Frequency of blood glucose testing in well educated patients with diabetes mellitus type 1: how often is enough?

A head-to-head comparison between Guardian Connect and FreeStyle Libre systems and an evaluation of user acceptability of sensors in patients with type 1 diabetes

AIMS

A user-calibrated real-time continuous glucose monitoring (rt-CGM) system is compared to a factory-calibrated flash glucose monitoring (FGM) system and assessed in terms of accuracy and acceptability in patients with type 1 diabetes (T1D).

METHODS

Ten participants with T1D were enroled from a specialist diabetes centre in Singapore and provided with the Guardian Connect with Enlite Sensor (Medtronic, Northridge, CA, USA) and first-generation Freestyle Libre System (Abbott Diabetes Care, Witney, UK), worn simultaneously. Participants had to check capillary blood glucose four times per day. At the end of week 1 and week 2, participants returned for data download and were given a user evaluation survey.

RESULTS

Accuracy evaluation between Guardian Connect and Freestyle Libre includes the overall mean absolute relative difference value (9.7 ± 11.0% vs. 17.5 ± 10.9%), Clarke Error Grid zones A + B (98.6% vs. 98.1%), sensitivity (78.9% vs. 63.4%), and specificity (93.4% vs. 81.0%). Notably, time below range (<3.9 mmol/L) was 10.5% for FGM versus 2% for rt-CGM. From the evaluation survey, 90% of participants perceived rt-CGM to be accurate versus 40% for FGM, although the majority found both devices to be easy to use, educational, and useful in improving glycaemic control. However, due to the cost of sensors, only 30% were keen to use either device for continuous monitoring.

CONCLUSIONS

Although rt-CGM was superior to FGM in terms of accuracy, the value of glucose trends in both devices is still useful in diabetes self-management. Patients and clinicians may consider either technology depending on their requirements.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

Physiological reconstruction of blood glucose level using CGMS-signals only

Patient with diabetes must regularly monitor blood glucose level. Drawing a blood sample is a painful and discomfort experience. Alternatively, the patient measures interstitial fluid glucose level with a sensor installed in subcutaneous tissue. Then, a model of glucose dynamics calculates blood glucose level from the sensor-measured, i.e., interstitial fluid glucose level of subcutaneous tissue. Interstitial fluid glucose level can significantly differ from blood glucose level. The sensor is either factory-calibrated, or the patient calibrates the sensor periodically by drawing blood samples, when glucose levels of both compartments are steady. In both cases, the sensor lifetime is limited up to 14 days. This is the present state of the art. With a physiological model, we would like to prolong the sensor lifetime with an adaptive approach, while requiring no additional blood sample. Prolonging sensor’s lifetime, while reducing the associated discomfort, would considerably improve patient’s quality of life. We demonstrate that it is possible to determine personalized model parameters from multiple CGMS-signals only, using an animal experiment with a hyperglycemic clamp. The experimenter injected separate glucose and insulin boluses to trigger rapid changes, on which we evaluated the ability to react to non-steady glucose levels in different compartments. With the proposed model, 70%, 80% and 95% of the calculated blood glucose levels had relative error less than or equal to 21.9%, 32.5% and 43.6% respectively. Without the model, accuracy of the sensor-estimated blood glucose level decreased to 39.4%, 49.9% and 99.0% relative errors. This confirms feasibility of the proposed method.

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.

The prevalence of retinopathy in patients with type 1 diabetes treated with education-based intensified insulin therapy and its association with parameters of glucose control

AIM

Prevalence of retinopathy (DR) in patients with type 1 diabetes treated with education-based intensified insulin therapy (EBIIT) and its association with parameters of glucose control.

METHODS

151 patients with mean diabetes duration of 14.3 years [SD ± 5.8]) were analyzed. Eyes were examined using standardized 7 field ETDRS (Early Treatment Diabetic Retinopathy Study) settings and images analyzed by a professional external reading center. The glucose exposure over time was defined as HbA1c years, i.e. the sum of the differences between annual mean HbA1c (in %) minus the ideal HbA1c of 6.0% (42 mmol/mol) for each diabetes year (e.g. HbA1c of 8% (64 mmol/mol) over 6 years gives an excess HbA1c of 2.0% (22 for mmol/mol) for 6 years, resulting in 12 HbA1c years (or 131 for mmol/mol)).

