Glycemic variability: measurement and utility in clinical medicine and research--one viewpoint

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial

The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L-1 (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391 .

Delayed Eating Schedule Raises Mean Glucose Levels in Young Adult Males: a Randomized Controlled Cross-Over Trial

BACKGROUND

Misalignment of meals to the biological clock may cause adverse effects on glucose metabolism. However, the effects of repeated different eating schedules (early compared with late) on glucose concentration throughout the day are poorly understood.

OBJECTIVES

We examined the effects of different eating schedules on the 24-h glucose response using a continuous glucose monitor (CGM).

METHODS

Eight young adult males (age, 20.9 ± 3.4 y; body mass index: 21.3 ± 1.8 kg/m²) each followed 2 different eating schedules (early [08:30, 13:30, and 19:30] and late [12:00, 17:00, and 23:00]) in random order. These diet interventions were conducted for 8 d, with an experimental period of 3 d and 2 nights (from dinner on day 7) after 7 d of free living. The 3 meals in each intervention were nutritionally equivalent (55% carbohydrate, 15% protein, and 30% fat). The 24-h mean interstitial glucose concentration on day 8 was obtained under controlled conditions using the CGM (primary outcome). These concentrations were compared among the following 3 schedules using Dunnett's test, with the early eating schedule as reference (1 compared with 2 and 1 compared with 3): 1) early eating schedule (control), 2) late eating schedule according to the clock time (08:00 on day 8 to 08:00 on day 9), and 3) late eating schedule according to the time elapsed since the first meal for 24 h.

RESULTS

The 24-h mean ± SD interstitial glucose concentrations when participants followed the late eating schedule were higher than those when they followed the early eating schedule in terms of clock time (91.2 ± 2.9 compared with 99.2 ± 4.6 mg/dL, P = 0.003) and time elapsed (91.2 ± 2.9 compared with 98.3 ± 3.8 mg/dL, P < 0.001).

CONCLUSIONS

A late eating schedule increases the mean 24-h interstitial glucose concentration in young adult males. This insight will have useful implications in determining meal timings, especially for those with conditions such as diabetes.

Practical constraint definition in safety schemes for artificial pancreas systems

Introduction:

Artificial pancreas systems usually define an insulin-on-board constraint ([Formula: see text]) for safety schemes to limit the insulin infusion and avoid hypoglycemia during the closed-loop performance. Several methods have been proposed with impractical considerations requiring information from the prandial events or complex procedures for ambulatory use.

Methods:

This paper presents a simple method that consists of two novel rules that allow finding an [Formula: see text] based only on common clinical parameters that do not require patient intervention. The method robustness was evaluated using a control system coupled to a safety layer under demanding scenarios implemented on the FDA-approved simulator for preclinical studies.

Results:

The method maintains a safe performance, even in the face of interpatient variability, hybrid and fully automatic implementations of an artificial pancreas system, and uncertain settings. Both proposed rules work as effectively or even better and without the patient intervention than other methods that have already been clinically validated.

Conclusion:

This method can be used to define a constant [Formula: see text] that ensures performance and safety of the control system, even under scenarios with incorrect clinical data. Unlike other methods, this method only requires reliable information that is easily obtained from the patient, such as their total daily dose of insulin or body mass.

Identifying Glycemic Variability in Diabetes Patient Cohorts and Evaluating Disease Outcomes

Glycemic variability (GV) is an obstacle to effective blood glucose control and an autonomous risk factor for diabetes complications. We, therefore, explored sample data of patients with diabetes mellitus who maintained better amplitude of glycemic fluctuations and compared their disease outcomes with groups having poor control. A retrospective study was conducted using electronic data of patients having hemoglobin A1C (HbA1c) values with five recent time points from Think Whole Person Healthcare (TWPH). The control variability grid analysis (CVGA) plot and coefficient of variability (CV) were used to identify and cluster glycemic fluctuation. We selected important variables using LASSO. Chi-Square, Fisher’s exact test, Bonferroni chi-Square adjusted residual analysis, and multivariate Kruskal–Wallis tests were used to evaluate eventual disease outcomes. Patients with very high CV were strongly associated (p < 0.05) with disorders of lipoprotein (p = 0.0014), fluid, electrolyte, and acid–base balance (p = 0.0032), while those with low CV were statistically significant for factors influencing health status such as screening for other disorders (p = 0.0137), long-term (current) drug therapy (p = 0.0019), and screening for malignant neoplasms (p = 0.0072). Reducing glycemic variability may balance alterations in electrolytes and reduce differences in lipid profiles, which may assist in strategies for managing patients with diabetes mellitus.

Glycemic variability in newly diagnosed diabetic cats treated with the glucagon-like peptide-1 analogue exenatide extended release

Background

Glycemic variability (GV) is an indicator of glycemic control and can be evaluated by calculating the SD of blood glucose measurements. In humans with diabetes mellitus (DM), adding a glucagon‐like peptide‐1 (GLP‐1) analogue to conventional therapy reduces GV. In diabetic cats, the influence of GLP‐1 analogues on GV is unknown.

Objective

To evaluate GV in diabetic cats receiving the GLP‐1 analogue exenatide extended release (EER) and insulin.

Animals

Thirty client‐owned cats with newly diagnosed spontaneous DM.

Methods

Retrospective study. Blood glucose curves from a recent prospective placebo‐controlled clinical trial generated 1, 3, 6, 10, and 16 weeks after starting therapy were retrospectively evaluated for GV. Cats received either EER (200 μg/kg) or 0.9% saline SC once weekly, insulin glargine and a low‐carbohydrate diet. Mean blood glucose concentrations were calculated and GV was assessed by SD. Data were analyzed using nonparametric tests.

Results

In the EER group, GV (mean SD [95% confidence interval]) was lower at weeks 6 (1.69 mmol/L [0.9‐2.48]; P = .02), 10 (1.14 mmol/L [0.66‐1.62]; P = .002) and 16 (1.66 mmol/L [1.09‐2.23]; P = .02) compared to week 1 (4.21 mmol/L [2.48‐5.93]) and lower compared to placebo at week 6 (3.29 mmol/L [1.95‐4.63]; P = .04) and week 10 (4.34 mmol/L [2.43‐6.24]; P < .000). Cats achieving remission (1.21 mmol/L [0.23‐2.19]) had lower GV compared to those without remission (2.96 mmol/L [1.97‐3.96]; P = .01) at week 6.

