exercise (P=0.022), indicated marginally lower LBGI (P=0.3) and fewer hypoglycemic events during exercise (none vs. two events; P=0.16), and resulted in overall higher percentage time within the target range (81% vs. 75%; P=0.2). LBGI and average BG remained unchanged overall, during recovery, and overnight.

CONCLUSIONS

HR-informed closed-loop control can be implemented in a portable artificial pancreas. Although closed loop has been shown to reduce hypoglycemia, adding HR signal may further limit the risk for hypoglycemia during and immediately after exercise. The most prominent effect of adding HR information is reduced BG decline during exercise, without deterioration of overall glycemic control.

Predicting Plasma Glucose From Interstitial Glucose Observations Using Bayesian Methods

One way of constructing a control algorithm for an artificial pancreas is to identify a model capable of predicting plasma glucose (PG) from interstitial glucose (IG) observations. Stochastic differential equations (SDEs) make it possible to account both for the unknown influence of the continuous glucose monitor (CGM) and for unknown physiological influences. Combined with prior knowledge about the measurement devices, this approach can be used to obtain a robust predictive model. A stochastic-differential-equation-based gray box (SDE-GB) model is formulated on the basis of an identifiable physiological model of the glucoregulatory system for type 1 diabetes mellitus (T1DM) patients. A Bayesian method is used to estimate robust parameters from clinical data. The models are then used to predict PG from IG observations from 2 separate study occasions on the same patient. First, all statistically significant diffusion terms of the model are identified using likelihood ratio tests, yielding inclusion of [Formula: see text], [Formula: see text], and [Formula: see text]. Second, estimates using maximum likelihood are obtained, but prediction capability is poor. Finally a Bayesian method is implemented. Using this method the identified models are able to predict PG using only IG observations. These predictions are assessed visually. We are also able to validate these estimates on a separate data set from the same patient. This study shows that SDE-GBs and a Bayesian method can be used to identify a reliable model for prediction of PG using IG observations obtained with a CGM. The model could eventually be used in an artificial pancreas.

Closed-loop artificial pancreas systems: physiological input to enhance next-generation devices

To provide an understanding of both the preclinical and clinical aspects of closed-loop artificial pancreas systems, we provide a discussion of this topic as part of this two-part Bench to Clinic narrative. Here, the Bench narrative provides an in-depth understanding of insulin-glucose-glucagon physiology in conditions that mimic the free-living situation to the extent possible in type 1 diabetes that will help refine and improve future closed-loop system algorithms. In the Clinic narrative, Doyle and colleagues compare and evaluate technology used in current closed-loop studies to gain further momentum toward outpatient trials and eventual approval for widespread use.

Postchallenge plasma glucose and glycemic spikes are more strongly associated with atherosclerosis than fasting glucose or HbA1c level

OBJECTIVE

To observe the relationship of fasting plasma glucose (FPG), postchallenge plasma glucose (PG) (30, 60, 90, and 120 min during an oral glucose tolerance test [OGTT], as well as maximal PG during an OGTT, postchallenge glucose spikes [PGS], and glucose under the OGTT curve), and HbA1c to intima-media thickness (IMT) as a marker of atherosclerosis.

RESEARCH DESIGN AND METHODS

OGTT, ultrasound measurement of carotid IMT, and various atherosclerosis risk factors, such as family history of diabetes, obesity, and/or hyperlipoproteinemia, but without known diabetes, were analyzed in 582 individuals aged 40-70 years and at risk for type 2 diabetes.

RESULTS

In univariate analysis, all examined glycemic parameters were significantly correlated to IMT. The 2-h postchallenge plasma glucose showed the strongest odds ratio (OR) of 1.88 (1.34-2.63) in relation to abnormal IMT. All PG variables, except for 30-min glucose in OGTT, showed a significant OR, whereas the OR for HbA1c and FPG was not significant. In logistic regression analysis, 2-h PG was identified as the strongest determinant of IMT from all glycemic parameters. The 2-h PG and PGS, but not FPG, were associated with a significant rise of IMT in tertiles of HbA1c. Glycemic parameters were strongly related to each other and to many atherosclerosis risk factors. In multivariate analysis including a variety of atherosclerosis risk factors, 2-h PG was a significant independent determinant of IMT.

CONCLUSIONS

PG and PGS are more strongly associated with carotid IMT than FPG and HbA1c level and modify substantially the risk for atherosclerosis, estimated by HbA1c alone, in a cohort at risk for diabetes and in the early diabetes stage.