A comparison of average daily risk range scores for young children with type 1 diabetes mellitus using continuous glucose monitoring and self-monitoring data

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring

CONTEXT

Although glycated hemoglobin A1c is currently the best parameter used clinically to assess risk for the development of diabetes complications, it does not provide insight into short-term fluctuations in glucose levels. This review summarizes the relationship between continuous glucose monitoring (CGM)-derived metrics of glycemic variability and diabetes-related complications.

EVIDENCE ACQUISITION

PubMed and Embase databases were searched from January 1, 2010 to August 22, 2020, using the terms type 1 diabetes, type 2 diabetes, diabetes-related microvascular and macrovascular complications, and measures of glycaemic variability. Exclusion criteria were studies that did not use CGM and studies involving participants who were not diabetic, acutely unwell (post stroke, post surgery), pregnant, or using insulin pumps.

EVIDENCE SYNTHESIS

A total of 1636 records were identified, and 1602 were excluded, leaving 34 publications in the final review. Of the 20 852 total participants, 663 had type 1 diabetes (T1D) and 19 909 had type 2 diabetes (T2D). Glycemic variability and low time in range (TIR) showed associations with all studied microvascular and macrovascular complications of diabetes. Notably, higher TIR was associated with reduced risk of albuminuria, retinopathy, cardiovascular disease mortality, all-cause mortality, and abnormal carotid intima-media thickness. Peripheral neuropathy was predominantly associated with standard deviation of blood glucose levels (SD) and mean amplitude of glycemic excursions (MAGE).

CONCLUSION

The evidence supports the association between diabetes complications and CGM-derived measures of intraday glycemic variability. TIR emerged as the most consistent measure, supporting its emerging role in clinical practice. More longitudinal studies and trials are required to confirm these associations, particularly for T1D, for which there are limited data.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP): Protocol for a Randomized Clinical Trial to Test a Video-Based Telehealth Intervention

Background

Despite the introduction of new insulin analogs, insulin pumps, and continuous glucose monitoring (CGM), young children with type 1 diabetes mellitus (T1D) remain vulnerable to episodes of hypoglycemia because of their unpredictable eating and activity patterns and high degree of insulin sensitivity. Caregivers and young children living with T1D learn to fear hypoglycemia because it is uncomfortable, unpredictable, and dangerous. Up to 60% of caregivers of young children with T1D report moderate to severe levels of fear of hypoglycemia, and caregiver fear of hypoglycemia relates to lower quality of life for families and suboptimal child glycemic control. Yet, until recently, there have been no studies reporting on a targeted intervention to treat caregiver fear of hypoglycemia in families of young children.

Objective

The aim of this project is to conduct a randomized clinical trial of an innovative, video-based telehealth intervention to treat fear of hypoglycemia in caregivers of young children with T1D versus a relevant, age-appropriate attention control intervention.

Methods

We created the Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP) intervention by merging age-appropriate T1D education and behavioral parenting strategies with cognitive behavioral therapy strategies that are effective for reducing fear and promoting adaptive coping. REDCHiP uses 10 video-based telehealth sessions that are a combination of group and individual sessions. We will recruit up to 180 families of young children with T1D to participate in this clinical trial from two pediatric diabetes clinics located in the midwestern and southern United States. Once families have been enrolled, we will randomize caregivers based on child age (age 2-3 years or 4-5 years), child sex, and family CGM use to participate in the REDCHiP or attention control intervention. Families will complete 3 assessment visits that coincide with study entry, end of treatment, and 3-month posttreatment. At each assessment visit, we will collect questionnaire data from caregivers, accelerometry data from caregivers and children, CGM data from children, and a blood sample to measure glycated hemoglobin levels from children.

Results

Recruitment began in July 2019, and enrollment is ongoing. The first wave of intervention delivery began in December 2019. We anticipate completing enrollment in 2023. Final reporting of results will occur within 12 months of the primary completion date.