Conclusions and Clinical Importance

The combination of EER, insulin, and a low‐carbohydrate diet might be advantageous in the treatment of newly diagnosed diabetic cats.

Independent Association of Glucose Variability With Hospital Mortality in Adult Intensive Care Patients: Results From the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Binational Registry

Supplemental Digital Content is available in the text.

Wide variations in blood glucose excursions in critically ill patients may influence adverse outcomes such as hospital mortality. However, whether blood glucose variability is independently associated with mortality or merely captures the excess risk attributable to hyperglycemic and hypoglycemic episodes is not established. We investigated whether blood glucose variability independently predicted hospital mortality in nonhyperglycemic critical care patients.

Efficacy of Real-Time Continuous Glucose Monitoring to Improve Effects of a Prescriptive Lifestyle Intervention in Type 2 Diabetes: A Pilot Study

INTRODUCTION

Optimising patient adherence to prescribed lifestyle interventions to achieve improved blood glucose control remains a challenge. Combined use of real-time continuous glucose monitoring systems (RT-CGM) may promote improved glycaemic control. This pilot study examines the effects of a prescriptive lifestyle modification programme when combined with RT-CGM on blood glucose control and cardiovascular disease risk markers.

METHODS

Twenty adults (10 men, 10 women) with obesity and type-2 diabetes (T2D) (age 60.55 ± 8.38 years, BMI 34.22 ± 4.67 kg/m²) were randomised to a prescriptive low-carbohydrate diet and lifestyle plan whilst continuously wearing either an RT-CGM or an 'offline-blinded' monitor (control) for 12 weeks. Outcomes were glycaemic control (HbA1c, fasting glucose, glycaemic variability [GV]), diabetes medication (MeS), weight, blood pressure and lipids assessed pre- and post-intervention.

RESULTS

Both groups experienced reductions in body weight (RT-CGM - 7.4 ± 4.5 kg vs. control - 5.5 ± 4.0 kg), HbA1c (- 0.67 ± 0.82% vs. - 0.68 ± 0.74%), fasting blood glucose (- 1.2 ± 1.9 mmol/L vs. - 1.0 ± 2.2 mmol/L), LDL-C (- 0.07 ± 0.34 mmol/L vs. - 0.26 ± 0.42 mmol/L) and triglycerides (- 0.32 ± 0.46 mmol/L vs. - 0.36 ± 0.53 mmol/L); with no differential effect between groups (P ≥ 0.10). At week 12, GV indices were consistently lower by at least sixfold in RT-CGM compared to control (CONGA-1 - 0.27 ± 0.36 mmol/L vs. 0.06 ± 0.19 mmol/L; CONGA-2 - 0.36 ± 0.54 mmol/L vs. 0.05 ± 2.88 mmol/L; CONGA-4 - 0.44 ± 0.67 mmol/L vs. - 0.02 ± 0.42 mmol/L; CONGA-8 - 0.36 ± 0.61 vs. 0.02 ± 0.52 mmol/L; MAGE - 0.69 ± 1.14 vs. - 0.09 ± 0.08 mmol/L, although there was insufficient power to achieve statistical significance (P ≥ 0.11). Overall, there was an approximately 40% greater reduction in blood glucose-lowering medication (MeS) in RT-CGM (- 0.30 ± 0.59) compared to control (0.02 ± 0.23).

CONCLUSION

This study provides preliminary evidence that RT-CGM may be an effective strategy to optimise glucose control whilst following a low-carbohydrate lifestyle programme that targets improved glycaemic control, with minimal professional support.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry identifier, ANZTR: 372898.

FUNDING

Grant funding was received for the delivery of the clinical trial only, by the Diabetes Australia Research Trust (DART).

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (α1, α2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and α exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol²/l²): 21.71 ± 9.82 vs. 7.25 ± 5.92, and α1: 1.94 ± 0.12 vs. 1.75 ± 0.12, α2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol²/l²), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents α1 and α2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (β = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though α1 and MSE failed. In the regression models, including SFE, AFE, and α2 (β = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.

Phase 1 Study of the Pharmacology of BTI320 Before High-Glycemic Meals

BTI320 is a proprietary fractionated mannan polysaccharide being studied for attenuation of postprandial glucose excursion. The apparent blood glucose‐lowering effect of this compound is effective in lowering postprandial hyperinsulinemia, participating in the metabolic regulation of other lipid molecules; the consequence of this activity is yet to be validated with BTI320 with respect to the risk of cardiovascular disease. The primary objective of the study was to determine the postprandial glucose and insulin responses to 3 test meals containing rice alone or consumed with BTI320 (study A) or 3 test meals (Sprite^TM) alone or consumed with BTI320 (study B). Twenty overweight but otherwise healthy volunteers, 4 female and 6 male (mean age 29 years, BMI 27–28 kg/m²) in study A and 6 female and 4 male (mean age 32 years, BMI 25‐32 kg/m²) in study B participated in the BTI320 evaluations. Standardized postprandial response methodology was utilized. In study A the addition of 6‐ and 12‐g BTI320 tablets reduced postprandial glucose responses to white rice by 19% and 32% and reduced postprandial insulin responses by 16% and 24%, respectively (P ≤ .05). In study B 2.6 and 5.2 g BTI320 reduced the glycemic index by 10% and 14%, respectively, and led to 14% and 18% decreases in the insulinemic index of the soft drink (P ≤ .05). These 2 studies demonstrated that the consumption of BTI320 before carbohydrate food or sugary beverage significantly reduced postprandial glucose levels and insulin responses to that meal or beverage in a dose‐dependent manner.

