Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care

Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

Effect of metformin adjunct therapy on cardiometabolic parameters in Indian adolescents with type 1 diabetes: a randomized controlled trial

Introduction

Insulin resistance is being increasingly reported in type-1 Diabetes (T1D) and is known to accelerate microvascular complications. The Asian Indian population has a higher risk of double diabetes development compared to Caucasians. Hence, we studied the effect of adding Metformin to standard insulin therapy on glycemic control, insulin sensitivity (IS), cardiometabolic parameters and body composition in Indian adolescents with T1D.

Methods

A Randomized controlled trial was conducted spanning 9 months (Registration number:CTRI/2019/11/022126). Inclusion: Age 10-19 years, T1D duration>1year, HbA1c>8% Exclusion: Uncontrolled vascular complications/comorbidities, Metformin intolerance, concomitant drugs affecting insulin sensitivity. Participants were randomized to Metformin/Placebo (n=41 each) groups and age, sex, duration-matched. Assessments were performed at baseline, 3 and 9 months.

Results

82 participants aged 14.7 ± 3years (40 females) were enrolled, with a mean diabetes duration of 5.2 ± 2.3 years. Over 9 months, HbA1c decreased significantly by 0.8 (95% confidence interval: -1.2 to -0.3) from 9.8 ± 1.8% to 9.1 ± 1.7% on Metformin but remained largely unchanged (difference of 0.2, 95% confidence interval: -0.7 to 0.2) i.e. 9.9 ± 1.6% and 9.7 ± 2.2% on placebo. HbA1c improvement correlated negatively with baseline IS (EGDR:r= -0.3;SEARCH:r = -0.24, p<0.05) implying better HbA1c-lowering in those with decreased initial IS. CGM-based glycemic variability (standard deviation) reduced by 6.3 mg/dL (95% confidence interval: -12.9 to 0.2) from 100.2 ± 19.1 mg/dL to 93.7 ± 19.9 mg/dL in those on Metformin (p=0.05) but not placebo (94.0 ± 20.5; 90.0 ± 22.6 mg/dL). Insulin sensitivity: CACTIexa & SEARCH scores demonstrated no change with Metformin but significant worsening on placebo. Significant increase in LDL-C(42%), total cholesterol(133.6 to 151.1 mg/dL), triglyceride (60.0 to 88.0 mg/dL) and carotid intima-media thickness was noted on placebo but not Metformin. Weight, BMI, fat Z-scores increased significantly on placebo but not Metformin. Adverse events (AE) were minor; AE, compliance and safety parameters were similar between the two groups.

Conclusion

Metformin as an adjunct to insulin in Asian Indian adolescents with T1D demonstrated beneficial effect on glycemic control, glycemic variability, IS, lipid profile, vascular function, weight and body fat, with a good safety profile when administered for 9 months.

Glycemic Control Among Adults With Type 1 Diabetes During Passover: A Pre-Post Single-center Study

OBJECTIVES

No data are available regarding glycemic control of patients with type 1 diabetes (T1D) during Passover. Our aim in this study was to assess the effect of Passover on diabetes management and glycemic control in adult patients with T1D with nutritional changes during Passover (observant) compared with patients who did not change their dietary habits during Passover (nonobservant).

METHODS

Observational pre-post study of adult patients with T1D, followed in a diabetes clinic in Israel. Data were downloaded from insulin pumps and continuous glucose monitoring for 37 days: 2 weeks before Passover; 9 days of Passover; and 2 weeks thereafter. Differences in percentage of time spent above target (>10.0 to >13.9 mmol/L), at target (3.9 to 10.0 mmol/L) and below target (<3.9 to <3.0 mmol/L), were compared using paired t tests or paired signed rank tests.

RESULTS

The study cohort included 43 patients (23 observant, 20 nonobservant). The average blood glucose was significantly higher during Passover compared with the period before Passover---in nonobservant patients 8.2±1.5 mmol/L and 7.9±1.3 mmol/L (p=0.043), respectively, and in observant patients 8.7±1.6 mmol/L and 8.4±1.6 mmol/L (p=0.048), respectively. Time above range 10 to 13.9 mmol/L was increased in observant patients during Passover, as compared with the period before Passover, was 24.9±16.2% and 20.6±12.4% (p=0.04), respectively. The dose of bolus insulin had increased significantly in observant patients: 27.4±13.9 units during Passover, as compared with 24.2±11.2 units before Passover (p=0.02).

CONCLUSIONS

Passover alters glycemic control and insulin needs in Jewish patients with T1D. It is advisable to make specific adjustments to maintain the recommended glycemic control.

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.

