Evaluation of the analytical performance of the coulometry-based Optium Omega blood glucose meter

Background

The goal of diabetes treatment is maintaining near normoglycemia based on self-monitoring of blood glucose (SMBG). In this study, an evaluation of the analytical performance of the coulometry-based Optium Omega™ glucose meter designed for SMBG has been carried out.

Methods

The assessment of precision and between-lot variability was based on glucose measurements in ethylene-diaminetetraacetic acid venous blood samples. Glucose concentrations measured in 289 fresh capillary blood samples using the Omega glucose meter and the Biosen C_line analyzer were compared.

Results

Within-run imprecision coefficient of variation for the lower and higher glucose concentrations amounted to 5.09 and 2.1%, respectively. The relative lot-dependent differences found for the lower and higher glucose concentrations were equal to 6.8 and 2.6%, respectively. The glucose meter error calculated for various concentration ranges amounted from 2.22 to 4.48%. The glucose meter error met the accuracy criteria recommended by the International Organization for Standardization and the American Diabetes Association. The Passing-Bablok agreement test and error grid analysis with 96% of results in zone A indicated good concordance of results, including glucose concentrations below 100 mg/dl.

Conclusions

The evaluated Optium Omega glucose meter fits the analytical requirements for its use in blood glucose monitoring in diabetes patients.     

Impact of Blood Glucose Self-Monitoring Errors on Glucose Variability, Risk for Hypoglycemia, and Average Glucose Control in Type 1 Diabetes: An In Silico Study

Background

Clinical trials assessing the impact of errors in self-monitoring of blood glucose (SMBG) on the quality of glycemic control in diabetes are inherently difficult to execute. Consequently, the objectives of this study were to employ realistic computer simulation based on a validated model of the human metabolic system and to provide potentially valuable information about the relationships among SMBG errors, risk for hypoglycemia, glucose variability, and long-term glycemic control.

Methods

Sixteen thousand computer simulation trials were conducted using 100 simulated adults with type 1 diabetes. Each simulated subject was used in four simulation experiments aiming to assess the impact of SMBG errors on detection of hypoglycemia (experiment 1), risk for hypoglycemia (experiment 2), glucose variability (experiment 3), and long-term average glucose control, i.e., estimated hemoglobin A1c (HbA1c)(experiment 4). Each experiment was repeated 10 times at each of four increasing levels of SMBG errors: 5, 10, 15, and 20% deviation from the true blood glucose value.

Results

When the permitted SMBG error increased from 0 to 5–10% to 15–20%-the current level allowed by International Organization for Standardization 15197–(1) the probability for missing blood glucose readings of 60 mg/dl increased from 0 to 0–1% to 3.5–10%; (2) the incidence of hypoglycemia, defined as reference blood glucose ≤70 mg/dl, changed from 0 to 0–0% to 0.1–5.5%; (3) glucose variability increased as well, as indicated by control variability grid analysis; and (4) the incidence of hypoglycemia increased from 15.0 to 15.2–18.8% to 22–25.6%. When compensating for this increase, glycemic control deteriorated with HbA1c increasing gradually from 7.00 to 7.01–7.12% to 7.26–7.40%.

Conclusions

A number of parameters of glycemic control deteriorated substantially with the increase of permitted SMBG errors, as revealed by a series of computer simulations (e.g., in silico) experiments. A threshold effect apparent between 10 and 15% permitted SMBG error for most parameters, except for HbA1c, which appeared to be increasing relatively linearly with increasing SMBG error above 10%.     

Treatment Satisfaction and Quality of Life for an Integrated Continuous Glucose Monitoring/Insulin Pump System Compared to Self-Monitoring Plus an Insulin Pump

Background

Little is known about how the most advanced technology affects treatment satisfaction and health-related quality of life (HRQOL) in adults with diabetes. This study was designed to assess treatment satisfaction and HRQOL among users of an integrated real-time (RT) continuous glucose monitoring (CGM)/continuous subcutaneous insulin infusion (CSII) system compared with those using self-monitoring of blood glucose (SMBG) with CSII.

