Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects

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.

Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.

Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN

Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.

Acute hypoglycemia and risk of cardiac arrhythmias in insulin-treated type 2 diabetes and controls

OBJECTIVE

Hypoglycemia is associated with an increased risk of cardiovascular disease including cardiac arrhythmias. We investigated the effect of hypoglycemia in the setting of acute glycemic fluctuations on cardiac rhythm and cardiac repolarization in insulin-treated patients with type 2 diabetes compared with matched controls without diabetes.

DESIGN

A non-randomized, mechanistic intervention study.

METHODS

Insulin-treated patients with type 2 diabetes (n = 21, age (mean ± s.d.): 62.8 ± 6.5 years, BMI: 29.0 ± 4.2 kg/m2, HbA1c: 6.8 ± 0.5% (51.0 ± 5.4 mmol/mol)) and matched controls (n = 21, age: 62.2 ± 8.3 years, BMI 29.2 ± 3.5 kg/m2, HbA1c: 5.3 ± 0.3% (34.3 ± 3.3 mmol/mol)) underwent a sequential hyperglycemic and hypoglycemic clamp with three steady-states of plasma glucose: (i) fasting plasma glucose, (ii) hyperglycemia (fasting plasma glucose +10 mmol/L) and (iii) hyperinsulinemic hypoglycemia (plasma glucose < 3.0 mmol/L). Participants underwent continuous ECG monitoring and blood samples for counterregulatory hormones and plasma potassium were obtained.

RESULTS

Both groups experienced progressively increasing heart rate corrected QT (Fridericia's formula) interval prolongations during hypoglycemia ((∆mean (95% CI): 31 ms (16, 45) and 39 ms (24, 53) in the group of patients with type 2 diabetes and controls, respectively) with similar increases from baseline at the end of the hypoglycemic phase (P = 0.43). The incidence of ventricular premature beats increased significantly in both groups during hypoglycemia (P = 0.033 and P < 0.0001, respectively). One patient with type 2 diabetes developed atrial fibrillation during recovery from hypoglycemia.

CONCLUSIONS

In insulin-treated patients with type 2 diabetes and controls without diabetes, hypoglycemia causes clinically significant and similar increases in cardiac repolarization that might increase vulnerability for serious cardiac arrhythmias and sudden cardiac death.

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.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.

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.

Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review

Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.

Corrected QT Interval Prolongation during Severe Hypoglycemia without Hypokalemia in Patients with Type 2 Diabetes

BACKGROUND

To evaluate the effects of severe hypoglycemia without hypokalemia on the electrocardiogram in patients with type 2 diabetes in real-life conditions.

METHODS

Electrocardiograms of adult type 2 diabetic patients during the episodes of severe hypoglycemia and the recovered stage were obtained and analysed between October 1, 2011 and May 31, 2012. Patients who maintained the normal serum sodium and potassium levels during the episodes of severe hypoglycemia were only selected as the subjects of this study. Severe hypoglycemia was defined, in this study, as the condition requiring active medical assistance such as administering carbohydrate when serum glucose level was less than 60 mg/dL.

RESULTS

Nine type 2 diabetes patients (seven men, two women) were included in the study. The mean subject age was 73.2±7.7 years. The mean hemoglobin A1c level was 6.07%±1.19%. The median duration of diabetes was 10 years (range, 3.5 to 30 years). Corrected QT (QTc) intervals were significantly increased during the episodes of severe hypoglycemia compared to the recovered stage (447.6±18.2 ms vs. 417.2±30.6 ms; P<0.05). However, the morphology and the amplitude of the T waves were not changed and ST-segment elevation and/or depression were not found during the episodes of severe hypoglycemia.

CONCLUSION

In this study, QTc interval prolongation during the episodes of severe hypoglycemia was observed without hypokalemia. Therefore, the distinct alterations in cardiac repolarization during the episodes of severe hypoglycemia may not be associated with hypokalemia.

Hypoglycemia-associated electroencephalogram and electrocardiogram changes appear simultaneously

BACKGROUND

Tight glycemic control in type 1 diabetes mellitus (T1DM) may be accomplished only if severe hypoglycemia can be prevented. Biosensor alarms based on the body's reactions to hypoglycemia have been suggested. In the present study, we analyzed three lead electrocardiogram (ECG) and single-channel electroencephalogram (EEG) in T1DM patients during hypoglycemia.

MATERIALS AND METHODS

Electrocardiogram and EEG recordings during insulin-induced hypoglycemia in nine patients were used to assess the presence of ECG changes by heart rate, and estimates of QT interval (QTc) and time from top of T wave to end of T wave corrected for heartbeat interval and EEG changes by extraction of the power of the signal in the delta, theta, and alpha bands. These six features were assessed continuously to determine the time between changes and severe hypoglycemia.

RESULTS

QT interval changes and EEG theta power changes were detected in six and eight out of nine subjects, respectively. Rate of false positive calculations was one out of nine subjects for QTc and none for EEG theta power. Detection time medians (i.e., time from significant changes to termination of experiments) was 13 and 8 min for the EEG theta power and QTc feature, respectively, with no significant difference (p = .25).

CONCLUSIONS

Severe hypoglycemia is preceded by changes in both ECG and EEG features in most cases. Electroencephalogram theta power may be superior with respect to timing, sensitivity, and specificity of severe hypoglycemia detection. A multiparameter algorithm that combines data from different biosensors might be considered.

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.