Conclusions

If the REDCHiP intervention is efficacious, next steps will be to examine multiple implementation strategies to determine how best to disseminate the intervention to pediatric diabetes clinics around the world.

Trial Registration

ClinicalTrials.gov NCT03914547; https://clinicaltrials.gov/ct2/show/NCT03914547

International Registered Report Identifier (IRRID)

PRR1-10.2196/17877

Lifestyle changes and glycemic control in type 1 diabetes mellitus: a trial protocol with factorial design approach

BACKGROUND

Type 1 diabetes (T1D) has been increasing globally over the past three decades. Self-monitoring of blood glucose is a challenge in both developed as well as developing countries. Self-management guidelines include maintaining logbooks for blood glucose, physical activity, and dietary intake that affect glycated hemoglobin (HbA1c) and a multitude of life-threatening acute complications. Innovative, cost-effective interventions along with beneficial lifestyle modifications can improve home-based self-monitoring of blood glucose in T1D patients. The overall objective of this study is to evaluate the relationship between maintaining log books for blood glucose levels, reinforcement by e-messages, and/or daily step count and changes in HbA1c.

METHODS/DESIGN

A randomized controlled trial will enroll participants aged 15 years and above in four groups. Each group of 30 participants will be working with a newly designed standard log book for documenting their blood glucose. The first group will be entirely on routine clinical care, the second group will be on routine care and will receive an additional e-device for recording step count (fit bit), the third group will receive routine care and daily motivational e-messages to maintain the log book, and the fourth group along with routine care will receive an e-device for measuring step count (fit bit) and e-messages about maintaining the log book. Patients will be enrolled from pediatric and endocrine clinics of a tertiary care hospital in Karachi. All groups will be followed up for a period of 6 months to evaluate outcomes. Log book data will be obtained every 3 months electronically or during a patient's clinic visit. HbA1c as a main outcome will be measured at baseline and will be evaluated twice every 3 months. A baseline questionnaire will determine the socio-demographic, nutritional, and physical activity profile of patients. Clinical information for T1D and other co-morbidities for age of onset, duration, complications, hospitalizations, habits for managing T1D, and other lifestyle characteristics will be ascertained. Behavioral modifications for maintaining daily log books as a routine, following e-messages alone, fit bit alone, or e-messages plus using fit bit will be assessed for changes in HbA1c using a generalized estimated equation.

DISCUSSION

The proposed interventions will help identify whether maintaining log books for blood glucose, motivational e-messages, and/or daily step count will reduce HbA1c levels.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03864991. March 6, 2019.

Iterative development of a web-based intervention for families of young children with type 1 diabetes: DIPPer Academy

Objective

A new diagnosis of Type 1 diabetes mellitus (T1D) can be stressful for families as they are expected to learn a large amount of information regarding disease management and treatment in a short period of time. Currently, parents complete diabetes education at the time of diagnosis. However, this format may not be sustainable as rates of T1D climb. The current study aimed to develop an online platform to provide educational material in the form of video micro-lectures using an iterative, user-centered, design process.

Methods

In the first stage of development, parents of young children with T1D identified information they felt most important to their child's T1D care. In the second stage, healthcare providers contributed qualitative and quantitative feedback regarding the educational material and video mock-ups. The third stage involved parents of young children with T1D providing feedback on the completed video micro-lectures.

Results

Providers and parents reported that the videos were highly useful, important to T1D care, and the majority would recommend them to other parents.

Conclusions

The iterative design process used by the research team incorporated multiple perspectives and ultimately developed educational resources that were well-received by providers, researchers, and parents.

Implications for Impact Statement

This study used an iterative, user-centered design to develop a series of web-based videos for parents of young children with type 1 diabetes. Primary stakeholders, both healthcare providers and parents, described videos as highly important, useful, enjoyable, and would recommend these resources to others. Parents rated videos highly across a variety of presentations that ranged in development time and cost, indicating that researchers can feasibly and cost-effectively create web-based resources for parents.

