The Burden of Suboptimal Insulin Dosing in People with Diabetes in Spain: Barriers and Solutions from the Physician Perspective

Background

This study aimed to determine physicians' perceptions of the extent of suboptimal insulin dosing and the barriers and solutions to optimal dosing in people with diabetes (PwD) treated with insulin.

Methods

A cross-sectional online survey was conducted in four countries with primary care physicians and endocrinologists treating PwD using insulin pens, which included 53 questions on physicians' characteristics and their perceptions of the behaviors of PwD in relation to insulin dosing routines, unmet needs and potential solutions. Analyses were descriptive.

Results

Of the 160 physicians (80 primary care physicians, 80 specialists) surveyed in Spain, 58.1% were male and 88.8% had been qualified to practice for more than five years. Most physicians (>65%) indicated that 0-30% of PwD missed or skipped, mistimed, or miscalculated an insulin dose in the last 30 days. Common reasons for these actions were that PwD forgot, were out of their normal routine, were too busy or distracted, or were unsure of how much insulin to take. To optimize insulin dosing, over 75% of physicians considered it very helpful for PwD to have real-time insulin dosing calculation guidance, mobile app reminders, a device automatically recording glucose measurements and/or insulin, having insulin and glucose data in one place, and having the time for more meaningful conversations about insulin dosing routines.

Conclusion

According to physicians' perspectives, suboptimal insulin dosing remains common among PwD. This survey highlights the need for integrated and automated insulin dosing support to manage the complexity of insulin treatment, improve communications between PwD and physicians, and ultimately improve outcomes for PwD.

The Need for Data Standards and Implementation Policies to Integrate Insulin Delivery Data Into the Electronic Health Record

Integration of insulin dosing data into the electronic health record (EHR), combined with other patient-generated health care data, would facilitate the use of wirelessly connected insulin delivery systems, including smart insulin pens, insulin pumps, and advanced hybrid closed-loop systems. In 2022, Diabetes Technology Society developed the Integration of Continuous Glucose Monitoring Data into the EHR (iCoDE) Project, which is the first consensus standard for integrating data from a wearable device into the EHR. The iCoDE Standard is a comprehensive guide for any health care delivery organization or hospital for automatically integrating continuous glucose monitoring data into the EHR. Diabetes Technology Society is following iCoDE with the Integration of Connected Diabetes Device Data into the EHR (iCoDE-2) Project, to similarly provide guidance for integrating insulin delivery data into the EHR alongside continuous glucose monitoring data.

Does dietary fat cause a dose dependent glycemic response in youth with type 1 diabetes?

OBJECTIVE

To determine the glycemic impact of dietary fat alone consumed without prandial insulin in individuals with T1D.

RESEARCH DESIGN AND METHODS

Thirty participants with T1D (aged 8-18 years) consumed a test drink with either 20 g glucose or 1, 13, 26, 39, 51 g of fat with negligible carbohydrate/protein on 6 consecutive evenings, in a randomized order without insulin. Continuous glucose monitoring was used to measure glucose levels for 8 h postprandially. Primary outcome was mean glycemic excursion at each 30 min interval for each test condition. Generalized linear mixed models with a random effect for people with diabetes were used to test for an increase in blood glucose excursion with increasing quantity of fat.

RESULTS

Glycemic excursions after 20 g glucose were higher than after fat drinks over the first 2 h (p < 0.05). Glycemic excursion for the fat drinks demonstrated a dose response, statistically significant from 4 h (p = 0.026), such that increasing loads of fat caused a proportionally larger increase in glycemic excursion, remaining statistically significant until 8 h (p < 0.05). Overall, for every 10 g fat added to the drink, glucose concentrations rose by a mean of 0.28 mmol L^-1 from 330 min (95% CI 0.15 to 0.39, p < 0.001).

CONCLUSIONS

Fat ingested without other macronutrients increases glucose excursions from 4 to 8 h after ingestion, in a dose dependent manner. These observations may impact on insulin dosing for high-fat foods in individuals with T1D.