Continuous glucose monitoring versus blood glucose monitoring for risk of severe hypoglycaemia and diabetic ketoacidosis in children, adolescents, and young adults with type 1 diabetes: a population-based study

BACKGROUND

The effect of continuous glucose monitoring on the risk of severe hypoglycaemia and ketoacidosis in patients with diabetes is unclear. We investigated whether rates of acute diabetes complications are lower with continuous glucose monitoring, compared with blood glucose monitoring, and which metrics predict its risk in young patients with type 1 diabetes.

METHODS

In this population-based cohort study, patients were identified from 511 diabetes centres across Austria, Germany, Luxembourg, and Switzerland participating in the Diabetes Prospective Follow-up initiative. We included people with type 1 diabetes aged 1·5-25·0 years, with a diabetes duration of more than 1 year, who had been treated between Jan 1, 2014, and June 30, 2021, and had an observation time of longer than 120 days in the most recent treatment year. Severe hypoglycaemia and ketoacidosis rates during the most recent treatment year were examined in people using continuous glucose monitoring and in those using blood glucose monitoring. Adjustments of statistical models included age, sex, diabetes duration, migration background, insulin therapy (pump or injections), and treatment period. Rates of severe hypoglycaemia and diabetic ketoacidosis were evaluated by several continuous glucose monitoring metrics, including percentage of time below target glucose range (<3·9 mmol/L), glycaemic variability (measured as the coefficient of variation), and mean sensor glucose.

FINDINGS

Of 32 117 people with type 1 diabetes (median age 16·8 years [IQR 13·3-18·1], 17 056 [53·1%] males), 10 883 used continuous glucose monitoring (median 289 days per year), and 21 234 used blood glucose monitoring. People using continuous glucose monitoring had lower rates of severe hypoglycaemia than those using blood glucose monitoring (6·74 [95% CI 5·90-7·69] per 100 patient-years vs 8·84 [8·09-9·66] per 100 patient-years; incidence rate ratio 0·76 [95% CI 0·64-0·91]; p=0·0017) and diabetic ketoacidosis (3·72 [3·32-4·18] per 100 patient-years vs 7·29 [6·83-7·78] per 100 patient-years; 0·51 [0·44-0·59]; p<0·0001). Severe hypoglycaemia rates increased with percentage of time below target glucose range (incidence rate ratio 1·69 [95% CI 1·18-2·43]; p=0·0024, for 4·0-7·9% vs <4·0% and 2·38 [1·51-3·76]; p<0·0001, for ≥8·0% vs <4·0%) and glycaemic variability (coefficient of variation ≥36% vs <36%; incidence rate ratio 1·52 [95% CI 1·06-2·17]; p=0·022). Diabetic ketoacidosis rates increased with mean sensor glucose (incidence rate ratio 1·77 [95% CI 0·89-3·51], p=0·13, for 8·3-9·9 mmol/L vs <8·3 mmol/L; 3·56 [1·83-6·93], p<0·0001, for 10·0-11·6 mmol/L vs <8·3 mmol/L; and 8·66 [4·48-16·75], p<0·0001, for ≥11·7 mmol/L vs <8·3 mmol/L).

INTERPRETATION

These findings provide evidence that continuous glucose monitoring can reduce severe hypoglycaemia and ketoacidosis risk in young people with type 1 diabetes on insulin therapy. Continuous glucose monitoring metrics might help to identify those at risk for acute diabetes complications.

FUNDING

German Center for Diabetes Research, German Federal Ministry of Education and Research, German Diabetes Association, and Robert Koch Institute.

The Beneficial Effects of a Multispecies Probiotic Supplement on Glycaemic Control and Metabolic Profile in Adults with Type 1 Diabetes: A Randomised, Double-Blinded, Placebo-Controlled Pilot-Study

OBJECTIVE

In this pilot-study, the effects of a multispecies probiotic supplement on glycaemic control and metabolic parameters in adults with type 1 diabetes (T1DM) were explored.

MATERIAL AND METHODS

A total of 50 T1DM patients were enrolled and randomly placed into a group receiving capsules containing multi-probiotic strains (Bifidobacterium longum, Lactobacterium bulagricumi, Streptococcus thermophilus) and insulin (probiotics group, n = 27) or a group receiving a placebo and insulin (placebo group, n = 23). All patients underwent continuous glucose monitoring at baseline and 12 weeks after intervention. The primary outcomes were determined by comparing factors such as changes in fasting blood glucose (FBG) and haemoglobin A1c (HbA1c) between the groups.

