Glycemic Variability Is Associated with Frequency of Blood Glucose Testing and Bolus: Post Hoc Analysis Results from the ProAct Study

Factors Influencing the Control of Diabetes Measured via Glycated Hemoglobin Concentrations in Adults with Type 1 Diabetes

Numerous complications of type 1 diabetes (T1D) may be prevented through suitable glycemic control. Glycated hemoglobin (HbA1c) may be one of the markers for the early detection of the metabolic imbalance characteristic of the disease. However, optimal control of diabetes is not achieved in a large group of patients. It was demonstrated that numerous factors (sociodemographic, psychological, and clinical) contributed to this condition. The aim of the study was to identify factors influencing the control of diabetes measured via glycated hemoglobin concentrations in people with T1D. Independent factors influencing better diabetes control measured via HbA1c in the study group included higher disease acceptance, higher nutritional adherence, lower BMI, and a lower risk of eating disorders. Describing the determinants will allow for the improvement of the system of care provided to people with T1D and for it to comprise important psychological variables related to self-care and acceptance of the disease.

[Blood glucose self monitoring]

Self monitoring of blood glucose contributes to the integrated management of diabetes mellitus. It, thus, should be available for all patients with diabetes mellitus. Self monitoring of blood glucose improves patients safety, quality of life and glucose control. The current article represents the recommendations of the Austrian Diabetes Association for the use of blood glucose self monitoring according to current scientific evidence.

Reduced Rate of Acute Diabetes Events with Flash Glucose Monitoring Is Sustained for 2 Years After Initiation: Extended Outcomes from the RELIEF Study

Background: The RELIEF study has previously shown a fall in the rate of acute diabetes events (ADEs) in people living with type 1 diabetes (PwDT1) or people living with type 2 diabetes (PwDT2) in the 12 months after initiation of flash glucose monitoring (FLASH) in France. The 2-year follow-up has provided new insights on the frequency of ADEs, including severe hypoglycemia and diabetic ketoacidosis (DKA), during use of FLASH. Methods: The RELIEF study included 31,446 PwDT1 and 41,027 PwDT2 with a first delivery of FreeStyle Libre (FSL) between August 1 and December 31, 2017. Hospitalizations for DKA, severe hypoglycemia, diabetes-related coma, and hyperglycemia were recorded for the 12 months before and 24 months after FSL initiation. Persistence of the FSL system use was estimated through a Kaplan-Meier survival curve. Change in usual blood glucose monitoring was estimated through acquisition of blood glucose test strips. Results: In the 2 years after FSL initiation, hospitalizations for ADEs were reduced by 49% and by 48% in PwDT1 or PwDT2, respectively, driven by reductions in DKA. After 2 years, 88% of patients persisted with the system and estimated mean consumption of blood glucose test strips had fallen after 2 years by -82% and by -84% in type 1 diabetes mellitus and type 2 diabetes mellitus, respectively. Conclusion: Use of FSL consistently reduces the rates of hospitalization for ADEs, mainly DKA, 2 years after initiation, confirming this is not a transitory effect. Use of FSL also results in a clear and progressive drop in use of blood glucose test strips over the 2-year period.

Glucose Regulation Beyond HbA1c in Type 2 Diabetes Treated With Insulin: Real-World Evidence From the DIALECT-2 Cohort

OBJECTIVE

To investigate glucose variations associated with glycated hemoglobin (HbA1c) in insulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Patients included in Diabetes and Lifestyle Cohort Twente (DIALECT)-2 (n = 79) were grouped into three HbA1c categories: low, intermediate, and high (≤53, 54-62, and ≥63 mmol/mol or ≤7, 7.1-7.8, and ≥7.9%, respectively). Blood glucose time in range (TIR), time below range (TBR), time above range (TAR), glucose variability parameters, day and night duration, and frequency of TBR and TAR episodes were determined by continuous glucose monitoring (CGM) using the FreeStyle Libre sensor and compared between HbA1c categories.

