Influence of autonomic neuropathy on QTc interval lengthening during hypoglycemia in type 1 diabetes

Cardiac autonomic neuropathy modified the association between obesity and hypoglycemia in type 2 diabetes

BACKGROUND

Previous studies have shown that increasing body mass index (BMI) was associated with decreased hypoglycemia in type 2 diabetes, but it remains uncertain whether this finding could be applied to patients with and without cardiac autonomic neuropathy (CAN).

METHODS

The study included 7789 participants with type 2 diabetes from action to control cardiovascular risk in diabetes (ACCORD) trail. CAN was defined as SDNN < 8.2 ms and RMSSD < 8.0 ms. Obesity was defined as BMI ≥ 30 kg/m2. Outcomes were identified as severe hypoglycemia requiring any assistance (HAA) or requiring medical assistance (HMA). We assessed the association between obesity and severe hypoglycemia in type 2 diabetes with or without CAN using COX regression models adjusted for baseline characteristics.

RESULTS

Over a median follow-up of 4.7 years, a total of 893 participants developed HAA and 584 participants developed HMA. Compared with non-obesity, obesity was associated with lower risk of severe hypoglycemia (HAA: hazard ratio [HR] 0.51, 95% confidence interval [CI] 0.38-0.68, P < 0.001; HMA: HR 0.57, 95% CI 0.40-0.82, P = 0.002) in CAN present group, but not in CAN absent group (HAA: HR 0.98, 95% CI 0.83-1.16, P = 0.830; HMA: HR 0.97, 95% CI 0.79-1.19, P = 0.754). Similarly, increasing BMI was associated with reduced severe hypoglycemic events in participants with CAN, but not in participants without CAN.

CONCLUSIONS

CAN modifies the association between obesity and hypoglycemia in type 2 diabetes. Type 2 diabetic individuals with CAN who are under weight control should pay attention to hypoglycemic events.

TRIAL REGISTRY

http://www.

CLINICALTRIALS

gov . Unique identifier: NCT00000620.

Characterizing Associations of QTc Interval with Nocturnal Glycemic Control in Children with Type 1 Diabetes

An association between QT prolongation (Bazett's corrected QT interval, QTcB) of 7 milliseconds and nocturnal hypoglycemia, compared with euglycemia, has been observed in children with type 1 diabetes (T1D). The objective of this pharmacometric analysis was to understand this association and other sources of QTc variability quantitatively. Data originate from a prospective observational study (25 cardiac healthy children with T1D, aged 8.1-17.6 years) with continuous subcutaneous glucose and electrocardiogram measurements for 5 consecutive nights. Mixed-effect modeling was used to compare QTcB with individual heart-rate correction (QTcI). Covariate models accounting for circadian variation, age, and sex were evaluated, followed by an investigation of glucose-QTc relationships (with univariable and combined adjusted analysis). Factors potentially modifying sensitivity to QTc lengthening were explored. Random inter-individual variability was reduced in the QTcI versus QTcB model (±12.6 vs 14.1 milliseconds), and was further reduced in the adjusted covariate model (±9.7 milliseconds), accounting for the significantly (P < .01) shortened QTc in adolescent boys (-14.6 milliseconds), circadian variation (amplitude, 19.2 milliseconds; shift, 2.9 hours), and linear glucose-QTc relationship (delay rate, 0.56-h ; slope, 0.76 milliseconds [95%CI 0.67- 0.85 milliseconds] per 1 mmol/L decrease in glucose). Differing sensitivity was suggested to depend upon hemoglobin A1c (HbA1c), T1D duration, and time spent in nocturnal hypoglycemia. In conclusion, a clinically mild association of QTc prolongation with nocturnal hypoglycemia was confirmed and quantified in this pharmacometric analysis, and the longest QTc interval was around 03:00 a.m. The characterized delayed association with glucose highlights the relevance of both the extent and the duration of hypoglycemia. Further clinical studies are warranted to investigate whether these factors contribute to increased risk of hypoglycemia-associated cardiac arrhythmia in children with T1D.

Dysglycemia and arrhythmias

Disorders in glucose metabolism can be divided into three separate but interrelated domains, namely hyperglycemia, hypoglycemia, and glycemic variability. Intensive glycemic control in patients with diabetes might increase the risk of hypoglycemic incidents and glucose fluctuations. These three dysglycemic states occur not only amongst patients with diabetes, but are frequently present in other clinical settings, such as during critically ill. A growing body of evidence has focused on the relationships between these dysglycemic domains with cardiac arrhythmias, including supraventricular arrhythmias (primarily atrial fibrillation), ventricular arrhythmias (malignant ventricular arrhythmias and QT interval prolongation), and bradyarrhythmias (bradycardia and heart block). Different mechanisms by which these dysglycemic states might provoke cardiac arr-hythmias have been identified in experimental studies. A customized glycemic control strategy to minimize the risk of hyperglycemia, hypoglycemia and glucose variability is of the utmost importance in order to mitigate the risk of cardiac arrhythmias.

Effects of Hypoglycemia on Cardiovascular Function in Patients with Diabetes

Hypoglycemia is common in patients with type 1 and type 2 diabetes (T1D, T2D), treated with insulin or sulfonylureas, and has multiple short- and long-term clinical implications. Whether acute or recurrent, hypoglycemia significantly affects the cardiovascular system with the potential to cause cardiovascular dysfunction. Several pathophysiological mechanisms have been proposed linking hypoglycemia to increased cardiovascular risk, including hemodynamic changes, myocardial ischemia, abnormal cardiac repolarization, cardiac arrhythmias, prothrombotic and proinflammatory effects, and induction of oxidative stress. Hypoglycemia-induced changes can promote the development of endothelial dysfunction, which is an early marker of atherosclerosis. Although data from clinical trials and real-world studies suggest an association between hypoglycemia and cardiovascular events in patients with diabetes, it remains uncertain whether this association is causal. New therapeutic agents for patients with T2D do not cause hypoglycemia and have cardioprotective benefits, whereas increasing the use of new technologies, such as continuous glucose monitoring devices and insulin pumps, has the potential to reduce hypoglycemia and its adverse cardiovascular outcomes in patients with T1D.

Non-invasive method for blood glucose monitoring using ECG signal

Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters.

Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal.

Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper.

Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.

Acoustic Measures of Voice and Physiologic Measures of Autonomic Arousal During Speech as a Function of Cognitive Load in Older Adults

OBJECTIVES/HYPOTHESIS

The purpose of this study was to determine the relationships among cognitive loading, autonomic arousal, and acoustic measures of voice in healthy older adults.

STUDY DESIGN

Prospective and observational.

METHODS

Twelve healthy older adults (six females) produced a sentence containing an embedded Stroop task in each of two cognitive load conditions: congruent and incongruent. Three physiologic measures of autonomic arousal (pulse volume amplitude, pulse period, and skin conductance response amplitude) and four acoustic measures of voice (cepstral peak prominence, low-to-high spectral energy ratio, fundamental frequency, and sound pressure level) were analyzed in each cognitive load condition.

RESULTS

A logistic regression model was used to predict the cognitive load condition using participant as a categorical predictor and the four acoustic measures and three autonomic measures as continuous predictors. Skin conductance response amplitude and pulse volume amplitude were both predictive of cognitive load; however, no acoustic measures of voice were statistically significant predictors of cognitive load for older adults.

