Association Between Heart Rate Variability Measures and Blood Glucose Levels: Implications for Noninvasive Glucose Monitoring for Diabetes

The relationship between heart rate variability and glucose clearance in healthy men and women

Heart rate variability (HRV) is a non-invasive indicator of the activity of the autonomic nervous system, which regulates many physiological functions including metabolism. The purpose of this study was to quantify the relationship between resting markers of HRV and oral glucose tolerance test (OGTT) response. Eighteen healthy individuals (10 males, 8 females, (23.8±2.9 years) underwent a 10-minute resting HRV recording. The final five minutes were evaluated via Kubios HRV Standard for: root mean square of successive differences (RMSSD), standard deviation of normal-to-normal sinus beats (SDNN), high frequency (HF), and low frequency (LF). A standard 2-hour OGTT was then administered. Glucose was measured via finger stick before, 30-minutes post, 1-hour post, and 2-hours post OGTT. Pearson correlations demonstrated that RMSSD, SDNN, HF and LF were strongly correlated to fasting blood glucose (FBG) for the group (p<0.05) but not for glucose area under the curve (AUC). When analyzed by sex, only males demonstrated significant correlations between AUC and RMSSD, SDNN, and LF (p<0.05). An independent samples t-test revealed no sex differences for FBG, AUC, RMSSD, SDNN, HF and LF. These findings provide new and interesting insights into the relationship of autonomic activity and glucose uptake, highlighting sex-based relationships.

The associations among glycemic control, heart variability, and autonomic brain function in healthy individuals: Age- and sex-related differences

INTRODUCTION

The purpose of this study was to clarify the relationships between glycemia and function of the autonomic nervous system (ANS), assessed via resting-state functional connectivity (FC) and heart-rate variability (HRV).

METHODS

Data for this study were extracted from the Leipzig Study for Mind-Body-Emotion Interactions, including 146 healthy adults (114 young, 32 older). Variables of interest were glycated hemoglobin (HbA1c), resting-state FC in the salience aspect of the central-autonomic (S-CAN) and salience network (SN) and HRV (RMSSD and high-frequency HRV (HF-HRV)).

RESULTS

HbA1c was inversely correlated with FC in the S-CAN but not SN. HbA1c was inversely correlated with HRV. Both RMSSD and log(HF-HRV) were correlated with FC in the S-CAN and SN. Age- (not sex-related) differences were observed in the Hb1Ac-FC associations (stronger in older adults) while sex- (not age-related) differences were observed in the HRV-FC (stronger in females).

CONCLUSIONS

These findings extend the diabetes literature to healthy adults in relating glycemia and brain function. The age- and sex-related differences in these relationships highlight the need to account for the potential effects of age and sex in future investigations.

Learning Difference Equations With Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose management requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, and also artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. This study proposes a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the two-hour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Associations of glucose metabolism and diabetes with heart rate variability: a population-based cohort study

The present study aimed to investigate the potential causal pathways and temporal relationships of glucose metabolism and diabetes with heart rate variability (HRV). This cohort study was conducted among a sample of 3858 Chinese adults. At baseline and 6 years follow-up, participants underwent HRV measurement (low frequency [LF], high frequency [HF], total power [TP], standard deviation of all normal-to-normal intervals [SDNN], and square root of the mean squared difference between adjacent normal-to-normal intervals [r-MSSD]) and determination of glucose homeostasis (fasting plasma glucose [FPG] and insulin [FPI], homeostatic model assessment for insulin resistance [HOMA-IR]). The temporal relationships of glucose metabolism and diabetes with HRV were evaluated using cross-lagged panel analysis. FPG, FPI, HOMA-IR, and diabetes were cross-sectionally negatively associated with HRV indices at baseline and follow-up (P < 0.05). Cross-lagged panel analyses demonstrated significant unidirectional paths from baseline FPG to follow-up SDNN (β = -0.06), and baseline diabetes to follow-up low TP group (β = 0.08), low SDNN group (β = 0.05), and low r-MSSD group (β = 0.10) (P < 0.05). No significant path coefficients were observed from baseline HRV to follow-up impaired glucose homeostasis or diabetes. These significant findings persisted even after excluding participants who were taking antidiabetic medication. The results support that elevated FPG and the presence of diabetes may be the causes rather than the consequences of HRV reduction over time.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier.