Adherence to Insulin Pump Behaviors in Young Children With Type 1 Diabetes Mellitus

BACKGROUND

Parents of young children are responsible for daily type 1 diabetes (T1DM) cares including insulin bolusing. For optimal insulin pump management, parents should enter a blood glucose result (SMBG) and a carbohydrate estimate (if food will be consumed) into the bolus advisor in their child's pump to assist in delivering the recommended insulin bolus. Previously, pump adherence behaviors were described in adolescents; we describe these behaviors in a sample of young children.

METHODS

Pump data covering between 14-30 consecutive days were obtained for 116 children. Assessed adherence to essential pump adherence behaviors (eg, SMBG, carbohydrate entry, and insulin use) and adherence to 3 Wizard/Bolus Advisor steps: SMBG-carbohydrate entry-insulin bolus delivered.

RESULTS

Parents completed SMBG ≥4 times on 99% of days, bolused insulin ≥3 times on 95% of days, and entered carbohydrates ≥3 times on 93% of days, but they corrected for hyperglycemia (≥250 mg/dl or 13.9 mmol/l) only 63% of the time. Parents completed Wizard/Bolus Advisor steps (SMBG, carbohydrate entry, insulin bolus) within 30 minutes for 43% of boluses. Inverse correlations were found between children's mean daily glucose and the percentage of days with ≥4 SMBG and ≥3 carbohydrate entries as well as the percentage of boluses where all Wizard/Bolus Advisor steps were completed.

CONCLUSIONS

Parents of young children adhered to individual pump behaviors, but showed some variability in their adherence to Wizard/Bolus Advisor steps. Parents showed low adherence to recommendations to correct for hyperglycemia. Like adolescents, targeting pump behaviors in young children may have the potential to optimize glycemic control.

Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes

BACKGROUND

Time series analysis provides information on blood glucose dynamics that is unattainable with conventional glycemic variability (GV) indices. To date, no studies have been published on these parameters in pediatric patients with type 1 diabetes. Our aim is to evaluate the relationship between time series analysis and conventional GV indices, and glycosylated hemoglobin (HbA1c) levels.

METHODS

This is a transversal study of 41 children and adolescents with type 1 diabetes. Glucose monitoring was carried out continuously for 72 h to study the following GV indices: standard deviation (SD) of glucose levels (mg/dL), coefficient of variation (%), interquartile range (IQR; mg/dL), mean amplitude of the largest glycemic excursions (MAGE), and continuous overlapping net glycemic action (CONGA). The time series analysis was conducted by means of detrended fluctuation analysis (DFA) and Poincaré plot.

RESULTS

Time series parameters (DFA alpha coefficient and elements of the ellipse of the Poincaré plot) correlated well with the more conventional GV indices. Patients were grouped according to the terciles of these indices, to the terciles of eccentricity (1: 12.56-16.98, 2: 16.99-21.91, 3: 21.92-41.03), and to the value of the DFA alpha coefficient (> or ≤1.5). No differences were observed in the HbA1c of patients grouped by GV index criteria; however, significant differences were found in patients grouped by alpha coefficient and eccentricity, not only in terms of HbA1c, but also in SD glucose, IQR, and CONGA index.

CONCLUSIONS

The loss of complexity in glycemic homeostasis is accompanied by an increase in variability.

Evaluation of the Average Daily Risk Range as a Measure of Glycemic Variability in Youths with Type 1 Diabetes

OBJECTIVE

The Average Daily Risk Range (ADRR) is a measure of glycemic variability (GV) developed for adults with diabetes. Although the ADRR is increasingly being reported in pediatric diabetes research and may also be used in clinical management, it has never been examined for its sensitivity to predicting hyper- and hypoglycemia in youths or compared for its predictive ability with other measures of GV in youths. Thus, we present predictive validity data for the ADRR in youths with type 1 diabetes.

