Are Changes in Heart Rate Variability During Hypoglycemia Confounded by the Presence of Cardiovascular Autonomic Neuropathy in Patients with Diabetes?

Hypoglycemia event prediction from CGM using ensemble learning

This work sought to explore the potential of using standalone continuous glucose monitor (CGM) data for the prediction of hypoglycemia utilizing a large cohort of type 1 diabetes patients during free-living. We trained and tested an algorithm for the prediction of hypoglycemia within 40 minutes on 3.7 million CGM measurements from 225 patients using ensemble learning. The algorithm was also validated using 11.5 million synthetic CGM data. The results yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.988 and a precision-recall area under the curve (PR AUC) of 0.767. In an event-based analysis for predicting hypoglycemic events, the algorithm had a sensitivity of 90%, a lead-time of 17.5 minutes and a false-positive rate of 38%. In conclusion, this work demonstrates the potential of using ensemble learning to predict hypoglycemia, using only CGM data. This could help alarm patients of a future hypoglycemic event so countermeasures can be initiated.

Effect of Hypoglycemia on Heart Rate Variability in People with Type 1 Diabetes and Impaired Awareness of Hypoglycemia

BACKGROUND

People with impaired awareness of hypoglycemia (IAH) are at elevated risk of severe, potentially hazardous, hypoglycemia and would benefit from a device alerting to hypoglycemia. Heart rate variability (HRV) changes with hypoglycemia due to sympathetic activity. Since IAH is associated with suppressed sympathetic activity, we investigated whether hypoglycemia elicits a measurable change in HRV in patients with T1D and IAH.

METHOD

Eligible participants underwent a modified hyperinsulinemic euglycemic hypoglycemic clamp (glucose nadir, 43.1 ± 0.90 mg/dl), while HRV was measured by a VitalConnect HealthPatch. Measurements of HRV included Root Mean Square of the Successive Differences (RMSSD) and low to high frequency (LF:HF) ratio. Wilcoxon rank-sum test was used for testing within-subject HRV changes.

RESULTS

We included 12 participants (8 female, mean age 57 ± 12 years, mean HbA1c 57 ± 5 mmol/mol (7.4 ± 0.4%)). Symptoms increased from 4.0 (1.5-7.0) at euglycemia to 7.5 (5.0-11.0) during hypoglycemia (P = .003). In response to hypoglycemia, the LF:HF ratio and RMSSD increased when normalized for data obtained during euglycemia (both P < .01). The LF:HF ratio increased in 6 participants (50%) and declined in one other participant (8%). The RMSSD decreased in 3 (25%) and increased in 4 (33%) participants. In 2 patients, no change in HRV could be detected in response to hypoglycemia.

CONCLUSIONS

This study reveals that hypoglycemia-induced changes in HRV are retained in the majority of people with T1D and IAH, and that these changes can be detected by a wearable device. Real-time HRV seems usable for detection of hypoglycemia in patients with IAH.

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Background

Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.

Objective

The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.

Methods

A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.

Results

On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.

Conclusions

Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

Disturbances in the intraventricular conduction system in teenagers with type 1 diabetes. A pilot study

Body Surface Potential Mapping (BSPM) is a multi-electrode synchronous method for examining electrocardiographic records on the patients' body surface that allows the assessment of changes in the heart conduction system. The aim of the study was to visualize and evaluate changes in the intraventricular system in adolescents with T1D.

PATIENTS AND METHODS

Inclusion criteria: age > 12 years, T1D duration >3 years, HbA1c >8%.

EXCLUSION CRITERIA

diagnosis of autonomic neuropathy, heart structural defects, heart failure. BSPM data were processed into map plotting to illustrate differences in ventricular activation time (VAT, isochron lines).

RESULTS

33 teenagers (20 boys), mean age 15.0 ± 2.1 years, T1D from 6.8 ± 4.1 years were included. Mean HbA1c was 9.6 ± 2.0%. In the standard ECG recording abnormalities were not present. The distribution of isolines on the group-mean map plotted for T1D patients only initially resembles the course of isolines on the group-map for normal subjects (N = 30), in whom the electrical impulse stimulating the heart ventricles passes through the atrio-ventricular node, then symmetrically excites the branches of His bundle and finally the Purkinje fibers. In T1D patients, after proper onset of intraventricular stimulation, the isolines reflecting the both ventricles reach higher time values, which indicates problems in the propagation of the ventricular depolarization.

