Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data

[Evaluation of fructosamine as a parameter of blood glucose control in diabetic pregnant women]

OBJECTIVE

To evaluate the alternative parameters to monitor glycemia in pregnant women with diabetes studying the relationship between fructosamine testing and self monitoring of blood glucose in pregnant women with diabetes.

MATERIALS AND METHODS

Serum fructosamine levels and the self monitoring of blood glucose over 14 days before the collection of fructosamine were evaluated in 47 diabetic pregnant women.

RESULTS

Seventy-one fructosamine levels and 2,238 glucose measurements (CGs) were analysed. Levels of fructosamine correlated with high blood glucose index (HBGI) and the standard deviation of glycemias (r = 0.28; p = 0.021 and r = 0.26; p = 0.03, respectively). The comparison between the mothers of the newborns with appropriated or large birthweight and those who gave birth to small newborns for their gestational age (SGA) showed that the latter had a lower glycemic mean (105 vs. 114 and 119 mg/dL), a higher low blood glucose index (5.8 vs. 1.3 and 0.7) and a higher percentage of hyperglycemias (11 vs. 0 and 0%) even when the fructosamine falls within the reference values (242 vs. 218 and 213 μmol/l).

CONCLUSION

The levels of fructosamine can be used as further parameter to aid self monitoring of blood glucose to evaluate hyperglycemias and glycemic variability, however, this can underestimate hypoglycemias in pregnant women carrying small-for-gestational age fetuses.

Glycaemic variability in diabetes: a tool for assessing the quality of glycaemic control and the risk of complications

The routine approach to evaluating the effectiveness of diabetes treatment based on the level of glycated haemoglobin (HbA. 1c) accounts for the average glucose level but does not consider the scope and frequency of its fluctuations. The development of computational methods to analyse glycaemic oscillations has made it possible to propose the concept of glycaemic variability (GV). The interest in research focused on GV increased dramatically after continuous glucose monitoring (CGM) technology was introduced, which provided the opportunity to study in detail the temporal structure of blood glucose curves. Numerous methods for assessing GV proposed over the past five decades characterize glycaemic fluctuations as functions of concentration and time and estimate the risks of hypoglycaemia and hyperglycaemia. Accumulating evidence indicates that GV may serve as a significant predictor of diabetic complications. Prospective studies demonstrate that certain GV parameters have independent significance for predicting diabetic retinopathy, nephropathy and cardiovascular diseases. There is evidence that GV correlates with the severity of atherosclerotic vascular lesions and cardiovascular outcomes in diabetic patients. The mechanisms underlying the relationship between GV and vascular complications are being intensively studied, and recent data show that the effect of GV on vascular walls may be mediated by oxidative stress, chronic inflammation and endothelial dysfunction. Average blood glucose levels and GV are considered independent predictors of hypoglycaemia. Increased GV is associated with impaired hormonal response to hypoglycaemia and is a long-term predictor of hypoglycaemia unawareness. These data allow us to conclude that computational methods for analysing GV in patients with diabetes may serve as a promising tool for personalized assessment of glycaemic control and the risk of vascular complications and hypoglycaemia. Thus, the reduction of GV can be regarded as one of the therapeutic targets to treat diabetes.

Lower glucose variability and hypoglycemia measured by continuous glucose monitoring with novel long-acting insulin LY2605541 versus insulin glargine

OBJECTIVE

To use continuous glucose monitoring (CGM) to evaluate the impact of the novel, long-acting basal insulin analog LY2605541 on hypoglycemia and glycemic variability in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Hypoglycemia and glucose variability were assessed with CGM of interstitial glucose (IG) in a subset of patients with type 2 diabetes from a phase II, randomized, open-label, parallel study of LY2605541 (n = 51) or insulin glargine (GL) (n = 25). CGM was conducted on 3 consecutive days (72-84 h) during the week before week 0, 6, and 12 study visits.

RESULTS

Measured by CGM for 3 days prior to the 12-week visit, fewer LY2605541-treated patients experienced hypoglycemic events overall (50.0 vs. 78.3%, P = 0.036) and nocturnally (20.5 vs. 47.8%, P = 0.027) and spent less time with IG ≤70 mg/dL than GL-treated patients during the 24-h (25 ± 6 vs. 83 ± 16 min, P = 0.012) and nocturnal periods (11 ± 5 vs. 38 ± 13 min, P = 0.024). These observations were detected without associated differences in the average duration of individual hypoglycemic episodes (LY2605541 compared with GL 57.2 ± 5.4 vs. 69.9 ± 10.2 min per episode, P = NS). Additionally, LY2605541-treated patients had lower within-day glucose SD for both 24-h and nocturnal periods.

CONCLUSIONS

By CGM, LY2605541 treatment compared with GL resulted in fewer patients with hypoglycemic events and less time in the hypoglycemic range and was not associated with protracted hypoglycemia.

Glucose variability: An emerging target for the treatment of diabetes mellitus

Alterations in glucose metabolism in individuals with diabetes have been considered for many years, as they appear at first glance, i.e., simply as hyperglycemia, and its surrogate marker, glycated hemoglobin (HbA1c), used both to estimate the risk of developing diabetic complications and to define the targets and measure the efficacy of diabetes treatments. However, over time diabetes-related glycemic alterations have been considered in more complex terms, by attempting to identify the role of fasting glycemia, postprandial glycemia and hypoglycemia in the overall assessment of the disease. This set of evaluations has led to the concept of glucose variability. Although intuitively easy to understand, it cannot be equally simply translated into terms of definition, measuring, prognostic and therapeutic impact. The literature available on glucose variability is extensive yet confused, with the only common element being the need to find out more on the subject. The purpose of this manuscript is not only to review the most recent evidence on glucose variability, but also to help the reader to better understand the available measurement options, and how the various definitions can differently be related with the development of diabetic complications. Finally, we provide how new and old drugs can impact on glucose variability.

Intensive structured self-monitoring of blood glucose and glycemic control in noninsulin-treated type 2 diabetes: the PRISMA randomized trial

OBJECTIVE

We aimed to evaluate the added value of intensive self-monitoring of blood glucose (SMBG), structured in timing and frequency, in noninsulin-treated patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

The 12-month, randomized, clinical trial enrolled 1,024 patients with noninsulin-treated type 2 diabetes (median baseline HbA1c, 7.3% [IQR, 6.9-7.8%]) at 39 diabetes clinics in Italy. After standardized education, 501 patients were randomized to intensive structured monitoring (ISM) with 4-point glycemic profiles (fasting, preprandial, 2-h postprandial, and postabsorptive measurements) performed 3 days/week; 523 patients were randomized to active control (AC) with 4-point glycemic profiles performed at baseline and at 6 and 12 months. Two primary end points were tested in hierarchical order: HbA1c change at 12 months and percentage of patients at risk target for low and high blood glucose index.

RESULTS

Intent-to-treat analysis showed greater HbA1c reductions over 12 months in ISM (-0.39%) than in AC patients (-0.27%), with a between-group difference of -0.12% (95% CI, -0.210 to -0.024; P=0.013). In the per-protocol analysis, the between-group difference was -0.21% (-0.331 to -0.089; P=0.0007). More ISM than AC patients achieved clinically meaningful reductions in HbA1c (>0.3, >0.4, or >0.5%) at study end (P<0.025). The proportion of patients reaching/maintaining the risk target at month 12 was similar in ISM (74.6%) and AC (70.1%) patients (P=0.131). At visits 2, 3, and 4, diabetes medications were changed more often in ISM than in AC patients (P<0.001).

CONCLUSIONS

Use of structured SMBG improves glycemic control and provides guidance in prescribing diabetes medications in patients with relatively well-controlled noninsulin-treated type 2 diabetes.