RESULTS

Probiotic supplementation significantly reduced FBG (-1.0 ± 4.7 vs 1.8 ± 4.7 mmol/L, p = 0.048), 30 min postprandial glucose (-0.5 ± 4.6 vs 1.9 ± 3.3 mmol/L, p = 0.0495), and low-density lipoprotein cholesterol (-0.07 ± 0.45 vs 0.32 ± 0.78 mmol/L, p = 0.0413), compared with the placebo. Although not statistically significant, probiotic supplementation also lowered HbA1c levels by 0.49% (-5.33 mmol/mol, p = 0.310). Besides, no significant difference was observed in the continuous glucose monitoring (CGM) parameters between the two groups. Further subgroup analysis revealed a significant reduction in mean sensor glucose (MSG; -0.75 (-2.11, 0.48) mmol/L vs 1.51 (-0.37, 2.74) mmol/L, p = 0.010) and time above range (TAR; -5.47 (-20.1, 3.04)% vs 18.9 (-1.11, 35.6)%, p = 0.006), as well as an greater improvement in the time in range (TIR; 9.32 (-4.84, 16.6)% vs -19.9 (-31.4, 0.69)%, p = 0.005) in male patients than female patients in the probiotics group.

CONCLUSION

Multispecies probiotics exerted beneficial effects on fasting and postprandial glucose and lipid profiles in adult T1DM patients, especially for male patients and those with higher baseline FBG levels.

Insulin: evolution of insulin formulations and their application in clinical practice over 100 years

The first preparation of insulin extracted from a pancreas and made suitable for use in humans after purification was achieved 100 years ago in Toronto, an epoch-making achievement, which has ultimately provided a life-giving treatment for millions of people worldwide. The earliest animal-derived formulations were short-acting and contained many impurities that caused adverse reactions, thereby limiting their therapeutic potential. However, since then, insulin production and purification improved with enhanced technologies, along with a full understanding of the insulin molecule structure. The availability of radio-immunoassays contributed to the unravelling of the physiology of glucose homeostasis, ultimately leading to the adoption of rational models of insulin replacement. The introduction of recombinant DNA technologies has since resulted in the era of both rapid- and long-acting human insulin analogues administered via the subcutaneous route which better mimic the physiology of insulin secretion, leading to the modern basal-bolus regimen. These advances, in combination with improved education and technologies for glucose monitoring, enable people with diabetes to better meet individual glycaemic goals with a lower risk of hypoglycaemia. While the prevalence of diabetes continues to rise globally, it is important to recognise the scientific endeavour that has led to insulin remaining the cornerstone of diabetes management, on the centenary of its first successful use in humans.

A Prospective Study on Continuous Glucose Monitoring in Glycogen Storage Disease Type Ia: Toward Glycemic Targets

CONTEXT

Although previous research has shown the benefit of continuous glucose monitoring (CGM) for hepatic glycogen storage diseases (GSDs), current lack of prospectively collected CGM metrics and glycemic targets for CGM-derived outcomes in the hepatic GSD population limits its use.

OBJECTIVE

To assess CGM metrics for glycemic variation and glycemic control in adult patients with GSDIa as compared to matched healthy volunteers.

DESIGN

Prospective CGM data were collected during the ENGLUPRO GSDIa trial (NCT04311307) in which a Dexcom G6 device was used. Ten adult patients with GSDIa and 10 age-, sex- and body mass index-matched healthy volunteers were enrolled. Capillary blood glucose was concurrently measured during 2 standardized 2-hour time intervals. Descriptive [eg, glycemic variability (GV), time below range, time in range (TIR), time above range (TAR)] and advanced (ie, first- and second-order derivatives, Fourier analysis) CGM outcomes were calculated. For each descriptive CGM outcome measure, 95% CIs were computed in patients with GSDIa and healthy volunteers, respectively.

RESULTS

CGM overestimation was higher under preprandial and level 1 hypoglycemia (ie, capillary glucose values ≥ 3.0 mmol/L and < 3.9 mmol/L) conditions. GV and TAR were higher while TIR was lower in patients with GSDIa compared to healthy volunteers (P < 0.05). Three patients with GSDIa showed descriptive CGM outcomes outside the calculated 95% CI in GSDIa patients. Advanced CGM analysis revealed a distinct pattern (ie, first- and second-order derivatives and glucose curve amplitude) in each of these 3 patients within the patients group.

CONCLUSIONS

This is the first study to prospectively compare CGM outcomes between adult patients with GSDIa and matched healthy volunteers. The generation of a set of CGM metrics will provide guidance in using and interpreting CGM data in GSDIa and will be useful for the definition of glycemic targets for CGM in patients with GSDIa. Future studies should investigate the prognostic value of CGM outcomes and their major determinants in patients with GSDIa.

Glucose variability is associated with an adverse vascular profile but only in the presence of insulin resistance in individuals with type 1 diabetes: An observational study

AIMS/HYPOTHESIS

We hypothesised that the detrimental effect of high glucose variability (GV) in people with type 1 diabetes is mainly evident in those with concomitant insulin resistance.