Methods

Participants were 311 adult respondents to an Internet survey, 162 using RT-CGM/CSII, 149 using SMBG + CSII (median age 43 years; type 1 diabetes 94%; diabetes duration >15 years 61%; median insulin use 15 years). Respondents completed instruments assessing glucose monitoring system and insulin delivery system convenience, interference, burden, glucose control efficacy, cost satisfaction, overall satisfaction, and treatment preference, as well as quality of life (diabetes-related worries, social burden, and psychological well-being). Real-time CGM/CSII users also assessed specific elements of the RT-CGM/CSII system. Group differences were assessed using analysis of covariance controlling for respondent characteristics.

Results

The RT-CGM/CSII group gave significantly better ratings than the SMBG + CSII group for their glucose monitoring system''s glucose control efficacy, overall satisfaction, desire to switch, and willingness to recommend, and significantly worse ratings for interference with daily activities. The RT-CGM/CSII group gave significantly better ratings than the SMBG + CSII group for their insulin delivery system''s convenience and glucose control efficacy, overall satisfaction, desire to switch, and willingness to recommend. Real-time CGM/CSII users gave positive ratings of all system features.

Conclusions

Users of the integrated RT-CGM/CSII system reported more benefits of treatment, higher treatment satisfaction and quality of life, and greater preference for this system than SMBG + CSII users.     

Evolution of Data Management Tools for Managing Self-Monitoring of Blood Glucose Results: A Survey of iPhone Applications

Background

Studies have indicated that sharing of self-monitoring of blood glucose (SMBG) data and subsequent feedback from the health care provider (HCP) can help achieve glycemic goals such as a reduction in glycated hemoglobin. Electronic SMBG data management and sharing tools for the PC and smartphones may help in reducing the effort to manage SMBG data.

Methods

We reviewed software and top-ranking applications (Apps) for the iPhone platform to document the variety of useful features. Additionally, in an attempt to assess metrics such as task analysis and user friendliness of diabetes Apps, we observed and surveyed patients with diabetes as they recorded and relayed sample SMBG results to their hypothetical HCP using three Apps.

Results

Observation and survey demonstrated that the WaveSense Diabetes Manager allowed the participants to complete preselected SMBG data entry and relay tasks faster than other Apps. The survey revealed patient behavior patterns that would be useful in future App development.

Conclusion

Being able to record, analyze, seamlessly share, and obtain feedback on the SMBG data using an iPhone/iTouch App might potentially benefit patients. Trends in SMBG data management and the possibility of having interoperability of blood glucose monitors and smartphones may open up new avenues of diabetes management for the technologically savvy patient.     

Benefits of blinded continuous glucose monitoring during a randomized clinical trial

Background:

Real-time, personal continuous glucose monitoring (CGM) is a validated technology that can help patients improve glycemic control. Blinded CGM is a promising technology for obtaining retrospective data in clinical research where the quantity and quality of blood glucose information is important. This study was designed to investigate the use of novel procedures to enhance data capture from blinded CGM.

Methods:

Following a 4-week run-in, 46 patients with type 1 diabetes were randomized to one of two prandial insulins for a 12-week treatment period, after which they were crossed over to the alternate treatment for 12 weeks. Continuous glucose monitoring was implemented at the end of run-in (practice only) and during the last 2 weeks of each treatment period. Eighty percent of 288 possible daily glucose values were required for at least three days. Continuous glucose monitoring was extended for an additional week if these criteria were not met, and patients were allowed to insert sensors at home when necessary. Continuous glucose monitoring results were compared to reference eight-point self-monitoring of blood glucose (SMBG).

Results:

Higher than expected sensor failure rate was approximately 25%. During run-in, 12 of 45 attempted profiles failed adequacy criteria. However, treatment periods had only 1 of 82 attempted profiles considered inadequate (6 cases required an additional week of CGM). Using SMBG as reference, 93.7% of 777 CGM values were in Clarke error grid zones A+B.