Prospective, randomized trial on intensive SMBG management added value in non-insulin-treated T2DM patients (PRISMA): a study to determine the effect of a structured SMBG intervention

Self-monitoring of blood glucose (SMBG) is a core component of diabetes management. However, the International Diabetes Federation recommends that SMBG be performed in a structured manner and that the data are accurately interpreted and used to take appropriate therapeutic actions. We designed a study to evaluate the impact of structured SMBG on glycemic control in non-insulin-treated type 2 diabetes (T2DM) patients. The Prospective, Randomized Trial on Intensive SMBG Management Added Value in Non-insulin-Treated T2DM Patients (PRISMA) is a 12-month, prospective, multicenter, open, parallel group, randomized, and controlled trial to evaluate the added value of an intensive, structured SMBG regimen in T2DM patients treated with oral agents and/or diet. One thousand patients (500 per arm) will be enrolled at 39 clinical sites in Italy. Eligible patients will be randomized to the intensive structured monitoring (ISM) group or the active control (AC) group, with a glycosylated hemoglobin (HbA1c) target of <7.0%. Intervention will comprise (1) structured SMBG (4-point daily glucose profiles on 3 days per week [ISM]; discretionary, unstructured SMBG [AC]); (2) comprehensive patient education (both groups); and (3) clinician's adjustment of diabetes medications using an algorithm targeting SMBG levels, HbA1c and hypoglycemia (ISM) or HbA1c and hypoglycemia (AC). The intervention and trial design build upon previous research by emphasizing appropriate and collaborative use of SMBG by both patients and physicians. Utilization of per protocol and intent-to-treat analyses facilitates assessment of the intervention. Inclusion of multiple dependent variables allows us to assess the broader impact of the intervention, including changes in patient and physician attitudes and behaviors.

Which patients should be evaluated for blood glucose variability?

Diabetes is characterized by glycaemic disorders that include both sustained chronic hyperglycaemia and acute fluctuations (i.e. glycaemic variability). Increasing attention is being paid to the role of glycaemic variability as a relevant determinant for diabetes control and prevention of its vascular complications. As a consequence, it is strongly suggested that a global antidiabetic strategy should be aimed at reducing to a minimum the different components of glycaemic control (i.e. HbA1c, fasting and postprandial glucose, as well as glycaemic variability). Subjects at risk of hypoglycaemia, subjects with postprandial hyperglycaemia and patients who need to adjust or start insulin seem to be the categories that require glycaemic variability monitoring. The analysis of blood glucose variability represents an additional tool in the global assessment of glycaemic control and can serve as a guide to the clinician in the management of therapy and for the patients both in the prevention of acute complications, in particular hypoglycaemia, and chronic disease, in particular macrovascular complications.

Examining the Behaviour subscale of the Hypoglycaemia Fear Survey: an international study

AIMS

The Hypoglycemia Fear Survey (HFS)-II Behaviour and Worry subscales were developed to measure behaviours and anxiety related to hypoglycaemia in diabetes. However, previous studies found lower reliability in the HFS Behaviour subscale and inconsistent relationships with glucose control. The purpose of this study was to conduct extensive analyses of the internal structure of the HFS Behaviour subscale's internal structure and its relationships with diabetes outcomes, including HbA1c and episodes of severe hypoglycaemia.

METHODS

HFS-II survey data from 1460 adults with Type 1 diabetes were collected from five countries. This aggregated sample underwent exploratory factor analysis and item analysis to determine the internal structure of the survey and subscales.

RESULTS

A three-factor solution showed the best fit for the HFS, with two subscales emerging from the HFS Behaviour representing tendencies towards (1) maintenance of high blood glucose and (2) avoidance of hypoglycaemic risks by other behaviours, and a third single HFS Worry subscale. Subscale item analysis showed excellent fit, separation and good point-measure correlations. All subscales demonstrated acceptable (0.75) to excellent (0.94) internal reliability. HbA(1c) correlated with Maintain High Blood Glucose subscale scores, r = 0.14, P < 0.001, and severe hypoglycaemia frequency correlated with all subscales.

CONCLUSIONS

The HFS Worry subscale measures one construct of anxiety about various aspects of hypoglycaemia. In contrast, the HFS Behaviour subscale appears to measure two distinct aspects of behavioural avoidance to prevent hypoglycaemia, actions which maintain high blood glucose and other behaviours to avoid hypoglycaemic risk. These results demonstrate the clinical importance of the HFS Behaviour subscales and their differential relationships with measures of diabetes outcome such as HbA1c .

Impact of telemedicine assessment on glycemic variability in children with type 1 diabetes mellitus

BACKGROUND

Experimental and in vitro studies have related glycemic variability (GV) with activation of oxidative stress, which could be involved in the micro- and macrovascular damage found in diabetes mellitus and in acute complications such as hypoglycemia. Some GV indexes are currently integrated in specialized Web sites for the analysis and assessment of diabetes patients through telemedicine. We aimed to identify the impact of telemedicine on metabolic control and GV in prepubescent patients with type 1 diabetes mellitus (T1DM).

SUBJECTS AND METHODS

Eighty patients between 6 and 10 years old were eligible for enrollment. Participation was accepted by the parents of only 15 (18%) patients, of whom 13 (86%) completed the study. These 13 patients were assessed fortnightly for 3 months through Accu-Chek (Roche, Mannheim, Germany) Smart Pix software; this period was compared with a subsequent 4-month period without telemedical support. The variables analyzed were mean glycated hemoglobin (HbA1c), mean blood glucose (MBG), and indexes of GV (SD, low blood glucose index [LBGI], high blood glucose index [HBGI], and the average daily risk range [ADRR]). In both periods, the patients attended their regular appointments. The statistical analysis was carried out with nonparametric tests (the Wilcoxon and Friedman tests, P<0.017).

RESULTS

At the end of the assessment phase, mean HbA1c levels were significantly reduced (P=0.012) with no significant reductions in the LBGI (P=0.115), ADRR (P=0.552), or SD (P=0.700). No significant increases were observed in MBG (P=0.861) or the HBGI (P=0.807). HbA1c and the LBGI, ADRR, and SD indexes increased when telemedical assistance was suspended, whereas MBG and the HBGI showed a nonsignificant reduction.

CONCLUSIONS

Telemedical assessment for 3 months in children improved metabolic control, by reducing HbAlc values and, to a lesser extent, by decreasing GV, without increasing acute complications. Metabolic control was reduced when the advice was suspended.

Glycemic variability in relation to oral disposition index in the subjects with different stages of glucose tolerance

BACKGROUND

Glucose variability could be an independent risk factor for diabetes complications in addition to average glucose. The deficiency in islet β cell secretion and insulin sensitivity, the two important pathophysiological mechanisms of diabetes, are responsible for glycemic disorders. The oral disposition index evaluated by product of insulin secretion and sensitivity is a useful marker of islet β cell function. The aim of the study is to investigate glycemic variability in relation to oral disposition index in the subjects across a range of glucose tolerance from the normal to overt type 2 diabetes.

METHODS

75-g oral glucose tolerance test (OGTT) was performed in total 220 subjects: 47 with normal glucose regulation (NGR), 52 with impaired glucose metabolism (IGM, 8 with isolated impaired fasting glucose [IFG], 18 with isolated impaired glucose tolerance [IGT] and 26 with combined IFG and IGT), 61 screen-diagnosed diabetes by isolated 2-h glucose (DM2h) and 60 newly diagnosed diabetes by both fasting and 2-h glucose (DM). Insulin sensitivity index (Matsuda index, ISI), insulin secretion index (ΔI30/ΔG30), and integrated β cell function measured by the oral disposition index (ΔI30/ΔG30 multiplied by the ISI) were derived from OGTT. All subjects were monitored using the continuous glucose monitoring system for consecutive 72 hours. The multiple parameters of glycemic variability included the standard deviation of blood glucose (SD), mean of blood glucose (MBG), high blood glucose index (HBGI), continuous overlapping net glycemic action calculated every 1 h (CONGA1), mean of daily differences (MODD) and mean amplitude of glycemic excursions (MAGE).