CONCLUSIONS

These findings support the idea that increased cognitive load is associated with increased autonomic nervous system activity in older adults. The lack of changes in acoustic measures of voice with increased cognitive load may result from age-related changes in vocal quality and speech subsystems.

Blood glucose estimation based on ECG signal

Successful self-management of diabetes requires Continuous Glucose Monitors (CGMs). These CGMs have several limitations such as being invasive, expensive and limited in terms of use. Many techniques, in vain, have been proposed to overcome these limitations. Nowadays, with the help of the Internet of Medical Things (IoMT) technologies, researchers are working to find alternative solutions. They succeed to predict hypoglycemia and hyperglycemia peaks using Electrocardiogram (ECG) signals. However, they failed to use it to estimate the Blood Glucose Concentration (BGC) directly and in real time. Three patients with 08 days of measurements from the D1namo dataset contributed to the study. A new technique has been proposed to estimate the BGC curves based on ECG signals. We used a convolutional neural network to segment the different regions of ECG signals as well as we extracted ECG features that were required for the next step. Then, five regression models have been employed to estimate BGC using as input sixth ECG parameters. We were able to segment the ECG signals with an accuracy of 94% using the convolutional neural network algorithm. The best performance among all simulated models was provided by Exponential Gaussian Process Regression (GPR) with Root Mean Squared Error (RMSE) values of 0.32, 0.41, 0.67 and R-squared (R²) values of 98%, 80%, and 70% for patients 01, 02 and 03 respectively. The method indicates the potential use of ECG wearable devices as non-invasive for continuous blood glucose monitoring, which is affordable and durable.

Sustained heart rate-corrected QT prolongation during recovery from hypoglycaemia in people with type 1 diabetes, independently of recovery to hyperglycaemia or euglycaemia

AIM

To investigate changes in cardiac repolarization abnormalities (heart rate-corrected QT [QT_c ] [primary endpoint], T-wave abnormalities) and heart-rate variability measures in people with type 1 diabetes during insulin-induced hypoglycaemia followed by recovery hyperglycaemia versus euglycaemia.

METHODS

In a randomized crossover study, 24 individuals with type 1 diabetes underwent two experimental clamps with three steady-state phases during electrocardiographic monitoring: (1) a 45-minute euglycaemic phase (5-8 mmol/L), (2) a 60-minute insulin-induced hypoglycaemic phase (2.5 mmol/L), and (3) 60-minute recovery in either hyperglycaemia (20 mmol/L) or euglycaemia (5-8 mmol/L).

RESULTS

All measured markers of arrhythmic risk indicated increased risk during hypoglycaemia. These findings were accompanied by a decrease in vagal tone during both hyperglycaemia and euglycaemia clamps. Compared with baseline, the QT_c interval increased during hypoglycaemia, and 63% of the participants exhibited a peak QT_c of more than 500 ms. The prolonged QT_c interval was sustained during both recovery phases with no difference between recovery hyperglycaemia versus euglycaemia. During recovery, no change from baseline was observed in heart-rate variability measures.

CONCLUSIONS

In people with type 1 diabetes, insulin-induced hypoglycaemia prolongs cardiac repolarization, which is sustained during a 60-minute recovery period independently of recovery to hyperglycaemia or euglycaemia. Thus, vulnerability to serious cardiac arrhythmias and sudden cardiac death may extend beyond a hypoglycaemic event, regardless of hyperglycaemic or euglycaemic recovery.

Exercise-related hypoglycaemia induces QTc-interval prolongation in individuals with type 1 diabetes

AIMS

To investigate changes in cardiac repolarisation during exercise-related hypoglycaemia compared to hypoglycaemia induced at rest in people with type 1 diabetes.

MATERIAL AND METHODS

In a randomised crossover study, 15 men with type 1 diabetes underwent two separate hyperinsulinaemic euglycaemic-hypoglycaemic clamp experiments during Holter-ECG monitoring. One experiment included a bout of moderate-intensity cycling exercise (60 min) along with declining plasma glucose (PG; Clamp-exercise). In the other experiment, hypoglycaemia was induced with the participants at rest (Clamp-rest). We studied QTc interval, T-peak to T-end (Tpe) interval and hormonal responses during three steady-state phases: (i) baseline (PG 4.0-8.0 mmol/L); (ii) hypoglycaemic phase (PG <3.0 mmol/L); and (iii) recovery phase (PG 4.0-8.0 mmol/L).

RESULTS

Both QTc interval and Tpe interval increased significantly from baseline during the hypoglycaemic phase but with no significant difference between test days. These changes were accompanied by an increase in plasma adrenaline and a decrease in plasma potassium on both days. During the recovery phase, ΔQTc interval was longer during Clamp-rest compared to Clamp-exercise, whereas ΔTpe interval remained similar on the two test days.

CONCLUSIONS

We found that both exercise-related hypoglycaemia and hypoglycaemia induced at rest can cause QTc-interval prolongation and Tpe-interval prolongation in people with type 1 diabetes. Thus, both scenarios may increase susceptibility to ventricular arrhythmias.

Severe Hypoglycemia and Incidence of QT Interval Prolongation Among Adults With Type 2 Diabetes

CONTEXT

There is a paucity of large-scale epidemiological studies on the link between severe hypoglycemia (SH) and corrected QT (QTc) interval prolongation in type 2 diabetes (T2DM).

OBJECTIVE

To evaluate the association of SH with QTc prolongation in adults with T2DM.

METHODS

Prospective cohort analysis of participants enrolled in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) study without QTc prolongation at baseline. SH was assessed over a 24-month period. Incident QTc prolongation was ascertained using follow-up electrocardiograms. Modified Poisson regression was used to generate the risk ratio (RR) and 95% CI for QTc prolongation.

RESULTS

Among 8277 participants (mean age 62.6 years [SD 6.5], 38.7% women, 62.8% White), 324 had ≥1 SH episode (3.9%). Over a median of 5 years, 517 individuals developed QTc prolongation (6.3%). Participants with SH had a 66% higher risk of QTc prolongation (RR 1.66, 95% CI 1.16-2.38). The incidence of QTc prolongation was 10.3% (27/261) and 14.3% (9/63) for participants with 1 and ≥2 SH, respectively. Compared with no SH, RRs for patients with 1 and ≥2 SH episodes were 1.57 (95% CI 1.04-2.39) and 2.01 (95% CI 1.07-3.78), respectively. Age modified the association of SH with QTc prolongation (PInteraction = .008). The association remained significant among younger participants (<61.9 years [median age]: RR 2.63, 95% CI 1.49-4.64), but was nonsignificant among older participants (≥61.9 years: RR 1.37, 95% CI 0.87-2.17).

CONCLUSION

In a large population with T2DM, SH was associated with an increased risk of QTc prolongation independently of other risk factors such as cardiac autonomic neuropathy. The association was strongest among younger participants.

The missing link: Unlocking the power of cardiac rhythm monitoring device based QT interval detection

Background

The QT interval is of high clinical value as QT prolongation can lead to Torsades de Pointes (TdP) and sudden cardiac death. Insertable cardiac monitors (ICMs) have the capability of detecting both absolute and relative changes in QT interval. In order to determine feasibility for long‐term ICM based QT detection, we developed and validated an algorithm for continuous long‐term QT monitoring in patients with ICM.