How Should We Assess Glycemic Variability in Type 1 Diabetes? Contribution of Principal Component Analysis for Interstitial Glucose Indices in 142 Children

BACKGROUND

Glycemic variability (GV) can be used to assess glycemic control in diabetes, but there is no clear consensus concerning the methods to use for its assessment. Methodological differences have resulted in differences in the outcome of GV metrics used in research studies, controversies over clinical impact, and an absence of integration into routine care.

AIM

To identify the indicators of GV most meaningful for clinicians, patients, and clinical researchers.

MATERIALS AND METHODS

Continuous glucose monitoring data were collected during the first 3 months of a pediatric diabetes clinical trial (Start-In!; n = 142). We used principal component analysis (PCA) to analyze weekly averages for 22 parameters relating to GV.

RESULTS

PCA identified five groups of parameters and three components explaining 85.7% of the variance. These components represented the amplitude, direction (hypoglycemia vs. hyperglycemia), and timing (within-day vs. between-days) of glucose excursions.

CONCLUSIONS

This study provides elements that could make GV parameters more useful in clinical practice and research. No single parameter was sufficient to represent the complexity of GV, but it was possible to restrict the number of indicators required. The five groups of parameters identified by PCA could facilitate the choice of the most relevant outcomes for GV analysis in pediatric diabetes according to the purpose of the analysis (e.g., exploration of GV associated with hypo- or hyperglycemia, with short- or long-term periodicity, or GV in its entirety).

The effect of brief intermittent stair climbing on glycemic control in people with type 2 diabetes: a pilot study

We examined the effect of brief intermittent stair climbing exercise on glycemic control using continuous glucose monitoring in people with type 2 diabetes (n = 7, 5 men; 2 women; age, 21-70 years). The protocol involved three 60-s bouts of vigorously ascending and slowly descending a flight of stairs. Mean 24-h blood glucose was unchanged after an acute session (p = 0.43) and following 18 sessions over 6 weeks (p = 0.13). The protocol was well tolerated by participants but seemingly insufficient to alter glycemic control.

Glucose concentrations after insulin-induced hypoglycemia and glycemic variability in healthy and diabetic cats

Background

Little information is available about posthypoglycemic hyperglycemia (PHH) in diabetic cats, and a causal link between hypoglycemia and subsequent hyperglycemia is not clear. Fluctuations in blood glucose concentrations might only represent high glycemic variability.

Hypothesis

Insulin induces PHH in healthy cats, and PHH is associated with poorly regulated diabetes and increased glycemic variability in diabetic cats.

Animals

Six healthy cats, 133 diabetic cats.

Methods

Insulin (protamine‐zinc and degludec; 0.1‐0.3 IU/kg) administered to healthy cats. Blood glucose curves were generated with portable glucose meter to determine the percentage of curves with PHH. Data from insulin‐treated diabetic cats with blood glucose curves showing hypoglycemia included data of cats with and without PHH. Post‐hypoglycemic hyperglycemia was defined as blood glucose concentrations <4 mmol/L followed by blood glucose concentrations >15 mmol/L within 12 hours. Glycemic variability was calculated as the standard deviation of the blood glucose concentrations.

Results

In healthy cats, all insulin doses caused hypoglycemia but PHH was not observed; glycemic variability did not differ between insulin preparations. Among diabetic cats with hypoglycemia, 33 (25%) had PHH. Compared with cats without PHH, their daily insulin dose was higher (1.09 ± 0.55 versus 0.65 ± 0.56 IU/kg; P < .001), serum fructosamine concentration was higher (565 ± 113 versus 430 ± 112 µmol/L; P < .001), remission was less frequent (10% versus 56%; P < .001), and glycemic variability was larger (8.1 ± 2.4 mmol/L versus 2.9 ± 2.2 mmol/L; P < .001).

Conclusions and Clinical Importance

Insulin‐induced hypoglycemia did not cause PHH in healthy cats but it occurred in 25% of diabetic cats with hypoglycemia, particularly when diabetes was poorly controlled. Glycemic variability was increased in cats with PHH.

Associations of blood glucose dynamics with antihyperglycemic treatment and glycemic variability in type 1 and type 2 diabetes

AIMS

The dynamical structure of glucose fluctuation has largely been disregarded in the contemporary management of diabetes.

METHODS

In a retrospective study of patients with diabetes, we evaluated the relationship between glucose dynamics, antihyperglycemic therapy, glucose variability, and glucose exposure, while taking into account potential determinants of the complexity index. We used multiscale entropy (MSE) analysis of continuous glucose monitoring data from 131 subjects with type 1 (n = 18), type 2 diabetes (n = 102), and 11 nondiabetic control subjects. We compared the MSE complexity index derived from the glucose time series among the treatment groups, after adjusting for sex, age, diabetes duration, body mass index, and carbohydrate intake.

RESULTS

In type 2 diabetic patients who were on a diet or insulin regimen with/without oral agents, the MSE index was significantly lower than in nondiabetic subjects but was lowest in the type 1 diabetes group (p < 0.001). The decline in the MSE complexity across the treatment groups correlated with increasing glucose variability and glucose exposure. Statistically, significant correlations existed between higher MSE complexity indices and better glycemic control. In multivariate regression analysis, the antidiabetic therapy was the most powerful predictor of the MSE (β = -0.940 ± 0.242, R ² = 0.306, p < 0.001), whereas the potential confounders failed to contribute.

CONCLUSIONS

The loss of dynamical complexity in glucose homeostasis correlates more closely with therapy modalities and glucose variability than with clinical measures of glycemia. Thus, targeting the glucoregulatory system by adequate therapeutic interventions may protect against progressive worsening of diabetes control.

Role of Continuous Glucose Monitoring in Clinical Trials: Recommendations on Reporting

Thanks to significant improvements in the precision, accuracy, and usability of continuous glucose monitoring (CGM), its relevance in both ambulatory diabetes care and clinical research is increasing. In this study, we address the latter perspective and derive provisional reporting recommendations. CGM systems have been available since around the year 2000 and used primarily in people with type 1 diabetes. In contrast to self-measured glucose, CGM can provide continuous real-time measurement of glucose levels, alerts for hypoglycemia and hyperglycemia, and a detailed assessment of glycemic variability. Through a broad spectrum of derived glucose data, CGM should be a useful tool for clinical evaluation of new glucose-lowering medications and strategies. It is the only technology that can measure hyperglycemic and hypoglycemic exposure in ambulatory care, or provide data for comprehensive assessment of glucose variability. Other advantages of current CGM systems include the opportunity for improved self-management of glycemic control, with particular relevance to those at higher risk of or from hypoglycemia. We therefore summarize the current status and limitations of CGM from the perspective of clinical trials and derive suggested recommendations for how these should facilitate optimal CGM use and reporting of data in clinical research.