METHODS

We conducted secondary analyses on continuous glucose monitoring (CGM) using baseline observational data from three randomised controlled trials and assessed the relationship with established vascular markers. We used standard CGM summary statistics and principal component analysis to generate individual glucose variability signatures for each participant. Cluster analysis was then employed to establish three GV clusters (low, intermediate, or high GV, respectively). The relationship with thrombotic biomarkers was then investigated according to insulin resistance, assessed as estimated glucose disposal rate (eGDR).

RESULTS

Of 107 patients, 45%, 37%, and 18% of patients were assigned into low, intermediate, and high GV clusters, respectively. Thrombosis biomarkers (including fibrinogen, plasminogen activator inhibitor-1, tissue factor activity, and tumour necrosis factor-alpha) increased in a stepwise fashion across all three GV clusters; this increase in thrombosis markers was evident in the presence of low but not high eGDR and at a threshold of eGDR <5.1 mg/kg/min.

CONCLUSION

Higher GV is associated with increased thrombotic biomarkers in type 1 diabetes but only in those with concomitant insulin resistance.

Glycemic outcomes of children 2-6 years of age with type 1 diabetes during the pediatric MiniMed™ 670G system trial

BACKGROUND

Highly variable insulin sensitivity, susceptibility to hypoglycemia and inability to effectively communicate hypoglycemic symptoms pose significant challenges for young children with type 1 diabetes (T1D). Herein, outcomes during clinical MiniMed™ 670G system use were evaluated in children aged 2-6 years with T1D.

METHODS

Participants (N = 46, aged 4.6 ± 1.4 years) at seven investigational centers used the MiniMed™ 670G system in Manual Mode during a two-week run-in period followed by Auto Mode during a three-month study phase. Safety events, mean A1C, sensor glucose (SG), and percentage of time spent in (TIR, 70-180 mg/dl), below (TBR, <70 mg/dl) and above (TAR, >180 mg/dl) range were assessed for the run-in and study phase and compared using a paired t-test or Wilcoxon signed-rank test.

RESULTS

From run-in to end of study (median 87.1% time in auto mode), mean A1C and SG changed from 8.0 ± 0.9% to 7.5 ± 0.6% (p < 0.001) and from 173 ± 24 to 161 ± 16 mg/dl (p < 0.001), respectively. Overall TIR increased from 55.7 ± 13.4% to 63.8 ± 9.4% (p < 0.001), while TBR and TAR decreased from 3.3 ± 2.5% to 3.2 ± 1.6% (p = 0.996) and 41.0 ± 14.7% to 33.0 ± 9.9% (p < 0.001), respectively. Overnight TBR remained unchanged and TAR was further improved 12:00 am-6:00 am. Throughout the study phase, there were no episodes of severe hypoglycemia or diabetic ketoacidosis (DKA) and no serious adverse device-related events.

CONCLUSIONS

At-home MiniMed™ 670G Auto Mode use by young children safely improved glycemic outcomes compared to two-week open-loop Manual Mode use. The improvements are similar to those observed in older children, adolescents and adults with T1D using the same system for the same duration of time.

Glucose and unstructured physical activity coupling during sleep and wake in young adults with type 1 diabetes

Glucose variations have a bidirectional relationship with the sleep/wake and circadian systems in type 1 diabetes (T1D); however, the mechanisms remain unclear. The aim of this study was to describe the coupling between glucose and unstructured physical activity over 168 h in young adults with T1D. We hypothesized that there would be differences in sleep and wake characteristics and circadian variations. Glucose was measured with a continuous glucose monitoring device every 5 min and activity with a non-dominant wrist-worn actigraph in 30-s epochs over 6–14 days. There was substantial glucose and unstructured physical activity coupling during sleep and wake, along with circadian variation based on the wavelet coherence analysis. The extent to which glucose fluctuations result in disrupted sleep over longer than one week should be examined considering the harmful effects on achieving glycemic targets. Further studies are needed to delineate the respective roles of glucose production and utilization and the potential for improved meal and insulin timing to optimize glucose and sleep in this population reliant on exogenous insulin.

Variations in Sleep Characteristics and Glucose Regulation in Young Adults With Type 1 Diabetes

CONTEXT

Short sleep duration and sleep disruptions are associated with impaired glucoregulation in type 1 diabetes (T1D). However, the mechanistic pathways between sleep and glucose variability remain unclear.

OBJECTIVE

To determine within- and between-person associations between objective sleep-wake characteristics and glucose variability indices.

