Impact of Glucose Meter Error on Glycemic Variability and Time in Target Range During Glycemic Control After Cardiovascular Surgery

Background:

We retrospectively studied the impact of glucose meter error on the efficacy of glycemic control after cardiovascular surgery.

Method:

Adult patients undergoing intravenous insulin glycemic control therapy after cardiovascular surgery, with 12-24 consecutive glucose meter measurements used to make insulin dosing decisions, had glucose values analyzed to determine glycemic variability by both standard deviation (SD) and continuous overall net glycemic action (CONGA), and percentage glucose values in target glucose range (110-150 mg/dL). Information was recorded for 70 patients during each of 2 periods, with different glucose meters used to measure glucose and dose insulin during each period but no other changes to the glycemic control protocol. Accuracy and precision of each meter were also compared using whole blood specimens from ICU patients.

Results:

Glucose meter 1 (GM1) had median bias of 11 mg/dL compared to a laboratory reference method, while glucose meter 2 (GM2) had a median bias of 1 mg/dL. GM1 and GM2 differed little in precision (CV = 2.0% and 2.7%, respectively). Compared to the period when GM1 was used to make insulin dosing decisions, patients whose insulin dose was managed by GM2 demonstrated reduced glycemic variability as measured by both SD (13.7 vs 21.6 mg/dL, P < .0001) and CONGA (13.5 vs 19.4 mg/dL, P < .0001) and increased percentage glucose values in target range (74.5 vs 66.7%, P = .002).

Conclusions:

Decreasing glucose meter error (bias) was associated with decreased glycemic variability and increased percentage of values in target glucose range for patients placed on intravenous insulin therapy following cardiovascular surgery.     

Sleep Apnea,Sleep Disturbance,and Fasting Glucose Variability: A Pilot Study

Background

Disturbed sleep and nocturnal altered breathing are related to disturbances of glucose metabolism. The present uncontrolled observational study explores the role of these factors on the variability of fasting glycemia.

Methods

The number and duration of nocturnal awakenings and the fasting glycemia of 97 patients with type 2 diabetes treated with diet, metformin, or gliptins were recorded over seven consecutive days. During the same time period, the main respiratory indexes—oxygen disturbance index, apnea/hypopnea index, and respiratory disturbance index—were recorded for one night.

Results

The three respiratory indexes and the number of nocturnal awakenings are highly correlated with the coefficient of variation of the fasting blood glucose recorded over the 7-day period at p <.005 level. A multiple regression analysis showed that the variables in the model explained 86% of the variability.

Results

Respiratory/sleep disturbances appear to be modulators superimposed on blood glucose levels determined by other factors.     

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%.     

Glycemic Variability and Its Impact on Quality of Life in Adults With Type 1 Diabetes

Background:

There is evidence suggesting that glycemic variability reduces quality of life (QoL) in people with type 2 diabetes, but this association has not been explored in type 1 diabetes. We aimed to assess whether glycemic variability has an impact on QoL in adults with established type 1 diabetes using multiple daily injections (MDI) of insulin or continuous subcutaneous insulin infusion (CSII).

Methods:

Participants wore a blinded continuous glucose monitor for up to 5 days and completed the diabetes quality of life (DQOL) questionnaire. Glycemic variability measures were calculated using the EasyGV version 9.0 software. A correlation analysis was performed to assess whether there was a relationship between glycemic variability and measures of QoL.

Results:

In all, 57 participants with type 1 diabetes (51% male, 65% on CSII, 35% on MDI, mean [SD] age 41 [13] years, duration of diabetes 21 [12] years, HbA1c 63 [12] mmol/mol [7.9% (1.1)], body mass index 25.2 [4.0] kg/m²) were included in the analysis. No significant associations between glycemic variability and DQOL total or subscale scores were demonstrated. The glycemic variability was significantly higher for MDI participants compared to CSII participants (P < .05 for all glycemic variability measures), but no significant difference in QoL between the 2 treatment modality groups was observed.

Conclusions:

Treatment with CSII is associated with lower glycemic variability compared to MDI. Despite this, and contrary to findings in type 2 diabetes, this study did not find an association between glycemic variability and QoL in adults with relatively well-controlled type 1 diabetes, irrespective of whether they are on MDI or CSII.     

Hypoglycemia in Type 2 Diabetes - More Common Than You Think: A Continuous Glucose Monitoring Study

Background:

Hypoglycemia is often the limiting factor for intensive glucose control in diabetes management, however its actual prevalence in type 2 diabetes (T2DM) is not well documented.