Methods

The QT detection algorithm, intended for use in ICMs, is designed to detect T‐waves and determine the beat‐to‐beat QT and QTc intervals. The algorithm was developed and validated using real‐world ICM data. The performance of the algorithm was evaluated by comparing the algorithm detected QT interval with the manually annotated QT interval using Pearson's correlation coefficient and Bland Altman plot.

Results

The QT detection algorithm was developed using 144 ICM ECG episodes from 46 patients and obtained a Pearson's coefficient of 0.89. The validation data set consisted of 136 ICM recorded ECG segments from 76 patients with unexplained syncope and 104 ICM recorded nightly ECG segments from 10 patients with diabetes and Long QT syndrome. The QT estimated by the algorithm was highly correlated with the truth data with a Pearson's coefficient of 0.93 (p < .001), with the mean difference between annotated and algorithm computed QT intervals of −7 ms.

Conclusions

Long‐term monitoring of QT intervals using ICM is feasible. Proof of concept development and validation of an ICM QT algorithm reveals a high degree of accuracy between algorithm and manually derived QT intervals.

The Prophylactic Effects of Metoprolol, Diltiazem, and Pilocarpine on Hypoglycemia-Induced Prolongation of QT Interval

Background Insulin-induced hypoglycemia has been demonstrated to prolong the corrected QT (QTc) interval. Prolongation of the QTc interval, especially in diabetic patients using insulin, can cause fatal ventricular arrhythmias. The aim of this study was to evaluate the effects of metoprolol, diltiazem, and pilocarpine on hypoglycemia-induced QTc prolongation. Methods Thirty male rats were randomly distributed into the following five groups: Group 1 (1 mL/kg saline, n=6), Group 2 (40 U/kg crystalline insulin + saline, n=6), Group 3 (40 U/kg crystalline insulin + 1 mg/kg metoprolol, n=6), Group 4 (40 U/kg crystalline insulin + 0.8 mg/kg pilocarpine, n=6), and Group 5 (40 U/kg crystalline insulin + 2 mg/kg diltiazem, n=6). Three hours after insulin injection, the blood glucose level was measured in all groups. Blood glucose <40 mg/dl was defined as hypoglycemia. Electrocardiograms (ECG) were taken in lead I (DI), and QTc was calculated by using Bazett's formula. Results Group 2 (insulin + saline) showed that it had a significantly prolonged QTc interval as compared to the control group (p<0.0001). However, treatments of the rats with metoprolol, pilocarpine, and diltiazem significantly prevented prolongation of the QTc interval as compared to the insulin + saline group (p<0.005, p<0.005, and p<0.01, respectively). Conclusion The findings of the present study demonstrated the efficacy of metoprolol, pilocarpine, and diltiazem in the prevention of hypoglycemia-induced QTc prolongation in male rats.

Hypoglycemia unawareness and autonomic dysfunction in diabetes: Lessons learned and roles of diabetes technologies

Impaired awareness of hypoglycemia (IAH) is a reduction in the ability to recognize low blood glucose levels that would otherwise prompt an appropriate corrective therapy. Identified in approximately 25% of patients with type 1 diabetes, IAH has complex pathophysiology, and might lead to serious and potentially lethal consequences in patients with diabetes, particularly in those with more advanced disease and comorbidities. Continuous glucose monitoring systems can provide real-time glucose information and generate timely alerts on rapidly falling or low blood glucose levels. Given their improvements in accuracy, affordability and integration with insulin pump technology, continuous glucose monitoring systems are emerging as critical tools to help prevent serious hypoglycemia and mitigate its consequences in patients with diabetes. This review discusses the current knowledge on IAH and effective diagnostic methods, the relationship between hypoglycemia and cardiovascular autonomic neuropathy, a practical approach to evaluating cardiovascular autonomic neuropathy for clinicians, and recent evidence from clinical trials assessing the effects of the use of CGM technologies in patients with type 1 diabetes with IAH.

Diabetes and the Risk of Sudden Cardiac Death

PURPOSE OF REVIEW

Persons with diabetes mellitus (DM) have increased morbidity and mortality rates compared with persons without DM. Sudden cardiac death (SCD) is a leading cause of death, and multiple studies have found an increased risk of SCD among individuals with DM. This review sought to collect the latest knowledge of the epidemiological and pathophysiological interplay between DM and SCD.

RECENT FINDINGS

Persons with DM have a two- to tenfold increased risk of SCD compared with persons without DM. The underlying mechanisms for the increased risk of SCD are complex and multifactorial. The main pathophysiological contributors are DM-induced cardiac autonomic neuropathy (CAN), metabolic changes, silent ischemia, and polypharmacy. Persons with DM have an increased risk of SCD. Future studies should focus on CAN and the combined risk of QT prolongation from the interplay between CAN, hypoglycemia, and polypharmacy. Genes and pathways involved in control of the autonomic nervous system and cardiac ion channels could be a future focal point.

Influence of cardiac autonomic neuropathy on cardiac repolarisation during incremental adrenaline infusion in type 1 diabetes

AIMS/HYPOTHESIS

We examined the effect of a standardised sympathetic stimulus, incremental adrenaline (epinephrine) infusion on cardiac repolarisation in individuals with type 1 diabetes with normal autonomic function, subclinical autonomic neuropathy and established autonomic neuropathy.

METHODS

Ten individuals with normal autonomic function and baroreceptor sensitivity tests (NAF), seven with subclinical autonomic neuropathy (SAN; normal standard autonomic function tests and abnormal baroreceptor sensitivity tests); and five with established cardiac autonomic neuropathy (CAN; abnormal standard autonomic function and baroreceptor tests) underwent an incremental adrenaline infusion. Saline (0.9% NaCl) was infused for the first hour followed by 0.01 μg kg^-1 min^-1 and 0.03 μg kg^-1 min^-1 adrenaline for the second and third hours, respectively, and 0.06 μg kg^-1 min^-1 for the final 30 min. High resolution ECG monitoring for QT_c duration, ventricular repolarisation parameters (T wave amplitude, T wave area symmetry ratio) and blood sampling for potassium and catecholamines was performed every 30 min.

RESULTS

Baseline heart rate was 68 (95% CI 60, 76) bpm for the NAF group, 73 (59, 87) bpm for the SAN group and 84 (78, 91) bpm for the CAN group. During adrenaline infusion the heart rate increased differently across the groups (p = 0.01). The maximum increase from baseline (95% CI) in the CAN group was 22 (13, 32) bpm compared with 11 (7, 15) bpm in the NAF and 10 (3, 18) bpm in the SAN groups. Baseline QT_c was 382 (95% CI 374, 390) ms in the NAF, 378 (363, 393) ms in the SAN and 392 (367, 417) ms in the CAN groups (p = 0.31). QT_c in all groups lengthened comparably with adrenaline infusion. The longest QT_c was 444 (422, 463) ms (NAF), 422 (402, 437) ms (SAN) and 470 (402, 519) ms (CAN) (p = 0.09). T wave amplitude and T wave symmetry ratio decreased and the maximum decrease occurred earlier, at lower infused adrenaline concentrations in the CAN group compared with NAF and SAN groups. AUC for the symmetry ratio was different across the groups and was lowest in the CAN group (p = 0.04). Plasma adrenaline rose and potassium fell comparably in all groups.