METHODS

Multilevel models were used to analyze concurrent sleep and glucose patterns over 7 days in 42 young adults with T1D in their natural home environment. Young adults with T1D (mean age 22.2 ± 3.0 years, HbA1c 7.2%, 32.6% male) for at least 6 months with no other medical or major psychiatric comorbidity were included. Sleep-wake characteristics were measured via wrist actigraphy and glucose variability indices via a continuous glucose monitor (CGM).

RESULTS

Lower sleep efficiency predicted higher glucose variability (less time in range β = 0.011 and more time in hyperglycemia β = -0.011) within-person. A longer wake after sleep onset and more sleep disruptions were associated with higher glucose variability between persons (β = 0.28 and 0.31). Higher glucose variability predicted poorer sleep within-person (delayed bedtime, waketime, mid-sleep time, and lower sleep efficiency), while higher glucose variability was associated with poorer sleep and more sleep disruptions between persons (lower sleep efficiency, longer wake after sleep onset, and a higher sleep fragmentation index).

CONCLUSION

Clinicians can address the reciprocal nature of the sleep-glucose relationship by optimizing sleep and targeting efforts toward a euglycemic range overnight. Sleep habits are a modifiable personal target in diabetes care.

Examination of the Igls Criteria for Defining Functional Outcomes of β-cell Replacement Therapy: IPITA Symposium Report

CONTEXT

The Igls criteria were developed to provide a consensus definition for outcomes of β-cell replacement therapy in the treatment of diabetes during a January 2017 workshop sponsored by the International Pancreas & Islet Transplant Association (IPITA) and the European Pancreas & Islet Transplant Association. In July 2019, a symposium at the 17th IPITA World Congress was held to examine the Igls criteria after 2 years in clinical practice, including validation against continuous glucose monitoring (CGM)-derived glucose targets, and to propose future refinements that would allow for comparison of outcomes with artificial pancreas system approaches.

EVIDENCE ACQUISITION

Utilization of the criteria in various clinical and research settings was illustrated by population as well as individual outcome data of 4 islet and/or pancreas transplant centers. Validation against CGM metrics was conducted in 55 islet transplant recipients followed-up to 10 years from a fifth center.

EVIDENCE SYNTHESIS

The Igls criteria provided meaningful clinical assessment on an individual patient and treatment group level, allowing for comparison both within and between different β-cell replacement modalities. Important limitations include the need to account for changes in insulin requirements and C-peptide levels relative to baseline. In islet transplant recipients, CGM glucose time in range improved with each category of increasing β-cell graft function.

CONCLUSIONS

Future Igls 2.0 criteria should consider absolute rather than relative levels of insulin use and C-peptide as qualifiers with treatment success based on glucose assessment using CGM metrics on par with assessment of glycated hemoglobin and severe hypoglycemia events.

Glucose as a modifiable cause of atherosclerotic cardiovascular disease: Insights from type 1 diabetes and transplantation

Diabetes is a major risk factor for cardiovascular (CV) disease. In contrast to the clear benefits from treatments which reduce blood pressure and lipids, clinical trials targeting blood glucose have not shown clear CV benefits. Interventions to intensify glycemic control early in the course of diabetes may have benefits in long term observational studies (DCCT-EDIC/UKPDS), but may not be helpful if introduced late in the course of type 2 diabetes (ACCORD, ADVANCE, VA-DT). More recent CVOT in high risk subjects suggest that the benefits of SGLT2 and GLP1-RA are glucose-independent. Type 1 diabetes provides a "cleaner" model to study the links between glucose and cardiovascular disease. Abnormalities of glucose regulation in type 1 diabetes is not restricted to hyperglycemia, but includes glycemic variability and hypoglycemia. Increasingly the mechanisms linking glycemic variability and hypoglycemia as key mediators of cardiovascular complications are being understood. Furthermore, data from pancreas and islet transplantation showing reduced cardiovascular mortality and regression of intima-media thickness supports a causal role for glucose in the pathogenesis of atherosclerosis, but suggests that restoration of normal glucose regulation may be required to demonstrate substantial impact on CV risk accrued over decades of type 1 diabetes. Considering the limited organ supply and risks of immunosuppression, advances in biology (stem cell derived beta cells) or technology (automated insulin delivery systems) will be required to provide a scalable solution to deliver optimal glucose control and reduce CV risk for people with type 1 diabetes.