MATERIALS AND METHODS

Glucometer data for 436 youths (mean age, 11.8±3.8 years) were collected from a clinical database. Using these data, we computed the ADRR, SD of blood glucose, coefficient of variation of blood glucose, Low Blood Glucose Index, High Blood Glucose Index, the percentage of glucose values ≥70 and ≤180 mg/dL, the percentage of high glucose values >180 mg/dL and >400 mg/dL, and the percentage of low glucose values <70 mg/dL and <40 mg/dL in Month 1. We then compared these with episodes of hypo- and hyperglycemia in Month 2.

RESULTS

The ADRR showed good concurrent validity with other measures of GV in youths experiencing hyperglycemic events but limited predictive validity in general and specifically with future hypoglycemic events. The percentages of current hyper- and hypoglycemic episodes appeared to be stronger predictors of future hyper- and hypoglycemic episodes, respectively.

CONCLUSIONS

In a large pediatric sample, the ADRR was not the strongest predictor of future glycemic excursion. The percentages of current hyper- and hypoglycemic episodes appear to be stronger predictors.

Fluctuations of Hyperglycemia and Insulin Sensitivity Are Linked to Menstrual Cycle Phases in Women With T1D

BACKGROUND

Factors influencing glycemic variability in type 1 diabetes (T1D) may play a significant role in the refinement of closed loop insulin administration. Phase of menstrual cycle is one such factor that has been inadequately investigated. We propose that unique individual patterns can be constructed and used as parameters of closed loop systems.

METHOD

Women with T1D on continuous subcutaneous insulin infusion and continuous glucose monitoring were studied for 3 consecutive menstrual cycles. Ovulation prediction kits and labs were used to confirm phase of menstrual cycle. Glycemic risks were assessed using the low- and high blood glucose indices (LBGI and HBGI). Insulin sensitivity (SI) was estimated using a Kalman filtering method from meal and insulin data. Overall change significance for glycemic risks was assessed by repeated measures ANOVA, with specific phases emphasized using contrasts.

RESULTS

Ovulation was confirmed in 33/36 cycles studied in 12 subjects (age = 33.1 ± 7.0 years, BMI = 25.7 ± 2.9 kg/m(2), A1c = 6.8 ± 0.7%). Risk for hyperglycemia changed significantly during the cycle (P = .023), with HBGI increasing until early luteal phase and returning to initial levels thereafter. LBGI was steady in the follicular phase, decreasing thereafter but not significantly. SI was depressed during the luteal phase when compared to the early follicular phase (P ≤ .05). Total daily insulin, carbohydrates, or calories did not show any significant fluctuations.

CONCLUSIONS

Women with T1D have glycemic variability changes that are specific to the individual and are linked to phase of cycle. An increased risk of hyperglycemia was observed during periovulation and early luteal phases compared to the early follicular phase; these changes appear to be associated with decreased insulin sensitivity during the luteal phase.

Evaluation of blood glucose fluctuation in Japanese patients with type 1 diabetes mellitus by self-monitoring of blood glucose and continuous glucose monitoring

AIMS

Accurate assessment of blood glucose fluctuation is essential for managing blood glucose control while avoiding hypoglycemia in patients with diabetes mellitus. In this study, blood glucose was measured by continuous glucose monitoring (CGM) in patients with type 1 diabetes mellitus (T1DM) whom self-monitoring of blood glucose (SMBG) was carried out three or more times per day, and evaluation was performed using blood glucose fluctuation parameters obtained by CGM and SMBG.

METHODS

Twenty-nine insulin-depleted patients with T1DM were enrolled. Their blood glucose fluctuations were measured at the same time by SMBG and CGM, and the correlations were evaluated.