Conclusions:

With appropriate training, adequate practice, and opportunity to repeat blinded CGM as needed, nearly 100% of attempted profiles can be obtained successfully.     

The Benefits of Continuous Glucose Monitoring and a Glucose Monitoring Schedule in Individuals with Type 1 Diabetes during Recreational Diving

Background

Our objective is to evaluate the Medtronic CGMS^® continuous glucose monitoring system and plasma glucose (PG) measurement performed in a monitoring schedule as tools to identify individuals with type 1 diabetes at risk when diving.

Methods

We studied 24 adults, 12 type 1 diabetes subjects and 12 controls, during 5 recreational scuba dives performed on 3 consecutive days. The CGMS was used by all participants on all the days and all the dives. Comparisons were made between PG performed in a monitoring schedule during the days of diving, self-monitored blood glucose (SMBG) performed 2 weeks prior to diving, and the CGMS during the study.

Results

One hundred seventeen dives were performed. Hypoglycemia (<70 mg/dl) was found in six individuals and on nine occasions. However, no symptoms of hypoglycemia were present during or immediately postdiving. In one case, repetitive hypoglycemia prediving gave rise to a decision not to dive. None of the dives were aborted. The number of hypoglycemic episodes, 10 min prediving or immediately postdiving, were related to the duration of diabetes, r = 0.83 and p =0.01, and the percentage of SMBG values below target (<72 mg/dl), r = 0.65 and p =0.02. Moreover, the number of hypoglycemic episodes was also related to the total duration below low limit (<70 mg/dl), measured by the CGMS, r =0.74 and p =0.006.

Conclusion

Safe dives are possible to achieve by well-informed, well-controlled individuals with type 1 diabetes. Using downloaded SMBG, CGMS, and repetitive PG in a monitoring schedule, it is possible to identify those subjects who are suitable for diving.     

Alarms for Continuous Glucose Monitors: Alarm Characterization for Continuous Glucose Monitors Used as Adjuncts to Self-Monitoring of Blood Glucose

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG). Alarm evaluation in the Clinical and Laboratory Standards Institute (CLSI) guideline for CGM does not specifically address devices that employ both CGM and SMBG. In this report, an alarm evaluation method is proposed for these devices.

Method

The proposed method builds on the CLSI method using data from an in-clinic study of subjects with type 1 diabetes. CGM was used to detect glycemic events, and SMBG was used to determine treatment. To optimize detection of a single glucose level, such as 70 mg/dl, a range of alarm threshold settings was evaluated. The alarm characterization provides a choice of alarm settings that trade off detection and false alarms. Detection of a range of high glucose levels was similarly evaluated.

Results

Using low glucose alarms, detection of 70 mg/dl within 30 minutes increased from 64 to 97% as alarm settings increased from 70 to100 mg/dl, and alarms that did not require treatment (SMBG >85 mg/dl) increased from 18 to 52%. Using high glucose alarms, detection of 180 mg/dl within 30 minutes increased from 87 to 96% as alarm settings decreased from 180 to 165 mg/dl, and alarms that did not require treatment (SMBG <180 mg/dl) increased from 24 to 42%.

Conclusion

The proposed alarm evaluation method provides information for choosing appropriate alarm thresholds and reflects the clinical utility of CGM alarms.     

Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Background:

The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series.

Methods:

Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data.

Results:

LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series.

Conclusions:

Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.     

Alarms for Continuous Glucose Monitors: Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring

Background

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG); all CGM alarms require SMBG confirmation before treatment. In this report, an analysis method is proposed to determine the CGM threshold alarm accuracy required to eliminate SMBG confirmation.