Circadian characteristics of the rest-activity rhythm, executive function, and glucose fluctuations in young adults with type 1 diabetes

Circadian alignment is an important element in individual health, and one behavioral marker, rest-activity rhythm, could influence self-management in young adults with type 1 diabetes (T1D). Little is known about the rest-activity rhythms, executive function, and glycemia among young adults with type 1 diabetes (T1D). The purpose of this study was to evaluate parametric and nonparametric circadian characteristics of the rest-activity rhythm and the associations between these variables, sleep-wake behavior, executive function, and glycemia among young adults with T1D. Young adults with T1D, recruited from diabetes clinics, wore wrist actigraphs and a continuous glucose monitor (CGM) concurrently for 6-14 days. Participants completed a 3-minute Trail Making Test on paper and electronic questionnaires - 8-item PROMIS v1.0 Emotional Distress Scale, 17-item Diabetes Distress Scale, including twice-daily Pittsburgh sleep diaries. Cosinor and nonparametric analyses were used to compute the rest-activity rhythm parameters, and linear regression modeling procedures were performed to determine the associations among the study variables. The sample included 46 young adults (mean age 22.3 ± 3.2; 32.6% male; 84.8% non-Hispanic White, HbA1c mean 7.2 ± 1.1%, BMI mean 27.0 ± 4.4 kg/m²). A number of parametric associations were observed between a stronger rhythm, better objective sleep-wake characteristics, and less daytime sleepiness. Nonparametric circadian parameters were significantly associated with several outcomes: a stronger rhythm adherence (higher inter-daily stability) with better objective sleep-wake characteristics, better executive function, lower diabetes distress, less hyperglycemia risk, and more time spent in hypoglycemia/hypoglycemia risk; and a more robust rhythm (higher relative amplitude) with better objective sleep-wake characteristics and more time spent in hypoglycemia/higher hypoglycemia risk. Future work should be directed at designs that test causality, such as interventions directed at the strength and stability of rest-activity rhythms, for the potential to improve glucoregulation and other diabetes outcomes.

Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes

STUDY OBJECTIVES

The purpose of this study was to describe objective sleep-wake characteristics and glycemia over 7 - 14 days in young adults with type 1 diabetes (T1D). Additionally, person level associations among objective sleep-wake characteristics (total sleep time, sleep variability, and sleep fragmentation index), daytime sleepiness, and glycemia (glycemic control and glucose variability) were examined.

METHODS

In this cross-sectional study, objective sleep-wake characteristics were measured via actigraphy and glucose variability via continuous glucose monitoring (CGM) over 6-14 days. At baseline, participants completed a psychomotor vigilance test (PVT), Trail Making Test, and questionnaires on daytime sleepiness, sleep quality, and sleep disturbance including sleep diaries.

RESULTS

Forty-six participants (mean age 22.3 ± 3.2 years) wore a wrist actigraph and CGM concurrently for 6-14 days. Greater sleep variability was directly associated with greater glucose variability (mean of daily differences) (r = 0.33, p = .036). Higher daytime sleepiness was directly associated with greater glucose variability (mean of daily differences) (r = 0.50, p = .001). The association between sleep variability and glucose variability (mean of daily differences) was no longer significant when accounting for daytime sleepiness and controlling for T1D duration (p > .05). A higher sleep fragmentation index was associated with greater glucose variability (B = 1.27, p = .010, pr² = .40) after controlling for T1D duration and accounting for higher daytime sleepiness.

CONCLUSIONS

Sleep-wake variability, sleep fragmentation, daytime sleepiness, and the associations with glycemia are new dimensions to consider in young adults with T1D. Sleep habits in this population may explain higher glucose variability and optimizing sleep may improve overall diabetes management.

Impact of Switching from Intermittently Scanned to Real-Time Continuous Glucose Monitoring Systems in a Type 1 Diabetes Patient French Cohort: An Observational Study of Clinical Practices

Aim: Assess the impact of switching from intermittently scanned (FreeStyle Libre [FSL]) to real-time (Dexcom G4 platinum [DG4]) continuous glucose monitoring systems on glycemia control in type 1 diabetes (T1D) patients with high risk of hypoglycemia and/or elevated glycated hemoglobin (HbA1c). Methods: We conducted an observational study in 18 T1D adults with poor glycemic control on FSL. Ambulatory glucose profile data were collected during the last 3 months of FSL use before inclusion (M0 period), during the first 3 months (M3 period) and the last 3 months (M6 period) of DG4 use. Data were then expressed as 24-h averages. Biological HbA1c was measured for all three periods. Patients were their own-controls and statistics were performed using paired t-test or Wilcoxon for matched-pairs. Results: The switch to DG4 at M3 resulted in a higher time-in-range (TIR) 70-180 mg/dL (median [Q1;Q3], 53.1 [44.5;67.3] vs. 41.5 [28.5;62.0], P = 0.0008), and a lower time-below-range <70 mg/dL (TBR mean ± standard deviation (SD), 5.4 ± 3.7 vs. 10.9 ± 7.1, P = 0.0009) and in the glucose % coefficient of variation (%CV mean ± SD, 40.1 vs. 46.9, P = 0.0001). Mean (SD) changes were +10.3 (8.0) percentage points for TIR, -5.5 (5.8) percentage points for TBR, and -6.8 (5.8) percentage points for %CV. These results were confirmed at the M6 period. Conclusions: Switching from FSL to DG4 appears to provide a beneficial therapeutic option without changing insulin delivery systems, regardless of the origin of the patient's initial glycemic issue.