RESULTS

The median (interquartile range) of individual mean HbA1c was 7.3% (6.8-7.8) [56 mmol/mol (51-62)]. and the median HbA1c years was 16.8 (9.1-29.1) [183 mmol/mol (99-319)]. No evidence for DR was found in 59 patients (39%), stage 1 DR in 43 (28.5%), stage 2 in 41 (27.2%), stage 3 in 7 (4.6%) and proliferative DR stage 4 in 1 patient. The best correlation between severity of DR and diabetes control measures was found for HbA1c years (Pearson r = 0.41, p < 0.001).

CONCLUSIONS

In type 1 diabetes EBIIT is associated with good diabetes control and a low prevalence of DR. The cumulative glucose exposure over time given as HbA1c years is the best predictor for development of DR. ClinicalTrials.gov Identifier: NCT02307110.

A review on electrochemical biosensing platform based on layered double hydroxides for small molecule biomarkers determination

The development of layered double hydroxides (LDHs), also known as anionic clays with uniform distribution of metal ions and facile exchangeability of intercalated anions, are now appealing an immense deal of attention in synthesis of multifunctional materials. In electrochemical biosensors, LDHs provide stable environment for immobilization of enzymes or other sensing materials and play crucial roles in development of clinical chemistry, point-of-care devices through analysis of various small molecule metabolites excreted by biological processes which in turn serve as molecular biomarkers for medical diagnostics. In this review, we summarize the recent development in fabrication of LDH based nanoarchitectures and their electrocatalytic applications in ultrasensitive in vitro determination of conventional biomarkers, i.e., H₂O₂, glucose, dopamine and other biomolecules. Moreover, detailed discussion has been compiled to differentiate electrochemical enzymatic and nonenzymatic biosensors, to evaluate useful concentration ranges of H₂O₂ and glucose for analytical circumstances and to distinguish tumorigenic and normal cells via quantifying the released H₂O₂ efflux from living cells. Here, we envision that electrochemical sensing platform based on structurally integrated LDH nanohybrids with highly conducting substrates will assist as diseases diagnostic probe further enhancing diagnosis as well as therapeutic window for chronic diseases. Finally, the perspective for fabrication and assembly of LDH electrode is proposed for the future innovation of electrochemical biosensors with high performance making them more reliable for in vitro diagnostics.

Both the frequency of HbA1c testing and the frequency of self-monitoring of blood glucose predict metabolic control: A multicentre analysis of 15 199 adult type 1 diabetes patients from Germany and Austria

BACKGROUND

The objective of this study was to examine the association between metabolic control and frequency of haemoglobin A_1c (HbA_1c ) measurements and of self-monitoring of blood glucose, as well as the interaction of both.

METHODS

Data of 15 199 adult type 1 diabetes patients registered in a standardized electronic health record (DPV) were included. To model the association between metabolic control and frequency of HbA_1c testing or of self-monitoring of blood glucose, multiple hierarchic regression models with adjustment for confounders were fitted. Tukey-Kramer test was used to adjust P values for multiple comparisons. Vuong test was used to compare non-nested models.

RESULTS

The baseline variables of the study population were median age 19.9 [Q1; Q3: 18.4; 32.2] years and diabetes duration 10.4 [6.8; 15.7] years. Haemoglobin A_1c was 60.4 [51.5; 72.5] mmol/mol. Frequency of HbA_1c testing was 8.0 [5.0; 9.0] within 2 years, and daily self-monitoring of blood glucose frequency was 5.0 [4.0; 6.0]. After adjustment, a U-shaped association between metabolic control and frequency of HbA_1c testing was observed with lowest HbA_1c levels in the 3-monthly HbA_1c testing group. There was an inverse relationship between self-monitoring of blood glucose and HbA_1c with lower HbA_1c associated with highest frequency of testing (>6 daily measurements). Quarterly HbA_1c testing and frequent self-monitoring of blood glucose were associated with best metabolic control. The adjusted Vuong Z statistic suggests that metabolic control might be better explained by HbA_1c testing compared to self-monitoring of blood glucose (P < .0001).

CONCLUSION

This research reveals the importance of quarterly clinical HbA_1c monitoring together with frequent self-monitoring of blood glucose in diabetes management to reach and maintain target HbA_1c .

Improved Glycemic Control in a Patient Group Performing 7-Point Profile Self-Monitoring of Blood Glucose and Intensive Data Documentation: An Open-Label, Multicenter, Observational Study

INTRODUCTION

Regular self-monitoring of blood glucose (SMBG) is recommended as an integral part of therapy for all patients with diabetes treated with insulin. In the current study, the effects on glycemic control of taking 7-point SMBG profiles and using a diabetes management system (DMA) on a smartphone were investigated.