RESULTS

Correlations were found between the following values obtained by SMBG and CGM: mean and standard deviation of blood glucose levels, average daily risk range, Morbus value and high-blood-glucose index. The hypoglycemia duration and the nocturnal hypoglycemia duration showed no correlation with any of the blood glucose fluctuation parameters obtained by SMBG.

CONCLUSIONS

The findings suggest that routine SMBG and glycated hemoglobin (HbA1c) measurement are sufficient for evaluation of hyperglycemia in T1DM. On the other hand, blood glucose fluctuation parameters obtained by SMBG and HbA1c have been shown to have no correlations with either hypoglycemia duration or nocturnal hypoglycemia duration.

Association between the extent of urinary albumin excretion and glycaemic variability indices measured by continuous glucose monitoring

AIMS

The contribution of glycaemic variability to the microvascular complication of diabetes has not been established. We examined whether there is an independent association between indices of glycaemic variability in continuous glucose monitoring and extent of albuminuria.

METHODS

A total of 173 patients with Type 2 diabetes (without insulin therapy, n = 96; with insulin therapy, n = 77) who had unexplained large fluctuations in blood glucose values underwent three-day continuous glucose monitoring. We used a multinomial logistic regression model to determine whether the indices of glycaemic variability independently affected the odds of having a spot urine albumin/creatinine ratio of 30-299 mg/g and ≥ 300 mg/g.

RESULTS

Higher standard deviation (P = 0.002), mean of daily differences (P = 0.023) and mean amplitude of glycaemic excursion (P = 0.043) significantly increased the odds of having a urine albumin/creatinine ratio of ≥ 300 mg/g. In multivariable analysis, only higher standard deviation, but not mean amplitude of glycaemic excursion and mean of daily differences, independently increased the odds of having a urine albumin/creatinine ratio of ≥ 300 mg/g (P = 0.025). Coefficient of variation (sd/mean) was not associated with the odds of having a urine albumin/creatinine ratio of 30-299 or ≥ 300 mg/g.

CONCLUSIONS

The independent association between standard deviation and the extent of albuminuria was lost when the measures were normalized by mean glucose level. At least in terms of relative measures of glycaemic variability, we failed to demonstrate an independent association between glycaemic variability and albuminuria extent in patients with inadequately controlled Type 2 diabetes.

Pilot study results for a novel behavior plus nutrition intervention for caregivers of young children with type 1 diabetes

OBJECTIVE

To present results for a parent-based educational intervention targeting mealtime behaviors plus nutrition among families of young children (mean age, 5.0 ± 1.2 years) with type 1 diabetes mellitus (T1DM).

METHODS

The researchers recruited 9 caregivers who participated in the 6-session intervention and completed baseline and posttreatment assessments, which included dietary intake, acceptability of diet changes, mealtime behavior, and mean blood glucose values.

RESULTS

Children's mean daily blood glucose levels decreased from 185 ± 46 mg/dL to 159 ± 40 mg/dL (P < .001). There were also decreases in problematic parent and child mealtime behaviors. There was no change in children's dietary intake indicators that could be detected.

CONCLUSIONS AND IMPLICATIONS

It appears promising that this targeted behavior plus nutrition intervention can improve glycemic control and behavior for young children with type 1 diabetes mellitus. Larger, randomized controlled trials will clarify significant results, limitations, and sustainability. Techniques within the program may have application to current practice.

Detection and treatment efficacy of hypoglycemic events in the everyday life of children younger than 7 yr

BACKGROUND

Mild hypoglycemia is commonly observed in children treated for type 1 diabetes mellitus (T1DM). Hypoglycemia disturbs cognition and learning.

OBJECTIVE

To describe how and to what extent hypoglycemia in young children with T1DM is detected in everyday life. To learn how parents and caregivers treat hypoglycemia and to evaluate how efficient this treatment is.