Individualizing Time-in-Range Goals in Management of Diabetes Mellitus and Role of Insulin: Clinical Insights From a Multinational Panel

Diabetes mellitus is a global health concern associated with significant morbidity and mortality. Inadequate control of diabetes leads to chronic complications and higher mortality rates, which emphasizes the importance of achieving glycemic targets. Although glycated hemoglobin (HbA_1c) is the gold standard for measuring glycemic control, it has several limitations. Therefore, in recent years, along with the emergence of continuous glucose monitoring (CGM) technology, glycemic control modalities have moved beyond HbA_1c. They encompass modern glucometrics, such as glycemic variability (GV) and time-in-range (TIR). The key advantage of these newer metrics over HbA_1c is that they allow personalized diabetes management with person-centric glycemic control. Basal insulin analogues, especially second-generation basal insulins with properties such as longer duration of action and low risk of hypoglycemia, have demonstrated clinical benefits by reducing GV and improving TIR. Therefore, for more effective and accurate diabetes management, the development of an integrated approach with second-generation basal insulin and glucometrics involving GV and TIR is the need of the hour. With this objective, a multinational group of endocrinologists and diabetologists reviewed the existing recommendations on TIR, provided their clinical insights into the individualization of TIR targets, and elucidated on the role of the second-generation basal insulin analogues in addressing TIR.

Does glucose influence multidien cycles of interictal and/or ictal activities?

PURPOSE

There are multidien patterns of seizure occurrence. Predicting seizure risk may be easier with biomarker correlates to multidien patterns. We hypothesize multiday hyper or hypoglycemia contributes to seizure risk.

METHODS

In a type I diabetic (T1D) with focal onset epilepsy with continuous glucose monitoring (CGM) and responsive neurostimulation (RNS) devices, we studied multiday interictal activities (IEA), seizures, and glucose. Hourly CGM data was matched to hourly RNS captures of interictal and ictal activities over 33 months. RNS detection settings were unchanged. Multidien cycles were analyzed, active blocks of IEA and ictal episodes defined, and tissue glucose averages studied.

RESULTS

Average glucose was 161 mg/dl. A 40-day cycle of interictal and ictal activities occurred, though no similar glucose cycle was evident. Glucose elevations relative to patient average were associated with increases in IEA but not seizure. Frequent seizures were not associated with obvious elevations or decreases of glucose from baseline, most seizures occurred at +/- 10 mg/dl of average daily glucose (i.e. 150-170 mg/dl).

CONCLUSION

Tissue glucose may influence IEA but may not influence multiday seizure activity or very frequent seizures. In an ambulatory T1D patient multiday hypo or hyperglycemic extremes do not appear to provoke seizure activities.

The Role of Glucagon in Glycemic Variability in Type 1 Diabetes: A Narrative Review

Type 1 diabetes mellitus (T1DM) is a progressive disease as a result of the severe destruction of islet β-cell function, which leads to high glucose variability in patients. However, α-cell function is also compromised in patients with T1DM, characterized by aberrant fasting and postprandial glucagon secretion. According to recent studies, this aberrant glucagon secretion plays an increasing role in hyperglycemia, insulin-induced hypoglycemia and exercise-associated hypoglycemia in patients with T1DM. With application of continuous glucose monitoring system, dozens of metrics enable the assessment of glycemic variability, which is an integral component of glycemic control for patients with T1DM. There is growing evidences to illustrate the contribution of glucagon secretion to the glycemic variability in patients with T1DM, which may promote the development of new treatment strategies aiming to mitigate glycemic variability associated with aberrant glucagon secretion.

Daily variations in sleep and glucose in adolescents with type 1 diabetes

OBJECTIVE

We used multilevel models (MLMs) to determine person (between-persons) and day level (within-person) associations between glucose variability indices and sleep characteristics in adolescents with type 1 diabetes (T1D).

METHODS

Adolescents with T1D (Mean age 13.4 ± 1.8 years; 37.8% male; mean HbA1c 8.2 ± 1.2%, 66 mmol/mol) monitored their sleep and glucose patterns concurrently for 3-7 days with a wrist actigraph on their non-dominant wrist and a continuous glucose monitor (CGM) (their own or a provided, blinded CGM). Glucose variability indices included J index, coefficient of variation, low and high blood glucose risk indices (LBGI and HBGI), time in range, and sleep characteristics, including bedtime, wake time, total sleep time, sleep efficiency, wake after sleep onset, awakenings, and sleep fragmentation index.