Retrospective Evaluation on the Use of a New Polysaccharide Complex in Managing Paediatric Type 1 Diabetes with Metabolic Syndrome (MetS)

Background: Children and adolescents affected by type 1 diabetes have an increased risk of being overweight or obese and of suffering from cardiometabolic symptoms. Aims: To retrospectively evaluate the effects of a new complex of polysaccharide macromolecules, Policaptil Gel Retard^® (PGR), on auxological and metabolic parameters, glycaemic variability and control parameters in paediatric patients with type 1 diabetes and metabolic syndrome (MetS). Patients and Methods: Data for 27 paediatric patients with a diagnosis of type 1 diabetes in conjunction with obesity and MetS of at least 5 years’ standing were collected and retrospectively studied. Of these, 16 (median age 12.9, range 9.5–15.8 years) had been adjunctively treated with PGR and 11 (median age 12.6, range 9.4–15.6 years) had not been treated with PGR. Auxological, metabolic and glycaemic control and variability parameters and insulin dosing were compared after 6 months in the two groups. Results: PGR significantly reduced BMI standard deviation score (SDS) (p < 0.005), waist SDS (p < 0.005), HbA1c (p < 0.05) and daily mean insulin dose requirement (p < 0.005). A significant improvement was also observed in the metabolic and glycaemic variability parameters of mean daily blood glucose (BG) levels (p < 0.005), SD of daily BG levels (p < 0.0001), mean coefficient of variation (p < 0.05), LBGI (p < 0.0001), HBGI (p < 0.0001), J-index (p < 0.005), total cholesterol (p < 0.005), HDL-cholesterol (p < 0.005) and LDL-cholesterol (p < 0.005) and triglycerides (p < 0.05). Conclusions: PGR produces a good auxological and metabolic response in obese patients with MetS who are affected by type 1 diabetes. It led to a significant reduction in BMI SDS, waist SDS and an improvement in glucose control and variability as well as in other MetS parameters. The use of polysaccharide compounds, especially if associated with appropriate dietary changes, may help achieve treatment targets in type 1 diabetes and reduce the risk that patients develop metabolic syndrome.

Optimising use of rate-of-change trend arrows for insulin dosing decisions using the FreeStyle Libre flash glucose monitoring system

Continuous glucose monitoring and flash glucose monitoring systems are increasingly used by people with diabetes on multiple daily injections of insulin and continuous subcutaneous insulin infusion. Along with real-time updates on current glucose levels, these technologies also use trend arrows to provide information on the direction and rate of change of glucose. Two systems, the Dexcom G5 and the FreeStyle Libre, have recently been approved for use without the need for adjunct capillary blood glucose, and there is a need for practical guidance for insulin dosing which incorporates rate of change in the insulin dosing algorithm. Here, we review the integration of rate of change trend arrow information into daily glucose management, including rapid-acting insulin dosing decisions. Based on the FreeStyle Libre flash glucose monitoring system, we also review a practical decision-support tool for actions to take when using trend arrows in conjunction with current glucose readings.

Laboratory and Benchtop Performance of a Mealtime Insulin-Delivery System

BACKGROUND

A basal bolus insulin regimen requires multiple daily insulin injections, which might discourage patient adherence. As a potential solution, a mealtime insulin-delivery system-a 3-day wearable bolus-only patch-was designed to manually administer mealtime insulin discreetly by actuating buttons through clothing, without the need for multiple needle sticks.

METHOD

Extensive functional testing of the patch included dose accuracy (from initial fill of the device to empty), pressure-vacuum leak testing, last-dose lockout and occlusion detection (safety alert features that lock the dosing buttons when no insulin is delivered), assessments of insulin drug stability, toxicological risk (including chemical testing), and system biocompatibility.

RESULTS

Dosing accuracy was 2 units ±10% (with U-100 insulin) over a range of environmental conditions, with ≥95% reliability and confidence. The fluid seal performance and the safety alert features performed with ≥95% reliability and ≥95% confidence. The system met acceptable standards for insulin (U-100 lispro and aspart) stability for its intended 3-day use, in addition to the operational requirements. The toxicological risk assessment and demonstrated biocompatibility suggested that the patch is safe for human use.

CONCLUSIONS

Benchtop performance showed that the bolus-only patch is a safe, accurate, and reliable device for mealtime insulin delivery.

Rate of Change of Premeal Glucose Measured by Continuous Glucose Monitoring Predicts Postmeal Glycemic Excursions in Patients With Type 1 Diabetes: Implications for Therapy

BACKGROUND

Patients with type 1 diabetes routinely utilize a single premeal fingerstick glucose to determine premeal insulin doses. Continuous glucose monitoring (CGM) provides much richer glycemic trend information, including glycemic slope (GS). How to incorporate this information into dosing decisions remains an open question.