METHODS

Twenty-three children [12 girls, mean age: 4.5 yr, mean HbA1c: 59 mmol/mol (7.5%)], 17 of whom were treated with an insulin pump, underwent blinded continuous glucose monitoring (CGM). Data on symptoms and treatment of hypoglycemia were collected in a logbook. Plasma glucose values were collected through self-monitoring of blood glucose and entered in the logbook, and glucometer memories were uploaded. Data were collected during 1 wk in autumn and 1 wk in spring.

RESULTS

Only 32% of all hypoglycemic events were detected despite plasma glucose being checked 10 times per day. Most hypoglycemic events were asymptomatic (90% overall and 98% of those occurring at night). Untreated hypoglycemic events were associated with a relapse into hypoglycemia within 3 h in the majority of events. Compared to treatment of hypoglycemia events with a defined dose of simple carbohydrates, treatment with a mixed meal resulted in a significantly higher glucose value 1 and 2 h after the hypoglycemia.

CONCLUSION

For optimum treatment, children younger than 7 yr with T1DM need better strategies and support for detecting hypoglycemia with real-time CGM. Hypoglycemia should be treated with a defined dose of carbohydrates rather than a mixed meal.

Use of technology when assessing adherence to diabetes self-management behaviors

During the last several decades, a proliferation of sophisticated technology has taken place to facilitate diabetes self-management and improve health outcomes. Blood glucose monitors, insulin pumps, and continuous glucose monitors have significant data storage capacity, which can be used to summarize diabetes health management and outcomes. In the absence of technology errors or failures, and in the context of the multiple psychosocial factors associated with nonadherence, these data have the potential to elucidate diabetes care because they reflect actual patient behaviors. This review provides a summary of the diabetes adherence literature in the context of current American Diabetes Association Clinical Practice Recommendations with a focus on studies that have used objective methods (ie, data derived from technology) to assess diabetes care provider and patient adherence in the areas of glucose monitoring; insulin administration and antihyperglycemic medications; medical nutrition therapy; and physical activity.

Average Daily Risk Range (ADRR) in Young Children With Type 1 Diabetes

OBJECTIVE

The objective was to examine the utility of the average daily risk range (ADRR) in young children with type 1 diabetes.

METHODS

Self-monitored blood glucose (BG) data and A1c values were collected from 134 children (ages 2-6). Other measures of BG variability and diabetes care were calculated using self-monitored BG data. ADRR, A1c, and other glycemic indices were compared to assess their distinctiveness and utility as measures of BG variability and glycemic control.

RESULTS

Of young children's ADRR values, 72% were in the "high-risk" range using adult guidelines. ADRR and A1c were highly correlated with indicators of hyperglycemia but only weakly correlated with measures of hypoglycemia. ADRR was moderately correlated with minimum BG value in the past 30 days but not percentage of BG values below 70 mg/dL. A1c was not correlated with either measure of hypoglycemia.

CONCLUSIONS

ADRR values confirm the high degree of BG variability present in young children with type 1 diabetes, particularly as compared with adults. New ADRR risk guidelines are needed for pediatric patients. ADRR and A1c are adequate indicators of hyperglycemia in young children. However, both ADRR and A1c failed to effectively capture hypoglycemia risk in this sample, and neither ADRR nor A1c can take the place of review of raw BG data to evaluate BG variability in young children.

[Glycemic variability and oxidative stress in children, with type 1 diabetes attending a summer camp]

OBJECTIVE

To assess glycemic variability, oxidative stress and their relationship in children and adolescents with type 1 diabetes (T1DM) attending a summer camp.

PATIENTS AND METHOD

Cross-sectional study that included 54 children and adolescents with T1DM aged 7-16, attending a 7 day summer camp. Sociodemographic information, clinical data, and blood glucose values measured using an Accu-Chek Nano® glucose meter were recorded. Glucose variability markers (standard deviation [SD], low blood glucose index [LBGI], high blood glucose index [HBGI], mean amplitude of glycemic excursions [MAGE] and mean of daily differences [MODD]) were calculated. Oxidative stress was assessed by the measurement of 8-iso-prostaglandin F2 alpha (PGF2α) in a 24-hour urine sample collected at the end of the camp in 14 children.