Methodology:

A total of 108 patients with T2DM wore a continuous glucose monitoring system (CGMS) for 5 days. Rates and patterns of hypoglycemia and glycemic variability (GV) were calculated. Patient and medication factors were correlated with rates, timing, and severity of hypoglycemia.

Results:

Of the patients, 49.1% had at least 1 hypoglycemic episode (mean 1.74 episodes/patient/ 5 days of CGMS) and 75% of those patients experienced at least 1 asymptomatic hypoglycemic episode. There was no significant difference in the frequency of daytime versus nocturnal hypoglycemia. Hypoglycemia was more frequent in individuals on insulin (alone or in combination) (P = .02) and those on oral hypoglycemic agents (P < .001) compared to noninsulin secretagogues. CGMS analysis resulted in treatment modifications in 64% of the patients. T2DM patients on insulin exhibited higher glycemic variability (GV) scores (2.3 ± 0.6) as compared to those on oral medications (1.8 ± 0.7, P = .017).

Conclusions:

CGMS can provide rich data that show glucose excursions in diabetes patients throughout the day. Consequently, unwarranted onset of hypo- and hyperglycemic events can be detected, intervened, and prevented by using CGMS. Hypoglycemia was frequently unrecognized by the patients in this study (75%), which increases their potential risk of significant adverse events. Incorporation of CGMS into the routine management of T2DM would increase the detection and self-awareness of hypoglycemia resulting in safer and potentially better overall control.     

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.     

Comparison of Fasting Glucose with Post-Load Glucose Values and Glycated Hemoglobin for Prediction of Type 2 Diabetes: The Isfahan Diabetes Prevention Study

OBJECTIVES: The aim of this study was to compare the ability of fasting plasma glucose (FPG), post-load plasma glucose values and glycated hemoglobin (HbA1c) to predict progression to diabetes in non-diabetic first-degree relatives (FDR) of patients with type 2 diabetes. METHODS: A total of 701 non-diabetic FDR of diabetic patients aged 20-70 years surveyed in 2003 to 2005 were followed until 2008 for the onset of type 2 diabetes mellitus. At baseline and at follow-ups, participants underwent a standard 75 g 2-hour oral glucose tolerance test (OGTT). Prediction of progression to type 2 diabetes was assessed by using area under the receiver-operating characteristic (ROC) curves based upon measurement of FPG, post-load glucose values and HbA1c. RESULTS: The incidence of type 2 diabetes was 33.9 per 1000 person-years in men and 48.6 in women. The incidence rates were 4.6, 50.7, and 99.7 per 1000 person-years in FDR with normal glucose tolerance, impaired fasting glucose and impaired glucose tolerance respectively. FPG value was a better predictor of progression to diabetes than any post-load glucose values or HbA1c. The areas under the ROC curves were 0.811 for fasting, 0.752 for 1/2-hour, 0.782 for 1-hour and 0.756 for 2-hour glucose vs. 0.634 for HbA1c (p < 0.001). CONCLUSIONS: FPG had more discriminatory power to distinguish between individuals at risk for diabetes and those who were not at risk than post-load glucose values during OGTT or HbA1c. Our findings support the American Diabetes Association recommendation of using FPG concentration to diagnose diabetes.     

Differences in Glycemic Variability Between Normoglycemic and Prediabetic Subjects

So far the criteria for NGT and abnormal glucose tolerance (AGT) are based on HbA1c and 75 g oGTT. We present data on GV and diurnal profiles in stratified cohorts with AGT versus controls. 28 NGT, 42 AGT (15 IGT, 11 IFG, 16 CGI) matched for age and BMI classified by 75 g oGTT underwent a CGM with test meal (TM). Diurnal profiles, glucose excursion after TM, and GV (SD, MAGE) were calculated for day 2 and 3. HbA1c, with its values of 5.5 ± 0.37% versus 5.65 ± 0.36%, was within normal range. Average interstitial glucose (AiG) was 5.84 ± 0.52 mmol/l) in NGT and 6.35 ± 0.65 mmol/l in AGT (P = .002). The 2 h incremental area under curve (iAUC) from TM until 2 h after TM was 1.94 ± 1.31 mmol/l*h versus 2.89 ( ± 1.75) mmol/l*h (P = .012), AiG 2 hours after TM was 5.99 ± 1.14 mmol/l*d versus 6.64 ± 1.30 mmol/l (P = .035). Peaks of AiG after TM were 7.69 ± 1.48 mmol/l*d versus 9.18 ± 1.67 mmol/l*d (P = .001). SD was significantly higher for AGT (1.12 ± 0.37 vs. 0.85 ± 0.32 mmol/l, P = .01) and MAGE 2.26 ± 0.84 vs. 1.60 ± 0.69 mmol/l, P = .005). In this comparative analysis NGT and AGT well matched for age, BMI, and comorbidities, CGM revealed significant differences in daytime AiG, pp glucose excursion and postprandial peaks. SD and MAGE was significantly higher for subjects with AGT. I Impaired glucose homeostasis a better characterizes degree of AGTe than HbA1c and 75 g OGTT.     