The Effect of Watermelon Juice Supplementation on Heart Rate Variability and Metabolic Response during an Oral Glucose Challenge: A Randomized, Double-Blind, Placebo-Controlled Crossover Trial

Heart rate variability (HRV) provides a simple method to evaluate autonomic function in health and disease. A reduction in HRV may indicate autonomic dysfunction and is strongly associated with aspects of cardiometabolic disease, including hyperglycemia. Reduced nitric oxide (NO) bioavailability is also implicated in the development of cardiometabolic disease and autonomic dysfunction. Watermelons are natural sources of L-arginine and L-citrulline, substrates used for NO synthesis. Watermelon consumption can improve NO bioavailability. We conducted a randomized, double-blind, placebo-controlled crossover trial to test the effects of 2 weeks of daily watermelon juice (WMJ) supplementation on HRV in response to an oral glucose challenge (OGC) in healthy young adults. We also performed indirect calorimetry to assess if our intervention altered the metabolic response to the OGC. WMJ supplementation preserved high-frequency power (HF) (treatment effect, p = 0.03) and the percentage of successive differences that differ by more than 50 ms (pNN50) (treatment effect, p = 0.009) when compared to the placebo treatment. There was no difference in resting energy expenditure or substate oxidation according to treatment. We report that WMJ supplementation attenuates OGC-induced reductions in HRV. Future work should emphasize the importance of NO bioavailability in autonomic dysfunction in cardiometabolic disease.

Association of depression and parasympathetic activation with glycemic control in type 2 diabetes mellitus

AIM

Patients with type 2 diabetes mellitus exhibited autonomic nervous system (ANS) dysfunction and comorbidities with depressive or anxiety symptoms were related to poor glycemic control. Heart rate variability (HRV) converted from electrocardiogram (ECG) has been used as the ANS index. The study aimed to explore the associations between depression, anxiety, HRV, and glycemic control in patients with type 2 diabetes mellitus.

METHODS

The Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) questionnaires were used to assess depressive and anxiety symptoms in 647 patients with type 2 diabetes mellitus (mean age was 63 ± 10 years, 56 % males). The ECG raw signals were collected from a 5-min sitting and resting baseline and then transformed to HRV indices referring ANS activation. Blood glucose and lipid profiles including glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride were obtained from the electronic medical records.

RESULTS

Ninety-nine (15 %) participants had depressive symptoms and 59 (9 %) had anxiety symptoms. Depression and HbA1c were negatively correlated with parasympathetic activation. Depression and anxiety were positively correlated with sympathetic activation. After controlling for demographic data and lipid profiles, depression was a significant positive predictor for HbA1c levels; and HRV indices (lnLF and lnHF) were the significant negative predictors for HbA1c levels. Mediation effect analysis showed that depression was a mediator between parasympathetic activation and glycemic control.

CONCLUSIONS

Lower parasympathetic activation and higher depressive symptoms may affect glycemic control in patients with type 2 diabetes mellitus. Intervention programs targeting to increase parasympathetic activities and reducing depression could be further tested for their effects on glycemic outcomes for potential clinical use.

Enhancing self-management in type 1 diabetes with wearables and deep learning

People living with type 1 diabetes (T1D) require lifelong self-management to maintain glucose levels in a safe range. Failure to do so can lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1D self-management for real-time glucose measurements, while smartphone apps are adopted as basic electronic diaries, data visualization tools, and simple decision support tools for insulin dosing. Applying a mixed effects logistic regression analysis to the outcomes of a six-week longitudinal study in 12 T1D adults using CGM and a clinically validated wearable sensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- and hyperglycemic events measured an hour later. We proceeded to develop a new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of meal and bolus insulin, and the sensor wristband to predict glucose levels and hypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE) of 35.28 ± 5.77 mg/dL with the Matthews correlation coefficients for detecting hypoglycemia and hyperglycemia of 0.56 ± 0.07 and 0.70 ± 0.05, respectively. The use of wristband data significantly reduced the RMSE by 2.25 mg/dL (p < 0.01). The well-trained model is implemented on the ARISES app to provide real-time decision support. These results indicate that the ARISES has great potential to mitigate the risk of severe complications and enhance self-management for people with T1D.

