The Impact of Nocturnal Hypoglycemia on Clinical and Cost-Related Issues in Patients With Type 1 and Type 2 Diabetes

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Clinical characteristics and influencing factors of hypoglycemia in hospitalized patients with type 2 diabetes mellitus: A cross-sectional study

AIM

Analysed clinical characteristics and influencing factors for hypoglycemia in hospitalized patients with type 2 diabetes mellitus (T2DM), and providing a scientific reference for precision nursing care of hypoglycemia in hospitalized patients with T2DM.

DESIGN

A cross-sectional study.

METHODS

This study involved 378 hospitalized patients diagnosed with T2DM who have suffered hypoglycemia. Through questionnaires and electronic medical records to obtain the data concerning the general information, clinical symptoms, all recorded blood glucose information, and the diabetes knowledge and self-management level of the patients. The clinical characteristics and influencing factors for hypoglycemia were analysed on the basis of the classification of hypoglycemia published by the American Diabetes Association in 2020.

RESULTS

Among 378 patients, 207 patients (54.76%) were experiencing Grade 1 hypoglycemia and 171 patients (45.24%) were experiencing Grade 2 hypoglycemia. Hypoglycemia and Grade 2 hypoglycemia in patients with T2DM occurred predominantly within the first 3 days of hospitalization. Hypoglycemia occurred most frequently after breakfast (74 cases, 19.6%), of which Grade 1 hypoglycemia and Grade 2 hypoglycemia accounted for 50%, respectively. Multivariable logistic regression identified risk factors for Grade 2 hypoglycemia in hospitalized patients with type 2 diabetes: older age, longer duration of diabetes, low body weight, diabetic nephropathy stages 4-5, diabetic autonomic neuropathy, inadequate self-management ability and diabetes knowledge, and lower educational background.

CONCLUSIONS

Hypoglycemia in type 2 diabetes occurs in the first 3 days during the hospitalization and most often after breakfast during the full day. Identifying high-risk individuals and providing a scientific reference for precision nursing care of hypoglycemia in hospitalized patients with T2DM.

Real-World Effects of Second-Generation Versus Earlier Intermediate/Basal Insulin Analogues on Rates of Hypoglycemia in Adults with Type 1 and 2 Diabetes (iNPHORM, US)

INTRODUCTION

Second-generation basal insulin analogues have been shown to reduce hypoglycemia in several trials and observational studies of select populations; however, it remains unclear whether these results persist in real-world settings. Using self-reported hypoglycemia events, we assessed whether second-generation basal insulin analogues reduce rates of hypoglycemia events (non-severe/severe; overall/daytime/nocturnal) compared to earlier intermediate/basal insulin analogues among people with insulin-treated type 1 or 2 diabetes.

METHODS

We used prospectively collected data from the Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-World Models (iNPHORM) panel survey. This US-wide, 1-year internet-based survey assessed hypoglycemia experiences and related sociodemographic and clinical characteristics of people with diabetes (February 2020-March 2021). We estimated population-average rate ratios for hypoglycemia comparing second-generation to earlier intermediate/basal insulin analogues using negative binomial regression, adjusting for confounders. Within-person variability of repeated observations was addressed with generalized estimating equations.

RESULTS

Among iNPHORM participants with complete data, N = 413 used an intermediate/basal insulin analogue for ≥ 1 month during follow-up. After adjusting for baseline and time-updated confounders, average second-generation basal insulin analogue users experienced a 19% (95% CI 3-32%, p = 0.02) lower rate of overall non-severe hypoglycemia and 43% (95% CI 26-56%, p < 0.001) a lower rate of nocturnal non-severe hypoglycemia compared to earlier intermediate/basal insulin users. Overall severe hypoglycemia rates were similar among second-generation and earlier intermediate/basal insulin users (p = 0.35); however, the rate of severe nocturnal hypoglycemia was reduced by 44% (95% CI 10-65%, p = 0.02) among second-generation insulin users compared to earlier intermediate/basal insulin users.

CONCLUSION

Our real-world results suggest second-generation basal insulin analogues reduce rates of hypoglycemia, especially nocturnal non-severe and severe events. Whenever possible and feasible, clinicians should prioritize prescribing these agents over first-generation basal or intermediate insulin in people with type 1 and 2 diabetes.