Method

The proposed method builds on the Clinical and Laboratory Standards Institute (CLSI) guideline for evaluating CGM threshold alarms using data from an in-clinic study of subjects with type 1 diabetes. The CLSI method proposes a maximum time limit of ±30 minutes for the detection of hypo- and hyperglycemic events but does not include limits for glucose measurement accuracy. The International Standards Organization (ISO) standard for SMBG glucose measurement accuracy (ISO 15197) is ±15 mg/dl for glucose <75 mg/dl and ±20% for glucose ≥75 mg/dl. This standard was combined with the CLSI method to more completely characterize the accuracy of CGM alarms.

Results

Incorporating the ISO 15197 accuracy margins, FreeStyle Navigator® CGM system alarms detected 70 mg/dl hypoglycemia within 30 minutes at a rate of 70.3%, with a false alarm rate of 11.4%. The device detected high glucose in the range of 140–300 mg/dl within 30 minutes at an average rate of 99.2%, with a false alarm rate of 2.1%.

Conclusion

Self-monitoring of blood glucose confirmation is necessary for detecting and treating hypoglycemia with the FreeStyle Navigator CGM system, but at high glucose levels, SMBG confirmation adds little incremental value to CGM alarms.     

The relation between HbA1c and hypoglycemia revisited; a secondary analysis from an intervention trial in patients with type 1 diabetes and impaired awareness of hypoglycemia

Aims

We aimed to re-assess the previously shown but recently disputed association between HbA1c and severe hypoglycemia.

Optimizing Display, Analysis, Interpretation and Utility of Self-Monitoring of Blood Glucose (SMBG) Data for Management of Patients with Diabetes

Background

Self-monitoring of blood glucose (SMBG) data have not been used to fullest advantage. Few physicians routinely download data from memory-equipped glucose meters and perform systematic analyses and interpretation of the data. There is need for improved methods for display and analysis of SMBG data, for a systematic approach for identification and prioritization of clinical problems revealed by SMBG, for characterization of blood glucose variability, and for clinical decision support.

Methods

We have developed a systematic approach to the analysis and interpretation of SMBG data to assist in the management of patients with diabetes. This approach utilizes the following criteria: 1) Overall quality of glycemic control; 2) Hypoglycemia (frequency, severity, timing); 3) Hyperglycemia; 4) Variability; 5) Pattern analysis; and 6) Adequacy of monitoring. The “Pattern analysis” includes assessment of: trends by date and by time of day; relationship of blood glucose to meals; post-prandial excursions; the effects of day of the week, and interactions between time of day and day of the week.

Results

The asymmetrical distribution of blood glucose values makes it difficult to interpret the mean and standard deviation. Use of the median (50^th percentile) and Inter-Quartile Range (IQR) overcomes these difficulties: IQR is the difference between the 75^th and 25^th percentiles. SMBG data can be used to predict the A1c level and indices of the risks of hyperglycemia and hypoglycemia.

Conclusion

Given reliable measures of glucose variability, one can apply a strategy to progressively reduce glucose variability and then increase the intensity of therapy so as to reduce median blood glucose and hence A1c, while minimizing the risk of hypoglycemia.     

Self-Measurement of Blood Glucose in Patients with Type 2 Diabetes: A Health Economic Assessment

Background

The clinical role and the potential benefit of self-measurement of blood glucose (SMBG) for patients with type 2 diabetes are still under discussion. Even less information is available on the cost-effectiveness of performing SMBG by this patient group. The goal of this study was to establish cost-effectiveness ratios of performing SMBG by patients afflicted by this disease.

Methods

We assessed the benefit and cost-effectiveness of SMBG in type 2 diabetes from a third-party payer perspective based on results of both a large epidemiologic cohort study reflecting the reality of care, and a Markov model calculation.

Results

Analysis of cohort study data revealed that total costs cumulated over the observation period of 8 years were lower in the SMBG group than in the non-SMBG group according to savings of € 1''714 [oral antidiabetic drugs (OAD) only] and € 13''815 (OAD + insulin) per patient. Several scenarios were considered in the model-based calculation. The cost-effectiveness ratio varied from € 20''768/life year gained to domination of SMBG use compared to nonusers in OAD treated patients and from € 59''057/life year gained to domination of SMBG use compared to nonusers in OAD + insulin treated patients.