METHODS

In a 12-week, open-label, multicenter, observational study, 51 patients [26 with type 1 diabetes mellitus (T1DM) and 25 with type 2 diabetes mellitus (T2DM)] were instructed to perform SMBG at least seven times a day using DMA combined with the iBGStar ^® SMBG system. HbA1c was measured at regular visits to the study sites. Patients reviewed and managed their data as well as their treatment on their own and there were no further assistance or treatment recommendations. Adverse events (AEs) were recorded throughout.

RESULTS

Overall, mean (SD) change from baseline in HbA1c at week 12 was -0.46 (0.57)% [-5 (6) mmol/mol (p < 0.0001)]. The change in HbA1c was observed in patients with T1DM [-0.27 (0.45)% (-3 [5] mmol/mol; p = 0.0063)] and T2DM [-0.65 (0.62)% (-7 [7] mmol/mol; p < 0.0001)]. The change in HbA1c was not correlated with an increased number of hypoglycemic events (blood glucose less than 55 mg/dL). The majority of AEs were symptomatic hypoglycemic events (42 events; nine patients).

CONCLUSIONS

Glycemic control can be improved, without receiving any recommendations or advice on insulin dose, by performing daily 7-point SMBG profiles and using electronic documentation with a smartphone app. These results must be confirmed in a larger controlled trial, but they already strengthen the importance of structured SMBG in diabetes therapy.

FUNDING

Sanofi.

Using meta-differential evolution to enhance a calculation of a continuous blood glucose level

We developed a new model of glucose dynamics. The model calculates blood glucose level as a function of transcapillary glucose transport. In previous studies, we validated the model with animal experiments. We used analytical method to determine model parameters. In this study, we validate the model with subjects with type 1 diabetes. In addition, we combine the analytic method with meta-differential evolution. To validate the model with human patients, we obtained a data set of type 1 diabetes study that was coordinated by Jaeb Center for Health Research. We calculated a continuous blood glucose level from continuously measured interstitial fluid glucose level. We used 6 different scenarios to ensure robust validation of the calculation. Over 96% of calculated blood glucose levels fit A+B zones of the Clarke Error Grid. No data set required any correction of model parameters during the time course of measuring. We successfully verified the possibility of calculating a continuous blood glucose level of subjects with type 1 diabetes. This study signals a successful transition of our research from an animal experiment to a human patient. Researchers can test our model with their data on-line at https://diabetes.zcu.cz.

Blood glucose self-monitoring and internet diabetes management on A1C outcomes in patients with type 2 diabetes

OBJECTIVES

The purpose of this study was to determine any correlation between frequency of self-monitoring of blood glucose (SMBG), frequency of patient-provider communication of SMBG (reporting), and hemoglobin A1C for patients with non-insulin-dependent diabetes solely on oral medications.

RESEARCH DESIGN AND METHODS

191 charts of patients with type 2 diabetes treated solely with oral hypoglycemic agents were reviewed retrospectively. A1C, SMBG frequency, and frequency of online communication with an endocrinologist within the most recent 6-month period were used in the analyses. Regression analysis was used to determine correlations to A1C. For subsequent subgroup analysis, patients were separated into infrequent and frequent SMBG groups, defined as those who test on average once or less per day or twice or more per day.

RESULTS

Although testing frequency did not correlate with A1C, higher reporting frequency correlated with lower A1C. Subgroup analysis of the frequent SMBG group showed a significantly lower A1C in frequent reporters when compared to infrequent reporters (N=118, p<0.05). This trend was not observed in the infrequent SMBG group (N=73, p=0.161).

CONCLUSIONS

The inverse correlation between reporting frequency and A1C, as well as the significant difference in A1C only for the frequent testers, suggests that frequent SMBG has an effect on reducing A1C only when combined with regular, frequent communication of SMBG with a healthcare provider.

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

INTRODUCTION

The ProAct study has shown that a pump switch to the Accu-Chek(®) Combo system (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) in type 1 diabetes patients results in stable glycemic control with significant improvements in glycated hemoglobin (HbA1c) in patients with unsatisfactory baseline HbA1c and shorter pump usage time.

