Glucose variability assessed by low blood glucose index is predictive of hypoglycemic events in patients with type 1 diabetes switched to pump therapy

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice

Background and objective

Hypoglycemia is a key barrier to achieving optimal glycemic control in people with diabetes, which has been proven to cause a set of deleterious outcomes, such as impaired cognition, increased cardiovascular disease, and mortality. Hypoglycemia prediction has come to play a role in diabetes management as big data analysis and machine learning (ML) approaches have become increasingly prevalent in recent years. As a result, a review is needed to summarize the existing prediction algorithms and models to guide better clinical practice in hypoglycemia prevention.

Materials and methods

PubMed, EMBASE, and the Cochrane Library were searched for relevant studies published between 1 January 2015 and 8 December 2022. Five hypoglycemia prediction aspects were covered: real-time hypoglycemia, mild and severe hypoglycemia, nocturnal hypoglycemia, inpatient hypoglycemia, and other hypoglycemia (postprandial, exercise-related).

Results

From the 5,042 records retrieved, we included 79 studies in our analysis. Two major categories of prediction models are identified by an overview of the chosen studies: simple or logistic regression models based on clinical data and data-based ML models (continuous glucose monitoring data is most commonly used). Models utilizing clinical data have identified a variety of risk factors that can lead to hypoglycemic events. Data-driven models based on various techniques such as neural networks, autoregressive, ensemble learning, supervised learning, and mathematical formulas have also revealed suggestive features in cases of hypoglycemia prediction.

Conclusion

In this study, we looked deep into the currently established hypoglycemia prediction models and identified hypoglycemia risk factors from various perspectives, which may provide readers with a better understanding of future trends in this topic.

Glycemic Variability in Type 1 Diabetes Mellitus Pregnancies—Novel Parameters in Predicting Large-for-Gestational-Age Neonates: A Prospective Cohort Study

Pregnancies with type 1 diabetes mellitus (T1DM) have a high incidence of large-for-gestational-age neonates (LGA) despite optimal glycemic control. In recent years, glycemic variability (GV) has emerged as a possible risk factor for LGA, but the results of the conducted studies are unclear. This study analyzed the association between GV and LGA development in pregnancies with T1DM. This was a prospective cohort study of patients with T1DM who used continuous glucose monitoring (CGM) during pregnancy. Patients were followed from the first trimester to birth. GV parameters were calculated for every trimester using the EasyGV calculator. The main outcomes were LGA or no-LGA. Logistic regression analysis was used to assess the association between GV parameters and LGA. In total, 66 patients were included. The incidence of LGA was 36%. The analysis extracted several GV parameters that were significantly associated with the risk of LGA. The J-index was the only significant parameter in every trimester of pregnancy (odds ratios with confidence intervals were 1.33 (1.02, 1.73), 3.18 (1.12, 9.07), and 1.37 (1.03, 1.82), respectively. Increased GV is a risk factor for development of LGA. The J-index is a possible novel GV parameter that may be assessed in all three trimesters of pregnancy together with glycated hemoglobin and time-in-range.

Continuous Glucose Monitoring for the Detection of Hypoglycemia in Patients With Diabetes of the Exocrine Pancreas

BACKGROUND

Detailed evaluations of hypoglycemia and associated indices based on continuous glucose monitoring (CGM) are limited in patients with diabetes of the exocrine pancreas. Our study sought to evaluate the frequency and pattern of hypoglycemic events and to investigate hypoglycemia-specific indices in this population.

METHODS

This was a cross-sectional study comprising 83 participants with diabetes of the exocrine pancreas. CGM and self-monitoring of blood glucose (SMBG) were performed on all participants for a minimum period of 72 hours. The frequency and pattern of hypoglycemic events, as well as hypoglycemia-related indices, were evaluated.