CONCLUSIONS/INTERPRETATION

Participants with CAN showed abnormal repolarisation in some measures at lower adrenaline concentrations. This may be due to denervation adrenergic hypersensitivity. Such individuals may be at greater risk of cardiac arrhythmias in response to physiological sympathoadrenal challenges such as stress or hypoglycaemia.

Myocardial ischaemia reperfusion injury and cardioprotection in the presence of sensory neuropathy: Therapeutic options

During the last decades, mortality from acute myocardial infarction has been dramatically reduced. However, the incidence of post‐infarction heart failure is still increasing. Cardioprotection by ischaemic conditioning had been discovered more than three decades ago. Its clinical translation, however, is still an unmet need. This is mainly due to the disrupted cardioprotective signalling pathways in the presence of different cardiovascular risk factors, co‐morbidities and the medication being taken. Sensory neuropathy is one of the co‐morbidities that has been shown to interfere with cardioprotection. In the present review, we summarize the diverse aetiology of sensory neuropathies and the mechanisms by which these neuropathies may interfere with ischaemic heart disease and cardioprotective signalling. Finally, we suggest future therapeutic options targeting both ischaemic heart and sensory neuropathy simultaneously.

LINKED ARTICLES

This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.23/issuetoc

Hypoglycaemia and cardiac arrhythmias in diabetes

Hypoglycaemia remains an inevitable risk in insulin-treated type 1 diabetes and type 2 diabetes and has been associated with multiple adverse outcomes. Whether hypoglycaemia is a cause of fatal cardiac arrhythmias in diabetes, or merely a marker of vulnerability, is still unknown. Since a pivotal report in 1991, hypoglycaemia has been suspected to induce cardiac arrhythmias in patients with type 1 diabetes, the so-called 'dead-in-bed syndrome'. This suspicion has subsequently been supported by the coexistence of an increased mortality and a three-fold increase in severe hypoglycaemia in patients with type 2 diabetes receiving intensive glucose-lowering treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Studies have investigated the association between hypoglycaemia-induced cardiac arrhythmias. In a rat-model, severe hypoglycaemia resulted in a specific pattern of cardiac arrhythmias including QT-prolongation, ventricular tachycardia, second- and third-degree AV block and ultimately cardiorespiratory arrest. In clinical studies of experimentally induced hypoglycaemia, QTc-prolongation, a risk factor of ventricular arrhythmias, is an almost consistent finding. The extent of QT-prolongation seems to be modified by several factors, including antecedent hypoglycaemia, diabetes duration and cardiac autonomic neuropathy. Observational studies indicate diurnal differences in the pattern of electrocardiographic alterations during hypoglycaemia with larger QTc-prolongations during daytime, whereas the risk of bradyarrhythmias may be increased during sleep. Daytime periods of hypoglycaemia are characterized by shorter duration, increased awareness and a larger increase in catecholamines. The counterregulatory response is reduced during nightly episodes of hypoglycaemia, resulting in prolonged periods of hypoglycaemia with multiple nadirs. An initial sympathetic activity at plasma glucose nadir is replaced by increased vagal activity, which results in bradycardia. Here, we provide an overview of the existing literature exploring potential mechanisms for hypoglycaemia-induced cardiac arrhythmias and studies linking hypoglycaemia to cardiac arrhythmias in patients with diabetes.

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta-analysis

AIMS

Hypoglycaemia is associated with increased cardiovascular risk among individuals with diabetes mellitus. It has been hypothesized that hypoglycaemia may trigger autonomic changes leading to increased cardiac arrhythmia risk. We conducted a systematic review and meta-analysis to explore this association.

MATERIALS AND METHODS

Ovid Medline, Embase, Scopus, Web of Science and Cochrane were searched from inception to October 10, 2017. We included studies of adults with diabetes (Type 1 or Type 2) that compared acute electrocardiogram (ECG) changes during episodes of hypoglycaemia and euglycaemia.

RESULTS

Our search resulted in 4625 citations, among which 20 studies met the predefined inclusion criteria. Finally, 12 studies were included in the descriptive analysis and 15 in the meta-analysis. Overall hypoglycaemia was associated with a reduction in heart rate variability and an increase in arrhythmia occurrence. QTc interval length was more significantly prolonged during hypoglycaemia compared to euglycaemia (pooled mean difference [95% confidence intervals] [0.64 (0.27-1.01], P = ·001). Subgroup analysis based on diabetes type showed that QTc prolongation occurred in individuals with Type 1 and Type 2 diabetes; however, the change between euglycaemia reached statistical significance only among individuals with Type 1 diabetes.

CONCLUSION

Our findings suggest that hypoglycaemia results in ECG alterations that are associated with increased risk of cardiac arrhythmia, which is associated with increased cardiovascular events and mortality. More clinical studies are needed to determine the cardiac risks of hypoglycaemia in individuals with diabetes, especially in Type 2 diabetes.

Incidence of prolonged QTc and severe hypoglycemia in type 1 diabetes: the EURODIAB Prospective Complications Study

AIMS

To assess the independent role of severe hypoglycemia on 7-year cumulative incidence of prolonged QTc in a large cohort of patients with type 1 diabetes.

METHODS

People with type 1 diabetes recruited by the EURODIAB Prospective Complications Study who had normal QTc were examined at baseline and after 7 years with standardized methods (n = 1415; mean age ± SD 32.1 ± 9.6 years; diabetes duration 14.2 ± 8.8 years). Hypoglycemic episodes were assessed by a questionnaire. QTc was calculated according to Bazett's formula. In logistic regression analysis, we examined the role of severe hypoglycemia (none, 1-2, or 3 and more episodes/year) on the cumulative incidence of prolonged QTc, independently of age, sex, HbA1c, blood pressure, BMI, physical activity, distal symmetrical and autonomic neuropathy.

RESULTS

In total, 264/1415 (17%) patients had incident prolonged QTc. Compared to those with persistently normal QTc, a greater proportion of incident cases had 3 and more hypoglycemic episodes at baseline (16.3 vs 11.2%, p = 0.03) and after 7 years (15.2 vs 9.6%, p = 0.01). In logistic regression analysis, 3 or more episodes of severe hypoglycemia at baseline did not increase cumulative incidence of prolonged QTc (OR 1.34, 95% CI 0.88-2.03). By contrast, severe hypoglycemia at the follow-up examination was associated with higher incidence of QTc prolongation (OR 1.68, 1.09-2.58), which reverted to not significant after adjustment for diabetic neuropathy.

CONCLUSIONS

Severe hypoglycemia was not associated with incidence QTc prolongation in type 1 diabetic patients from the EURODIAB PCS.

A Case of Hypoglycemiainduced QT Prolongation Leading to Torsade de Pointes and a Review of Pathophysiological Mechanisms

Torsades de pointes is a life-threatening cardiac arrhythmia. Occurrence of this arrhythmia as a result of hypoglycemia has not been reported in the literature. We describe an interesting case of an insulin-dependent diabetic patient presenting with torsades de pointes resulting from hypoglycemia. A 62-year-old male was admitted to the hospital following an episode of severe insulin-induced hypoglycemia and a cardiac arrest. He was found to unresponsive at home after taking insulin. His serum glucose was found to be 18. He was given juice initially to normalize his glucose and was then transferred by EMS to ER where he was given 5% dextrose infusion. Analysis of the LifeVest rhythm recording showed torsades de pointes that was terminated by defibrillation of the LifeVest. Several mechanisms are responsible for torsade, including QT interval prolongation, adrenalin secretion and calcium overload leading to intracellular calcium oscillations. These mechanisms are a trigger to torsade de pointes. Predisposing factors were present leading torsade to occur.