PATIENTS AND METHODS

In this post hoc analysis of the ProAct database, we investigated the glycemic control and glycemic variability at baseline by determination of several established parameters and scores (HbA1c, hypoglycemia frequency, J-score, Hypoglycemia and Hyperglycemia Indexes, and Index of Glycemic Control) in participants with different daily bolus and blood glucose measurement frequencies (less than four day, four or five per day, and more than five per day, in both cases). The data were derived from up to 299 patients (172 females, 127 males; age [mean±SD], 39.4±15.2 years; pump treatment duration, 7.0±5.2 years).

RESULTS

Participants with frequent glucose readings had better glycemic control than those with few readings (more than five readings per day vs. less than four readings per day: HbA1c, 7.2±1.1% vs. 8.0±0.9%; mean daily blood glucose, 151±22 mg/dL vs. 176±30 mg/dL; percentage of readings per month >300 mg/dL, 10±4% vs. 14±5%; percentage of readings in target range [80-180 mg/dL], 59% vs. 48% [P<0.05 in all cases]) and had a lower glycemic variability (J-score, 49±13 vs. 71±25 [P<0.05]; Hyperglycemia Index, 0.9±0.5 vs. 1.9±1.2 [P<0.05]; Index of Glycemic Control, 1.9±0.8 vs. 3.1±1.6 [P<0.05]; Hypoglycemia Index, 0.9±0.8 vs. 1.2±1.3 [not significant]). Frequent self-monitoring of blood glucose was associated with a higher number of bolus applications (6.1±2.2 boluses/day vs. 4.5±2.0 boluses/day [P<0.05]). Therefore, a similar but less pronounced effect on glycemic variability in favor of more daily bolus applications was observed (more than five vs. less than four bolues per day: J-score, 57±17 vs. 63±25 [not significant]; Hypoglycemia Index, 1.0±1.0 vs. 1.5±1.4 [P<0.05]; Hyperglycemia Index, 1.3±0.6 vs. 1.6±1.1 [not significant]; Index of Glycemic Control, 2.3±1.1 vs. 3.1±1.7 [P<0.05]).

CONCLUSIONS

Pump users who perform frequent daily glucose readings have a better glycemic control with lower glycemic variability.

The relationship between the frequency of self-monitoring of blood glucose and glycemic control in patients with type 1 diabetes mellitus on continuous subcutaneous insulin infusion or on multiple daily injections

Aims/Introduction

We investigated the relationship between the frequency of self-monitoring of blood glucose (SMBG) and glycemic control in type 1 diabetes mellitus patients on continuous subcutaneous insulin infusion (CSII) or on multiple daily injections (MDI) using data management software.

Materials and Methods

We recruited 148 adult type 1 diabetes mellitus patients (CSII n = 42, MDI n = 106) and downloaded their SMBG records to the MEQNET™ SMBG Viewer software (Arkray Inc., Kyoto, Japan). The association between the SMBG frequency and the patients' hemoglobin A1c (HbA1c) levels was analyzed using the χ²-test and linear regression analysis was carried out to clarify their relationship.

Results

The odds ratio of achieving a target HbA1c level of <8% (63.9 mmol/mol) was significantly higher in subjects with SMBG frequencies of ≥3.5 times/day compared with those with SMBG frequencies of <3.5 times/day in the CSII group (odds ratio 7.00, 95% confidence interval 1.72–28.54), but not in the MDI group (odds ratio 1.35, 95% CI 0.62–2.93). A significant correlation between SMBG frequency and the HbA1c level was detected in the CSII group (HbA1c [%] = –0.24 × SMBG frequency [times/day] + 8.60 [HbA1c {mmol/L} = –2.61 × SMBG frequency {times/day} + 70.5], [r = –0.384, P = 0.012]), but not in the MDI group.

Conclusions

A SMBG frequency of <3.5 times per day appeared to be a risk factor for poor glycemic control (HbA1c ≥8%) in type 1 diabetes mellitus patients on CSII.

7(th) Annual Symposium on Self-Monitoring of Blood Glucose (SMBG), May 8-10, 2014, Helsinki, Finland

International experts in the fields of diabetes, diabetes technology, endocrinology, mobile health, sport science, and regulatory issues gathered for the 7(th) Annual Symposium on Self-Monitoring of Blood Glucose (SMBG). The aim of this meeting was to facilitate new collaborations and research projects to improve the lives of people with diabetes. The 2014 meeting comprised a comprehensive scientific program, parallel interactive workshops, and two keynote lectures.