Severe Hypoglycemia and the Use of Glucagon Rescue Agents: An Observational Survey in Adults With Type 1 Diabetes

Severe hypoglycemia (SH) is the most frequent and potentially serious complication affecting individuals with type 1 diabetes and can have major clinical and psychosocial consequences. Glucagon is the only approved treatment for SH that can be administered by non-health care professionals (HCPs); however, reports on the experiences and emotions of people with type 1 diabetes associated with SH and glucagon rescue use are limited. This survey study demonstrated that an increasing number of individuals with type 1 diabetes have current and filled prescriptions for glucagon and have been educated about glucagon rescue use by an HCP. Despite this positive trend, challenges with SH remain, including a high level of health care resource utilization, considerable out-of-pocket expenses for glucagon kits, a high prevalence of hypoglycemia unawareness, and a negative emotional impact on individuals with diabetes. Nocturnal and exercise-related hypoglycemia were concerns for most survey participants.

Hypoglycaemia events with iGlarLixi versus premix biphasic insulin aspart 30 (BIAsp 30) in people with type 2 diabetes advancing from basal insulin: An analysis of the SoliMix trial

AIMS

To explore details of the incidence and rates of daytime and nocturnal hypoglycaemia, levels of hypoglycaemia, and relationship to glycated haemoglobin (HbA1c), when comparing iGlarLixi versus premixed biphasic insulin aspart 30 (BIAsp 30) in the SoliMix randomized controlled trial.

MATERIALS AND METHODS

This exploratory analysis of SoliMix used logistic regression and negative binomial regression analyses to assess between-treatment differences in the incidence and rates of hypoglycaemia by time of day. A negative binomial model was used to derive estimated annualized hypoglycaemia rates as a function of HbA1c.

RESULTS

iGlarLixi was associated with lower incidence and rates of American Diabetes Association Level 2 (<54 mg/dL [<3.0 mmol/L]) hypoglycaemia during both night and day versus BIAsp 30. Incidence and rates of Level 1 (<70 to ≥54 mg/dL [<3.9 to ≥3.0 mmol/L]) hypoglycaemia were also mostly shown to be reduced with iGlarLixi versus BIAsp 30. Severe (Level 3) events were too few for analysis (n = 3). iGlarLixi was associated with lower modelled event rates of Level 2 and Level 1 hypoglycaemia over a wide range of HbA1c levels versus BIAsp 30.

CONCLUSIONS

These results show that the lower HbA1c levels and weight benefit seen with iGlarLixi versus premixed BIAsp 30 in people with type 2 diabetes advancing their basal insulin therapy in the SoliMix trial are also accompanied by a lower risk of hypoglycaemia at any time of day and across a broad range of HbA1c levels.

Hypoglycemia unawareness identified by continuous glucose monitoring system is frequent in outpatients with type 2 diabetes without receiving intensive therapeutic interventions

BACKGROUND

Patients with diabetes are prone to asymptomatic hypoglycemia (AH) due to diminished ability to perceive the onset of hypoglycemia. However, the actual prevalence and influencing factors of AH in outpatients with type 2 diabetes (T2DM) have not been well investigated.

METHODS

A total of 351 outpatients with T2DM underwent glucose monitoring by continuous glucose monitoring system (CGMS) for consecutive 72 h without changing their lifestyle and treatment regimens. Hypoglycemia is defined as a blood glucose level less than 3.9 mmol/L, which was further divided into Level 1 hypoglycemia (blood glucose 3.0-3.9 mmol/L) and Level 2 hypoglycemia (blood glucose < 3.0 mmol/L). Univariate and multivariate logistic regression analyses were used to determine the possible risk factors of AH.

RESULTS

In all 351 subjects studied, 137 outpatients (39.0%) were captured AH events, in which Level 1 AH and Level 2 AH accounted for 61.3% and 38.7%, respectively. 85 (62.0%) of the AH patients experienced nocturnal asymptomatic hypoglycemia (NAH) and 25 (18.2%) exclusively NAH. Multivariate logistic regression analysis demonstrated that patients with younger age, lower hemoglobin A1c (HbA1c), and higher systolic blood pressure (SBP) levels were associated with increased risk of AH. While after further grading of AH, male sex and Dipeptidylpeptidase-4 inhibitors (DPP4i) regime were shown to be associated with lower risk of Level 2 AH.

CONCLUSIONS

Hypoglycemia unawareness could be frequently observed at either daytime or nighttime, although NAH was more common, in outpatients with T2DM. Relative relax HbA1c targets should be considered for patients who are prone to AH.

Scientific advisory on nocturnal hypoglycemia in insulin-treated patients with diabetes: Recommendations from Indian experts

INTRODUCTION

Insulin is one of the commonly prescribed glucose lowering agents in diabetes. Hypoglycemia is the most common complication, and severe hypoglycemia is the most serious complication of insulin therapy. Almost half of all severe hypoglycemia episodes (HEs) occur at night. However, patients are often unaware of their nocturnal hypoglycaemia (NH) risk. Additionally, both healthcare professionals and patients find it difficult to manage NH. The purpose of this expert group meeting is to improve NH awareness and provide guidance for the physicians to recognize and manage NH.