RESULTS

Hypoglycemia was detected in 90.4% of patients using CGM and 38.5% of patients using SMBG. Nocturnal hypoglycemic events were more frequent (1.9 episodes/patient) and prolonged (142 minutes) compared with day-time events (1.1 episodes/patient; 82.8 minutes, P < 0.05). The mean low blood glucose index was 2.1, and glycemic risk assessment diabetes equation hypoglycemia was 9.1%. The mean time spent below (TSB) <70 mg/dL was 9.2%, and TSB <54 mg/dL was 3.7%. The mean area under curve (AUC) <70 mg/dL was 1.7 ± 2.5 mg/dL/hour and AUC <54 mg/dL was 0.6 ± 1.3 mg/dL/hour. All of the CGM-derived hypoglycemic indices were significantly more deranged at night compared with during the day (P < 0.05).

CONCLUSION

Patients with diabetes of the exocrine pancreas have a high frequency of hypoglycemic episodes that are predominantly nocturnal. CGM is superior to SMBG in the detection of nocturnal and asymptomatic hypoglycemic episodes. CGM-derived hypoglycemic indices are beneficial in estimating the risk of hypoglycemia.

Low Blood Glucose Index and Hypoglycaemia Risk: Insulin Glargine 300 U/mL Versus Insulin Glargine 100 U/mL in Type 2 Diabetes

INTRODUCTION

We examined differences in hypoglycaemia risk between insulin glargine 300 U/mL (Gla-300) and insulin glargine 100 U/mL (Gla-100) in individuals with type 2 diabetes (T2DM) using the low blood glucose index (LBGI).

METHODS

Daily profiles of self-monitored plasma glucose (SMPG) from the EDITION 2, EDITION 3 and SENIOR treat-to-target trials of Gla-300 versus Gla-100 were used to compute the LBGI, which is an established metric of hypoglycaemia risk. The analysis also examined documented (blood glucose readings < 3.0 mmol/L [54 mg/dL]) symptomatic hypoglycaemia (DSH).

RESULTS

Overall LBGI in EDITION 2 and SENIOR and night-time LBGI in all three trials were significantly (p < 0.05) lower with Gla-300 versus Gla-100. The largest differences between Gla-300 and Gla-100 were observed during the night. In all three trials, individual LBGI results correlated with the observed number of DSH episodes per participant (EDITION 2 [r = 0.35, p < 0.001]; EDITION 3 [r = 0.26, p < 0.001]; SENIOR [r = 0.30, p < 0.001]). Participants at moderate risk of experiencing hypoglycaemia (defined as LBGI > 1.1) reported 4- to 8-fold more frequent DSH events than those at minimal risk (LBGI ≤ 1.1) (p ≤  0.009).

CONCLUSIONS

The LBGI identified individuals with T2DM at risk for hypoglycaemia using SMPG data and correlated with the number of DSH events. Using the LBGI metric, a lower risk of hypoglycaemia with Gla-300 than Gla-100 was observed in all three trials. The finding that differences in LBGI are greater at night is consistent with previously published differences in the pharmacokinetic profiles of Gla-300 and Gla-100, which provides the physiological foundation for the presented results.

Beyond HbA1c : using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision-making

Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user-friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research-related goals and clinical guidance for individuals. Traditionally, HbA_1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA_1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo- and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review presents several clinical scenarios of glycaemic outcomes from CGM data that can be analysed to describe glycaemic variability and its attendant risks of hyperglycaemia and hypoglycaemia, moving towards relevant interpretation of the complex CGM data streams.

Day-to-day fasting self-monitored blood glucose variability is associated with risk of hypoglycaemia in insulin-treated patients with type 1 and type 2 diabetes: A post hoc analysis of the SWITCH Trials

AIMS

To investigate the association between day-to-day fasting self-monitored blood glucose (SMBG) variability and risk of hypoglycaemia in type 1 (T1D) and type 2 diabetes (T2D), and to compare day-to-day fasting SMBG variability between treatments with insulin degludec (degludec) and insulin glargine 100 units/mL (glargine U100).

MATERIALS AND METHODS

Data were retrieved from two double-blind, randomized, treat-to-target, two-period (32 weeks each) crossover trials of degludec vs glargine U100 in T1D (SWITCH 1, n = 501) and T2D (SWITCH 2, n = 720). Available fasting SMBGs were used to determine the standard deviation (SD) of day-to-day fasting SMBG variability for each patient and the treatment combination. The association between day-to-day fasting SMBG variability and overall symptomatic, nocturnal symptomatic and severe hypoglycaemia was analysed for the pooled population using linear regression, with fasting SMBG variability included as a three-level factor defined by population tertiles. Finally, day-to-day fasting SMBG variability was compared between treatments.