Conclusion

Results indicate that SMBG in type 2 diabetes offers an excellent opportunity to get a high investment–outcome ratio in the treatment of this pandemic disease.     

Defining the relationship between average glucose and HbA1c in patients with type 2 diabetes and chronic kidney disease

Aims

To examine the relationship between average glucose (AG) and HbA_1c in patients with and without chronic kidney disease (CKD) and type 2 diabetes.

Materials and methods

43 patients with diabetes and CKD (stages 3–5) with stable glycaemic control, and glucose-lowering and erythropoiesis stimulating agent (ESA) doses, were prospectively studied for 3 months and compared to 104 age-matched controls with diabetes, without CKD from the ADAG study. Over 3 months, AG was calculated from 7 to 8 point self-monitored blood glucose measurements (SMBG) and from continuous glucose monitoring (CGMS), and mean HbA_1c was calculated from 4 measurements. AG and HbA_1c relationships were determined using multivariable linear regression analyses.

Results

The CKD and non-CKD groups were well matched for age and gender. Mean AG tended to be higher (p = 0.08) but HbA_1c levels were similar (p = 0.68) in the CKD compared with non-CKD groups. A linear relationship between AG and HbA_1c was observed irrespective of the presence and stage of CKD. The relationship was weaker in patients with stage 4–5 CKD (non-CKD R² = 0.75, stage 3 CKD R² = 0.79 and stage 4–5 CKD R² = 0.34, all p < 0.01). The inclusion of ESA use in the model rendered the effect of CKD stage insignificant (R² = 0.67, p < 0.01).

Conclusions

In patients with type 2 diabetes and CKD there is a linear relationship between HbA_1c and AG that is attenuated by ESA use, suggesting that ESA results in a systematic underestimation of AG derived from HbA_1c.     

Combined Insulin Pump Therapy with Real-Time Continuous Glucose Monitoring Significantly Improves Glycemic Control Compared to Multiple Daily Injection Therapy in Pump Na?ve Patients with Type 1 Diabetes; Single Center Pilot Study Experience

Objectives

This study assessed the safety and clinical effectiveness of the training protocol for initiating insulin pump therapy with real-time continuous glucose monitoring (MiniMed Paradigm REAL-Time System) in a stepwise approach on pump naive subjects with type 1 diabetes compared to a control group who remained on multiple daily injection (MDI) therapy.

Methods

This was a 15-week treat-to-target pilot study of 16 adult subjects (n = 50% male, age 45.9 ± 16 years) with type 1 diabetes (duration of diabetes 21.9 ± 11 years) on MDI therapy with hemoglobin A1c levels at or above 7.5% at baseline. Subjects were randomized to either the study arm (using a combined insulin pump and real-time continuous glucose monitoring system) or the control arm [which continued on MDI therapy with self-monitored blood glucose (SMBG) only]. All subjects dosed insulin according to results of SMBG by finger stick and uploaded data into the CareLink data management software.

Results

Significant improvements in glycemic control were observed from baseline in both study groups—study arm: pre-A1c 9.45 ± 0.55 and post-A1c 7.4 ± 0.66 (p = 0.00037); control arm: pre-A1c 8.58 ± 1.30 and post-A1c 7.5 ±1.01 (p = 0.04). Both arms had no incidence of severe hypoglycemia.

Conclusion

In this pilot study, the Paradigm REAL-Time System was initiated safely and effectively in type 1 diabetes patients who were pump naïve using a stepwise educational protocol.     

Increased insulinogenic index is an independent determinant of nonalcoholic fatty liver disease activity score in patients with normal glucose tolerance

Background

Although insulin resistance is involved in nonalcoholic fatty liver disease, role of abnormalities in early phase of insulin secretion has not been examined.