METHOD

The panel of experts in an e-board deliberated extensively upon the available literature and guidelines on hypoglycemia and NH discussed the consensus on definition, detection, reporting, monitoring, treatment, and optimization of therapy in NH.

RESULT

& Conclusion: Though there are many guidelines on the management of HEs in patients with diabetes, very few touch the topic of NH. This scientific advisory on management of NH in insulin treated patients with diabetes is formulated to address this gap in understanding regarding management of NH. The experts provide recommendations for the nocturnal window, defining NH based on blood glucose values, recognition, prevention and management of NH.

Largest Amplitude of Glycemic Excursion Calculating from Self-Monitoring Blood Glucose Predicted the Episodes of Nocturnal Asymptomatic Hypoglycemia Detecting by Continuous Glucose Monitoring in Outpatients with Type 2 Diabetes

AIMS

Nocturnal asymptomatic hypoglycemia (NAH) is a serious complication of diabetes, but it is difficult to be detected clinically. This study was conducted to determine the largest amplitude of glycemic excursion (LAGE) to predict the episodes of NAH in outpatients with type 2 diabetes.

METHODS

Data were obtained from 313 outpatients with type 2 diabetes. All subjects received continuous glucose monitoring (CGM) for consecutive 72 hours. The episodes of NAH and glycemic variability indices (glucose standard deviation [SD], mean amplitude of plasma glucose excursion [MAGE], mean blood glucose [MBG]) were accessed via CGM. LAGE was calculated from self-monitoring blood glucose (SMBG).

RESULTS

A total of 76 people (24.3%) had NAH. Compared to patients without NAH, patients with NAH showed higher levels of glucose SD (2.4 ± 0.9 mmol/L vs 1.7 ± 0.9 mmol/L, p <0.001), MAGE (5.2 ± 2.1 mmol/L vs 3.7 ± 2.0, p<0.001) and LAGE (4.6 ± 2.3 mmol/L vs 3.8 ± 1.9 mmol/L, p=0.007), and lower level of MBG (7.5 ± 1.5 mmol/L vs 8.4 ± 2.2 mmol/L, p=0.002). LAGE was significantly associated with the incidence of NAH and time below rang (TBR) in model 1 [NAH: 1.189 (1.027-1.378), p=0.021; TBR: 0.008 (0.002-0.014), p=0.013] with adjustment for age, BMI, sex, work, hyperlipidemia, complication and medication, and in model 2 [NAH: 1.177 (1.013-1.367), p=0.033; TBR: 0.008 (0.002-0.014), p=0.012] after adjusting for diabetes duration based on model 1, as well as in model 3 [NAH: 1.244 (1.057-1.464), p=0.009; TBR: 0.009 (0.002-0.016), p=0.007] with further adjustment for HbA1c based on model 2. In addition, no significant interactions were found between LAGE and sex, age, HbA1c, duration of diabetes, BMI and insulin therapy on the risk of NAH. The receiver operator characteristic (ROC) curve shows the ideal cutoff value of LAGE for the prediction of NAH was 3.48 mmol/L with 66.7% sensitivity, 50% specificity and 0.587 (95% CI: 0.509-0.665) of area under the ROC curve.

CONCLUSIONS

High glycemic variability is strongly associated with the risk of NAH. The LAGE based on SMBG could be an independent predictor of NAH for outpatients with type 2 diabetes, and LAGE greater than 3.48 mmol/L could act as a warning alarm for high risk of NAH in daily life.

Nursing interventions for people with type 1 diabetes and frequent hypoglycaemia

Type 1 diabetes is a lifelong condition which affects all age ranges, for reasons unknown, and the UK has one of the highest incidences of this complex condition in the world. Type 1 diabetes is caused by autoimmune damage to the insulin-producing β-cells found in the pancreatic islet cells, leading to severe insulin deficiency. People with diabetes need to achieve a target glyosylated haemoglobin level to avoid macro- and microvascular complications, but there is the associated risk of hypoglycaemic events. These can vary in severity and consequences but will likely always cause worry for the person living with diabetes. There are many risk factors and reasons to be explored when looking at hypoglycaemia. This case study explores the nursing interventions that can be safely worked through and prioritised, within the community setting, to allow people with diabetes to be safe from severe hypoglycaemia, thus improving their quality of life and safety, as well as reducing costs for the NHS.