Diurnal Differences in Risk of Cardiac Arrhythmias During Spontaneous Hypoglycemia in Young People With Type 1 Diabetes

OBJECTIVE

Hypoglycemia may exert proarrhythmogenic effects on the heart via sympathoadrenal stimulation and hypokalemia. Hypoglycemia-induced cardiac dysrhythmias are linked to the "dead-in-bed syndrome," a rare but devastating condition. We examined the effect of nocturnal and daytime clinical hypoglycemia on electrocardiogram (ECG) in young people with type 1 diabetes.

RESEARCH DESIGN AND METHODS

Thirty-seven individuals with type 1 diabetes underwent 96 h of simultaneous ambulatory ECG and blinded continuous interstitial glucose monitoring (CGM) while symptomatic hypoglycemia was recorded. Frequency of arrhythmias, heart rate variability, and cardiac repolarization were measured during hypoglycemia and compared with time-matched euglycemia during night and day.

RESULTS

A total of 2,395 h of simultaneous ECG and CGM recordings were obtained; 159 h were designated hypoglycemia and 1,355 h euglycemia. A median duration of nocturnal hypoglycemia of 60 min (interquartile range 40-135) was longer than daytime hypoglycemia of 44 min (30-70) (P = 0.020). Only 24.1% of nocturnal and 51.0% of daytime episodes were symptomatic. Bradycardia was more frequent during nocturnal hypoglycemia compared with matched euglycemia (incident rate ratio [IRR] 6.44 [95% CI 6.26, 6.63], P < 0.001). During daytime hypoglycemia, bradycardia was less frequent (IRR 0.023 [95% CI 0.002, 0.26], P = 0.002) and atrial ectopics more frequent (IRR 2.29 [95% CI 1.19, 4.39], P = 0.013). Prolonged QTc, T-peak to T-end interval duration, and decreased T-wave symmetry were detected during nocturnal and daytime hypoglycemia.

CONCLUSIONS

Asymptomatic hypoglycemia was common. We identified differences in arrhythmic risk and cardiac repolarization during nocturnal versus daytime hypoglycemia in young adults with type 1 diabetes. Our data provide further evidence that hypoglycemia is proarrhythmogenic.

Changes in cardiac repolarisation during spontaneous nocturnal hypoglycaemia in subjects with type 1 diabetes: a preliminary report

AIMS

Experimental studies have revealed that hypoglycaemia can result in morphological changes in electrocardiographic repolarisation in subjects with type 1 diabetes. However, the influence of spontaneous nocturnal hypoglycaemia on repolarisation morphology in a 'real life' situation is not clear.

METHODS

Adults with type 1 diabetes (n = 11) underwent continuous glucose monitoring with a subcutaneous sensor and digital 12-lead ECG recording for three nights. T-wave morphology was analysed with custom-made software during both hypoglycaemia (glucose <3.5 mmol/l at least 20 min) from ten consecutive heart beats in the middle of the deepest hypoglycaemia and from a control nonhypoglycaemic period (glucose ≥5.0 mmol/l) from the same recording.

RESULTS

In the comparison of 10 hypoglycaemia-control pairs, heart rate (65 ± 12 beats/min during normoglycaemia versus 85 ± 19 beats/min during hypoglycaemia, p = 0.028) increased and the QT_c interval (439 ± 5 vs. 373 ± 5 ms, respectively, p = 0.025) decreased significantly during hypoglycaemia. The spatial QRS-T angle (TCRT) was reduced, and the roughness of the T-wave loop (T-E) increased significantly (p = 0.037 for both) in the patients during hypoglycaemia.

CONCLUSIONS

In adults with type 1 diabetes, spontaneous nocturnal hypoglycaemia results in morphological changes and increased heterogeneity of global cardiac repolarisation. These changes may contribute to the risk of 'dead in bed' syndrome encountered in young individuals with type 1 diabetes.

Diabetic autonomic neuropathy

Diabetes mellitus is the commonest cause of an autonomic neuropathy in the developed world. Diabetic autonomic neuropathy causes a constellation of symptoms and signs affecting cardiovascular, urogenital, gastrointestinal, pupillomotor, thermoregulatory, and sudomotor systems. Several discrete syndromes associated with diabetes cause autonomic dysfunction. The most prevalent of these are: generalized diabetic autonomic neuropathy, autonomic neuropathy associated with the prediabetic state, treatment-induced painful and autonomic neuropathy, and transient hypoglycemia-associated autonomic neuropathy. These autonomic manifestations of diabetes are responsible for the most troublesome and disabling features of diabetic peripheral neuropathy and result in a significant proportion of the mortality and morbidity associated with the disease.

Major adverse cardiovascular events with basal insulin peglispro versus comparator insulins in patients with type 1 or type 2 diabetes: a meta-analysis

BACKGROUND

To identify possible differences in cardiovascular (CV) risk among different insulin therapies, we performed pre-specified meta-analyses across the clinical program for basal insulin peglispro (BIL), in patients randomized to treatment with BIL or comparator insulin [glargine (IG) or NPH].

METHODS

One phase 2 (12-week) and 6 phase 3 (26 to 78-week) randomized studies of BIL compared to IG or NPH, in patients with type 1 or type 2 diabetes, were included. The participants were diverse with respect to demographics, baseline glycemic control, and concomitant disease or medications, but treatment groups were comparable in each study. For any potential CV or neurovascular event, relevant medical information was provided to a blinded external clinical events committee (C5Research, Cleveland Clinic, Cleveland, OH, USA) for adjudication. Cox regression analysis was used to compare treatment groups. The primary endpoint was a composite of adjudicated MACE+ [CV death, myocardial infarction (MI), stroke, or hospitalization for unstable angina].

RESULTS

The pooled population included 5862 patients in the safety evaluation, with randomization to BIL:IG:NPH of 3578:2072:212. Mean age was 54.1 years, 27 % had type 1 diabetes, 56 % were male, and 88 % were white. Baseline demographic and clinical characteristics, including use of statins or other lipid-lowering drugs, were comparable between BIL and comparators. A total of 83 patients experienced at least 1 MACE+ and 70 patients experienced at least 1 MACE (CV death, MI, or stroke). Overall, there were no treatment-associated differences in time to MACE+ [hazard ratio (HR) for BIL versus comparator insulin (95 % CI): 0.82 (0.53-1.27)] or MACE [0.83 (0.51-1.33)]. In 4297 patients with type 2 diabetes, there were 71 MACE+ events [HR: 1.02 (95 % CI: 0.63-1.65), p = 0.94]. In 1565 patients with type 1 diabetes, there were only 12 MACE+ [0.24 (0.07-0.85), p = 0.027]. There were no differences in all-cause death between BIL and comparators. Sub-group analyses did not identify any sub-population with increased risk with BIL versus comparator insulins.