RESULTS

CGM was performed for a median (interquartile range) of 10 (7-12) days/patient. TIR was not different for low and intermediate HbA1c categories (76.8% [68.3-88.2] vs. 76.0% [72.5.0-80.1]), whereas in the low category, TBR was higher and TAR lower (7.7% [2.4-19.1] vs. 0.7% [0.3-6.1] and 8.2% [5.7-17.6] vs. 20.4% [11.6-27.0], respectively, P < 0.05). Patients in the highest HbA1c category had lower TIR (52.7% [40.9-67.3]) and higher TAR (44.1% [27.8-57.0]) than the other HbA1c categories (P < 0.05), but did not have less TBR during the night. All patients had more (0.06 ± 0.06/h vs. 0.03 ± 0.03/h; P = 0.002) and longer (88.0 [45.0-195.5] vs. 53.4 [34.4-82.8] minutes; P < 0.001) TBR episodes during the night than during the day.

CONCLUSIONS

In this study, a high HbA1c did not reduce the occurrence of nocturnal hypoglycemia, and low HbA1c was not associated with the highest TIR. Optimal personalization of glycemic control requires the use of newer tools, including CGM-derived parameters.

Impact of flash glucose monitoring on glucose control and hospitalization in type 1 diabetes: A nationwide cohort study

BACKGROUND

We evaluated the impact of flash continuous glucose monitoring (FCGM) on glycemic control and healthcare burden in a large real-world cohort of patients with type 1 diabetes (T1D) initiating FCGM technology.

METHODS

In this retrospective cohort study, we included adults (age ≥18 years) with T1D from a large Health Maintenance Organization in Israel, who initiated FCGM during 2018. Primary outcomes included change in HbA1c ≥3 months following FCGM commencement and change in rate of internal-medicine hospitalization. Additional outcomes included changes in glucose test strip purchases, diabetes related outpatient health care visits and hospitalization for diabetic ketoacidosis (DKA) and/or severe hypoglycemia.

RESULTS

The study included 3490 patients, followed for a median of 14 (inter-quartile range 11-15) months after FCGM commencement. Among 2682 patients with an HbA1c measured both at baseline and ≥3 months after FCGM initiation, average HbA1c declined from 8.1% ± 1.46% to 7.9% ± 1.31% (P < .001) at first measurement and was maintained during follow up. Specifically, in those with HbA1c ≥8%, a mean decline of 0.5% (P < .001) was observed. A clinically significant HbA1c reduction of ≥0.5% was experienced by 25.5% of the patients. The rate of internal medicine hospitalization, visits to primary care, or visits to endocrine/diabetes specialists in the period following FCGM commencement vs the 6 months prior was significantly reduced (P < .001). Hospitalization for DKA and/or hypoglycemia declined as well (P = .004).

CONCLUSIONS

FCGM was associated with significant and durable improvement in glycemic control as well as reduced consumption of healthcare services.

Increased Time in Range and Fewer Missed Bolus Injections After Introduction of a Smart Connected Insulin Pen

Background: This observational study investigated whether the connected NovoPen^® 6 could influence insulin regimen management and glycemic control in people with type 1 diabetes (T1D) using a basal-bolus insulin regimen and continuous glucose monitoring in a real-world setting. Methods: Participants from 12 Swedish diabetes clinics downloaded pen data at each visit (final cohort: n = 94). Outcomes included time in range (TIR; sensor glucose 3.9-10.0 mmol/L), time in hyperglycemia (>10 mmol/L), and hypoglycemia (L1: 3.0- <3.9 mmol/L; L2: <3.0 mmol/L). Missed bolus dose (MBD) injections were meals without bolus injection within -15 and +60 min from the start of a meal. Outcomes were compared between the baseline and follow-up periods (≥5 health care professional visits). Data were analyzed from the first 14 days following each visit. For the TIR and total insulin dose analyses (n = 94), a linear mixed model was used, and for the MBD analysis (n = 81), a mixed Poisson model was used. Results: TIR significantly increased (+1.9 [0.8; 3.0]_{95% CI} h/day; P < 0.001) from baseline to follow-up period, with a corresponding reduction in time in hyperglycemia (-1.8 [-3.0; -0.6]_{95% CI} h/day; P = 0.003) and L2 hypoglycemia (-0.3 [-0.6; -0.1]_{95% CI} h/day; P = 0.005), and no change in time in L1 hypoglycemia. MBD injections decreased by 43% over the study (P = 0.002). Change in MBD injections corresponded to a decrease from 25% to 14% based on the assumption that participants had three main meals per day. Conclusions: Our study highlights the potential benefit on glycemic control and dosing behavior when reliable insulin dose data from a connected pen contribute to insulin management in people with T1D.