Aims

We examined which anthropometric and metabolic parameters, including insulinogenic index during oral glucose tolerant test, were independently associated with the disease activity of nonalcoholic fatty liver disease.

Methods

A total of 114 consecutive biopsy-proven nonalcoholic fatty liver disease patients without type 2 diabetes were enrolled.

Results

Age, aspartate aminotransferase, free fatty acid, ferritin type IV collagen, hyaluronic acid, procollagen N-terminal peptide, fasting plasma glucose and 2-h insulin after glucose loading were significantly higher in patients with impaired glucose tolerance than those with normal glucose tolerance. Multiple stepwise regression analysis revealed that glycated haemoglobin, decreased density ratio of liver to spleen in computed tomography and increased insulinogenic index were independently associated with nonalcoholic fatty liver disease activity score in normal glucose tolerance patients, whereas aspartate aminotransferase and 2-h insulin in impaired glucose tolerance subjects. However, there were no significant independent correlations between insulinogenic index and steatosis grade/fibrosis stage in normal glucose tolerance patients.

Conclusion

The present study suggests that increased early phase of insulin secretion may contribute to nonalcoholic fatty liver disease activity score in patients with normal glucose tolerance.     

Are Type 2 Diabetes Patients Who Self-Monitor Blood Glucose Special? The Role of Confounders in the Observational ROSSO Study

Background

In the German multicenter, retrospective cohort study (ROSSO), those patients with type 2 diabetes who performed self-monitoring of blood glucose (SMBG) had a better long-term clinical outcome. We analyzed whether confounders accounted for the lower rate of clinical events in the SMBG cohort.

Methods

ROSSO followed 3268 persons from diagnosis of type 2 diabetes for a mean of 6.5 years. Data were retrieved from patient files of randomly contacted primary care practices.

Results

In total, more than 60 potential confounders were documented, including nondisease-associated parameters such as patient''s health insurance, marital status, habitation, and characteristics of diabetes centers. There were only modest differences for these parameters between groups with versus without SMBG, and multiple adjustments did not weaken the association of SMBG use with better outcome (odds ratio 0.65, 95% confidence interval 0.53–0.81, p < .001). This was also true for subgroups of patients defined by type of antidiabetes treatment. Propensity score analysis confirmed the association of SMBG use with outcome. Using key baseline parameters, 813 matching pairs of patients were identified. The analysis again showed a better long-term outcome in the SMBG group (hazard ratio 0.67 p = .004).

Conclusion

An influence of nonrecognized confounders on better outcome in the SMBG group is rendered improbable by similar results obtained with adjustments for disease-associated or disease-independent parameters, by the analysis of patient subgroups, by propensity score analysis and by performing a matched-pair analysis. The higher flexibility in pharmacological antidiabetes treatment regimens in the SMBG cohort suggests a different attitude of treating physicians and patients in association with SMBG.     

Self-Monitoring of Blood Glucose: One STeP Forward?

Introduction

In times of short health care budgets, reimbursement for self-monitoring of blood glucose (SMBG) in diabetes patients without insulin treatment is subject to debate. The Structured Testing Program (STeP) trial found a positive correlation of test frequency and improved hemoglobin A1c (HbA1c) levels in poorly controlled type 2 diabetes patients not treated with insulin.

Methods

A structured literature search for other clinical studies reporting on SMBG frequency was performed.

Results

There is scarce evidence: three trials, including STeP, noted a significant and relevant correlation between testing frequency and improved HbA1c levels (FA effect), whereas two studies did not. The comparability between the identified studies is problematic.

Conclusion

Future research should consider correlations between testing frequency and level of glycemic control. More emphasis should be placed on a structured approach to use SMBG and to address adherence to testing and therapy.     

Blood Glucose Pattern Management in Diabetes: Creating Order from Disorder

Background

Self-monitoring of blood glucose (SMBG) is the most accessible way to assess glycemic patterns, and interpretation of these patterns can provide reasons for poor glycemic control and suggest management strategies. Furthermore, diabetes management based on blood glucose (BG) patterns is associated with improved patient outcomes. The aim of this review is therefore to evaluate the impact of pattern management in clinical practice.