CONCLUSIONS

Treatment with BIL versus comparator insulin in patients with type 1 diabetes or type 2 diabetes was not associated with increased risk for major CV events in the studies analyzed.

Considerations for assessing the potential effects of antidiabetes drugs on cardiac ventricular repolarization: A report from the Cardiac Safety Research Consortium

Thorough QT studies conducted according to the International Council on Harmonisation E14 guideline are required for new nonantiarrhythmic drugs to assess the potential to prolong ventricular repolarization. Special considerations may be needed for conducting such studies with antidiabetes drugs as changes in blood glucose and other physiologic parameters affected by antidiabetes drugs may prolong the QT interval and thus confound QT/corrected QT assessments. This review discusses potential mechanisms for QT/corrected QT interval prolongation with antidiabetes drugs and offers practical considerations for assessing antidiabetes drugs in thorough QT studies. This article represents collaborative discussions among key stakeholders from academia, industry, and regulatory agencies participating in the Cardiac Safety Research Consortium. It does not represent regulatory policy.

Hypoglycaemia in diabetes mellitus: epidemiology and clinical implications

Hypoglycaemia is a frequent adverse effect of treatment of diabetes mellitus with insulin and sulphonylureas. Fear of hypoglycaemia alters self-management of diabetes mellitus and prevents optimal glycaemic control. Mild (self-treated) and severe (requiring help) hypoglycaemia episodes are more common in type 1 diabetes mellitus but people with insulin-treated type 2 diabetes mellitus are also exposed to frequent hypoglycaemic events, many of which occur during sleep. Hypoglycaemia can disrupt many everyday activities such as driving, work performance and leisure pursuits. In addition to accidents and physical injury, the morbidity of hypoglycaemia involves the cardiovascular and central nervous systems. Whereas coma and seizures are well-recognized neurological sequelae of hypoglycaemia, much interest is currently focused on the potential for hypoglycaemia to cause dangerous and life-threatening cardiac complications, such as arrhythmias and myocardial ischaemia, and whether recurrent severe hypoglycaemia can cause permanent cognitive impairment or promote cognitive decline and accelerate the onset of dementia in middle-aged and elderly people with diabetes mellitus. Prevention of hypoglycaemia is an important part of diabetes mellitus management and strategies include patient education, glucose monitoring, appropriate adjustment of diet and medications in relation to everyday circumstances including physical exercise, and the application of new technologies such as real-time continuous glucose monitoring, modified insulin pumps and the artificial pancreas.

Hypoglycaemia and QT interval prolongation in type 1 diabetes--bridging the gap between clamp studies and spontaneous episodes

AIMS

We propose a study design with controlled hypoglycaemia induced by subcutaneous injection of insulin and matched control episodes to bridge the gap between clamp studies and studies of spontaneous hypoglycaemia. The observed prolongation of the heart rate corrected QT interval (QTc) during hypoglycaemia varies greatly between studies.

METHODS

We studied ten adults with type 1 diabetes (age 41±15years) without cardiovascular disease or neuropathy. Single-blinded hypoglycaemia was induced by a subcutaneous insulin bolus followed by a control episode on two occasions separated by 4weeks. QT intervals were measured using the semi-automatic tangent approach, and QTc was derived by Bazett's (QTcB) and Fridericia's (QTcF) formulas.

RESULTS

QTcB increased from baseline to hypoglycaemia (403±20 vs. 433±39ms, p<0.001). On the euglycaemia day, QTcB also increased (398±20 vs. 410±27ms, p<0.01), but the increase was less than during hypoglycaemia (p<0.001). The same pattern was seen for QTcF. Plasma adrenaline levels increased significantly during hypoglycaemia compared to euglycaemia (p<0.01). Serum potassium levels decreased similarly after insulin injection during both hypoglycaemia and euglycaemia.

CONCLUSIONS

Hypoglycaemia as experienced after a subcutaneous injection of insulin may cause QTc prolongation in type 1 diabetes. However, the magnitude of prolongation is less than typically reported during glucose clamp studies, possible because of the study design with focus on minimizing unwanted study effects.

The clinical impact of inpatient hypoglycemia

Hypoglycemia is common in hospitalized patients and is associated with poor outcomes, including increased mortality. Older individuals and those with comorbidities are more likely to suffer the adverse consequences of inpatient hypoglycemia. Observational studies have shown that spontaneous inpatient hypoglycemia is a greater risk factor for death than iatrogenic hypoglycemia, suggesting that hypoglycemia acts as a marker for more severe illness, and may not directly cause death. Initial randomized controlled trials of intensive insulin therapy in intensive care units demonstrated improvements in mortality with tight glycemic control, despite high rates of hypoglycemia. However, follow-up studies have not confirmed these initial findings, and the largest NICE-SUGAR study showed an increase in mortality in the tight control group. Despite these recent findings, a causal link between hypoglycemia and mortality has not been clearly established. Nonetheless, there is potential for harm from inpatient hypoglycemia, so evidence-based strategies to treat hyperglycemia, while preventing hypoglycemia should be instituted, in accordance with current practice guidelines.

Risk of cardiac arrhythmias during hypoglycemia in patients with type 2 diabetes and cardiovascular risk

Recent trials of intensive glycemic control suggest a possible link between hypoglycemia and excess cardiovascular mortality in patients with type 2 diabetes. Hypoglycemia might cause arrhythmias through effects on cardiac repolarization and changes in cardiac autonomic activity. Our aim was to study the risk of arrhythmias during spontaneous hypoglycemia in type 2 diabetic patients with cardiovascular risk. Twenty-five insulin-treated patients with type 2 diabetes and a history of cardiovascular disease or two or more risk factors underwent simultaneous continuous interstitial glucose and ambulatory electrocardiogram monitoring. Frequency of arrhythmias, heart rate variability, and markers of cardiac repolarization were compared between hypoglycemia and euglycemia and between hyperglycemia and euglycemia matched for time of day. There were 134 h of recording at hypoglycemia, 65 h at hyperglycemia, and 1,258 h at euglycemia. Bradycardia and atrial and ventricular ectopic counts were significantly higher during nocturnal hypoglycemia compared with euglycemia. Arrhythmias were more frequent during nocturnal versus daytime hypoglycemia. Excessive compensatory vagal activation after the counterregulatory phase may account for bradycardia and associated arrhythmias. QT intervals, corrected for heart rate, >500 ms and abnormal T-wave morphology were observed during hypoglycemia in some participants. Hypoglycemia, frequently asymptomatic and prolonged, may increase the risk of arrhythmias in patients with type 2 diabetes and high cardiovascular risk. This is a plausible mechanism that could contribute to increased cardiovascular mortality during intensive glycemic therapy.