One Hundred Years of Insulin: Value Beyond Price in Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is a chronic, progressive disease that frequently necessitates treatment with basal insulin to maintain adequate glycaemic control. In considering the value of different basal insulin therapies, although acquisition costs are of increasing importance to budget-constrained healthcare systems, value beyond simple price considerations should be taken into account. Whilst human basal insulins are of lower acquisition cost compared to long-acting insulin analogues, this difference in price has the potential to be offset in terms of total healthcare system value through the ultra-long duration of action and low variability in glucose-lowering activity which have been translated into real clinical benefits, in particular a reduced risk of hypoglycaemic events. The maintenance of glycaemic targets and avoidance of hypoglycaemia that have been associated with insulin analogues represent a significant value consideration, beyond price, for the use of basal insulin analogues to manage type 2 diabetes mellitus from the perspective of all stakeholders within the healthcare system, including payers, healthcare professionals, patients and society.

The impact of hypoglycaemia on quality of life outcomes among adults with type 1 diabetes: A systematic review

Hypoglycaemia is a common barrier to optimal glycaemic management and often feared among adults with type 1 diabetes. The aim of this systematic review was to summarize current evidence regarding the impact of hypoglycaemia on quality of life (QoL) and related outcomes. Electronic searches of MEDLINE, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials were conducted. Peer-reviewed empirical studies investigating the relationship between hypoglycaemia and QoL were eligible for inclusion. Thirty studies met the inclusion criteria. Extracted data was summarized in a narrative synthesis according to Synthesis Without Meta-Analysis guidelines. None of the studies examined the impact of hypoglycaemia on general QoL. There was no association between hypoglycaemia and diabetes-specific QoL in four of the 30 studies. Severe hypoglycaemia was associated with greater fear of hypoglycaemia and diabetes distress, and lower general emotional well-being, but not with depression, anxiety, or health status. Self-treated hypoglycaemia was associated with greater fear of hypoglycaemia. With the exception of fear of hypoglycaemia, this review shows mixed associations between hypoglycaemia and psychological outcomes. Further research is needed to investigate the impact of hypoglycaemia on other domains of QoL.

Fear of hypoglycemia in adults with diabetes mellitus switching to treatment with IDegAsp co-formulation to examine real-world setting: an observational study (The HATICE study)

OBJECTIVES

To evaluate the clinical results of insulin degludec/aspart (IDEgAsp) therapy and its effect on the fear of hypoglycemia.

METHODS

A prospective observational study has been conducted through surveys of 36 patients using insulin because of type 2 diabetes mellitus who initiated treatment with IDegAsp switching from other insulins. Patients, 18-75 years old, were recruited to the study, consecutively. Participants' age, gender, height, weight, body mass index (BMI), daily insulin dose, glycated hemoglobin (HbA1c), hypoglycemia rate, hypoglycemia fear survey (HFS) were recorded at the beginning of the study. By the end of 12th month, data was re-measured and compared with each other.

RESULTS

HbA1c was declined by mean of -1.59% (95% CI -1.06 to -2.12, p<0.001). There was also a significant decrease in mean, daily insulin dose, weight and BMI values of patients via IDegAsp. While there was an increase in the amount of dipeptidyl peptidase 4-inhibitors (DPP4-i) and sodium-glucose co-transporter 2-inhibitors (SGLT2-i), there was a decrease in daily injection frequency. There was also a significant decrease in the median values of monthly hypoglycemia rate (from 2.0 to 1.0, p<0.001) and the entire HFS scores (HFS-T: from 1.09 to 0.73, p<0.001; HFS-B: from 0.83 to 0.60, p<0.001; HFS-W: from 1.33 to 0.88, p<0.001). There was a strong positive correlation between ΔHFS-B and daily injection frequency (Rho: 0.398; P: 0.016).

CONCLUSIONS

IDegAsp co-formulation, combined with DPP4-i and/or SGLT2-i, can provide usefulness in terms of rates of hypoglycemia, reduced HbA1c, less injection administration, and decreased the fear of hypoglycemia in diabetics.

Real-World Improvements in Hypoglycemia in an Insulin-Dependent Cohort With Diabetes Mellitus Pre/Post Tandem Basal-Iq Technology Remote Software Update

OBJECTIVE

Software updatable insulin pumps, such as the t:slim X2 pump from Tandem Diabetes Care, enable access to new technology as soon as it is commercialized. The remote software update process allows for minimal interruption in therapy compared to purchasing a new pump; however, little quantitative data exist on the software update process or on pre/post therapeutic outcomes. We examined real-world usage and impact of a remote software updatable predictive low-glucose suspend (PLGS) technology designed to reduce hypoglycemic events in people with insulin-dependent diabetes.