Use of a Mobile App to Facilitate Blood Glucose Monitoring in Adolescents With Type 1 Diabetes: Single-Subject Nonrandomized Clinical Trial

Background

Cloud-based glucose monitoring programs allow users with diabetes to wirelessly synchronize their glucometers to their mobile phones. They also provide visualization and remote access of their data through its mobile app. There have been very few studies evaluating their effectiveness in managing diabetes among adolescents with type 1 diabetes (T1D).

Objective

The purpose of this study was to assess the feasibility of using a mobile app to improve daily average blood glucose (BG) levels and increase BG monitoring frequency.

Methods

We used an ABA single-subject prospective study design. We recruited five participants aged 13 to 17 years with uncontrolled T1D, glycated hemoglobin A1c 9.0%-10.7%, self-monitoring behavior of ≤5 checks/day, and on multiple daily insulin injections. The study consisted of 4-week intervals of three phases: (1) phase A: usual glucose monitoring log (fax); (2) phase B: mobile app; and (3) phase A': second phase A. A certified diabetes educator and endocrinologist reviewed logs and provided recommendations weekly. Data were analyzed using a quasi-Poisson model to adjust for overdispersion among individual participants, and a generalized estimating equation model for overall intervention effect in aggregate.

Results

For mean daily BG (mg/dL) levels, participant 1 had decreased values on the mobile app (298 to 281, P=.03) and maintained in phase A'. Participant 4 had an increase in mean daily BG in phase A' (175 to 185, P=.01), whereas participant 5 had a decrease in mean daily BG in phase A' (314 to 211, P=.04). For daily monitoring (checks/day), participant 3 increased in phase B (4.6 to 8.3, P=.01) and maintained in phase A'. Participant 5 also had increased daily monitoring at each phase (2.1 to 2.4, P=.01; 2.4 to 3.4, P=.02). For the five participants combined, the overall mean BG and BG checks per day in phase A were mean 254.8 (SD 99.2) and mean 3.6 (SD 2.0), respectively, mean 223.1 (SD 95.7) and mean 4.5 (SD 3.0) in phase B, and mean 197.5 (SD 81.3) and mean 3.7 (SD 2.1) in phase A'. Compared to phase A, mean glucose levels declined during phase B and remained lower during phase A' (P=.002). There was no overall change in BG checks by phase (P=.25). However, mean BG levels negatively correlated with daily BG checks (r=–.47, P<.001). Although all participants had positive opinions about the app, its utilization was highly variable.

Conclusions

We demonstrated modest feasibility of adolescents with uncontrolled T1D utilizing a glucose monitoring mobile app. Further study is needed to better determine its effects on BG level and monitoring frequency. Psychosocial factors and motivational barriers likely influence adoption and continuous use of technology for diabetes management.

Factors associated with high levels of glycated haemoglobin in patients with type 1 diabetes: a multicentre study in Brazil

OBJECTIVE

Long-term complications of type 1 diabetes mellitus (DM1) can be prevented with adequate glycaemic control. However, high levels of glycated haemoglobin (HbA1c) occur in 60%-90% of the patients with DM1. Thus, we aimed to investigate the role of sociodemographic, behavioural and clinical factors on the HbA1c levels of patients with DM1 in Brazil.

DESIGN, SETTING AND PARTICIPANTS

A cross-sectional study was conducted in ambulatory patients with DM1 aged ≥18 years from 10 Brazilian cities. Sociodemographic, behavioural and clinical data were obtained through interviews.

MAIN OUTCOME MEASURES

HbA1c level was measured by liquid chromatography. Hierarchical multiple variable linear regression models were used to identify factors correlated with high levels of HbA1c.