METHODS

We examined the relationship between premeal GS and postmeal glycemic excursions in 240 individuals with type 1 diabetes receiving CGM augmented insulin pump therapy. Over 23.5 million CGM values were synchronized with 264 500 meals. CGM values were integrated 2 hours premeal to compute GS and 2 hours postmeal to compute glycemic excursion outcomes. Postmeal hyperglycemia (integrated CGM glucose >180 mg/dL*hr) and postmeal hypoglycemic events (any CGM glucose < 70 mg/dL) were tabulated according to positive/negative premeal GS and according to GS bins commonly displayed as rate-of-change arrows on CGM devices.

RESULTS

Positive versus negative premeal GS was associated with a 2.28-fold (95% CI 2.25-2.32) risk of postmeal hyperglycemia. Negative versus positive premeal GS was associated with a 2.36-fold (95% CI 2.25-2.43) increase in one or more postprandial hypoglycemic events. Premeal GS in the bin currently displayed as "no change" on existing CGM devices (-1 to 1 mg/dL/min), conferred a 1.82-fold (95% CI 1.79-1.86) risk of postprandial hyperglycemia when positive and a 2.06-fold (95% CI 1.99-2.15) increased risk of postprandial hypoglycemia when negative.

CONCLUSION

Premeal GS predicts postmeal glycemic excursions and may help inform insulin dosing decisions. Rate-of-change arrows on existing devices obscure clinically actionable glycemic trend information from CGM users.

A Practical Approach to Using Trend Arrows on the Dexcom G5 CGM System to Manage Children and Adolescents With Diabetes

After assessing previously published methods, we developed a practical approach to adjusting insulin doses using rtCGM trend arrows in pediatric patients with diabetes.

Self-Management Behaviors in Adults on Insulin Pump Therapy

BACKGROUND

Successful diabetes management requires behavioral changes. Little is known about self-management behaviors (SMB) in adults on insulin pump (IP) therapy.

OBJECTIVE

Analyze and characterize observed common diabetes SMB in adult participants with type 1 diabetes (T1D) using IPs and to correlate behaviors with glycemic outcomes based on participant's individual glucose targets.

MATERIALS AND METHODS

One month of IP data from adults with T1D were downloaded. Computer programs were written to automatically quantify the observed frequency of expected behaviors such as: insulin bolusing, checking blood glucose (BG), and recording carbohydrate intake, and other interactions with the IP.

RESULTS

Nineteen participants were recruited and 4,249 IP interactions were analyzed to ascertain behaviors. Intersubject variability of adherence to minimally expected behaviors was observed: daily documentation of carbohydrates and BG checks in 76.6 (31.7)% and 60.0 (32.5)%, respectively, and bolusing without consulting the IPBC in 13.0 (16.9)% of delivered boluses, while daily insulin bolus delivery was consistent 96.8 (5.7)%. Higher frequency of adherence to daily behaviors correlated with a higher number of glucose readings at target.

CONCLUSION

Results indicate variability in SMB and do not always match recommendations. Case-scenarios based on observed real-life SMB could be incorporated into interviews/surveys to elucidate ways to improve SMB.

Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy

BACKGROUND

There is a lack of systematic ways to analyze how diabetes patients use their insulin pumps to self-manage blood glucose to compensate for alcohol ingestion and exercise. The objective was to analyze "real-life" insulin dosing decisions occurring in conjunction with alcohol intake and exercise among patients using insulin pumps.

METHODS

We recruited adult type 1 diabetes (T1D) patients on insulin pump therapy. Participants were asked to maintain their daily routines, including those related to exercising and consuming alcohol, and keep a 30-day journal on exercise performed and alcohol consumed. Thirty days of insulin pump data were downloaded. Participants' actual insulin dosing behaviors were compared against their self-reported behaviors in the setting of exercise and alcohol.

RESULTS

Nineteen T1D patients were recruited and over 4000 interactions with the insulin pump were analyzed. The analysis exposed variability in how subjects perceived the effects of exercise/alcohol on their blood glucose, inconsistencies between self-reported and observed behaviors, and higher rates of blood glucose control behaviors for exercise versus alcohol.