METHODS

Approximately 15,000 U.S. Tandem pump users remotely updated their t:slim X2 software to Basal-IQ PLGS technology since its commercial release. We performed a retrospective analysis of users who uploaded at least 21 days of pre/post PLGS update usage data to the Tandem t:connect web application between August 28, 2018, and October 21, 2019 (N = 6,170). Insulin delivery and sensor-glucose values were analyzed per recent international consensus and American Diabetes Association guidelines. Software update performance was also assessed.

RESULTS

Median software update time was 5.36 minutes. Overall glycemic outcomes for pre and post software update showed a decrease in sensor time <70 mg/dL from 2.14 to 1.18% (-1.01; 95% confidence interval [CI], -0.97, -1.05; P<.001), with overall sensor time 70 to 180 mg/dL increasing from 57.8 to 58.5% (0.64; 95% CI, 0.04, 1.24; P<.001). These improvements were sustained at 3, 6, and 9 months after the update.

CONCLUSION

Introduction of a software updatable PLGS algorithm for the Tandem t:slim X2 insulin pump resulted in sustained reductions of hypoglycemia.

ABBREVIATIONS

ADA = American Diabetes Association; CGM = continuous glucose monitoring; CI = confidence interval; PLGS = predictive low-glucose suspend; SG = sensor glucose; T1D = type 1 diabetes; T2D = type 2 diabetes; TIR = time-in-range.

Modeling continuous glucose monitoring (CGM) data during sleep

We introduce a multilevel functional Beta model to quantify the blood glucose levels measured by continuous glucose monitors for multiple days in study participants with type 2 diabetes mellitus. The model estimates the subject-specific marginal quantiles, quantifies the within- and between-subject variability, and produces interpretable parameters of blood glucose dynamics as a function of time from the actigraphy-estimated sleep onset. Results are validated via simulations and by studying the association between the estimated model parameters and hemoglobin A1c, the gold standard for assessing glucose control in diabetes.

Epidemiology, Quality of Life, and Costs Associated with Hypoglycemia in Patients with Diabetes in Spain: A Systematic Literature Review

OBJECTIVE

To assess the burden of hypoglycemia in patients with diabetes mellitus (DM) in Spain, including epidemiological data and information relating to healthcare resource utilization (HRU) and costs, and patients' quality of life (QoL).

METHODS

A systematic literature review (SLR) was conducted to identify studies that included original information on epidemiology, HRU and costs, and/or QoL associated with hypoglycemia in patients with DM in Spain, published in either Spanish or English, between January 2007 and April 2017.

RESULTS

Fifteen articles, involving 14 studies, were identified in the SLR and included in the analysis. The estimated rate of severe hypoglycemia (SH) events per patient per year ranged from 0.90 to 1.50 in patients with type 1 DM (T1DM) and from 0.30 to 0.63 in patients with type 2 DM (T2DM). The data on HRU differed extensively between studies, making it difficult to draw a conclusion. Total costs per SH event ranged from €409.97 in patients with T1DM to €713.10 in patients with DM. Work absence was reported in 11.80-18% of the working patients. Further, patients who experienced hypoglycemic events expressed a higher fear and had a poorer QoL than those who did not report these events.

CONCLUSION

Although the data included in the SLR were difficult to synthesize due to heterogeneity of the study designs and patient characteristics in the 14 studies, our search identified a high burden associated with hypoglycemic events in terms of HRU and costs, and patients' QoL. Further research is recommended to reach a consensus on hypoglycemia definition and study design to provide robust evidence on the burden of hypoglycemia and to accurately weigh the impact of this acute complication in Spain.

FUNDING

Eli Lilly and Company.

A post-hoc pooled analysis to evaluate the risk of hypoglycaemia with insulin glargine 300 U/mL (Gla-300) versus 100 U/mL (Gla-100) over wider nocturnal windows in individuals with type 2 diabetes on a basal-only insulin regimen

The EDITION trials in type 2 diabetes demonstrated comparable glycaemic control with less nocturnal and anytime (24-hour) hypoglycaemia for insulin glargine 300 U/mL (Gla-300) versus glargine 100 U/mL (Gla-100). However, the predefined nocturnal window (0:00-5:59 AM) may not be the most relevant for clinical practice. This post-hoc analysis compared expansions of the predefined nocturnal interval during basal insulin treatment without prandial insulin. Patient-level, 6-month data, pooled from the EDITION 2 and 3 trials and the EDITION JP 2 trial (N = 1922, basal insulin only) were analysed. Accompanying hypoglycaemia during treatment with Gla-300 was compared to that during treatment with Gla-100, using predefined (0:00-5:59 AM) and expanded (10:00 PM-5:59 AM, 0:00-7:59 AM, 10:00 PM to pre-breakfast SMPG) windows. Confirmed (≤3.9 mmol/L [≤70 mg/dL]) or severe hypoglycaemic events were reported most frequently between 6:00 AM and 8:00 AM. Windows expanded beyond 6:00 AM included more events than other windows. The percentage of participants with at least one event was lower with Gla-300 than Gla-100 in all windows examined. Expanding the nocturnal interval allows better assessment of the risk of hypoglycaemia associated with basal insulin. The risk of nocturnal hypoglycaemia was consistently lower with Gla-300 versus Gla-100 using all four windows.