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

Recent Advances in the Generation of β-Cells from Induced Pluripotent Stem Cells as a Potential Cure for Diabetes Mellitus

Diabetes mellitus (DM) is a group of metabolic disorders characterized by high blood glucose levels due to insufficient insulin secretion, insulin action, or both. The present-day solution to diabetes mellitus includes regular administration of insulin, which brings about many medical complications in diabetic patients. Although islet transplantation from cadaveric subjects was proposed to be a permanent cure, the increased risk of infections, the need for immunosuppressive drugs, and their unavailability had restricted its use. To overcome this, the generation of renewable and transplantable β-cells derived from autologous induced pluripotent stem cells (iPSCs) has gained enormous interest as a potential therapeutic strategy to treat diabetes mellitus permanently. To date, extensive research has been undertaken to derive transplantable insulin-producing β-cells (iβ-cells) from iPSCs in vitro by recapitulating the in vivo developmental process of the pancreas. This in vivo developmental process relies on transcription factors, signaling molecules, growth factors, and culture microenvironment. This review highlights the various factors facilitating the generation of mature β-cells from iPSCs. Moreover, this review also describes the generation of pancreatic progenitors and β-cells from diabetic patient-specific iPSCs, exploring the potential of the diabetes disease model and drug discovery. In addition, the applications of genome editing strategies have also been discussed to achieve patient-specific diabetes cell therapy. Last, we have discussed the current challenges and prospects of iPSC-derived β-cells to improve the relative efficacy of the available treatment of diabetes mellitus.

Continuous Glucose and Heart Rate Monitoring in Young People with Type 1 Diabetes: An Exploratory Study about Perspectives in Nocturnal Hypoglycemia Detection

A combination of information from blood glucose (BG) and heart rate (HR) measurements has been proposed to investigate the HR changes related to nocturnal hypoglycemia (NH) episodes in pediatric subjects with type 1 diabetes (T1D), examining whether they could improve hypoglycemia prediction. We enrolled seventeen children and adolescents with T1D, monitored on average for 194 days. BG was detected by flash glucose monitoring devices, and HR was measured by wrist-worn fitness trackers. For each subject, we compared HR values recorded in the hour before NH episodes (before-hypoglycemia) with HR values recorded during sleep intervals without hypoglycemia (no-hypoglycemia). Furthermore, we investigated the behavior after the end of NH. Nine participants (53%) experienced at least three NH. Among these nine subjects, six (67%) showed a statistically significant difference between the before-hypoglycemia HR distribution and the no-hypoglycemia HR distribution. In all these six cases, the before-hypoglycemia HR median value was higher than the no-hypoglycemia HR median value. In almost all cases, HR values after the end of hypoglycemia remained higher compared to no-hypoglycemia sleep intervals. This exploratory study support that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns can be helpful to improve NH detection and prevent fatal events.

Windows Into Human Health Through Wearables Data Analytics

Background

Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers.

Objectives

The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators.

Methods

A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1).

Conclusions

Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.

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.

Heart-Rate Variability-More than Heart Beats?

Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa. But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion-cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.

Cardiac Autonomic Neuropathy: Why Should Cardiologists Care about That?

BACKGROUND

Cardiac autonomic neuropathy (CAN) is a frequent but underdiagnosed complication of diabetes mellitus. It has a strong influence on various cardiac disorders including myocardial ischemia and infarction, hypertension, orthostatic hypotonia, heart failure, and arrhythmias. CAN can lead to severe morbidity and mortality and increase the risk of sudden cardiac death.

METHODS

This review article summarizes the latest evidence regarding the epidemiology, pathogenesis, influence on the cardiovascular system, and diagnostic methods for CAN. The methodology of this review involved analyzing available data from recent papers relevant to the topic of diabetic autonomic neuropathy and cardiac disorders.

CONCLUSIONS

The early diagnosis of CAN can improve the prognosis and reduce adverse cardiac events. Methods based on heart rate variability enable the diagnosis of CAN even at a preclinical stage. These methods are simple and widely available for use in everyday clinical practice. According to the recently published Toronto Consensus Panel on Diabetic Neuropathy, all diabetic patients should be screened for CAN. Because diabetes mellitus often coexists with heart diseases and the most common methods used for diagnosis of CAN are based on ECG, not only diabetologists but also cardiologists should be responsible for diagnosis of CAN.