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.     

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.     

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.     

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.     

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.     

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.     

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.     

A Novel Algorithm for Prediction and Detection of Hypoglycemia Based on Continuous Glucose Monitoring and Heart Rate Variability in Patients With Type 1 Diabetes

Background:Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device.Methods:Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L.Results:A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device.Conclusions:Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.     

IDegLira Improves Both Fasting and Postprandial Glucose Control as Demonstrated Using Continuous Glucose Monitoring and a Standardized Meal Test

Objective:

IDegLira is a novel, fixed-ratio combination of the long-acting basal insulin, insulin degludec, and the long-acting glucagon-like peptide-1 analog liraglutide. We studied the effect of IDegLira versus its components on postprandial glucose (PPG) in type 2 diabetes.

Methods:

In this substudy, 260 (15.6%) of the original 1663 patients with inadequate glycemic control participating in a 26-week, open-label trial (DUAL I) were randomized 2:1:1 to once-daily IDegLira, insulin degludec or liraglutide. Continuous glucose monitoring (CGM) for 72 hours and a meal test were performed.

Results:

At week 26, IDegLira produced a significantly greater decrease from baseline in mean PPG increment (normalized iAUC_0-4h) than insulin degludec (estimated treatment difference [ETD] −12.79 mg/dl [95% CI: −21.08; −4.68], P = .0023) and a similar magnitude of decrease as liraglutide (ETD −1.62 mg/dl [95% CI: −10.09; 6.67], P = .70). CGM indicated a greater reduction in change from baseline in PPG increment (iAUC_0-4h) for IDegLira versus insulin degludec over all 3 main meals (ETD −6.13 mg/dl [95% CI: −10.27, −1.98], P = .0047) and similar reductions versus liraglutide (ETD −1.80 mg/dl [95% CI: −2.52, 5.95], P = .4122). Insulin secretion ratio and static index were greater for IDegLira versus insulin degludec (P = .048 and P = .006, respectively) and similar to liraglutide (P = .45 and P = .895, respectively).

Conclusions:

Once-daily IDegLira provides significantly better PPG control following a mixed meal test than insulin degludec. The improvement is at least partially explained by higher endogenous insulin secretion and improved beta cell function with IDegLira. The benefits of liraglutide on PPG control are maintained across all main meals in the combination.