Comparison of CGM-Derived Measures of Glycemic Variability Between Pancreatogenic Diabetes and Type 2 Diabetes Mellitus

Background:To compare glycemic variability (GV) indices between patients with fibrocalculous pancreatic diabetes (FCPD) and type 2 diabetes mellitus (T2D) using continuous glucose monitoring (CGM).Methods:We measured GV indices using CGM (iPro™2 Professional CGM, Medtronic, USA) data in 61 patients each with FCPD and T2D who were matched for glycated hemoglobin A1c (HbA1c) and duration of diabetes. GlyCulator2 software was used to estimate the CGM-derived measures of GV (SD, mean amplitude of glycemic excursion [MAGE], continuous overall net glycemic action [CONGA], absolute means of daily differences [MODD], M value, and coefficient of variance [%CV]), hypoglycemia (time spent below 70 mg/dL, AUC below 70 mg/dL, glycemic risk assessment diabetes equation hypoglycemia, Low Blood Glucose Index), and hyperglycemia (time spent above 180 mg/dL at night [TSA > 180], AUC above 180 mg/dL [AUC > 180], glycemic risk assessment diabetes equation hyperglycemia, High Blood Glucose Index [HBGI], and J index). The correlation of GV indices with HbA1c, duration of diabetes, and demographic and biochemical parameters were also assessed.Results:All the CGM-derived measures of GV (SD, MAGE, CONGA, MODD, and %CV), except M value, were significantly higher in the FCPD group than in the T2D group (P < 0.05). Measures of hyperglycemia (TSA >180, AUC >180, HBGI, and J index) were significantly higher in the FCPD group than in the T2D group (P < 0.05). The measures of hypoglycemia were not significantly different between the two groups. All the hyperglycemia indices showed a positive correlation with HbA1c in both groups.Conclusions:FCPD is associated with higher GV than is T2D. The findings of higher postprandial glycemic excursions in patients with FCPD could have potential therapeutic implications.     

Vascular Glucose Sensor Symposium: Continuous Glucose Monitoring Systems (CGMS) for Hospitalized and Ambulatory Patients at Risk for Hyperglycemia,Hypoglycemia, and Glycemic Variability

Hyperglycemia, hypoglycemia, and glycemic variability have been associated with increased morbidity, mortality, length of stay, and cost in a variety of critical care and non–critical care patient populations in the hospital. The results from prospective randomized clinical trials designed to determine the risks and benefits of intensive insulin therapy and tight glycemic control have been confusing; and at times conflicting. The limitations of point-of-care blood glucose (BG) monitoring in the hospital highlight the great clinical need for an automated real-time continuous glucose monitoring system (CGMS) that can accurately measure the concentration of glucose every few minutes. Automation and standardization of the glucose measurement process have the potential to significantly improve BG control, clinical outcome, safety and cost.     

Significance of Glycemic Variability in Diabetes Mellitus

The goal of diabetes treatment is to maintain good glycemic control, prevent the development and progression of diabetic complications, and ensure the same quality of life and life expectancy as healthy people. Hemoglobin A1c (HbA1c) is used as an index of glycemic control, but strict glycemic control using HbA1c as an index may lead to severe hypoglycemia and cardiovascular death. Glycemic variability (GV), such as excessive hyperglycemia and hypoglycemia, is associated with diabetic vascular complications and has been recognized as an important index of glycemic control. Here, we reviewed the definition and evaluated the clinical usefulness of GV, and its relationship with diabetic complications and therapeutic strategies to reduce GV.     

Time in Specific Glucose Ranges,Glucose Management Indicator,and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

Background:International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system.Method:A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics.Results:Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%).Conclusion:Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.     

Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is It Important? How to Measure It?