RESULTS

Of 979 patients with DM1, 63.8% were women, and the mean age was 40 (SD 14.6) years. The mean HbA1c level was 9.4% (SD 2.2%), and 89.6% of the patients had HbA1c ≥7.0%. Factors independently correlated with increased HbA1c levels included: lower education, non-participation in diabetes classes/lecture during the year before, having a self-perception of poor adherence to diet and insulin, not having private medical care and not measuring the HbA1c levels in the prior year. Of note, poor adherence to diet and insulin were the independent factors most strongly associated with high levels of HbA1c (mean increment in HbA1c levels of 0.88% and 1.25%, respectively).

CONCLUSION

Poor glycaemic control, which is common among Brazilian patients with DM1, is associated with lower education, self-perception of insufficient adherence to diet and insulin and inadequate monitoring of HbA1c levels. Specific actions, particularly those targeting improving adherence to diet and insulin, may contribute to successful management of patients with DM1.

Behavioral Patterns and Associations with Glucose Control During 12-Week Randomized Free-Living Clinical Trial of Day and Night Hybrid Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes

OBJECTIVES

We evaluated patterns of meal intake, insulin bolus delivery, and fingerstick glucose measurements during hybrid closed-loop and sensor-augmented pump (SAP) therapy, including associations with glucose control.

METHODS

Data were retrospectively analyzed from pump-treated adults with type 1 diabetes who underwent, in random order, 12 weeks free-living closed-loop (n = 32) and 12 weeks SAP (n = 33) periods. We quantified daily patterns of main meals, snacks, prandial insulin boluses, correction boluses, and fingerstick glucose measurements by analyzing data recorded on the study glucometer and on study insulin pump.

RESULTS

We analyzed 1942 closed-loop days and 2530 SAP days. The total number of insulin boluses was reduced during closed-loop versus SAP periods by mean 1.0 per day (95% confidence interval 0.6-1.4, P < 0.001) mainly because of a reduced number of correction boluses by mean 0.7 per day (0.4-1.0, P < 0.001). Other behavioral patterns were unchanged. The carbohydrate content of snacks but not the number of snacks was positively correlated with (1) glycemic variability as measured by standard deviation of sensor glucose (closed-loop P < 0.05; SAP P < 0.01), (2) mean sensor glucose (P < 0.05), and (3) postintervention HbA1c (P < 0.05). Behavioral patterns explained 47% of between-subject variance in glucose variability during SAP period and 30%-33% of variance of means sensor glucose and postintervention HbA1c.

CONCLUSION

Fewer correction boluses are delivered during closed-loop period. The size of snacks appears to worsen glucose control possibly because of carbohydrate-rich content of snacks. Modifiable behavioral patterns may be important determinants of glucose control.

Lower Basal Insulin Dose is Associated with Better Control in Type 1 Diabetes

OBJECTIVE

To test the hypothesis that lower basal insulin doses may be paradoxically associated with better diabetic control, we assessed the association between the basal insulin dose and hemoglobin A1c (HbA1c) level in a group of children and young adults with type 1 diabetes.

STUDY DESIGN

This was a retrospective study of 89 patients with type 1 diabetes (mean age, 14.67 ± 4.8 years; range, 3-29 years) treated in a single outpatient clinic. Forty-six of the 89 patients were treated with continuous subcutaneous insulin infusion, and the other 43 were treated with multiple daily injections (glargine as basal insulin). The daily basal insulin dose was taken either as downloaded from the insulin pump or as registered in the chart at the most recent clinic visit. Glucose data were taken from computerized registration of downloaded patient glucometers. The mean time between data download and HbA1c determination was 0.9 ± 0.78 months. HbA1c level and basal insulin dose were entered with other variables in a multivariable linear regression model.

RESULTS

There was a significant correlation between injection of less total daily basal insulin and lower HbA1c level (Pearson correlation, 0.441; P < .001). Optimal HbA1c level was associated with use of 0.28 ± 0.08 U/kg/day of basal insulin (35 ± 10% basal/total).