RESULTS

The Median SD, MAGE and MODD indexes were in the high range (61, 131 and 58 mg/dl, respectively), LBGI in the moderate range (3.3), and HBGI in the low range (4.5). The mean HbA1c was 7.6% and the median urinary excretion rate of 8-iso-PGF2α was 864.39 pg/mg creatinine. The Spearman correlation coefficients between markers of glycemic variability (SD, HBGI, MAGE, MODD) were significant. Non-significant correlations were found between markers of glycemic variability and urinary 8-iso-PGF2α.

CONCLUSIONS

High glycemic variability was observed in children and adolescents attending a summer camp. However, no correlations were found between markers of glycemic variability and oxidative stress measured by urinary 8-iso-PGF2α. Further studies are needed to address the relationship between oxidative stress and glycemic variability in children with T1DM.

Identification and prediction of group-based glycemic control trajectories during the transition to adolescence

OBJECTIVE

To identify trajectories of glycemic control over a period of 3 years in a pediatric sample of youth diagnosed with Type 1 diabetes transitioning to adolescence. A second aim was to examine a set of modifiable individual and family level baseline predictors of glycemic control group membership.

METHODS

This multisite, prospective study included 239 children and adolescents (ages 9-11 years at baseline) diagnosed with Type 1 diabetes and their caregivers. Glycemic control was based on hemoglobin A1c (HbA1c) collected at 6-month intervals over a period of 3 years. Predictors of glycemic control membership included baseline global executive functioning, diabetes self-management, diabetes-specific family conflict, blood glucose monitoring frequency, and relevant individual and family level covariates.

RESULTS

Group-based trajectory analyses were used to describe patterns of glycemic control from baseline to 36 months and 3 trajectories were identified: low risk (42.9%), elevated risk (44.6%), and high risk (12.1%) subgroups. Baseline maternal-reported family conflict, blood glucose monitoring frequency, and gender were significant predictors of glycemic control group membership. Higher levels of baseline family conflict, lower frequency of blood glucose monitoring, and female gender were associated with elevated and high-risk group membership.

CONCLUSIONS

These findings underscore the importance of examining trajectories of HbA1c across time. These results suggest that problematic trajectories of glycemic control are evident during the transition to adolescence. Furthermore, there are modifiable individual and family level characteristics that predict group membership and hence could be targeted in interventions to ensure adequate glycemic control is maintained over time and that risks for diabetes-related complications are reduced.

Average daily risk range as a measure for clinical research and routine care

There is emerging evidence suggesting that glycemic variability may relate to risk for diabetes-related complications. This article provides a description of average daily risk range (ADRR), a diabetes-specific measure of risk for hyperglycemia and hypoglycemia, and provides a summary of research using ADRR and clinical applications of ADRR. Average daily risk range is a variability metric that is based on "risk" values obtained from glucose levels that are mathematically transformed to give equal weight to hyperglycemic and hypoglycemic excursions. It can be calculated using self-monitoring of blood glucose or continuous glucose monitoring (CGM) data. The ADRR is scored based on risk categories: low risk, 0-19; moderate risk, 20-40; and high risk, 40 and above. Research using ADRR has found it to be a reliable predictor of extreme blood glucose values regardless of diabetes type and patients' age. Moreover, in treatment studies, ADRR presents as a very conservative measure of variability. Clinically, ADRR can provide meaningful data related to patients' risk for hyperglycemia and hypoglycemia that is not available from glycated hemoglobin values. Average daily risk range scores may also help clinicians to identify patients who may be overtreating blood glucose levels, leading to very high or low values. To expand the utility of ADRR, future research should examine the validity of existing risk cutoff scores for pediatric patients, determine if ADRR cutoff scores need to be modified for CGM data, and investigate whether patients' ADRR scores also relate to the development of long-term complications, including retinopathy and microalbuminuria.