RESULTS

From the NGR to IGM to DM2h to DM group, the respective values of SD (mean ± SD) (0.9 ± 0.3, 1.5 ± 0.5, 1.9 ± 0.6 and 2.2 ± 0.6 mmol/), MBG (5.9 ± 0.5, 6.7 ± 0.7, 7.7 ± 1.0 and 8.7 ± 1.5 mmol/L), HGBI [median(Q1-Q3)][0.8(0.2-1.2), 2.0(1.2-3.7), 3.8(2.4-5.6) and 6.4(3.2-9.5)], CONGA1 (1.0 ± 0.2, 1.3 ± 0.2, 1.5 ± 0.3 and 1.8 ± 0.4 mmol/L), MODD (0.9 ± 0.3, 1.4 ± 0.4, 1.8 ± 0.7 and 2.1 ± 0.7 mmol/L) and MAGE (2.1 ± 0.6, 3.3 ± 1.0, 4.3 ± 1.4 and 4.8 ± 1.6 mmol/L) were all increased progressively (all p < 0.05), while their oral disposition indices [745(546-947), 362(271-475), 203(134-274) and 91(70-139)] were decreased progressively (p < 0.05). In addition, SD, MBG, HGBI, CONGA1, MODD and MAGE were all negatively associated with the oral disposition index in each group (all p < 0.05) and in the entire data set (r = -0.66, -0.66, -0.72, -0.59, -0.61 and -0.65, respectively, p < 0.05).

CONCLUSIONS

Increased glycemic variability parameters are consistently associated with decreased oral disposition index in subjects across the range of glucose tolerance from the NGR to IGM to DM2h to DM group.

The effect of bolus and food calculator Diabetics on glucose variability in children with type 1 diabetes treated with insulin pump: the results of RCT

BACKGROUND

The calculation of prandial insulin dose is a complex process in which many factors should be considered. High glucose variability during the day, arising from difficulties which include errors made in food counting and inappropriate insulin adjustments, influence hemoglobin A1c levels. During this study, in children using insulin pumps to manage type 1 diabetes, we compared 2-h postprandial blood glucose levels (BGL) and glucose variability when calorie tables and mental calculation were used, to when Diabetics software was used.

METHODS

This 3-month, randomized, open-label study involved 48 children aged 1-18 yr. Patients were educated in food counting system used in the Warsaw Pump Therapy School (WPTS) where the carbohydrate unit (CU) and the fat-protein unit (FPU) are taken into account. The children were randomly allocated to an experimental group (A) who used diabetics software and a control group (B) who used caloric tables and mental calculations.

RESULTS

We observed significant differences (p < 0.05) between the groups in 2-h postprandial BGL's and the glucose variability parameters mean(T), SD(T), % BGL in the target range 70-180 mg/dL, and high blood glucose index HBGI. We did not observe statistically significant differences in hypoglycemic events or low blood glucose index (LBGI) nor in HbA1c or insulin requirements.

CONCLUSIONS

The use of the Diabetics software by patients educated at the WPTS is safe and reduces 2-h postprandial BGL's and glucose variability.

Diabetes technology: markers, monitoring, assessment, and control of blood glucose fluctuations in diabetes

People with diabetes face a life-long optimization problem: to maintain strict glycemic control without increasing their risk for hypoglycemia. Since the discovery of insulin in 1921, the external regulation of diabetes by engineering means has became a hallmark of this optimization. Diabetes technology has progressed remarkably over the past 50 years-a progress that includes the development of markers for diabetes control, sophisticated monitoring techniques, mathematical models, assessment procedures, and control algorithms. Continuous glucose monitoring (CGM) was introduced in 1999 and has evolved from means for retroactive review of blood glucose profiles to versatile reliable devices, which monitor the course of glucose fluctuations in real time and provide interactive feedback to the patient. Technology integrating CGM with insulin pumps is now available, opening the field for automated closed-loop control, known as the artificial pancreas. Following a number of in-clinic trials, the quest for a wearable ambulatory artificial pancreas is under way, with a first prototype tested in outpatient setting during the past year. This paper discusses key milestones of diabetes technology development, focusing on the progress in the past 10 years and on the artificial pancreas-still not a cure, but arguably the most promising treatment of diabetes to date.

Impact of glycemic variability on cardiovascular outcomes beyond glycated hemoglobin. Evidence and clinical perspectives

AIMS

The aim of this review is to focus on intra-day glucose variability (GV), specifically reviewing its correlation with HbA1c, the methods currently available to measure it, and finally the relationship between GV and cardiovascular outcomes, in type 1 and type 2 diabetic patients, and in the non-diabetic population.

DATA SYNTHESIS

The term GV has been used in the literature to express many different concepts; in the present review, we focus our attention on intra-day GV. In particular, we try to assess whether GV provides additional information on glycemic control beyond HbA1c, since GV seems to be incompletely expressed by HbA1c, particularly in patients with good metabolic control. Many different indexes have been proposed to measure GV, however at the moment no "gold standard" procedure is available. Evidence in vitro, in experimental settings and in animal studies, shows that fluctuating glucose levels display a more deleterious effect than constantly high glucose exposure. However, these findings are not completely reproducible in human settings. Moreover, the relationship between GV and cardiovascular events is still controversial.

CONCLUSIONS

The term GV should be reserved to indicate intra-day variability and different indexes of GV should be used, depending on the metabolic profile of the population studied and the specific issue to be investigated. Self glucose monitoring or continuous glucose monitoring should be used for assessing glucose variability.

A study on the association of serum 1,5-anhydroglucitol levels and the hyperglycaemic excursions as measured by continuous glucose monitoring system among people with type 2 diabetes in China

BACKGROUND

Blood glucose excursion is an important component of the glycaemic burden, but there are no indexes that can directly reflect them. The aim was to evaluate the values and significance of serum 1,5-anhydroglucitol (1,5-AG) in people with type 2 diabetes mellitus in China and to elucidate the relationship between 1,5-AG and traditional indexes of glycaemic excursions by continuous glucose monitoring.

METHODS

A total of 576 healthy adults and 292 patients were included, and their 1,5-AG, fasting blood glucose and postprandial blood glucose and glycated haemoglobin were measured. For the 34 patients, their mean blood glucose, standard deviation of blood glucose, mean amplitude of glucose excursion, mean of daily differences, low blood glucose M-value index and the area under the curve for blood glucose above 180 mg/dL were calculated by use of a continuous glucose monitoring system.

RESULTS

Serum levels of 1,5-AG among healthy adults were 28.44 ± 8.76 µg/mL with a significant gender bias rather than age bias. The 1,5-AG levels in people with type 2 diabetes mellitus were 4.57 ± 3.71 µg/mL, which were lower than those seen in the healthy adults. There was a correlation between 1,5-AG and glycated haemoglobin, fasting blood glucose, and postprandial blood glucose (r = -0.251, -0.195 and -0.349, respectively; all had p < 0.05). The continuous glucose monitoring system demonstrated that 1,5-AG presents a negative correlation with mean blood glucose, standard deviation of blood glucose, mean amplitude of glucose excursion and mean of daily differences for 7 days and with the area under the curve for blood glucose above 180 mg/dL on the third, fourth and seventh days.

CONCLUSIONS

1,5-AG may serve as a marker of hyperglycaemia and 7-day hyperglycaemic excursions as well as being a useful adjunct to glycated haemoglobin for blood glucose monitoring in patients with diabetes.