The proarrhythmic effect of hypoglycemia: evidence for increased risk from ischemia and bradycardia

Hypoglycemia increases the risk for both overall and sudden death. At a cellular level, hypoglycemia causes alterations in the physiology of myocardial tissue that are identical to proarrhythmic medications. Reduced serum glucose blocks the repolarizing K(+) channel HERG, which leads to action potential and QT prolongation and is uniformly associated with risk for torsades de pointes ventricular tachycardia. The sympathetic response induced by hypoglycemia also increases the risk of arrhythmias from Ca(2+) overload, which occur with sympathomimetic medications and excessive beta adrenergic stimulation. Thus, hypoglycemia can be considered a proarrhythmic event. This review focuses on emerging evidence for two other important changes induced by hypoglycemia that promote arrhythmias: ischemia and bradycardia. Studies of patients with "insulin shock" therapy from the early twentieth century and other more recent data strongly suggest that hypoglycemia can cause ischemia of myocardial tissue, both in association with coronary artery obstructions and by cellular mechanisms. Ischemia induces multiple proarrhythmic responses. Since ischemia itself reduces the possibility of using energy substrates other than glucose, hypoglycemia may generate positive feedback for electrophyisologic destabilization. Recent studies also show that hypoglycemia can cause bradycardia and heart block. Bradycardia is known to cause action potential prolongation and potentiate the development of torsades de pointes, particularly with low-serum K(+) which can be induced by hypoglycemic episodes. Thus, hypoglycemia-induced bradycardia may also create a dynamic, positive feedback for the development of arrhythmias and sudden death. These studies further support the hypothesis that hypoglycemia is a proarrhythmic event.

Impact of hypoglycemia in hospitalized patients

Hypoglycemia is a common problem in hospitalized patients, particularly the elderly, frail, and severely ill. Hypoglycemia has been implicated in the development of adverse clinical outcomes, including increased mortality. Fear of iatrogenic hypoglycemia remains an obstacle to adequate inpatient glycemic control. However, evidence from large clinical trials is mixed: several intensive care unit studies have shown either reduced or no change in mortality with intensive glycemic control, despite high rates of iatrogenic hypoglycemia, and only 1 large study showed higher mortality. In the general ward setting, the association of hypoglycemia with worse outcomes and mortality has been frequently reported, but after multivariate adjustment for comorbidities this association disappears. Spontaneous hypoglycemia, rather than iatrogenic hypoglycemia, is strongly associated with mortality suggesting that hypoglycemia behaves as a biomarker rather than a causative factor of adverse outcomes. Inpatient glycemic management should be patient-centered, follow the current guidelines, and aimed at preventing hypoglycemia.

NON-INVASIVE NOCTURNAL HYPOGLYCEMIA DETECTION FOR INSULIN-DEPENDENT DIABETES MELLITUS USING GENETIC FUZZY LOGIC METHOD

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

A1C and cardiovascular outcomes in type 2 diabetes: a nested case-control study

OBJECTIVE

Type 2 diabetes is associated with increased cardiovascular risk. The role of aggressive glycemic control in preventing cardiovascular events is unclear. A nested case-control study design was used to evaluate the association between average A1C and cardiovascular outcomes.

RESEARCH DESIGN AND METHODS

Adults with type 2 diabetes were identified among members of Kaiser Permanente Southern California. Type 2 diabetes was identified based on ICD-9 diagnosis codes and either A1C >7.5% or prescriptions for hypoglycemic agents. Case subjects were defined based on nonfatal myocardial infarction, nonfatal stroke, or death attributed to cardiovascular events during a 3-year window. Four type 2 diabetes control subjects were matched to each case subject based on age, sex, and index date for the corresponding case. A conditional logistic regression model was used to estimate the odds ratio of cardiovascular events and compare three patient groups based on average A1C measured in the preindex period (≤6, >6-8, >8%).

RESULTS

A total of 44,628 control subjects were matched to 11,157 case subjects. Patients with an average A1C ≤6% were 20% more likely to experience a cardiovascular event than the group with an average A1C of >6-8% (P < 0.0001). Patients with an average A1C >8% experienced a 16% increase in the likelihood of a cardiovascular event (P < 0.0001). We found evidence of statistical interaction with A1C category and LDL level (P = 0.0002), use of cardiovascular medications (P = 0.02), and use of antipsychotics (P = 0.001).

CONCLUSIONS

High-risk patients with type 2 diabetes who achieved mean A1C levels of ≤6% or failed to decrease their A1C to <8% are at increased risk for cardiovascular events.

QT Measurement and Heart Rate Correction during Hypoglycemia: Is There a Bias?

Introduction. Several studies show that hypoglycemia causes QT interval prolongation. The aim of this study was to investigate the effect of QT measurement methodology, heart rate correction, and insulin types during hypoglycemia. Methods. Ten adult subjects with type 1 diabetes had hypoglycemia induced by intravenous injection of two insulin types in a cross-over design. QT measurements were done using the slope-intersect (SI) and manual annotation (MA) methods. Heart rate correction was done using Bazett's (QTcB) and Fridericia's (QTcF) formulas. Results. The SI method showed significant prolongation at hypoglycemia for QTcB (42(6) ms; P < .001) and QTcF (35(6) ms; P < .001). The MA method showed prolongation at hypoglycemia for QTcB (7(2) ms, P < .05) but not QTcF. No difference in ECG variables between the types of insulin was observed. Discussion. The method for measuring the QT interval has a significant impact on the prolongation of QT during hypoglycemia. Heart rate correction may also influence the QT during hypoglycemia while the type of insulin is insignificant. Prolongation of QTc in this study did not reach pathologic values suggesting that QTc prolongation cannot fully explain the dead-in-bed syndrome.

Sleep and the response to hypoglycaemia

Undetected nocturnal hypoglycaemia frequently occurs in patients with diabetes, having a negative influence on well-being, counterregulation against and awareness of subsequent hypoglycaemia, and even causing sudden death in some cases most likely by inducing cardiac arrhythmia. Sleep markedly weakens the neuroendocrine defence mechanism against hypoglycaemia by shifting the glycaemic threshold for counterregulatory activation to lower levels. While hypoglycaemia triggers awakening in healthy subjects, patients with type 1 diabetes frequently fail to awake in the presence of low plasma glucose levels. Little is known about the frequency of and responses to nocturnal hypoglycaemia in patients with type 2 diabetes. Unfortunately, effective strategies to prevent or even safely detect nocturnal hypoglycaemia are still lacking. Taken together, hypoglycaemia occurring during sleep presents a major, often neglected problem in the management of diabetic patients. Different aspects of this phenomenon such as responses to and consequences of nocturnal hypoglycaemia as well as strategies for its prevention are highlighted in this review.

QT interval prolongation during spontaneous episodes of hypoglycaemia in type 1 diabetes: the impact of heart rate correction

AIMS/HYPOTHESIS

Prolongation of the heart rate corrected QT interval (QTc) is seen during episodes of hypoglycaemia in type 1 diabetes. We studied the relationship between spontaneous hypoglycaemia and the QT interval and hypothesised that the choice of heart rate correction affects the observed change in QTc.

METHODS

Twenty-one participants with type 1 diabetes (aged 58 +/- 10 years with duration of diabetes 34 +/- 12 years) had continuous glucose and ECG monitoring for 72 h. QT and RR intervals were measured during hypoglycaemia (blood glucose or continuous glucose measurements <or=3.5 mmol/l) and compared with euglycaemia (5-12 mmol/l). QT intervals were measured using the semi-automated tangent method from signal-averaged ECG and corrected using Bazett's formula, Fridericia's formula, the nomogram method and a linear subject-specific method.

RESULTS

Hypoglycaemia was present in 14 participants. With Bazett's formula, QTc changed significantly from euglycaemia to hypoglycaemia (422 +/- 30 vs 432 +/- 33 ms; p = 0.02). Heart rate, QT intervals and QTc corrected with formulas other than Bazett's were not associated with a significant change (p = 0.07-0.29). During hypoglycaemia, significantly lower values of QTc compared with the subject-specific method were seen for Fridericia's formula (p = 0.02) and the nomogram method (p = 0.04).