Effect of Peripheral Electrical Stimulation (PES) on Nocturnal Blood Glucose in Type 2 Diabetes: A Randomized Crossover Pilot Study

BACKGROUND

Regulation of hepatic glucose production has been a target for antidiabetic drug development, due to its major contribution to glucose homeostasis. Previous pre-clinical study demonstrated that peripheral electrical stimulation (PES) may stimulate glucose utilization and improve hepatic insulin sensitivity. The aim of the present study was to evaluate safety, tolerability, and the glucose-lowering effect of this approach in patients with type 2 diabetes (T2DM).

METHODS

Twelve patients with T2DM were recruited for an open label, interventional, randomized trial. Eleven patients underwent, in a crossover design, an active, and a no-intervention control periods, separated with a two-week washout phase. During the active period, the patients received a daily lower extremity PES treatment (1.33Hz/16Hz burst mode), for 14 days. Study endpoints included changes in glucose levels, number of hypoglycemic episodes, and other potential side effects. Endpoints were analyzed based on continuous glucose meter readings, and laboratory evaluation.

RESULTS

We found that during the active period, the most significant effect was on nocturnal glucose control (P < 0.0004), as well as on pre-meal mean glucose levels (P < 0.02). The mean daily glucose levels were also decreased although it did not reach clinical significance (P = 0.07). A reduction in serum cortisol (P < 0.01) but not in insulin was also detected after 2 weeks of treatment. No adverse events were recorded.

CONCLUSIONS

These results indicate that repeated PES treatment, even for a very short duration, can improve blood glucose control, possibly by suppressing hepatic glucose production. This effect may be mediated via hypothalamic-pituitary-adrenal axis modulation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02727790.

Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring

OBJECTIVE

To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia.

METHODS

Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated.

RESULTS

Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADE_hypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADE_hypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use.

CONCLUSIONS

CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADE_hypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

IMPACT OF GLUCOSE MANAGEMENT TEAM ON OUTCOMES OF HOSPITALIZARON IN PATIENTS WITH TYPE 2 DIABETES ADMITTED TO THE MEDICAL SERVICE

OBJECTIVE

To improve glycemic control of hospitalized patients with diabetes and hyperglycemia, many medical centers have established dedicated glucose management teams (GMTs). However, the impact of these specialized teams on clinical outcomes has not been evaluated.

METHODS

We conducted a retrospective study of 440 patients with type 2 diabetes admitted to the medical service for cardiac or infection-related diagnosis. The primary endpoint was a composite outcome of several well-recognized markers of morbidity, consisting of: death during hospitalization, transfer to intensive care unit, initiation of enteral or parenteral nutrition, line infection, new in-hospital infection or infection lasting more than 20 days of hospitalization, deep venous thrombosis or pulmonary embolism, rise in plasma creatinine, and hospital re-admissions.

RESULTS

Medical housestaff managed the glycemia in 79% of patients (usual care group), while the GMT managed the glycemia in 21% of patients (GMT group). The primary outcome was similar between cohorts (0.95 events per patient versus 0.99 events per patient in the GMT and usual care cohorts, respectively). For subanalysis, the subjects in both groups were stratified into those with average glycemia of <180 mg/dL versus those with glycemia >180 mg/dL. We found a significant beneficial impact of glycemic management by the GMT on the composite outcome in patients with average glycemia >180 mg/dL during their hospital stay. The number of patients who met primary outcome was significantly higher in the usual care group (40 of 83 patients, 48%) than in the GMT-treated cohort (8 of 33 patients, 25.7%) (P<.02).

CONCLUSION

Our data suggest that GMTs may have an important role in managing difficult-to-control hyperglycemia in the inpatient setting.

ABBREVIATIONS

BG = blood glucose GMT = glucose management team HbA1c = hemoglobin A1c ICU = intensive care unit POC = point of care T2D = type 2 diabetes.

Clinical implications of oxidative stress and potential role of natural antioxidants in diabetic vascular complications

AIMS

The possible link between hyperglycaemia-induced oxidative stress (OxS) and diabetic complications is suggested by many in vitro studies. However, not much attention has been paid to the clinical evidence supporting this hypothesis, as well as to their possible therapeutic implications.

DATA SYNTHESIS

Some prospective studies show a direct correlation between an increase in OxS biomarkers and the appearance of diabetes complications. This is consistent with the evidence that any acute increase of glycaemia, particularly post-prandial, and hypoglycaemia causes endothelial dysfunction and inflammation, through the generation of an OxS. However, the detection of free radicals is difficult as they are highly reactive molecules with a short half-life. Instead, the metabolites of OxS are measured. Interventional trials with supplemented antioxidants have failed to show any beneficial effects. Conversely, natural foods show very promising results.

CONCLUSIONS

The "new antioxidant" approach includes the possibility of controlling free radical production and increasing intracellular antioxidant defence, a concept different from the old one, when antioxidant activities implied scavenging the free radicals already produced. A synergistic action in this respect could convincingly be obtained with a balanced 'Mediterranean Diet' (MedD) type. Early intensive glucose control is still the best strategy to avoid OxS and its associated diabetes complications.

Impact of Real-Time Continuous Glucose Monitoring Use on Glucose Variability and Endothelial Function in Adolescents with Type 1 Diabetes: New Technology--New Possibility to Decrease Cardiovascular Risk?