RESULTS

Linear regression showed that day-to-day fasting SMBG variability was significantly associated with overall symptomatic, nocturnal symptomatic and severe hypoglycaemia risk in T1D and T2D (P < 0.05). Day-to-day fasting SMBG variability was significantly associated (P < 0.01) with all categories of hypoglycaemia risk, with the exception of severe hypoglycaemia in T2D when analysed within tertiles. Degludec was associated with 4% lower day-to-day fasting SMBG variability than glargine U100 in T1D (P = 0.0082) and with 10% lower day-to-day fasting SMBG variability in T2D (P < 0.0001).

CONCLUSIONS

Higher day-to-day fasting SMBG variability is associated with an increased risk of overall symptomatic, nocturnal symptomatic and severe hypoglycaemia. Degludec has significantly lower day-to-day fasting SMBG variability vs glargine U100.

Is insulin pump therapy effective in Type 1 diabetes?

There continues to be uncertainty about the effectiveness in Type 1 diabetes of insulin pump therapy (continuous subcutaneous insulin infusion, CSII) vs. multiple daily insulin injections (MDI). This narrative review discusses the reasons for this uncertainty, summarizes the current evidence base for CSII and suggests some future research needs. There are difficulties in interpreting trials of CSII because effectiveness varies widely due to factors such as differing baseline control, suboptimal use of best CSII practices, and psychological factors, for example, high external locus of control, non-adherence and lack of motivation. Many summary meta-analyses are also misleading because of poor trial selection (e.g. short duration, obsolete pumps, low baseline rate of hypoglycaemia) and reliance on mean effect size for decision-making. Both MDI and CSII can achieve strict glycaemic control without hypoglycaemia in some people with Type 1 diabetes, especially those who are motivated and have undergone structured diabetes education, and with high levels of ongoing input from healthcare professionals. CSII is particularly effective in those people with Type 1 diabetes who have not achieved target HbA_1c levels without disabling hypoglycaemia using best attempts with MDI, and here there can be valuable and substantial improvement. Insulin pumps are safe, effective and accepted when used in newly diagnosed diabetes, particularly in children, where MDI may not be practicable. Future research needs include more studies on mortality associated with insulin pumps where registry data have suggested lower rates vs. MDI; and psychological strategies to improve non-adherence and suboptimal glycaemic outcomes on CSII.

Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus

INTRODUCTION

Recent publications frequently introduce new indexes to measure glycemic variability (GV), quality of glycemic control, or glycemic risk; however, there is a lack of evidence supporting the use of one particular parameter, especially in clinical practice.

METHODS

A cohort of type 2 diabetes mellitus (T2DM) patients in ambulatory care were followed using continuous glucose monitoring sensors (CGM). Mean glucose (MG), standard deviation, coefficient of variation (CV), interquartile range, CONGA1, 2, and 4, MAGE, M value, J index, high blood glucose index, and low blood glucose index (LBGI) were estimated. Hypoglycemia incidence (<54 mg/dl) was calculated. Area under the curve (AUC) was determined for different indexes as identifiers of patients with risk of hypoglycemia (IRH). Optimal cutoff thresholds were determined from analysis of the receiver operating characteristic curves.

RESULTS

CGM data for 657 days from 140 T2DM patients (4.69 average days per patient) were analyzed. Hypoglycemia was present in 50 patients with 144 hypoglycemic events in total (incidence rate of 0.22 events per patient/day). In the multivariate analysis, both CV (OR 1.20, 95% CI 1.12-1.28, P < .001) and LBGI (OR 4.83, 95% CI 2.41-9.71, P < .001) were shown to have a statistically significant association with hypoglycemia. The highest AUC were for CV (0.84; 95% CI 0.77-0.91) and LBGI (0.95; 95% CI 0.92-0.98). The optimal cutoff threshold for CV as IRH was 34%, and 3.4 for LBGI.