CONCLUSIONS/INTERPRETATION

Spontaneous hypoglycaemia was associated with a modest increase in QTc. Bazett's formula resulted in overcorrection of QTc while both Fridericia's formula and the nomogram method undercorrected the QTc compared with the subject-specific method during hypoglycaemia. The results may indicate that the use of a fixed heart rate correction formula can lead to misleading results in investigations of spontaneous hypoglycaemia.

The case for hypoglycaemia as a proarrhythmic event: basic and clinical evidence

Recent clinical studies show that hypoglycaemia is associated with increased risk of death, especially in patients with coronary artery disease or acute myocardial infarction. This paper reviews data from cellular and clinical research supporting the hypothesis that acute hypoglycaemia increases the risk of malignant ventricular arrhythmias and death in patients with diabetes by generating the two classic abnormalities responsible for the proarrhythmic effect of medications, i.e. QT prolongation and Ca(2+) overload. Acute hypoglycaemia causes QT prolongation and the risk of ventricular tachycardia by directly suppressing K(+) currents activated during repolarisation, a proarrhythmic effect of many medications. Since diabetes itself, myocardial infarction, hypertrophy, autonomic neuropathy and congestive heart failure also cause QT prolongation, the arrhythmogenic effect of hypoglycaemia is likely to be greatest in patients with pre-existent cardiac disease and diabetes. Furthermore, the catecholamine surge during hypoglycaemia raises intracellular Ca(2+), thereby increasing the risk of ventricular tachycardia and fibrillation by the same mechanism as that activated by sympathomimetic inotropic agents and digoxin. Diabetes itself may sensitise myocardium to the arrhythmogenic effect of Ca(2+) overload. In humans, noradrenaline (norepinephrine) also lengthens action potential duration and causes further QT prolongation. Finally, both hypoglycaemia and the catecholamine response acutely lower serum K(+), which leads to QT prolongation and Ca(2+) loading. Thus, hypoglycaemia and the subsequent catecholamine surge provoke multiple, interactive, synergistic responses that are known to be proarrhythmic when associated with medications and other electrolyte abnormalities. Patients with diabetes and pre-existing cardiac disease may therefore have increased risk of ventricular tachycardia and fibrillation during hypoglycaemic episodes.

Continuous glucose monitoring reveals associations of glucose levels with QT interval length

BACKGROUND

QTc interval lengthening during hypoglycemia is discussed as a mechanism linked to sudden death in diabetes patients and the so-called "dead in bed syndrome." Previous research reported a high interindividual variability in the glucose-QTc association. The present study aimed at deriving parameters for direction and strength of the glucose-QTc association on the patient level using combined Holter electrocardiogram (ECG) and continuous glucose monitoring.

METHODS

Twenty type 1 diabetes patients were studied: mean (SD, range) age, 43.6 (10.8, 22-65) years; gender male (n [%]), 10 (50.0%); mean (SD) hemoglobin A1C, 8.5% (1.0%); and impaired hypoglycemia awareness (n [%]), six (30.0%). Continuous interstitial glucose monitoring and Holter ECG monitoring were performed for 48 h. Hierarchical (mixed) regression modeling was used to account for the structure of the data.

RESULTS

Glucose levels during nighttime were negatively associated with QTc interval length if the data structure was accounted for (b [SE] = -0.76 [0.17], P = 0.000). Exploratory regression analysis revealed hypoglycemia awareness as the only predictor of the individual strength of the glucose-QTc association, with the impaired awareness group showing less evidence for an association of low glucose with QTc lengthening.

CONCLUSIONS

Mixed regression allows for deriving parameters for the glucose-QTc association on the patient level. Consistent with previous studies, we found a large interindividual variability in the glucose-QTc association. The finding on impaired hypoglycemia awareness patients has to be interpreted with caution but provides some support for the role of sympathetic activation for the QTc-glucose link.

A physiological model of the effect of hypoglycemia on plasma potassium

BACKGROUND

Adrenaline release and excess insulin during hypoglycemia stimulate the uptake of potassium from the bloodstream, causing low plasma potassium (hypokalemia). Hypokalemia has a profound effect on the heart and is associated with an increased risk of malignant cardiac arrhythmias. It is the aim of this study to develop a physiological model of potassium changes during hypoglycemia to better understand the effect of hypoglycemia on plasma potassium.

METHOD

Potassium counterregulation to hypokalemia was modeled as a linear function dependent on the absolute potassium level. An insulin-induced uptake of potassium was modeled using a negative exponential function, and an adrenaline-induced uptake of potassium was modeled as a linear function. Functional expressions for the three components were found using published data.

RESULTS

The performance of the model was evaluated by simulating plasma potassium from three published studies. Simulations were done using measured levels of adrenaline and insulin. The mean root mean squared error (RMSE) of simulating plasma potassium from the three studies was 0.09 mmol/liter, and the mean normalized RMSE was 14%. The mean difference between nadirs in simulated and measured potassium was 0.12 mmol/liter.

CONCLUSIONS

The presented model simulated plasma potassium with good accuracy in a wide range of clinical settings. The limited number of hypoglycemic episodes in the test set necessitates further tests to substantiate the ability of the model to simulate potassium during hypoglycemia. In conclusion, the model is a good first step toward better understanding of changes in plasma potassium during hypoglycemia.

Evaluation and management of adult hypoglycemic disorders: an Endocrine Society Clinical Practice Guideline

OBJECTIVE

The aim is to provide guidelines for the evaluation and management of adults with hypoglycemic disorders, including those with diabetes mellitus.

EVIDENCE

Using the recommendations of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system, the quality of evidence is graded very low (plus sign in circle ooo), low (plus sign in circle plus sign in circle oo), moderate (plus sign in circle plus sign in circle plus sign in circle o), or high (plus sign in circle plus sign in circle plus sign in circle plus sign in circle).

CONCLUSIONS

We recommend evaluation and management of hypoglycemia only in patients in whom Whipple's triad--symptoms, signs, or both consistent with hypoglycemia, a low plasma glucose concentration, and resolution of those symptoms or signs after the plasma glucose concentration is raised--is documented. In patients with hypoglycemia without diabetes mellitus, we recommend the following strategy. First, pursue clinical clues to potential hypoglycemic etiologies--drugs, critical illnesses, hormone deficiencies, nonislet cell tumors. In the absence of these causes, the differential diagnosis narrows to accidental, surreptitious, or even malicious hypoglycemia or endogenous hyperinsulinism. In patients suspected of having endogenous hyperinsulinism, measure plasma glucose, insulin, C-peptide, proinsulin, beta-hydroxybutyrate, and circulating oral hypoglycemic agents during an episode of hypoglycemia and measure insulin antibodies. Insulin or insulin secretagogue treatment of diabetes mellitus is the most common cause of hypoglycemia. We recommend the practice of hypoglycemia risk factor reduction--addressing the issue of hypoglycemia, applying the principles of intensive glycemic therapy, and considering both the conventional risk factors and those indicative of compromised defenses against falling plasma glucose concentrations--in persons with diabetes.