Methods

We included a review of available literature, a discussion of obstacles to implementation of SMBG and pattern management, and suggestions on how clinicians and patients might work together to optimize this management feature.

Results

The literature review revealed eight publications specifically describing structured approaches to SMBG and pattern management. Specific information on how SMBG might be structured to detect BG patterns, however, remains limited. Barriers to pattern management include not just practical reasons, but emotional and psychological reasons as well.

Conclusions

Patterns are not always easy to detect or interpret, but on-meter and web-based tools can support both patients and clinicians. Ultimately, successful pattern management requires education and mutual commitment from the clinician and patient—ongoing collaboration is needed to obtain, review, and interpret SMBG values and to make changes based on the patterns.     

Interindividual and intraindividual variations in postprandial glycemia peak time complicate precise recommendations for self-monitoring of glucose in persons with type 1 diabetes mellitus

Background

In glycemic control, postprandial glycemia may be important to monitor and optimize as it reveals glycemic control quality, and postprandial hyperglycemia partly predicts late diabetic complications. Self-monitoring of blood glucose (SMBG) may be an appropriate technology to use, but recommendations on measurement time are crucial.

Method

We retrospectively analyzed interindividual and intraindividual variations in postprandial glycemic peak time. Continuous glucose monitoring (CGM) and carbohydrate intake were collected in 22 patients with type 1 diabetes mellitus. Meals were identified from carbohydrate intake data. For each meal, peak time was identified as time from meal to CGM zenith within 40–150 min after meal start. Interindividual (one-way Anova) and intraindividual (intraclass correlation coefficient) variation was calculated.

Results

Nineteen patients were included with sufficient meal data quality. Mean peak time was 87 ± 29 min. Mean peak time differed significantly between patients (p = 0.02). Intraclass correlation coefficient was 0.29.

Conclusions

Significant interindividual and intraindividual variations exist in postprandial glycemia peak time, thus hindering simple and general advice regarding postprandial SMBG for detection of maximum values.     

The effect of real-time continuous glucose monitoring on glycemic control in patients with type 2 diabetes mellitus

Background:

Real-time continuous glucose monitoring (RT-CGM) improves hemoglobin A1c (A1C) and hypoglycemia in people with type 1 diabetes mellitus and those with type 2 diabetes mellitus (T2DM) on prandial insulin; however, it has not been tested in people with T2DM not taking prandial insulin. We evaluated the utility of RT-CGM in people with T2DM on a variety of treatment modalities except prandial insulin.

Methods:

We conducted a prospective, 52-week, two-arm, randomized trial comparing RT-CGM (n = 50) versus self-monitoring of blood glucose (SMBG) (n = 50) in people with T2DM not taking prandial insulin. Real-time continuous glucose monitoring was used for four 2-week cycles (2 weeks on/1 week off). All patients were managed by their usual provider. This article reports on changes in A1C 0–12 weeks.

Results:

Mean (±standard deviation) decline in A1C at 12 weeks was 1.0% (±1.1%) in the RT-CGM group and 0.5% (±0.8%) in the SMBG group (p = .006). There were no group differences in the net change in number or dosage of hypoglycemic medications. Those who used the RT-CGM for ≥48 days (per protocol) reduced their A1C by 1.2% (±1.1%) versus 0.6% (±1.1%) in those who used it <48 days (p = .003). Multiple regression analyses statistically adjusting for baseline A1C, an indicator for usage, and known confounders confirmed the observed differences between treatment groups were robust (p = .009). There was no improvement in weight or blood pressure.

Conclusions:

Real-time continuous glucose monitoring significantly improves A1C compared with SMBG in patients with T2DM not taking prandial insulin. This technology might benefit a wider population of people with diabetes than previously thought.