CONCLUSION

This analysis shows that CV can be recommended as the preferred parameter of GV to be used in clinical practice for T2DM patients. LBGI is the preferred IRH between glycemic risk indexes.

Glucotypes reveal new patterns of glucose dysregulation

Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.

Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2)

AIMS/HYPOTHESIS

The Trial Comparing Cardiovascular Safety of Insulin Degludec vs Insulin Glargine in Patients with Type 2 Diabetes at High Risk of Cardiovascular Events (DEVOTE) was a double-blind, randomised, event-driven, treat-to-target prospective trial comparing the cardiovascular safety of insulin degludec with that of insulin glargine U100 (100 units/ml) in patients with type 2 diabetes at high risk of cardiovascular events. This paper reports a secondary analysis investigating associations of day-to-day fasting glycaemic variability (pre-breakfast self-measured blood glucose [SMBG]) with severe hypoglycaemia and cardiovascular outcomes.

METHODS

In DEVOTE, patients with type 2 diabetes were randomised to receive insulin degludec or insulin glargine U100 once daily. The primary outcome was the first occurrence of an adjudicated major adverse cardiovascular event (MACE). Adjudicated severe hypoglycaemia was the pre-specified secondary outcome. In this article, day-to-day fasting glycaemic variability was based on the standard deviation of the pre-breakfast SMBG measurements. The variability measure was calculated as follows. Each month, only the three pre-breakfast SMBG measurements recorded before contact with the site were used to determine a day-to-day fasting glycaemic variability measure for each patient. For each patient, the variance of the three log-transformed pre-breakfast SMBG measurements each month was determined. The standard deviation was determined as the square root of the mean of these monthly variances and was defined as day-to-day fasting glycaemic variability. The associations between day-to-day fasting glycaemic variability and severe hypoglycaemia, MACE and all-cause mortality were analysed for the pooled trial population with Cox proportional hazards models. Several sensitivity analyses were conducted, including adjustments for baseline characteristics and most recent HbA_1c.

RESULTS

Day-to-day fasting glycaemic variability was significantly associated with severe hypoglycaemia (HR 4.11, 95% CI 3.15, 5.35), MACE (HR 1.36, 95% CI 1.12, 1.65) and all-cause mortality (HR 1.58, 95% CI 1.23, 2.03) before adjustments. The increased risks of severe hypoglycaemia, MACE and all-cause mortality translate into 2.7-, 1.2- and 1.4-fold risk, respectively, when a patient's day-to-day fasting glycaemic variability measure is doubled. The significant relationships of day-to-day fasting glycaemic variability with severe hypoglycaemia and all-cause mortality were maintained after adjustments. However, the significant association with MACE was not maintained following adjustment for baseline characteristics with either baseline HbA_1c (HR 1.19, 95% CI 0.96, 1.47) or the most recent HbA_1c measurement throughout the trial (HR 1.21, 95% CI 0.98, 1.49).

CONCLUSIONS/INTERPRETATION

Higher day-to-day fasting glycaemic variability is associated with increased risks of severe hypoglycaemia and all-cause mortality.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01959529.

SAFETY AND EFFICACY OF A PERI-OPERATIVE PROTOCOL FOR PATIENTS WITH DIABETES TREATED WITH CONTINUOUS SUBCUTANEOUS INSULIN INFUSION WHO ARE ADMITTED FOR SAME-DAY SURGERY

OBJECTIVE

The number of people with diabetes using continuous subcutaneous insulin infusions (CSII) with an insulin pump has risen dramatically, creating new challenges when these patients are admitted to the hospital for surgical or other procedures. There is limited literature guiding CSII use during surgical procedures.

METHODS

The study was carried out in a large, urban, tertiary care hospital. We enrolled 49 patients using insulin pump therapy presenting for 57 elective surgeries. We developed a CSII peri-operative glycemic management protocol (PGMP) to standardize insulin pump management in patients admitted to a same-day surgery unit (SDSU). The purpose was evaluate the safety (% capillary blood glucose (CBG) <70 mg/dL and/or pump incidents) and efficacy (first postoperative CBG ≤200 mg/dL) of the CSII PGMP. We determine the contribution of admission CBG, type of anesthesia, surgery length, and peri-operative steroid use on postoperative glycemic control.