Similar glycaemic control with less nocturnal hypoglycaemia in a 38-week trial comparing the IDegAsp co-formulation with insulin glargine U100 and insulin aspart in basal insulin-treated subjects with type 2 diabetes mellitus

AIMS

To confirm non-inferiority of insulin degludec/insulin aspart (IDegAsp) once-daily (OD) versus insulin glargine (IGlar) U100 OD + insulin aspart (IAsp) OD for HbA_1c after 26 weeks, and compare efficacy and safety between groups at W26 + W38.

METHODS

A 38-week, randomised, open-label, treat-to-target (HbA_1c < 7.0%) trial in adults with type 2 diabetes mellitus (on basal insulin ± oral antidiabetic drugs; HbA_1c 7.0-10.0%). Randomisation (1:1): IDegAsp or IGlar U100 + IAsp. Intensification to IDegAsp twice daily (BID) was permitted at W26 + W32, or with additional IAsp injections at W26 (maximum IAsp BID) or W32 (maximum IAsp three-times daily).

RESULTS

For W0-W26, mean percentage-change (standard deviation) HbA_1c was: IDegAsp, -1.1 (0.9); IGlar U100 + IAsp, -1.1 (0.8); estimated treatment difference: 0.07% (95% confidence interval [CI]: -0.06; 0.21) confirmed non-inferiority. At W26 and W38, target HbA_1c achievement, and mean fasting and postprandial glucose were similar across groups. At W38, more subjects achieved target HbA_1c without hypoglycaemia with IDegAsp (22.5%) than with IGlar U100 + IAsp (21.1%), with significantly fewer nocturnal episodes (W0-W38, estimated rate ratio: 0.61 [95% CI: 0.40; 0.93]). Safety profiles were similar across treatment groups throughout.

CONCLUSIONS

IDegAsp OD/BID are effective treatment intensification options versus multiple injection basal-bolus therapies, achieving similar glycaemic control, with significantly less nocturnal hypoglycaemia.

Cost-effectiveness of G5 Mobile continuous glucose monitoring device compared to self-monitoring of blood glucose alone for people with type 1 diabetes from the Canadian societal perspective

AIMS

To evaluate the cost-effectiveness of real-time continuous glucose monitoring (CGM) compared to self-monitoring of blood glucose (SMBG) alone in people with type 1 diabetes (T1DM) using multiple daily injections (MDI) from the Canadian societal perspective.

METHODS

The IMS CORE Diabetes Model (v.9.0) was used to assess the long-term (50 years) cost-effectiveness of real-time CGM (G5 Mobile CGM System; Dexcom, Inc., San Diego, CA) compared with SMBG alone for a cohort of adults with poorly-controlled T1DM. Treatment effects and baseline characteristics of patients were derived from the DIAMOND randomized controlled clinical trial; all other assumptions and costs were sourced from published research. The accuracy and clinical effectiveness of G5 Mobile CGM is the same as the G4 Platinum CGM used in the DIAMOND randomized clinical trial. Base case assumptions included (a) baseline HbA1c of 8.6%, (b) change in HbA1c of -1.0% for CGM users vs -0.4% for SMBG users, and (c) disutilities of -0.0142 for non-severe hypoglycemic events (NSHEs) and severe hypoglycemic events (SHEs) not requiring medical intervention, and -0.047 for SHEs requiring medical resources. Treatment costs and outcomes were discounted at 1.5% per year.

RESULTS

The incremental cost-effectiveness ratio for the base case G5 Mobile CGM vs SMBG was $33,789 CAD/quality-adjusted life-year (QALY). Sensitivity analyses showed that base case results were most sensitive to changes in percentage reduction in hypoglycemic events and disutilities associated with hypoglycemic events. The base case results were minimally impacted by changes in baseline HbA1c level, incorporation of indirect costs, changes in the discount rate, and baseline utility of patients.

CONCLUSIONS

The results of this analysis demonstrate that G5 Mobile CGM is cost-effective within the population of adults with T1DM using MDI, assuming a Canadian willingness-to-pay threshold of $50,000 CAD per QALY.