Children with type 1 diabetes (T1DM) are the high-risk group of accelerated atherosclerosis. Real-time continuous glucose monitoring (RT-CGM) provides possibilities for the detection of glycaemic variability, newly recognized cardiovascular risk factor. The aim of the study was to assess the usefulness of RT-CGM as an educational tool to find and reduce glycaemic variability in order to improve endothelial function in T1DM adolescents. Forty patients aged 14.6 years were recruited. The study was based on one-month CGM sensors use. Parameters of glycaemic variability were analyzed during first and last sensor use, together with brachial artery flow-mediated dilatation (FMD) to assess endothelial function. In the whole group, FMD improvement was found (10.9% to 16.6%, p < 0.005), together with decrease in all studied glycaemic variability parameters. In patients with HbA1c improvement compared to the group without HbA1c improvement, we found greater increase of FMD (12% to 19%, p < 0.005 versus 8.2% to 11.3%, p = 0.080) and greater improvement of glucose variability. RT-CGM can be considered as an additional tool that offers T1DM adolescents the quick reaction to decrease glycaemic variability in short time observation. Whether such approach might influence improvement in endothelial function and reduction of the risk of future cardiovascular disease remains to be elucidated.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis

BACKGROUND

Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D).

METHODS

A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance.

RESULTS

A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D.

CONCLUSIONS

Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.

DPP-4 inhibitors: pharmacological differences and their clinical implications

INTRODUCTION

Recently, incretin-based therapy was introduced for the treatment of type 2 diabetes (T2D). In particular, dipeptidyl peptidase-4 inhibitors (DPP-4i) (sitagliptin, vildagliptin, saxagliptin, linagliptin and alogliptin) play an increasing role in the management of T2D.

AREAS COVERED

An extensive literature search was performed to analyze the pharmacological characteristics of DPP-4i and their clinical implications.

EXPERT OPINION

DPP-4i present significant pharmacokinetic differences. They also differ in chemical structure, in the interaction with distinct subsites of the enzyme and in different levels of selectivity and potency of enzyme inhibition. Moreover, disparities in the effects on glycated hemoglobin, glucagon-like peptide-1 and glucagon levels and on glucose variability have been observed. However, indirect comparisons indicate that all DPP-4i have a similar safety and efficacy profiles. DPP-4i are preferred in overweight/obese and elderly patients because of the advantages of minimal or no influence on weight gain and low risk of hypoglycemia. For the same reasons, DPP-4i can be safely combined with insulin. However, currently cardiovascular outcomes related to DPP-4i are widely debated and the available evidence is controversial. Today, long-term studies are still in progress and upcoming results will allow us to better define the strengths and limits of this therapeutic class.

Model of glucose sensor error components: identification and assessment for new Dexcom G4 generation devices

It is clinically well-established that minimally invasive subcutaneous continuous glucose monitoring (CGM) sensors can significantly improve diabetes treatment. However, CGM readings are still not as reliable as those provided by standard fingerprick blood glucose (BG) meters. In addition to unavoidable random measurement noise, other components of sensor error are distortions due to the blood-to-interstitial glucose kinetics and systematic under-/overestimations associated with the sensor calibration process. A quantitative assessment of these components, and the ability to simulate them with precision, is of paramount importance in the design of CGM-based applications, e.g., the artificial pancreas (AP), and in their in silico testing. In the present paper, we identify and assess a model of sensor error of for two sensors, i.e., the G4 Platinum (G4P) and the advanced G4 for artificial pancreas studies (G4AP), both belonging to the recently presented "fourth" generation of Dexcom CGM sensors but different in their data processing. Results are also compared with those obtained by a sensor belonging to the previous, "third," generation by the same manufacturer, the SEVEN Plus (7P). For each sensor, the error model is derived from 12-h CGM recordings of two sensors used simultaneously and BG samples collected in parallel every 15 ± 5 min. Thanks to technological innovations, G4P outperforms 7P, with average mean absolute relative difference (MARD) of 11.1 versus 14.2%, respectively, and lowering of about 30% the error of each component. Thanks to the more sophisticated data processing algorithms, G4AP resulted more reliable than G4P, with a MARD of 10.0%, and a further decrease to 20% of the error due to blood-to-interstitial glucose kinetics.

Glucose variability indices in type 1 diabetes: parsimonious set of indices revealed by sparse principal component analysis

BACKGROUND

Continuous glucose monitoring (CGM) time-series are often analyzed, retrospectively, to investigate glucose variability (GV), a risk factor for the development of complications in type 1 diabetes (T1D). In the literature, several tens of different indices for GV quantification have been proposed, but many of them carry very similar information. The aim of this article is to select a relatively small subset of GV indices from a wider pool of metrics, to obtain a parsimonious but still comprehensive description of GV in T1D datasets.

MATERIALS AND METHODS

A pool of 25 GV indices was evaluated on two CGM time-series datasets of 17 and 16 T1D subjects, respectively, collected during the European Union Seventh Framework Programme project "Diadvisor" (2008-2012) in two different clinical research centers using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus. After the indices were centered and scaled, the Sparse Principal Component Analysis (SPCA) technique was used to determine a reduced set of metrics that allows preserving a high percentage of the variance of the whole original set. In order to assess whether or not the selected subset of GV indices is dataset-dependent, the analysis was applied to both datasets, as well as to the one obtained by merging them.

RESULTS

SPCA revealed that a subset of up to 10 different GV indices can be sufficient to preserve more than the 60% of the variance originally explained by all the 25 variables. It is remarkable that four of these GV indices (i.e., Index of Glycemic Control, percentage of Glycemic Risk Assessment Diabetes Equation score due to euglycemia, percentage Coefficient of Variation, and Low Blood Glucose Index) were selected for all the considered T1D datasets.

CONCLUSIONS

The SPCA methodology appears a suitable candidate to identify, among the large number of literature GV indices, subsets that allow obtaining a parsimonious, but still comprehensive, description of GV.