Glycemic Profiles of Healthy Individuals with Low Fasting Plasma Glucose and HbA1c

Scant data exists on glucose profile variability in healthy individuals. Twenty-nine healthy subjects without diabetes (86% male; mean age, 38 years) were measured by a CGM system and under real-life conditions. The median percentage of time spent on the blood glucose >7.8 mmol/L for 24 hrs was greater than 10% in both NFG and IFG groups. When subjects were divided into either NFG group (i.e., FPG levels of <5.6 mmol/L; n = 22) or IFG group (FPG levels of 5.6-6.9 mmol/L; n = 7), all CGM indicators investigated but GRADE scores, including glucose variability measures, monitoring excursions, hyperglycemia, hypoglycemia, and 24-hour AUC, did not differ significantly between the two groups. GRADE score and its euglycemia% were significantly different between the two groups. Among various CGM indicators, GRADE score may be a sensitive indicator to discriminate glucose profiles between subjects with NFG and those with IFG.

Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

BACKGROUND

Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability.

METHODS

Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG).

RESULTS

Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2.

CONCLUSIONS

We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

A dynamic risk measure from continuous glucose monitoring data

BACKGROUND

The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics.

METHODS

A new dynamic risk (DR) is proposed to explicitly consider the rate of change of glucose as a threat factor for the patient (e.g., risk levels in hypoglycemia and hyperglycemia are amplified in the presence of a decreasing and increasing glucose trend, respectively). The practical calculation of DR is made possible by the use of a regularized deconvolution algorithm that is able to deal with noise in CGM data and with the ill-conditioning of the time-derivative calculation, even in online applications.

RESULTS

Results on simulated and real data show that DR can be effectively computed and fruitfully used in real time (e.g., to generate early warnings of hypo-/hyperglycemic threshold crossings). Further applications of DR in the quantification of the efficiency of glucose control are also suggested.

CONCLUSIONS

Exploiting the information on glucose trends empowers the strength of risk measures in interpreting CGM time-series.

Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

BACKGROUND

Glycemic variability has been proposed as a contributing factor in the development of diabetes complications. Multiple measures exist to calculate the magnitude of glycemic variability, but normative ranges for subjects without diabetes have not been described. For treatment targets and clinical research we present normative ranges for published measures of glycemic variability.

METHODS

Seventy-eight subjects without diabetes having a fasting plasma glucose of <120 mg/dL (6.7 mmol/L) underwent up to 72 h of continuous glucose monitoring (CGM) with a Medtronic Minimed (Northridge, CA) CGMS(®) Gold device. Glycemic variability was calculated using EasyGV(©) software (available free for non-commercial use at www.easygv.co.uk ), a custom program that calculates the SD, M-value, mean amplitude of glycemic excursions (MAGE), average daily risk ratio (ADRR), Lability Index (LI), J-Index, Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), continuous overlapping net glycemic action (CONGA), mean of daily differences (MODD), Glycemic Risk Assessment in Diabetes Equation (GRADE), and mean absolute glucose (MAG).

RESULTS

Eight CGM traces were excluded because there were inadequate data. From the remaining 70 traces, normative reference ranges (mean±2 SD) for glycemic variability were calculated: SD, 0-3.0; CONGA, 3.6-5.5; LI, 0.0-4.7; J-Index, 4.7-23.6; LBGI, 0.0-6.9; HBGI, 0.0-7.7; GRADE, 0.0-4.7; MODD, 0.0-3.5; MAGE-CGM, 0.0-2.8; ADDR, 0.0-8.7; M-value, 0.0-12.5; and MAG, 0.5-2.2.

CONCLUSIONS

We present normative ranges for measures of glycemic variability in adult subjects without diabetes for use in clinical care and academic research.

Treatment and risk factor analysis of hypoglycemia in diabetic rhesus monkeys

In order to anticipate and promptly treat hypoglycemia in diabetic monkeys treated with insulin or other glucose-lowering drugs, the relationships between the incidence and symptoms of hypoglycemia in these animals, and many factors involved in model development and sustainment were analyzed. Different procedures were performed on 22 monkeys for the induction of diabetes. The monkey models were evaluated by blood glucose, insulin, C-peptide levels and intravenous glucose tolerance tests. A glucose treatment program for the diabetic monkeys was administered and laboratory tests were regularly performed. A standard procedure of hypoglycemia treatment was established and the risk factors of hypoglycemia were analyzed by a logistic regression model. Furthermore, the relationships between the four methods of diabetes induction, renal function, glycemic control and hypoglycemia were studied using one-way analysis of variance and t-test. We found that the hypoglycemic conditions of diabetic monkeys were improved rapidly by our treatment. The statistical analysis suggested that the modeling methods, renal function and glycemic control were related to the incidence of hypoglycemia. In detail, the progress of diabetes, effects of glycemic control and, particularly, the severity of the hypoglycemia differed according to the induction strategy used. The models induced by partial pancreatectomy with low-dose streptozotocin were not prone to hypoglycemia and their glycemic controls were stable. However, the models induced by total pancreatectomy were more vulnerable to severe hypoglycemia and their glycemic controls were the most unstable. Moreover, the levels of blood creatinine and triglyceride increased after the development of diabetes, which was related to the occurrence of hypoglycemia. In conclusion, we suggested that total pancreatectomy and renal impairment are two important risk factors for hypoglycemia in diabetic monkeys. More attention should be paid to daily care of diabetic monkeys, particularly monitoring and protecting their renal function.

Psychometric properties of the hypoglycemia fear survey-ii for adults with type 1 diabetes

OBJECTIVE

To perform the first comprehensive psychometric evaluation of the Hypoglycemia Fear Survey-II (HFS-II), a measure of the behavioral and affective dimensions of fear of hypoglycemia, using modern test-theory methods, including item-response theory (IRT).

RESEARCH DESIGN AND METHODS

Surveys completed in four previous studies by 777 adults with type 1 diabetes were aggregated for analysis, with 289 subjects completing both subscales of the HFS-II and 488 subjects completing only the Worry subscale. The aggregated sample (53.3% female, 44.4% using insulin pumps) had a mean age of 41.9 years, diabetes duration of 23.8 years, HbA(1c) value of 7.7%, and 1.4 severe hypoglycemic episodes in the past year. Data analysis included exploratory factor analysis using polychoric correlations and IRT. Factors were analyzed for fit, trait-level locations, point-measure correlations, and separation values.

RESULTS

Internal and test-retest reliability was good, as well as convergent validity, as demonstrated by significant correlations with other measures of psychological distress. Scores were significantly higher in subjects who had experienced severe hypoglycemia in the past year. Factor analyses validated the two subscales of the HFS-II. Item analyses showed that 12 of 15 items on the Behavior subscale, and all of the items on the Worry subscale had good-fit statistics.

CONCLUSIONS

The HFS-II is a reliable and valid measure of the fear of hypoglycemia in adults with type 1 diabetes, and factor analyses and IRT support the two separate subscales of the survey.

Effect of automated bio-behavioral feedback on the control of type 1 diabetes

OBJECTIVE

To test the effect of an automated system providing real-time estimates of HbA(1c), glucose variability, and risk for hypoglycemia.

RESEARCH DESIGN AND METHODS

For 1 year, 120 adults with type 1 diabetes (69 female/51 male, age = 39.1 [14.3] years, duration of diabetes 20.3 [12.9] years, HbA(1c) = 8.0 [1.5]), performed self-monitoring of blood glucose (SMBG) and received feedback at three increasingly complex levels, each continuing for 3 months: level 1--routine SMBG; level 2--adding estimated HbA(1c), hypoglycemia risk, and glucose variability; and level 3--adding estimates of symptoms potentially related to hypoglycemia. The subjects were randomized to feedback sequences of either levels 1-2-3 or levels 2-3-1. HbA(1c), symptomatic hypoglycemia, and blood glucose awareness were evaluated at baseline and at the end of each level.