Association between glucose variability as assessed by continuous glucose monitoring (CGM) and diabetic retinopathy in type 1 and type 2 diabetes

There is a growing debate in the literature on whether glucose variability contributes, as well as high HbA1c levels and longstanding diabetes, to the onset and progression of diabetic retinopathy (DR) in patients with diabetes types 1 (DM1) and 2 (DM2). Few data, obtained only by self-monitoring of blood glucose, support this hypothesis. We used continuous glucose monitoring (CGM) to investigate the association between DR and glucose variability parameters (SD, CONGA 2, MAGE), acute hyperglycemia (HBGI) and chronic exposure to glucose (AG and AUC tot). We studied 68 patients from 19 to 69 years old, 35 with DM1 and 33 with DM2. The prevalence of retinopathy was 43 % in DM 1 patients and 39 % in DM 2 patients. The values of all indicators were obtained by CGM for 72 h. DR was diagnosed on direct or indirect ophthalmoscopic examination, after inducing mydriasis with tropicamide. HbA1c was measured at the baseline and 6 weeks after CGM to test the stability of the patients' glycemic control. Univariate analysis showed a close association between DR and duration of diabetes (OR 1.11; 1.04-1.19), intensive insulin therapy (OR 5.6, CI 1.14-27.30), SD (OR 1.03; CI 1.01-1.06) and CONGA 2 (OR 1.02; CI 1.00-1.04)-both indicators of variability and HBGI (OR 1.1, CI 1.01-1.18)-a parameter reflecting acute hyperglycemia. There was no significant correlation with HbA1c (p = 0.070). Multivariate regression analysis showed that disease duration is the parameter most significantly correlating with DR (OR 1.05; 1.01-1.15). These results reinforce the evidence that longstanding disease is the factor most closely associated with DR. Our data also suggest, however, that glucose variability-regardless of HbA1c-may also have a role as a risk factor for DR, particularly in the case of acute fluctuations (as represented by CONGA 2 and SD) and acute hyperglycemia (as represented by HBGI).

Effects of sensor-augmented pump therapy on glycemic variability in well-controlled type 1 diabetes in the STAR 3 study

BACKGROUND

Compared with multiple daily injections (MDI), sensor-augmented pump (SAP) insulin therapy may reduce glycemic variability and oxidative stress in type 1 diabetes in a glycosylated hemoglobin (A1C)-independent manner.

SUBJECTS AND METHODS

The STAR 3 study compared SAP with MDI therapy for 1 year. Week-long continuous glucose monitoring studies were conducted at baseline and 1 year for assessment of glycemic variability in both groups. Soluble CD40 ligand (CD40L), a biomarker of inflammation and thrombocyte function, was measured at baseline and 1 year. Subjects were classified according to treatment group and 1-year A1C levels (<6.5%, 6.5-6.9%, 7-7.9%, ≥8%). Glycemic parameters were compared between SAP and MDI subjects in each A1C cohort.

RESULTS

At 1 year, sensor glucose values at A1C levels ≥6.5% were similar in the SAP and MDI groups. However, sensor glucose SD and coefficient of variation (CV) values were lower at A1C levels <8% among SAP than among MDI subjects; the overall between-group difference was significant for both SD (P<0.01) and CV (P=0.01). The overall mean amplitude of glycemic excursion was similar in MDI and SAP groups (P=0.23). CD40L levels fell over the course of the study in both groups, but the between-group difference was not significant (P=0.18). CD40L concentrations were unrelated to A1C, change in A1C from baseline, or glycemic variability.

CONCLUSIONS

At comparable A1C levels of <8%, SAP reduced glycemic variability as measured by SD and CV compared with MDI. SAP may provide beneficial reductions in the number and severity of glycemic excursions.