CONCLUSION

Compensation techniques and perceptions on how exercise and alcohol affect their blood glucose levels vary between patients. Improved individualized educational techniques that take into consideration a patient's unique life style are needed to help patients effectively apply alcohol and exercise compensation techniques.

Analysis of "Comparison an Electronic Glycemic Management System Versus Provider Managed Subcutaneous Basal Bolus Insulin Therapy in the Hospital Setting"

Safety and efficacy of a nurse-directed electronic glycemic management system (eGMS) in comparison to basal-bolus subcutaneous insulin therapy managed by providers has been evaluated recently by Aloi et al. They included 993 non-critically ill patients across 9 different hospitals in a retrospective observational crossover study and compared mean blood glucose, number of hypoglycemic events <40 mg/dl and <70 mg/dl and the percentage of blood glucose in target (140-180 mg/dl) before, during and after the use of eGMS. Conclusion was that eGMS can lead to better glycemic control with less hypoglycemic events compared to provider managed basal-bolus insulin therapy (before and after eGMS). Although some limitations exist, the authors made a strong case that eGMS has positive impact on glycemic control in hospitalized patients with diabetes.

iDECIDE: A Mobile Application for Insulin Dosing Using an Evidence Based Equation to Account for Patient Preferences

Diabetes is a complex disease affecting 29.1 million (9.3%) US citizens [1]. It is a chronic illness that needs continual medical care and ongoing patient self-management, education, and support [2]. There is no cure for diabetes, requiring patients to conduct frequent self-monitoring of blood glucose and dosing of insulin in many cases. Evidence has shown that patients are more adherent to their diabetes management plan when they incorporate personal lifestyle choices [3]. To address the challenge of empowering patients to better manage their diabetes, we have developed a novel mobile application prototype, iDECIDE, that refines rapid-acting insulin dose calculations by incorporating two important patient variables in addition to carbohydrates consumed that are not a part of standard insulin dose calculation algorithms: exercise and alcohol intake [4, 5]. A retrospective analysis for the calibration and evaluation of iDECIDE is underway by comparing recommendations made by the application against dosing recommendations made by insulin pumps.

Bolus calculators

Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.

No higher dose requirements with insulin detemir than glargine in type 2 diabetes: a crossover, double-blind, and randomized study using continuous glucose monitoring

BACKGROUND

In a previous publication we reported no difference in the 24-hour glucose response between two basal analog insulins, detemir and glargine, when taken once a day in type 2 diabetes mellitus (T2DM). We now report the dose comparison observed within this randomized, double-blind, crossover study.

METHOD

Of 36 patients on basal insulin and other noninsulin treatments, 29 completed the study. Both insulins were given once a day at 8 pm and no food was taken between 6 pm and the following morning. The dose was titrated daily by continuous glucose monitoring (CGM) until the basal glucose (between 12 and 6 am) was <120 mg/dl but not >5% of CGM readings <70 mg/dl. Subjects were then crossed over to the other insulin and titrated similarly.

RESULTS

Glucose goals were achieved in all subjects. The mean dosage was 0.26 U/kg with very few subjects requiring >0.4 U/kg. Only 2 required an absolute dose less than 10 U/day and all others required more, some considerably higher. Of the 29 subjects, 7 required a greater, 6 a smaller, and 16 the same dose of detemir compared to glargine.

CONCLUSIONS

When given once daily in T2DM and titrated using CGM to the same fasting glucose, there was no difference in the glucose response between basal insulins during the basal titration period (4-10 hours after injection) nor during the entire 24-hour period following the injection. Further, the mean dosage to achieve this glucose goal was the same with both insulins.

An analysis of dosing equivalence of insulin detemir and insulin glargine: more evidence?

Current guidelines for the management of type 2 diabetes call for the use of basal insulin when glycemic targets are not achieved. Previous studies have demonstrated noninferiority of insulin detemir, dosed once or twice daily, and insulin glargine, dosed once daily. In this issue of Journal of Diabetes Science and Technology, Dr. Allen King provides additional data of his previously published randomized, double-blinded, crossover trial in which both insulins were restricted to once-daily use. In this trial of 29 patients, 24-hour continuous glucose monitoring profiles (published previously) and dosing requirements (in this publication) were shown to be statistically equivalent between the two insulins. The shortcomings of this trial are its short duration, small number of patients, and potential interference from endogenous insulin. Longer trials with more patients, studying once-daily use of these medications, will help better determine if any significant differences exist.