Peritoneal dialysis – risk factor for glycemic variability assessed by continuous glucose monitoring system

Background and Aims. Peritoneal dialysis (PD) is accompanied by a multitude of factors that influence glycemic variability, and HbA1c does not detect dynamic glucose changes. In this study we wanted to assess glycemic variability, using a 72-hour continuous glucose monitoring system (CGMS), in 31 patients stratified according to the presence of type 2 diabetes and PD. Materials and Methods. The study included 31 patients (11 type 2 diabetic PD patients, 9 non diabetic PD patients and 11 type 2 diabetic patients without PD). Glycemic variability was assessed on CGM readings by: Mean Amplitude of Glycemic Excursion (MAGE), Mean of Daily Differences (MODD), Fractal Dimensions (FD), Mean Interstitial Glucose (MIG), Area Under glycemia Curve (AUC), M100, % time with glucose >180/<70 mg/dl. Results. The PD diabetic patients presented AUC, MIG and inter-day glycemic variability (MODD) significantly higher than diabetic patients without PD. In PD patients, the type of dialysis fluid in the nocturnal exchange and peritoneal membrane status did not significantly influence glycemic variability. Conclusions. CGMS is more useful than HbA1c in quantifying the metabolic imbalance of PD patients. PD induces inter-day glycemic variability and poor glycemic control, thus being a potential risk factor for chronic complications progression in diabetic patients.

Sparse Principal Component Analysis for the parsimonious description of glucose variability in diabetes

Abnormal glucose variability (GV) is considered to be a risk factor for the development of diabetes complications. For its quantification from continuous glucose monitoring (CGM) data, tens of different indices have been proposed in the literature, but the information carried by them is highly redundant. In the present work, the Sparse Principal Component Analysis (SPCA) technique is used to select, from a wide pool of GV metrics, a smaller subset of indices that preserves the majority of the total original variance, providing a parsimonious but still comprehensive description of GV. In detail, SPCA is applied to a set of 25 literature GV indices evaluated on CGM time-series collected in 17 type 1 (T1D) and 13 type 2 (T2D) diabetic subjects. Results show that the 10 GV indices selected by SPCA preserve more than the 75% of the variance of the original set of 25 indices, both in T1D and T2D. Moreover, 6 indices of the parsimonious set are shared by T1D and T2D.

Minimal functional β-cell mass in intraportal implants that reduces glycemic variability in type 1 diabetic recipients

OBJECTIVE

Previous work has shown a correlation between β-cell number in cultured islet cell grafts and their ability to induce C-peptide secretion after intraportal implantation in C-peptide-negative type1 diabetic patients. In this cross-sectional study, we examined the minimal functional β-cell mass (FBM) in the implant that induces metabolic improvement.

RESEARCH DESIGN AND METHODS

Glucose clamps assessed FBM in 42 recipients with established implants. C-peptide release during each phase was expressed as percentage of healthy control values. Its relative magnitude during a second hyperglycemic phase was most discriminative and therefore selected as a parameter to be correlated with metabolic effects.

RESULTS

Recipients with functioning β-cell implants exhibited average FBM corresponding to 18% of that in normal control subjects (interquartile range 10-33%). Its relative magnitude negatively correlated with HbA1c levels (r = -0.47), daily insulin dose (r = -0.75), and coefficient of variation of fasting glycemia (CVfg) (r = -0.78, retained in multivariate analysis). A correlation between FBM and CVfg <25% appeared from the receiver operating characteristic curve (0.97 [95% CI 0.93-1.00]). All patients with FBM >37% exhibited CVfg <25% and a >50% reduction of their pretransplant CVfg; this occurred in none with FBM <5%. Implants with FBM >18% reduced CVfg from a median pretransplant value of 46 to <25%.

CONCLUSIONS

Glucose clamping assesses the degree of restoration in FBM achieved by islet cell implants. Values >37% of normal control subjects appear needed to reduce glycemic variability in type 1 diabetic recipients. Further studies should examine whether the test can help guide decisions on additional islet cell transplants and on adjusting or stopping immunotherapy.

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.

Modeling the glucose sensor error

Continuous glucose monitoring (CGM) sensors are portable devices, employed in the treatment of diabetes, able to measure glucose concentration in the interstitium almost continuously for several days. However, CGM sensors are not as accurate as standard blood glucose (BG) meters. Studies comparing CGM versus BG demonstrated that CGM is affected by distortion due to diffusion processes and by time-varying systematic under/overestimations due to calibrations and sensor drifts. In addition, measurement noise is also present in CGM data. A reliable model of the different components of CGM inaccuracy with respect to BG (briefly, "sensor error") is important in several applications, e.g., design of optimal digital filters for denoising of CGM data, real-time glucose prediction, insulin dosing, and artificial pancreas control algorithms. The aim of this paper is to propose an approach to describe CGM sensor error by exploiting n multiple simultaneous CGM recordings. The model of sensor error description includes a model of blood-to-interstitial glucose diffusion process, a linear time-varying model to account for calibration and sensor drift-in-time, and an autoregressive model to describe the additive measurement noise. Model orders and parameters are identified from the n simultaneous CGM sensor recordings and BG references. While the model is applicable to any CGM sensor, here, it is used on a database of 36 datasets of type 1 diabetic adults in which n = 4 Dexcom SEVEN Plus CGM time series and frequent BG references were available simultaneously. Results demonstrates that multiple simultaneous sensor data and proper modeling allow dissecting the sensor error into its different components, distinguishing those related to physiology from those related to technology.

How can we monitor glycaemic variability in the clinical setting?

No universal consensus exists on how to express glycaemic variability. Among other parameters, standard deviation of blood glucose values, mean amplitude of glycaemic excursions (MAGE), the Low Blood Glucose Index (LBGI) and the High Blood Glucose Index (HBGI), which were subsequently combined into the Average Daily Risk Range (ADRR), mean of daily differences (MODD) and glycaemic variability index (GVI) are highlighted. The continuous glucose monitoring in research and clinical settings has been a great help for a comprehensive approach to circadian blood glucose evaluation and identification of individual patterns, mainly in type 1 diabetes, but recently also in type 2 diabetes. In everyday clinical practice the judicious use of self-monitoring of blood glucose in an educational setting involving the patient and the care team is an unreplaceable tool to effectively and safely guide behavioural and drug therapy.