RESULTS

For all subjects, HbA(1c) was reduced from 8.0 to 7.6 from baseline to the end of study (P = 0.001). This effect was confined to subjects with baseline HbA(1c) >8.0 (from 9.3 to 8.5, P < 0.001). Incidence of symptomatic moderate/severe hypoglycemia was reduced from 5.72 to 3.74 episodes/person/month (P = 0.019), more prominently for subjects with a history of severe hypoglycemia (from 7.20 to 4.00 episodes, P = 0.008) and for those who were hypoglycemia unaware (from 6.44 to 3.71 episodes, P = 0.045). The subjects' ratings of the feedback were positive, with up to 89% approval of the provided features.

CONCLUSIONS

Feedback of SMBG data and summary SMBG-based measures resulted in improvement in average glycemic control and reduction in moderate/severe hypoglycemia. These effects were most prominent in subjects who were at highest risk at the baseline.

Hypoglycemia risk and glucose variability indices derived from routine self-monitoring of blood glucose are related to laboratory measures of insulin sensitivity and epinephrine counterregulation

BACKGROUND

the widely held assumptions that in type 1 diabetes glucose variability may correlate with insulin sensitivity and impaired epinephrine counterregulation have not been studied directly. Here we investigate possible relationships between outpatient measures of glucose variability and risk for hypoglycemia with physiological characteristics: insulin sensitivity and hypoglycemia counterregulation.

METHODS

thirty-four subjects with type 1 diabetes (14 women, 20 men; 37 ± 2.1 years old; glycosylated hemoglobin [HbA1c], 7.6  ±  0.21%) performed self-monitoring of blood glucose (SMBG) for a month, followed by an inpatient hyperinsulinemic euglycemic and hypoglycemic clamp. SMBG field data were used to calculate measures of glucose variability and risk of hypoglycemia, while the clamp procedure was used to evaluate insulin sensitivity and epinephrine response during induced hypoglycemia. Spearman partial correlations adjusted for age, duration of diabetes, body mass index, gender, and HbA1c were used to assess the relationship between the field indices of glucose variability and the physiological characteristics of diabetes.

RESULTS

two glucose variability measures correlated positively (P < 0.01) with insulin sensitivity: the Average Daily Risk Range (ADRR) (ρ = 0.5) and the Glycemic Lability Index (ρ = 0.48). The Low Blood Glucose Index, a measure of the risk for hypoglycemia, and the ADRR correlated negatively with maximum epinephrine response during hypoglycemia: ρ = -0.46, P < 0.01 and ρ = -0.4, P = 0.03, respectively.

CONCLUSIONS

higher insulin sensitivity and lower epinephrine response during hypoglycemia are related to increased glucose variability (as quantified by the ADRR), irrespective of HbA1c and other patient characteristics. Lower epinephrine relates to risk for hypoglycemia as well.

Fetal risk assessment in pregnancies complicated by diabetes mellitus

BACKGROUND

Hypoglycemia and hyperglycemia can pose a number of serious risks to pregnant mothers with diabetes, but these risks are not always related to glucose concentrations directly. Previous studies have shown the utility of using mathematical transformation functions to create patient risk profiles that can then be used to analyze and predict adverse outcomes in individuals with diabetes. We propose a novel use of these functions to analyze the risks posed to the fetus in pregnancies complicated by diabetes.

METHODS

We retrospectively analyzed 71 h continuous glucose monitoring system (CGMS Gold, Medtronic Northridge, CA) third trimester tracings obtained during a normal pregnancy and in those complicated by gestational diabetes mellitus (GDM), type 2 diabetes mellitus (T2DM), and type 1 diabetes mellitus (T1DM). We then used a transformation function to calculate fetal and maternal risk in each case.

RESULTS

In the normal pregnancy (0.93), the risk was at a minimum. Along with mean glucose values, the risk increased in those cases where gestation was complicated by GDM (3.12), T2DM (7.85), and T1DM (16.94). In contrast, the original patient risk profile yielded a minimal value for the GDM tracings.

CONCLUSIONS

Total fetal risk increases from normal to GDM to T2DM to T1DM pregnancies. This new risk assignment better distinguishes the stages of fetal risk than the original method and therefore may be useful in future clinical trials and applications to predict risk for adverse outcomes in pregnancies complicated by diabetes.

Driving mishaps among individuals with type 1 diabetes: a prospective study

OBJECTIVE

Hypoglycemia-related neuroglycopenia disrupts cognitive-motor functioning, which can impact driving safety. Retrospective studies suggest that drivers with type 1 diabetes experience more collisions and citations than their nondiabetic spouses. We present the first prospective data documenting the occurrence of apparent neuroglycopenia-related driving performance impairments.

RESEARCH DESIGN AND METHODS

We completed the initial screening of 452 drivers from three geographically diverse centers who then reported monthly occurrences of driving "mishaps," including collisions, citations, losing control, automatic driving, someone else taking over driving, and moderate or severe hypoglycemia while driving.

RESULTS

Over 12 months, 52% of the drivers reported at least one hypoglycemia-related driving mishap and 5% reported six or more. These mishaps were related to mileage driven, history of severe hypoglycemia, and use of insulin pump therapy.

CONCLUSIONS

Many individuals with type 1 diabetes report hypoglycemia-related driving events. Clinicians should explore the recent experiences with hypoglycemia while driving and the risk of future events.

The assessment of glycemic variability and its impact on diabetes-related complications: an overview

There is a growing body of evidence that the sole use of hemoglobin A1c is insufficient to adequately reflect the metabolic situation of patients with diabetes mellitus. The risk of developing diabetes-related complications apparently not only depends on the long-term stability of glucose values, but also on the presence or occurrence of short-term glycemic peaks and nadirs lasting for minutes or hours during a day. This leads to the phenomenon of glycemic variability. This article reviews the existing evidence for the clinical relevance of short-term glucose variations and the currently available different means of measuring glycemic variability.

Statistical tools to analyze continuous glucose monitor data

Continuous glucose monitors (CGMs) generate data streams that are both complex and voluminous. The analyses of these data require an understanding of the physical, biochemical, and mathematical properties involved in this technology. This article describes several methods that are pertinent to the analysis of CGM data, taking into account the specifics of the continuous monitoring data streams. These methods include: (1) evaluating the numerical and clinical accuracy of CGM. We distinguish two types of accuracy metrics-numerical and clinical-each having two subtypes measuring point and trend accuracy. The addition of trend accuracy, e.g., the ability of CGM to reflect the rate and direction of blood glucose (BG) change, is unique to CGM as these new devices are capable of capturing BG not only episodically, but also as a process in time. (2) Statistical approaches for interpreting CGM data. The importance of recognizing that the basic unit for most analyses is the glucose trace of an individual, i.e., a time-stamped series of glycemic data for each person, is stressed. We discuss the use of risk assessment, as well as graphical representation of the data of a person via glucose and risk traces and Poincaré plots, and at a group level via Control Variability-Grid Analysis. In summary, a review of methods specific to the analysis of CGM data series is presented, together with some new techniques. These methods should facilitate the extraction of information from, and the interpretation of, complex and voluminous CGM time series.

Reduced daily risk of glycemic variability: comparison of exenatide with insulin glargine

BACKGROUND

Conventional methods describing daily glycemic variability (i.e., standard deviation and coefficient of variation) do not express risk. Low and High Blood Glucose Indices (LBGI and HBGI, respectively) and Average Daily Risk Range (ADRR) are parameters derived from self-monitored blood glucose (SMBG) data that quantify risk of glycemic excursions and temporal aspects of variability. In the present study, variability parameters were used to assess effects of exenatide and insulin glargine on risk of acute blood glucose extremes.