Risk Factors for Nocturnal Hypoglycemia in Insulin-treated Patients With Type 2 Diabetes: A Secondary Analysis of Observational Data Derived From an Integrated Clinical Trial Database

PURPOSE

A trade-off exists in most diabetes therapies between the benefits of good glycemic control and the morbidity of hypoglycemia. Balancing these factors to achieve desired outcomes is a key consideration for personalized diabetes therapy. Hypoglycemia at night (nocturnal hypoglycemia [NH]) is a common but often under-reported problem in insulin-treated patients with type 2 diabetes. To better understand the risk for NH, we pooled data from multiple clinical trials of insulin treatment and specifically examined NH risk factors in relation to glycemic goals.

METHODS

Of 53 randomized trials involving insulin treatment, 18 trials that collected NH data were included. Risk factors associated with NH were identified by using gradient-boosting methods. A proportional hazards model was used to quantify the hazard ratio (HR) for risk factors. By modeling with individual patient data, a patient-level NH risk score distribution was created. Finally, results of the model were used to quantify an adjustment to the glycemic goal that would fully offset each risk factor, all other factors being equal.

FINDINGS

Data pooling resulted in the inclusion of 7341 patients with type 2 diabetes from 18 randomized clinical trials. In the mean 6-month treatment period, 43% of patients experienced at least 1 episode of NH (mean [SD], 1.1 [1.5] events/month). Reduction of glycosylated hemoglobin (HbA_1c) levels during the trial was a risk factor for NH (HR, 1.40 [95% CI, 1.38-1.43] per -1% of HbA_1c). Higher baseline HbA_1c level was a protective factor against NH (HR, 0.76 [95% CI, 0.74-0.77] per +1% of HbA_1c); and the adjustment to HbA_1c goal required to offset 1% higher baseline HbA_1c was -0.825%. Patient characteristics for risk of NH included older age (HR, 1.02 [95% CI, 1.01-1.02]) per 1-year increase), female sex (HR, 1.18 [95% CI, 1.15-1.22]), black or African-American race (HR, 1.41 [95% CI, 1.33-1.50] vs white race), longer diabetes duration (HR, 1.02 [95% CI, 1.01-1.02] per 1-year increase), diabetic nephropathy (HR, 1.40 [95% CI, 1.27-1.54]), and concomitant sulfonylurea use (HR, 1.10 [95% CI, 1.05-1.15]). Asian race was associated with a lower risk of NH (HR, 0.50 [95% CI, 0.48-0.53] vs white race); this finding could be offset with a 2.03% adjustment to the HbA_1c goal.

IMPLICATIONS

Data on NH are scarce. By pooling multiple clinical trials, this study was able to evaluate patient-level data. A quantitative understanding of the trade-off between individual risk factors for NH and glycemic reduction may help clinicians to personalize patients' glycemic goals, while effectively managing NH risk. Limitations of the study include that patients were selected through inclusion/exclusion criteria and that patient compliance may be better in a trial setting. Validating the findings in the real world will be helpful.

Review of insulin-associated hypoglycemia and its impact on the management of diabetes in Southeast Asian countries

Although the incidence of diabetes is rising in Southeast Asia, there is limited information regarding the incidence and manifestation of insulin-associated hypoglycemia. The aim of the present review was to discuss what is currently known regarding insulin-associated hypoglycemia in Southeast Asia, including its known incidence and impact in the region, and how the Southeast Asian population with diabetes differs from other populations. We found a paucity of data regarding the incidence of hypoglycemia in Southeast Asia, which has contributed to the adoption of Western guidelines. This might not be appropriate, as Southeast Asians have a range of etiological, educational and cultural differences from Western populations with diabetes that might place them at greater risk of hypoglycemia if not managed optimally. For example, Southeast Asians with type 2 diabetes tend to be younger, with lower body mass indexes than their Western counterparts, and the management of type 2 diabetes with premixed insulin preparations is more common in Southeast Asia. Both of these factors might result in higher rates of hypoglycemia. In addition, Southeast Asians are often poorly educated about hypoglycemia and its management, including during Ramadan fasting. We conclude there is a need for more information about Southeast Asian populations with diabetes to assist with the construction of more appropriate national and regional guidelines for the management of hypoglycemia, more closely aligned to patient demographics, behaviors and treatment practices. Such bespoke guidelines might result in a greater degree of implementation and adherence within clinical practice in Southeast Asian nations.

Glucose variability indices predict the episodes of nocturnal hypoglycemia in elderly type 2 diabetic patients treated with insulin

AIM

O determine the applicability of glucose variability (GV) indices derived from continuous glucose monitoring (CGM) data for prediction of nocturnal hypoglycemia (NH) in elderly patients with type 2 diabetes treated with insulin.