Do data in the literature indicate that glycaemic variability is a clinical problem? Glycaemic variability and vascular complications of diabetes

In recent years glycaemic variability (GV) has emerged as a determinant of vascular complications of both type 1 and type 2 diabetes mellitus. In type 1 diabetes analysis of data of GV show conflicting results on both micro- and macro-vascular complications. In non-diabetic subjects blood glucose after loading is a stronger predictor of cardiovascular complications than fasting glucose. In type 2 diabetes both coefficient of variation of fasting blood glucose and postprandial blood glucose predict cardiovascular events. Also, long term variability of HbA1c has been associated predominantly with diabetic nephropathy, less frequently with retinopathy. Intervention trials to evaluate the effect of postprandial glucose have been conducted only in prediabetes or in type 2 diabetes and the data are not conclusive. In vitro and in vivo data have shown the mechanisms that are at the basis of the adverse cardiovascular effects of GV, mainly associated with oxidative stress; the atherogenic action of postprandial glucose also involves insulin sensitivity, postprandial increase in serum lipids and glycaemic index of food. Thus, correction of GV emerges as a target to be pursued in clinical practice in order to safely reduce mean blood glucose (and thus glycated haemoglobin) and for its direct effects on vascular complications of diabetes.

Signal processing algorithms implementing the "smart sensor" concept to improve continuous glucose monitoring in diabetes

Glucose readings provided by current continuous glucose monitoring (CGM) devices still suffer from accuracy and precision issues. In April 2013, we proposed a new conceptual architecture to deal with these problems and render CGM sensors algorithmically smarter, which consists of three modules for denoising, enhancement, and prediction placed in cascade to a commercial CGM sensor. The architecture was assessed on a data set consisting of 24 type 1 diabetes patients collected in four clinical centers of the AP@home Consortium (a European project of 7th Framework Programme funded by the European Committee). This article, as a companion to our prior publication, illustrates the technical details of the algorithms and of the implementation issues.

Analysis of Chronic Kidney Disease – Asociated Glycemic Variability in Patients with Type 2 Diabetes Using Continuous Glucose Monitoring System

Background and Aims. In diabetic patients, chronic kidney disease (CKD) requires special attention due to the multitude of factors that determine glycemic variability. We aimed to assess glycemic variability in patients with CKD and type 2 diabetes mellitus (T2DM) using a continuous glucose monitoring system (CGMS) and identify the predictive value of inter-day and intra-day glycemic variability indices for metabolic imbalance. Material and method. We included 20 diabetic patients (10 CKD patients/10 patients without CKD) and 10 healthy volunteers. Anthropometric parameters, glycated hemoglobin (HbA1c), and glycemic variability indices on CGMS readings were registered. Results. CKD diabetic patients presented significantly higher inter-day and intra-day glycemic variability compared to the diabetic patients without CKD. HbA1c was not significantly different between diabetic subjects with/without CKD. ROC curves indicated that just some CGMS parameters had higher predictive value for metabolic imbalance (HbA1c≥6.5%) but only the percentage of time with glucose values>180 mg/dl (p=0.024) was an independent predictor for HbA1c≥6.5%. Conclusions. Subjects with CKD and T2DM had poor glycemic control and significantly higher glycemic variability comparative to those without CKD, and especially to healthy volunteers. Assessment of glycemic variability indices is more accurate than HbA1c for the quantification of glycemic control in CKD diabetic patients

Evaluation of the mean absolute glucose change as a measure of glycemic variability using continuous glucose monitoring data

BACKGROUND

The mean absolute glucose (MAG) change, originally developed to assess associations between glycemic variability (GV) and intensive care unit mortality, has not yet been validated. We used continuous glucose monitoring (CGM) datasets from patients with diabetes to assess the validity of MAG and to quantify associations with established measures of GV.

SUBJECTS AND METHODS

Validation was based on retrospective analysis of 72-h CGM data collected during clinical studies involving 815 outpatients (48 with type 1 diabetes and 767 with type 2 diabetes). Measures of GV included SD around the sensor glucose, interquartile range, mean amplitude of glycemic excursions, and the continuous overlapping net glycemic action indices at 1, 3, and 6 h. MAG was calculated using 5-min, 60-min, and seven-point glucose profile sampling intervals; correlations among the variability measures and effects of sampling frequency were assessed.

RESULTS

Strong linear correlations between MAG change and classical markers of GV were documented (r=0.587-0.809, P<0.001 for all), whereas correlations with both glycosylated hemoglobin and mean sensor glucose were found to be weak (r=0.246 and r=0.378, respectively). The magnitude of MAG change decreased in a nonlinear fashion (P<0.001), as intervals between glucose measurements increased. MAG change, as calculated from 5-min sensor glucose readings, did reflect relatively small differences in glucose fluctuations associated with glycemic treatment modality.

CONCLUSIONS

MAG change represents a valid GV index if closely spaced sensor glucose measurements are used, but does not provide any advantage over variability indices already used for assessing diabetes control.

Type 1 diabetes and celiac disease in adults: glycemic control and diabetic complications

The prevalence of celiac disease (CD) in patients with type 1 diabetes mellitus (T1DM) is 4.5 %. Objective of the study is to investigate (1) the course of glycemic control at CD diagnosis and after the initiation of a gluten-free diet (GFD) in T1DM patients; (2) the prevalence of diabetic complications in T1DM patients with adult onset of CD. In 20 hospitals in the Netherlands, we identified T1DM patients diagnosed with CD at adult age. We retrospectively collected glycated hemoglobin (HbA1c) levels before CD diagnosis, at CD diagnosis, and the most recent HbA1c levels as well as the presence of nephropathy and retinopathy. The control group consisted of patients with T1DM and negative CD serology matched for age, gender, T1DM duration, and HbA1c levels. Thirty-one patients were eligible with a median duration of T1DM and CD of 27 years (IQR 14-37) and 3 years (IQR 1-8), respectively. The matched control group consisted of 46 patients. HbA1c levels at the moment of CD diagnosis were 7.5 % (IQR 7.1-8) [58 mmol/mol] and at the most recent visit 7.4 % (IQR 6.9-7.9, P = 0.15) [57 mmol/mol] indicating no difference. Prevalence of retinopathy was lower in T1DM + CD group compared with controls, (38.7 vs 67.4 %, P < 0.05), whereas no difference in the prevalence of nephropathy was found between the groups (P = 0.09). In conclusion, T1DM + CD patients have less retinopathy compared to T1DM patients without CD. A GFD possibly favorable affects the development of vascular complications in T1DM patients.