METHODS

New (LBGI, HBGI, and ADRR) and conventional variability analyses were applied retrospectively to SMBG data from patients with type 2 diabetes suboptimally controlled with metformin and a sulfonylurea plus exenatide or insulin glargine as a next therapeutic step. Exenatide- (n = 282) and insulin glargine-treated (n = 267) patients were well matched.

RESULTS

Exenatide treatment reduced ADRR overall (exenatide, mean +/- SEM, 16.33 +/- 0.45; insulin glargine, 18.54 +/- 0.49; P = 0.001). Seventy-seven percent of exenatide-treated patients were at low risk for glucose variability compared with 62% of glargine-treated patients (P = 0.00023). LBGI for exenatide remained minimal for all categories and significantly lower than glargine for all comparisons, and HBGI for exenatide remained low or moderate for all categories and significantly lower than glargine after the morning and evening meals. Reduced variability in exenatide-treated patients was shown by conventional methods but provided no indications of risk.

CONCLUSIONS

Average glycemic control was similar for both treatment groups. However, exenatide treatment minimized risk for glycemic variability and extremes to a greater degree than insulin glargine treatment.

Diabetes: Models, Signals, and Control

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

The median is not the only message: a clinician's perspective on mathematical analysis of glycemic variability and modeling in diabetes mellitus

Hemoglobin A1c (HbA1c), a long-term, integrated average of tissue exposure to hyperglycemia, is the best reflection of average glucose concentrations and the best proven predictor of microvascular complications of diabetes mellitus. However, HbA1c fails to capture glycemic variability and the risks associated with extremes of hypoglycemia and hyperglycemia. These risks are the primary barrier to achieving the level of average glucose control that will minimize both the microvascular and the long-term macrovascular complications of type 1 diabetes. High blood glucose levels largely due to prandial excursions produce oxidative and inflammatory stress with potential acceleration of preexisting atherosclerosis and increased cardiovascular risk. Moreover, some temporal aspects of glycemic variation, including the rates of rise and fall of glucose, are associated with adverse cognitive and mood symptoms in those with diabetes. Methods to quantify the risk of glycemic extremes, both high and low, and the variability including its temporal aspects are now more precise than ever. These important endpoints should be included for use in clinical trials as useful metrics and recognized by regulatory agencies, which has not been the case in the past. Precise evaluation of glycemic variability and its attendant risks are essential in the design of optimal therapies; for these reasons, inclusion of these metrics and the pulsatile hormone patterns in mathematical models may be essential. For the clinician, the incursion of mathematical models that simulate normal and pathophysiological mechanisms of glycemic control is a reality and should be also gradually incorporated into clinical practice.

Pramlintide reduces the risks associated with glucose variability in type 1 diabetes

BACKGROUND

This study was designed to determine whether pramlintide added to insulin therapy reduced the risks associated with extreme blood glucose (BG) fluctuations in patients with type 1 diabetes.

METHODS

Self-monitored BG (SMBG) records were retrospectively analyzed from a randomized, double-blind, placebo-controlled study of the effects of pramlintide on intensively treated patients with type 1 diabetes. Two groups--pramlintide (n=119), 30/60 microg administered subcutaneously at each meal, or placebo (n=129)--were matched by age, gender, and baseline hemoglobin A1C. Using SMBG, daily BG profiles, BG rate of change, and low and high BG indices (LBGI and HBGI, respectively) measuring the risk for hypoglycemia and hyperglycemia were calculated.

RESULTS

Compared with placebo, pramlintide significantly attenuated the pre- to postprandial BG rate of change (F=83.8, P<0.0001). Consequently, in pramlintide-treated patients, the average post-meal BG (8.4 vs. 9.7 mmol/L [151.2 vs. 174.6 mg/dL]) and postprandial HBGI were significantly lower than placebo (both P<0.0001). Substantial daily BG variation was observed in placebo-treated patients, with most significant hyperglycemia occurring after breakfast and during the night; post-meal BG did not vary significantly throughout the day in pramlintide-treated patients. The reduction in postprandial hyperglycemia in pramlintide-treated patients occurred without increased risk for preprandial hypoglycemia as quantified by the LBGI.

CONCLUSIONS

Risk analysis of the effect of pramlintide treatment demonstrated risk-reduction effects independent of changes in average glycemia, most notably reduced rate and magnitude of postprandial BG fluctuations. These effects were not accompanied by an increased risk of hypoglycemia.

Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis

BACKGROUND

Advancements in subcutaneous continuous glucose monitoring and subcutaneous insulin delivery are stimulating the development of a minimally invasive artificial pancreas that facilitates optimal glycemic regulation in diabetes. The key component of such a system is the blood glucose controller for which different design strategies have been investigated in the literature. In order to evaluate and compare the efficacy of the various algorithms, several performance indices have been proposed.

METHODS

A new tool-control-variability grid analysis (CVGA)-for measuring the quality of closed-loop glucose control on a group of subjects is introduced. It is a method for visualization of the extreme glucose excursions caused by a control algorithm in a group of subjects, with each subject presented by one data point for any given observation period. A numeric assessment of the overall level of glucose regulation in the population is given by the summary outcome of the CVGA.

RESULTS

It has been shown that CVGA has multiple uses: comparison of different patients over a given time period, of the same patient over different time periods, of different control laws, and of different tuning of the same controller on the same population.

CONCLUSIONS

Control-variability grid analysis provides a summary of the quality of glycemic regulation for a population of subjects and is complementary to measures such as area under the curve or low/high blood glucose indices, which characterize a single glucose trajectory for a single subject.

Relating mean blood glucose and glucose variability to the risk of multiple episodes of hypoglycaemia in type 1 diabetes

AIMS/HYPOTHESIS

The main disadvantage of intensive treatment in the Diabetes Control and Complications Trial (DCCT) was an increased risk of hypoglycaemia that was not explained by the difference in HbA(1c) values alone. This study re-analysed DCCT data to establish whether mean blood glucose (MBG) and/or glucose variability add to the predictive value of HbA(1c) for hypoglycaemia risk in type 1 diabetes.

METHODS

The times to first and subsequent severe hypoglycaemic events were compared with MBG, HbA(1c) and within-day SD of blood glucose using Cox regression after adjusting for other known risk factors for hypoglycaemia.

RESULTS

On its own, the incidence of time to first hypoglycaemic event increased 1.05-fold for each 1 mmol/l decrease in MBG and 1.07-fold for every 1 mmol/l increase in glucose SD. MBG and SD of blood glucose also both added to the ability of HbA(1c) to predict repeated hypoglycaemic events: after adjusting for HbA(1c), a 1 mmol/l increase in SD was associated with a 1.09-fold increased risk of a first event, increasing to a 1.12-fold risk of a fifth event. A 1 mmol/l fall in MBG added a constant 1.02-1.03-fold risk of repeated events. Daytime events were predicted more accurately than nocturnal episodes.

CONCLUSIONS/INTERPRETATION

This study has established that HbA(1c), MBG and glucose variability measurements each have an independent role in determining an individual's risk of hypoglycaemia in type 1 diabetes. All three aspects of glycaemic assessment should thus be considered in patients in whom hypoglycaemia is a real or potential problem.

Prediction of severe hypoglycemia

OBJECTIVE

Prevention of severe hypoglycemia (SH) is premised partially on the ability to accurately anticipate its occurrence. This study prospectively tests methods for predicting SH using blood glucose meter readings.