The dysglycemia of diabetes includes two components: (1) sustained chronic hyperglycemia that exerts its effects through both excessive protein glycation and activation of oxidative stress and (2) acute glucose fluctuations. Glycemic variability seems to have more deleterious effects than sustained hyperglycemia in the development of diabetic complications as both upward (postprandial glucose increments) and downward (interprandial glucose decrements) changes activate the oxidative stress. For instance, the urinary excretion rate of 8-iso-PGF2α, a reliable marker of oxidative stress, was found to be strongly, positively correlated (r = 0.86, p < .001) with glycemic variability assessed from the mean amplitude of glycemic excursions (MAGE) as estimated by continuous glucose monitoring systems (CGMS). These observations therefore raise the question of whether we have the appropriate tools for assessing glycemic variability in clinical practice. From a statistical point of view, the standard deviation (SD) around the mean glucose value appears as the “gold standard.” By contrast, the MAGE index is probably more appropriate for selecting the major glucose swings that are calculated as the arithmetic mean of differences between consecutive peaks and nadirs, provided that the differences be greater than the SD around the mean values. Furthermore, calculating the MAGE index requires continuous glucose monitoring, which has the advantage to detect all isolated upward and downward acute glucose fluctuations. In conclusion, the increasing use of CGMSs will certainly promote better assessment and management of glycemic variability.     

Association between Changes in the Systolic Blood Pressure from Evening to the Next Morning and Night Glucose Variability in Heart Disease Patients

Objectve To assess the impact of glycemic variability on blood pressure in hospitalized patients with cardiac disease. Methods In 40 patients with cardiovascular disease, the glucose levels were monitored by flash continuous glucose monitoring (FGM; Free-Style Libre™ or Free-Style Libre Pro; Abbott, Witney, UK) and self-monitoring blood glucose (SMBG) for 14 days. Blood pressure measurements were performed twice daily (morning and evening) at the same time as the glucose level measurement using SMBG. Results The detection rate of hypoglycemia using the FGM method was significantly higher than that with the 5-point SMBG method (77.5% vs. 5.0%, p＜0.001). Changes in the systolic blood pressure from evening to the next morning [morning - evening (ME) difference] were significantly correlated with night glucose variability (r=0.63, P＜0.001). A multiple regression analysis showed that night glucose variability using FGM was more closely correlated with the ME difference [r=0.62 (95% confidence interval, 0.019-0.051); p＜0.001] than with the age, body mass index, or smoking history. Night glucose variability was also more closely associated with the ME difference in patients with unstable angina pectoris (UAP) than in those with acute myocardial infarction (AMI) or heart failure (HF) (r=0.83, p=0.058). Conclusion Night glucose variability is associated with the ME blood pressure difference, and FGM is more accurate than the 5-point SMBG approach for detecting such variability.     

Sensitivity of Traditional and Risk-Based Glycemic Variability Measures to the Effect of Glucose-Lowering Treatment in Type 2 Diabetes Mellitus

Background:Here we assess associations between glycemic variability (GV) measures and outcomes from glucose-lowering therapy in patients with type 2 diabetes (T2DM) to identify the metrics most sensitive to treatment response.Methods:Data from 1699 patients in 6 previously reported studies in adults with T2DM treated with basal insulin and/or oral glucose-lowering drugs were included in a post hoc meta-analysis. Using 7-point blood glucose (BG) profiles we compared the GV metrics standard deviation (SD), mean amplitude of glycemic excursion (MAGE), mean absolute glucose (MAG), low and high BG risk indices (LBGI, HBGI), and average daily risk range (ADRR). Treatment-related changes in GV and risk status and associations between end-of-trial GV/risk metrics with treatment outcomes (end-of-trial glycated hemoglobin A1c[A1C] level ≥7.0%, hypoglycemia, and composite outcome of A1C <7.0% and no hypoglycemia), were evaluated.Results:Significant changes from baseline to end of treatment were observed in all measures (all P < .0001), with the largest reduction following treatment for HBGI (–65.5%) and ADRR (–43.3%). The baseline risk classification for hyperglycemia based on the risk categories of HBGI improved for 66.8%, remained unchanged for 29.8%, and deteriorated for 3.3% of patients (chi-square P < .0001), while the risk for hypoglycemia did not change. HBGI showed the strongest association with A1C ≥7.0% at the end of treatment, and LBGI showed the strongest association with symptomatic hypoglycemia.Conclusions:During glucose-lowering therapy in T2DM, HBGI and LBGI offer insights into hyperglycemia and trends toward hypoglycemia, respectively; ADRR may be the optimal GV measure responsive to hypo- and hyperglycemic treatment effects.