Standardization versus customization of glucose reporting

Bergenstal et al. (Diabetes Technol Ther 2013;15:198-211) described an important approach toward standardization of reporting and analysis of continuous glucose monitoring and self-monitoring of blood glucose (SMBG) data. The ambulatory glucose profile (AGP), a composite display of glucose by time of day that superimposes data from multiple days, is perhaps the most informative and useful of the many graphical approaches to display glucose data. However, the AGP has limitations; some variations are desirable and useful. Synchronization with respect to meals, traditionally used in glucose profiles for SMBG data, can improve characterization of postprandial glucose excursions. Several other types of graphical display are available, and recently developed ones can augment the information provided by the AGP. There is a need to standardize the parameters describing glycemic variability and cross-validate the available computer programs that calculate glycemic variability. Clinical decision support software can identify and prioritize clinical problems, make recommendations for modifications of therapy, and explain its justification for those recommendations. The goal of standardization is challenging in view of the diversity of clinical situations and of computing and display platforms and software. Standardization is desirable but must be done in a manner that permits flexibility and fosters innovation.

Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence

Hyperglycemia, hypoglycemia, preexisting diabetes, and glycemic variability each may affect hospital outcomes. Observational findings derived from randomized trials or retrospective studies suggest that independent of hypoglycemia and hyperglycemia, a relationship exists between variability and hospital outcomes. A review of studies conducted in diverse hospital populations is reported here, showing a relationship between measures of variability and nonglycemic outcomes, including ICU and hospital mortality and length of stay. "Glycemic variability" has an intuitive meaning, understood as a propensity of a single patient to develop repeated episodes of excursions of BG over a relatively short period of time that exceed the amplitude expected in normal physiology. It is proposed that each of 3 dimensions of variability should be separately studied: (1) magnitude of glycemic excursions during intervals of relative stability of the moving average of BG, (2) frequency with which a critical magnitude of excursion is exceeded, and (3) presence or absence of fine tuning. Multiple hospital studies have found that the standard deviation (SD) of the data set of blood glucose values (BG) of individual patients predicts outcomes. An appropriate refinement would be to report the "Reverse-transformed group mean of the SD of the logarithmically transformed BG data set of each patient," with confidence intervals. In logarithmic space, group means of the SD of BGs of each patient may be compared, using an appropriate parametric test. Upon reverse transformation, the upper and lower bounds of the confidence intervals become asymmetric about the reverse-transformed group mean of the SD. There is a need to understand what patterns of dispersion of BG over time are captured by SD as a predictor of outcomes. Among the causes of high SD, a subgroup may consist of patients having frequent oscillations of BG. Another subgroup may consist of patients experiencing a major change of overall glycemia during the timeframe of data collection. Appropriate metrics should be developed to recognize both variability in the sense of recurrent large oscillations of BG, and separately to recognize any time-dependent change of overall glycemia during hospitalization. Especially in relation to uncontrolled diabetes, there is a need to know whether rapid correction of chronic hyperglycemia adversely affects hospital outcomes. We have some understanding of how to control or prevent change of overall glycemia, and less understanding of how to control variability. Each may be associated with outcomes, and each may be detected by a high SD, but it remains uncertain whether intervention to prevent either pattern of changing glycemia would affect outcomes.

The challenges of measuring glycemic variability

This commentary reviews several of the challenges encountered when attempting to quantify glycemic variability and correlate it with risk of diabetes complications. These challenges include (1) immaturity of the field, including problems of data accuracy, precision, reliability, cost, and availability; (2) larger relative error in the estimates of glycemic variability than in the estimates of the mean glucose; (3) high correlation between glycemic variability and mean glucose level; (4) multiplicity of measures; (5) correlation of the multiple measures; (6) duplication or reinvention of methods; (7) confusion of measures of glycemic variability with measures of quality of glycemic control; (8) the problem of multiple comparisons when assessing relationships among multiple measures of variability and multiple clinical end points; and (9) differing needs for routine clinical practice and clinical research applications.

[Benefits of subcutaneous continuous insulin infusion in type 1 diabetic patients with high glycemic variability]

BACKGROUND

Hypoglycemia limits the efficacy of intensive insulin therapy, especially in patients with great glucose variability. The extent to which continuous subcutaneous insulin infusion (CSII) overcomes this limitation is unclear. Our aim was to determine whether CSII is helpful for decreasing glucose variability and hypoglycemia, mainly in patients with the greatest variability.

METHOD

Twenty-four patients with type 1 diabetes wore a continuous glucose monitoring system sensor for three days before starting therapy with CSII and 6 months later. Glucose variability (SD, MAGE, M) and hypoglycemia duration (area under the curve (AUC) <70mg/dL) were compared in all patients and in those with the greatest MAGE (highest quartile).

RESULTS

At 6 months, a decreased glucose variability was seen, as measured by MAGE, M, and SD (median: -28mg/dL (interquartile range, -48 to 1), p=0.03; -22(-40 to 0), p=0.04; -11(-23 to 0), p=0.009; respectively). Patients with the greatest initial glucose variability (MAGE quartile 4) showed a greater decrease in both MAGE (-47mg/dL (-103 to -34) vs -20 (-36 to 17), p=0.01) and AUC <70 (-10.7mg/dL x day (-15 to 0) vs -1.1 (-4.7 to 3.8), p=0.03) as compared to all others. Patients with longer initial hypoglycemia (AUC quartile 4) achieved a greater reduction in AUC <70 (-9.7mg/dL x day(-15 to -6.5) vs -0.08 (-2.9 to 3.8), p=0.003). A correlation was found between ΔMAGE-ΔAUC (r 0.4, p=0.03).

CONCLUSIONS

During CSII, glucose variability significantly decreased, especially in patients with the greatest initial variability. Hypoglycemia was also markedly less in patients with greater variability, with the greatest reduction occurring in those who experienced more marked hypoglycemia with CSII.