CONCLUSION

With lower basal insulin levels, lower HbA1C was achieved despite the same total bolus dose. The optimal basal dose as determined by this study is similar to that found in fasting individuals of similar age.

Poststroke glycemic variability increased recurrent cardiovascular events in diabetic patients

BACKGROUND AND PURPOSE

The association between blood glucose fluctuation and poststroke cardiovascular outcome has been largely unknown. This study attempted to evaluate whether initial glycemic variability increases cardiovascular events and mortality in diabetic patients with acute ischemic stroke.

METHODS

We recruited consecutive patients with acute ischemic stroke or transient ischemic attack from March 2005 to December 2014. A total of 674 patients with diabetes within 72 hours from stroke onset were included. The serum glucose levels were checked 4 times per day during the initial 3 hospital days. J-index, coefficients of variation and standard deviation were calculated for glycemic variability. Composite outcome (nonfatal stroke, nonfatal myocardial infarction, cardiovascular death) and all-cause mortality at 3 months were prospectively captured. Multivariable logistic regression analyses were done adjusting for covariates which can influence on cardiovascular outcomes.

RESULTS

Cardiovascular composite outcomes at 3 months were identified in 71 (10.5%): 11 (6.5%), 15 (8.9%), 18 (10.7%) and 27 (16.0%) in each J-index quartiles (P = .035). The highest quartile of J-index had significantly higher cardiovascular death (4.2%, 3.6%, 6.5% and 11.8%; P = .008). In multivariable logistic regression, age (odds ratio [OR] 1.045; 95% confidence interval [CI] 1.006-1.084), P = .021), NIH stroke scale (OR 1.078; 95% CI 1.024-1.134, P = .004), and the highest J-index (OR 12.058; 95% 1.890-76.912, P = .008) were significantly associated with 3-month cardiovascular composite outcome. Increased cardiovascular outcomes in highest J-index quartile were similar in both euglycemic and hyperglycemic groups.

CONCLUSION

The initial glycemic variability might increase cardiovascular events in acute ischemic stroke patients with diabetes.

CGM Versus FGM; or, Continuous Glucose Monitoring Is Not Flash Glucose Monitoring

It remains to be seen as to what share of the market FGM will achieve if the manufacturer can supply any amount desired.Will a significant portion of the glucose monitoring market then be taken over by FGM? The availability of FGM as anew option for glucose monitoring can basically be evaluated positively and it does indeed clearly show the benefit of“more information” on the glucose trend. The relatively low price for glucose monitoring using FGM and the unusual market introduction (not first via the National Association of Statutory Health Insurance Funds, as was the case with CGM) have given increased attention to the use of more glucose information. It will likely take a certain amount of time before other providers are able to bring different FGM systems to the market.The option of coupling a CGM system with an insulin pump offers the perspective of an automated insulin application,that is, a closed-loop system. Such systems are currently being tested under everyday conditions, although it is not possible to predict when they will actually reach the market.There are, however, such couplings where algorithms are responsible for shutting off insulin delivery when the glucose concentration reaches a defined level or if it will be reached in the foreseeable future. This significantly helps prevent hypoglycemia. These options are only available with CGM. The aim of this commentary is to present the differences between CGM and FGM, including the advantages and disadvantages of both approaches. We see significant benefits in both options based on the different positioning of the approaches and the different user groups.

Towards a Physiological Prandial Insulin Profile: Enhancement of Subcutaneously Injected Prandial Insulin Using Local Warming Devices

The need to develop an insulin delivery system that can closely mimic physiologically induced changes in prandial insulin release has been a major research target since the discovery of insulin. The challenges facing existing insulin delivery systems, related to relatively slow pharmacokinetics and pharmacodynamics, have been further highlighted by rapid advances in diabetes technology and progress in artificial pancreas research. Despite the growing interest in alternative routes of insulin administration, the subcutaneous route remains-at least for now-the preferred route for insulin administration. In this article, we review efforts aimed at developing subcutaneously injected ultrafast-acting insulin and measures aimed at enhancing insulin absorption, focusing on local warming devices.