RESEARCH DESIGN AND METHODS

One hundred adults with type 1 diabetes were followed for 6 months, and 79 insulin-using adults with type 2 diabetes were followed for 4 months. During this time, subjects' routine self-monitored blood glucose (SMBG) readings were stored on and retrieved from memory meters, and participants were queried biweekly about occurrence of SH. Respective demographics for the two groups were age 40.7 and 50.2 years, duration of diabetes 20.0 and 12.2 years, A1C 7.6 and 8.8%, and male sex 43 and 39%, respectively.

RESULTS

Relative risk for SH, quantified by the ratio of an individual's low blood glucose index (LBGI) based on the previous 150 SMBG readings to the LBGI based on recent SMBG readings, increased significantly in the 24 h before SH episodes in individuals with type 1 and type 2 diabetes (t = 10.3, P < 0.0001, and t = 4.2, P < 0.001, respectively). A sliding algorithm detected 58% of imminent (within 24 h) SH episodes in the type 1 diabetic group and 60% of those in the type 2 diabetic group when three SMBG readings were available in the 24 h before an episode. Detection increased to 63 and 75%, respectively, if five SMBG readings were available in the 24 h before an episode.

CONCLUSIONS

SH often follows a specific blood glucose fluctuation pattern that is identifiable from SMBG. Thus, partial prediction of imminent SH is possible, providing a potential tool to trigger self-regulatory prevention of significant hypoglycemia.

A novel analytical method for assessing glucose variability: using CGMS in type 1 diabetes mellitus

BACKGROUND

Marked blood glucose (BG) fluctuations may increase the risk of some complications associated with diabetes. Acute BG excursions are common in patients with diabetes, but are not usually quantified, nor can they be captured by glycosylated hemoglobin level. This study evaluated the sensitivity of novel analytical methods for assessing BG variability using CGMS (Medtronic Minimed, Northridge, CA) data from patients treated with pramlintide, a drug that acutely reduces postprandial hyperglycemia when added to insulin therapy.

METHODS

Retrospective analyses were done on 24-h CGMS profiles obtained from 22 evaluable subjects with type 1 diabetes using insulin pumps and receiving preprandial three times daily injections of placebo (n = 6) or 30 microg of pramlintide (n = 16) for 4 weeks. CGMS data were recorded at baseline, after 4 weeks of treatment, and after 2 weeks off-treatment. Three parameters were calculated for each time period: variability (BG rate of change), an index for severe hypoglycemia [low BG index (LBGI)], and an index for marked hyperglycemia [high BG index (HBGI)].

RESULTS

The mean postprandial BG rate of change was significantly lower after 4 weeks of pramlintide treatment compared with placebo treatment (0.87 vs. 1.21 mg/dL/min, P < 0.01) without changes in average glycemia, illustrating the sensitivity of this parameter to medication effects. The HBGI and LBGI indicated a decreased risk of hyperglycemia without a significant increase in risk of hypoglycemia after 4 weeks of pramlintide.

CONCLUSIONS

These results suggest the potential utility of several novel methods for assessing variability and glycemic extremes to gauge the effects of pharmacological interventions not captured by glycosylated hemoglobin.

Is glycemic variability important to assessing antidiabetes therapies?

Traditionally, statistical estimation of glycemic variability includes computing standard deviation of glucose readings or related statistics (eg, M value, mean amplitude of glucose excursions, and so forth). We advocate an alternative approach using risk measures of variability, which have substantial clinical and numerical advantages. In addition, continuous glucose monitoring (CGM) data have clinically important inherent temporal structure that should be taken into consideration. Thus, temporal variability methods are discussed for the analysis and interpretation of CGM output.

Evaluation of a new measure of blood glucose variability in diabetes

OBJECTIVE

Recent studies show the importance of controlling blood glucose variability in relationship to both reducing hypoglycemia and attenuating the risk for cardiovascular and behavioral complications due to hyperglycemia. It is therefore important to design variability measures that are equally predictive of low and high blood glucose excursions.

RESEARCH DESIGN AND METHODS

We introduce the average daily risk range (ADRR), a variability measure computed from routine self-monitored blood glucose (SMBG) data. The ADRR was constructed using a development dataset for 39 and 31 adults with type 1 and type 2 diabetes, respectively. The formula was then fixed, and the ADRR was compared against other variability measures using an independent validation dataset containing approximately 4 months of SMBG for 254 and 81 adults with type 1 and type 2 diabetes.

RESULTS

From the 1st month of validation SMBG data, we computed the ADRR, blood glucose SD and coefficient of variation, daily blood glucose range and interquartile range, mean amplitude of glycemic excursion, M-value, and lability index. Then all measures were tested as predictors of low blood glucose (<2.2 mmol/l; <3.9 mmol/l) and high (>10 mmol/l; >22.2 mmol/l) events in the subsequent 3 months. The ADRR was the best predictor of both hypoglycemia and hyperglycemia, with a 6-fold increase in the likelihood of hypoglycemia and 3.5-fold increase in the likelihood of hyperglycemia across its risk categories.

CONCLUSIONS

In a large SMBG database, the ADRR showed strong association with subsequent out-of-control glucose readings. Compared with other variability measures, the ADRR demonstrated a superior balance of sensitivity to predicting both hypoglycemia and hyperglycemia. This prediction was independent from type of diabetes.

A stochastic model to assess the variability of blood glucose time series in diabetic patients self-monitoring

Several studies have shown that patients suffering from Diabetes Mellitus can significantly delay the onset and slow down the progression of diabetes micro- and macro-angiopathic complications through intensive monitoring and treatment. In general, intensive treatments imply a careful blood glucose level (BGL) self-monitoring. The analysis of BGL measurements is one of the most important tasks in order to assess the glucose metabolic control and to revise the therapeutic protocol. Recent clinical studies have shown the correlation between the glucose variability and the long-term diabetes related complications. In this paper, we propose a stochastic model to extract the time course of such variability from the self-monitoring BGL time series. This information can be conveniently combined with other analysis to evaluate the adequacy of the therapeutic protocol and to highlight periods characterized by an increasing glucose instability. The method here proposed has been validated on two simulated data sets and tested with success in the retrospective analysis of three patients' data sets.

Cost-utility analysis in a UK setting of self-monitoring of blood glucose in patients with type 2 diabetes

BACKGROUND

Self-monitoring of blood glucose (SMBG) in type 2 diabetes patients has been shown in meta-analyses of randomized trials to improve HbA(1c) by approximately 0.4% when compared to no SMBG. However, the cost of testing supplies is high, improvements in health utility due to improved glycaemic control may be possible and cost-effectiveness has not been evaluated.

METHODS

A peer-reviewed validated model projected improvements in lifetime quality-adjusted life years (QALYs), long-term costs and cost-effectiveness of SMBG versus no SMBG. Markov/Monte Carlo modelling simulated the progression of complications (cardiovascular, neuropathy, renal and eye disease). Transition probabilities and HbA(1c)-dependent adjustments came from the United Kingdom Prospective Diabetes Study (UKPDS) and other major studies. Effects of SMBG on HbA(1c) came from clinical studies, meta-analyses and population studies, but can only be considered 'moderate' levels of evidence. Costs of complications were retrieved from published sources. Direct costs of diabetes complications and SMBG were projected over patient lifetimes from a UK National Health Service perspective. Outcomes were discounted at 3.5% annually. Extensive sensitivity analyses were performed.

RESULTS

Depending on the type of diabetes treatment (diet and exercise/oral medications/insulin), improvements in glycaemic control with SMBG improved discounted QALYs anywhere from 0.165 to 0.255 years, with increased total costs of 1013 pounds sterlings- 2564 pounds sterlings/patient, giving incremental cost-effectiveness ratios of 4508 pounds sterlings: 15,515 pounds sterlings/QALY gained, well within current UK willingness-to-pay limits. Results were robust under a wide range of plausible assumptions.