RESULTS

More overall glucose variability was associated within person, more sleep disruptions (more awakenings and more fragmentation) or poorer sleep in our study (earlier wake time or longer wake after sleep onset). Also, more time spent in hypoglycemia <70 mg/dL and a higher LBGI was associated within person with earlier wake time indicating poorer sleep. However, a lower LBGI was associated with a later between-persons wake time.

CONCLUSIONS

Monitoring over a longer period of time in subsequent studies would allow researchers to determine the within person association between habitual short sleep duration and glucose variability. Providers should regularly assess sleep habits in adolescents as a way to improve glycemic control. Targeting a euglycemic range overnight is also important to promote better sleep and to decrease sleep disruptions.

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

Glucose variability (GV) has been recognized recently as a promoter of complications and therapeutic targets in diabetes. The aim of this study was to reconstruct and analyze gene networks related to GV in diabetes and its complications. For network analysis, we used the ANDSystem that provides automatic network reconstruction and analysis based on text mining. The network of GV consisted of 37 genes/proteins associated with both hyperglycemia and hypoglycemia. Cardiovascular system, pancreas, adipose and muscle tissues, gastrointestinal tract, and kidney were recognized as the loci with the highest expression of GV-related genes. According to Gene Ontology enrichment analysis, these genes are associated with insulin secretion, glucose metabolism, glycogen biosynthesis, gluconeogenesis, MAPK and JAK-STAT cascades, protein kinase B signaling, cell proliferation, nitric oxide biosynthesis, etc. GV-related genes were found to occupy central positions in the networks of diabetes complications (cardiovascular disease, diabetic nephropathy, retinopathy, and neuropathy) and were associated with response to hypoxia. Gene prioritization analysis identified new gene candidates (THBS1, FN1, HSP90AA1, EGFR, MAPK1, STAT3, TP53, EGF, GSK3B, and PTEN) potentially involved in GV. The results expand the understanding of the molecular mechanisms of the GV phenomenon in diabetes and provide molecular markers and therapeutic targets for future research.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Sotagliflozin: A Review in Type 1 Diabetes

Sotagliflozin (Zynquista™) is the first dual inhibitor of sodium-glucose co-transporter-1 and -2 (SGLT1 and 2). In the phase 3, inTANDEM 1-3 trials, adjunctive use of oral sotagliflozin (200 mg or 400 mg once daily) improved glycaemic control and reduced bodyweight and insulin requirements relative to placebo over 24 weeks of treatment in adults whose type 1 diabetes (T1D) was inadequately controlled by insulin therapy. Similar benefits were seen with the drug in patients who were overweight/obese [i.e. body mass index (BMI) ≥ 27 kg/m²] in inTANDEM 1 and 2 (pooled). The benefits of sotagliflozin were largely maintained over 52 weeks of treatment. Overall, use of sotagliflozin in this setting is generally well tolerated and reduces, or at least does not increase, the likelihood of hypoglycaemia; however, as with other SGLT inhibitors, sotagliflozin carries a risk of diabetic ketoacidosis (DKA). On the basis of its risk/benefit profile, sotagliflozin is indicated in the EU as an adjunct to insulin in adults with T1D with a BMI ≥ 27 kg/m² who have failed to achieve adequate glycaemic control despite optimal insulin therapy, thus expanding the currently limited adjunctive oral treatment options available for use in this population.

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

AIMS

Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c).

METHODS

Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro^™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro^™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).

RESULTS

A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.

CONCLUSIONS

This study validated the GMI formula using the iPro^™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.

Glycaemic variability: The under-recognized therapeutic target in type 1 diabetes care

Type 1 diabetes mellitus (T1DM) remains one of the most challenging long-term conditions to manage. Despite robust evidence to demonstrate that near normoglycaemia minimizes, but does not completely eliminate, the risk of complications, its achievement has proved almost impossible in a real-world setting. HbA1c to date has been used as the gold standard marker of glucose control and has been shown to reflect directly the risk of diabetes complications. However, it has been recognized that HbA1c is a crude marker of glucose control. Continuous glucose monitoring (CGM) provides the ability to measure and observe inter- and intraday glycaemic variability (GV), a more meaningful measure of glycaemic control, more relevant to daily living for those with T1DM. This paper reviews the relationship between GV and hypoglycaemia, and micro- and macrovascular complications. It also explores the impact on GV of CGM, insulin pumps, closed-loop technologies, and newer insulins and adjunctive therapies. Looking to the future, there is an argument that GV should become a key determinant of therapeutic success. Further studies are required to investigate the pathological and psychological benefits of reducing GV.