RESULTS

Overall, 63% of patients treated according to the CSII PGMP had a first postoperative CBG ≤200 mg/dL. There were no episodes of intra- or postoperative hypoglycemia. For patients treated with the CSII PGMP, the mean postoperative CBG was lower in patients with anticipated or actual surgical length ≤120 minutes (158.1 ± 53.9 vs. 216 ± 77.7 mg/dL, P<.01). No differences were observed with admission CBG, type of anesthesia, or steroid use.

CONCLUSIONS

This study demonstrates that a CSII PGMP is both safe and effective for patients admitted for elective surgical procedures and provides an example of a standardized protocol for use in clinical practice.

Improvement of β-cell function ameliorated glycemic variability in patients with newly diagnosed type 2 diabetes after short-term continuous subcutaneous insulin infusion or in combination with sitagliptin treatment: a randomized control trial

Glycemic variability (GV) has been proposed as contributor to diabetes-related macrovascular complications. This randomized control trial evaluated a new combination therapy with continuous subcutaneous insulin infusion (CSII) plus sitagliptin (CSII + sitagliptin) vs. CSII only in terms of metabolic control, GV and β-cell function in patients with newly diagnosed type 2 diabetes (T2DM). 217 patients were randomized to two weeks of CSII (n = 108) or CSII + sitagliptin (n = 109) therapy. As a measure of GV, the coefficient of variation (CV) was computed from capillary blood glucose during the first and second week, respectively. β-cell function before and after treatment was determined with the Insulin Secretion-Sensitivity Index-2 (ISSI-2). Good metabolic controls were established with both therapies. CSII + sitagliptin therapy resulted in greater improvements in CV and ISSI-2 than CSII alone (all P = 0.000). For each group, change in CV was inversely correlated with change in ISSI-2 (r = -0.529, P = 0.000 and r = -0.433, P = 0.000, respectively). The multivariate regression analysis demonstrated that improved ISSI-2 was the only independent contributor to reduced CV in both groups (standardized β = -0.388, P = 0.004 and standardized β = -0.472, P = 0.000, respectively). Correction of β-cell function in newly diagnosed T2DM patients via use of either CSII or CSII + sitagliptin therapy was feasible in controlling GV to prevent secondary complications of T2DM. Moreover, CSII + sitagliptin therapy was superior to CSII monotherapy in terms of GV.

Poincaré plot quantification for assessing glucose variability from continuous glucose monitoring systems and a new risk marker for hypoglycemia: application to type 1 diabetes patients switching to continuous subcutaneous insulin infusion

BACKGROUND

The Poincaré plot (PCP) is a valuable tool for describing glucose variability (GV) from continuous glucose monitoring (CGM) but remains only visual and qualitative. The aim of this work was to validate metrics for the geometry of the PCP in type 1 diabetes and to apply them to the study of a series of patients switching to continuous subcutaneous insulin infusion (CSII).

PATIENTS AND METHODS

We reviewed the CGM profiles of 44 patients with type 1 diabetes. A subgroup (n=13) used CGM before and after 6 months on CSII. Additionally, we prospectively collected seven recordings from healthy controls. The new PCP metrics were correlated with hypoglycemia and classical GV indices and were compared between groups.

RESULTS

SDs related to the PCP fitting ellipse (SD1, SD2) and area and shape of the fitting ellipse (SFE) were all higher in diabetes patients than in the controls and decreased significantly on CSII. SD1 represented short-term GV and was equivalent to continuous overlapping net glycemic action (CONGA). SD2 represented long-term GV and correlated with the SD of glucose levels (r ≥ 0.98), mean of daily differences (r ≥ 0.91), and mean amplitude of glycemic excursions (r ≥ 0.88). SFE correlated positively with CONGA at 1 h but not with the other indices and was inversely correlated with hypoglycemic episodes (Spearman's ρ=-0.42), independently of the coefficient of variation and the Low Blood Glucose Index in a multivariate analysis (partial r=-0.34).

CONCLUSIONS

PCP metrics are correlated with known GV indices and may be used for the study of CGM recording series in type 1 diabetes. SFE is a new risk marker for hypoglycemia.