CONCLUSIONS

Based on the moderate level of clinical evidence available to date, improvements in glycaemic control with interventions, including SMBG, can improve patient outcomes, with acceptable cost-effectiveness ratios in the UK setting.

How good is your glucose control?

BACKGROUND

Glycemic control is fundamental to the management of diabetes and maintenance of health. Popular measures of performance in glycemic control include A1c and self-monitoring of blood glucose (SMBG). As measures of performance, A1c has perspective, but it fails to recognize hypoglycemia, while SMBG lacking overall perspective finds use mainly by patients to simply evaluate their glycemic status and current response to therapy. An additional, preferably visual, measure of performance in diabetes management in general and glycemic control in particular is needed.

METHODS

To form a visual measure of performance, a graphical method of analysis from the statistician's toolbox (known as the lag plot) was adapted. It can utilize SMBG data sets from any source, including memory meters and registry databases in call centers. Data are retrieved, processed, formatted, and then plotted on a PC screen or printer. The resulting lag plots visually characterize the performance of glucose control achieved over periods (selectable by the user) from days to months. Supporting numerical statistics provide rigorous outcome measures that correlate with glycated hemoglobin.

RESULTS

Clinical use of the lag plot is illustrated in seven case studies spanning the range from no diabetes, through glucose intolerance, early-onset type 2 diabetes mellitus, type 1 diabetes, intensified therapy, pump therapy, and finally islet cell transplantation. Visual comparisons before and after action/referral show impacts of interventions, incidences of hypoglycemia, and changes in the polyglycemia of unstable diabetes. Statistical significance of observed changes are quantified.

CONCLUSIONS

The simple lag plot can empower patients and their providers to identify problems in glycemic control, seek proactive action, adopt beneficial strategies, evaluate outcomes, and, most importantly, rule out interventions with no benefit.

Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application

BACKGROUND

Continuous glucose monitors (CGMs) collect detailed blood glucose (BG) time series, which carry significant information about the dynamics of BG fluctuations. In contrast, the methods for analysis of CGM data remain those developed for infrequent BG self-monitoring. As a result, important information about the temporal structure of the data is lost during the translation of raw sensor readings into clinically interpretable statistics and images.

METHODS

The following mathematical methods are introduced into the field of CGM data interpretation: (1) analysis of BG rate of change; (2) risk analysis using previously reported Low/High BG Indices and Poincare (lag) plot of risk associated with temporal BG variability; and (3) spatial aggregation of the process of BG fluctuations and its Markov chain visualization. The clinical application of these methods is illustrated by analysis of data of a patient with Type 1 diabetes mellitus who underwent islet transplantation and with data from clinical trials.

RESULTS

Normative data [12,025 reference (YSI device, Yellow Springs Instruments, Yellow Springs, OH) BG determinations] in patients with Type 1 diabetes mellitus who underwent insulin and glucose challenges suggest that the 90%, 95%, and 99% confidence intervals of BG rate of change that could be maximally sustained over 15-30 min are [-2,2], [-3,3], and [-4,4] mg/dL/min, respectively. BG dynamics and risk parameters clearly differentiated the stages of transplantation and the effects of medication. Aspects of treatment were clearly visualized by graphs of BG rate of change and Low/High BG Indices, by a Poincare plot of risk for rapid BG fluctuations, and by a plot of the aggregated Markov process.

CONCLUSIONS

Advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of the data. The use of such methods improves the assessment of patients' glycemic control.

Can continuous glucose monitoring provide objective documentation of hypoglycemia unawareness?

OBJECTIVE

To establish criteria defining hypoglycemia as detected by the continuous glucose monitoring system (CGMS) in patients with type 1 diabetes that best predict hypoglycemia unawareness (HUN), established by a validated questionnaire.

METHODS

Adult patients were selected for inclusion in this study if they had long-standing type 1 diabetes, a fasting level of C peptide of < or = 0.6 ng/mL, commitment to achieving glycemic control, and a hemoglobin A1c value no higher than 9%. After clinical data and self-monitoring of plasma glucose data were collected, patients underwent a 72-hour glucose monitoring session with use of a Medtronic-MiniMed CGMS. The presence of HUN was determined by a questionnaire. Factors independently associated with HUN were estimated by multivariate independent analysis.

RESULTS

Our study group consisted of 60 patients (33 women and 27 men) who ranged in age from 18 to 84 years (mean, 50.4) and had had diabetes for 5 to 56 years (mean, 23.8). The best predictor of HUN was the maximal duration of hypoglycemia, as determined by the CGMS (P = 0.001). Detection of hypoglycemic episodes with a duration of more than 90 minutes identified patients who had HUN with an 88% specificity and 75% sensitivity. HUN was also significantly associated with use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (P = 0.003) and with a longer duration of diabetes (P = 0.008).

CONCLUSION

The CGMS can be used for objective detection of patients with HUN.

A novel approach to continuous glucose analysis utilizing glycemic variation

BACKGROUND

Various methodologies have been proposed for analysis of continuous glucose measurements. These methods have mainly focused on the proportion of low or high glucose readings and have not attempted to analyze other dimensions of the data obtained. This study proposes an algorithm for analysis of continuous glucose data including a novel method of assessing glycemic variability.

METHODS

Mean blood glucose and mean of daily differences (MODD) assessed the degree that the Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) trace was representative of the 3-month glycemic pattern. Percentages of times in low, normal, and high glucose ranges were used to assess marked glycemic excursion. Continuous overall net glycemic action (CONGA), a novel method developed by the authors, assessed intra-day glycemic variability. These methods were applied to 10 CGMS traces chosen randomly from those completed by children with type 1 diabetes from the Royal Children's Hospital, Melbourne, Victoria, Australia and 10 traces recorded by healthy volunteer controls.

RESULTS

The healthy controls had lower values for mean blood glucose, MODD, and CONGA. Patients with diabetes had higher percentages of time spent in high and low glucose ranges. There was no overlap between the CONGA values for patients with diabetes and for controls, and the difference between controls and patients with diabetes increased markedly as the CONGA time period increased.

CONCLUSIONS

We advocate an approach to the analysis of CGMS data based upon a hierarchy of relevant clinical questions alluding to the representative nature of the data, the amount of time spent in glycemic excursions, and the degree of glycemic variation. Integrated use of these algorithms distinguishes between various patterns of glycemic control in those with and without diabetes.

Hypoglycemia anticipation, awareness and treatment training (HAATT) reduces occurrence of severe hypoglycemia among adults with type 1 diabetes mellitus

Severe hypoglycemia (SH) can be a significant problem for patients around the world with Type 1 Diabetes Mellitus (T1DM). To avoid SH, patients need to better manage, and reduce the occurrence of, preceding mild hypoglycemia. Hypoglycemia Anticipation, Awareness and Treatment Training (HAATT), developed in the United States specifically to address such issues, was evaluated at short- and long-term follow-up in a medically, economically and culturally different setting; Bulgaria. Sixty adults with T1DM and a history of recurrent SH (20 each from Sofia, Russe, and Varna, Bulgaria) were randomized to Self-Monitoring of Blood Glucose (SMBG) or SMBG+ HAATT. For 6 months before and 1 to 6 and 13 to 18 months after intervention, participants recorded occurrence of moderate, severe, and nocturnal hypoglycemia. For 1-month pre- and post-intervention, participants completed daily diaries concerning their diabetes management. Relative to SMBG, HAATT produced significant improvement in occurrence of low BG, moderate, severe, and nocturnal hypoglycemia, and detection and treatment of low BG (p values < .05 to < .001), with no compromise in metabolic control. At long-term follow-up, HAATT participants continued to have significantly fewer episodes of moderate and severe hypoglycemia. These findings suggest that a structured, specialized psycho-educational treatment program (HAATT) can be highly effective in managing hypoglycemia.