METHODS

We observed 83 insulin-treated in-patients, 65-80 years of age. Blinded CGM data for 176 nights were analyzed. Daytime (06:00-22:59) mean glucose, Standard Deviation (SD), 2-h Continuous Overlapping Net Glycemic Action (CONGA2) and Mean Absolute Glucose (MAG), pre-midnight (23:00-23:59) mean glucose, SD and MAG, 24-h Mean Amplitude of Glucose Excursions (MAGE), were estimated. Pre-midnight glucose trends were estimated as the absolute difference between glucose values at 23:00 and 23:59 (deltaG). Episode of interstitial glucose ≤70mg/dL observed from 0:00 to 5:59 was considered as NH.

RESULTS

NH was present in 68 out of 176 24-h recordings (39%). Lower daytime mean glucose and CONGA2, and higher MAG values were found in patients with NH as compared to those without (p=0.0002, p=0.0001 and p=0.02, respectively). Pre-midnight mean glucose was lower, while pre-midnight deltaG was higher in patients with NH (p<0.0001 and p=0.02). Antecedent daytime hypoglycemia increased the risk of NH (p<0.0001). In logistic regression analysis, the combination of daytime MAG and pre-midnight mean glucose was the most reliable predictor of subsequent NH (accuracy 75.6%, p=0.0004).

CONCLUSION

The analysis of CGM-derived GV parameters could improve prediction of NH in elderly patients with type 2 diabetes treated with insulin.

Safety and efficacy of insulin glargine 300 u/mL compared with other basal insulin therapies in patients with type 2 diabetes mellitus: a network meta-analysis

OBJECTIVE

To compare the efficacy and safety of a concentrated formulation of insulin glargine (Gla-300) with other basal insulin therapies in patients with type 2 diabetes mellitus (T2DM).

DESIGN

This was a network meta-analysis (NMA) of randomised clinical trials of basal insulin therapy in T2DM identified via a systematic literature review of Cochrane library databases, MEDLINE and MEDLINE In-Process, EMBASE and PsycINFO.

OUTCOME MEASURES

Changes in HbA1c (%) and body weight, and rates of nocturnal and documented symptomatic hypoglycaemia were assessed.

RESULTS

41 studies were included; 25 studies comprised the main analysis population: patients on basal insulin-supported oral therapy (BOT). Change in glycated haemoglobin (HbA1c) was comparable between Gla-300 and detemir (difference: -0.08; 95% credible interval (CrI): -0.40 to 0.24), neutral protamine Hagedorn (NPH; 0.01; -0.28 to 0.32), degludec (-0.12; -0.42 to 0.20) and premixed insulin (0.26; -0.04 to 0.58). Change in body weight was comparable between Gla-300 and detemir (0.69; -0.31 to 1.71), NPH (-0.76; -1.75 to 0.21) and degludec (-0.63; -1.63 to 0.35), but significantly lower compared with premixed insulin (-1.83; -2.85 to -0.75). Gla-300 was associated with a significantly lower nocturnal hypoglycaemia rate versus NPH (risk ratio: 0.18; 95% CrI: 0.05 to 0.55) and premixed insulin (0.36; 0.14 to 0.94); no significant differences were noted in Gla-300 versus detemir (0.52; 0.19 to 1.36) and degludec (0.66; 0.28 to 1.50). Differences in documented symptomatic hypoglycaemia rates of Gla-300 versus detemir (0.63; 0.19 to 2.00), NPH (0.66; 0.27 to 1.49) and degludec (0.55; 0.23 to 1.34) were not significant. Extensive sensitivity analyses supported the robustness of these findings.

CONCLUSIONS

NMA comparisons are useful in the absence of direct randomised controlled data. This NMA suggests that Gla-300 is also associated with a significantly lower risk of nocturnal hypoglycaemia compared with NPH and premixed insulin, with glycaemic control comparable to available basal insulin comparators.

Concepts and clinical use of ultra-long basal insulin

Diabetes mellitus (DM) is a public health issue, affecting around 382 million people worldwide. In order to achieve glycemic goals, insulin therapy is the frontline therapy for type 1 DM patients; for patients with type 2 DM, use of insulin therapy is an option as initial or add-on therapy for those not achieving glycemic control. Despite insulin therapy developments seen in the last decades, several barriers remain for insulin initiation and optimal maintenance in clinical practice. Fear of hypoglycemia, weight gain, pain associated with blood testing and injection-related pain are the most cited reasons for not starting insulin therapy. However, new generation of basal insulin formulations, with longer length of action, have shown the capability of providing adequate glycemic control with lower risk of hypoglycemia.