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.

Heart Rate Variability in Hyperthyroidism: A Systematic Review and Meta-Analysis

Objective: Cardiovascular effects of thyroid hormones may be measured through heart rate variability (HRV). We sought to determine the impact of hyperthyroidism on HRV. Design: A systematic review and meta-analysis on the impact of hyperthyroidism on HRV. Methods: PubMed, Cochrane, Embase and Google Scholar were searched until 20 August 2021 for articles reporting HRV parameters in untreated hyperthyroidism and healthy controls. Random-effects meta-analysis was stratified by degree of hyperthyroidism for each HRV parameter: RR intervals (or Normal-to-Normal intervals—NN), SDNN (standard deviation of RR intervals), RMSSD (square root of the mean difference of successive RR intervals), pNN50 (percentage of RR intervals with >50 ms of variation), total power (TP), LFnu (low-frequency normalized unit) and HFnu (high-frequency), VLF (very low-frequency), and LF/HF ratio. Results: We included 22 studies with 10,811 patients: 1002 with hyperthyroidism and 9809 healthy controls. There was a decrease in RR (effect size = −4.63, 95% CI −5.7 to −3.56), SDNN (−6.07, −7.42 to −4.71), RMSSD (−1.52, −2.18 to −0.87), pNN50 (−1.36, −1.83 to −0.88), TP (−2.05, −2.87 to −1.24), HFnu (−3.51, −4.76 to −2.26), and VLF power (−2.65, −3.74 to −1.55), and an increase in LFnu (2.66, 1.55 to 3.78) and LF/HF ratio (1.75, 1.02 to 2.48) (p < 0.01). Most parameters had ES that was twice as high in overt compared to subclinical hyperthyroidism. Increased peripheral thyroid hormones and decreased TSH levels were associated with lower RR intervals. Conclusions: Hyperthyroidism is associated with a decreased HRV, which may be explained by the deleterious effect of thyroid hormones and TSH. The increased sympathetic and decreased parasympathetic activity may have clinical implications.

The effect of a high fat meal on heart rate variability and arterial stiffness in adolescents with or without type 1 diabetes

AIM

Type 1 diabetes (T1D) is associated with increased arterial stiffness and cardiac autonomic neuropathy. We tested whether those variables are acutely affected by a high fat meal (HFM).

METHODS

Responses to a HFM were measured in adolescents with T1D (N = 14) or without T1D (Control, N = 21). Heart rate variability (HRV), arterial stiffness, blood pressure (BP), and energy expenditure (EE) were measured before (baseline) and four times over 180 min postprandially.

RESULTS

T1D had higher blood glucose and insulin, but the suppression of fatty acids (~40%) and rise in triglycerides (~60%) were similar between groups. T1D had 9% higher EE, but postprandial increase in EE was similar to Controls. T1D had ~7 to 24% lower baseline HRV but a similar postprandial decline of ~8 to 25% as Controls. Both groups had a similar 2 to 5% increase in BP after the meal. Rate pressure product increased postprandially in both groups and was higher in T1D. Pulsewave velocity and augmentation index did not differ between groups or change postprandially.

CONCLUSION

Adolescents with T1D have evidence of cardiac autonomic dysfunction and increased EE, but those variables, along with arterial stiffness, are not acutely made worse by a HFM.

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.

Sugar-sweetened soft drink consumption acutely decreases spontaneous baroreflex sensitivity and heart rate variability

In healthy humans, fructose-sweetened water consumption increases blood pressure variability (BPV) and decreases spontaneous cardiovagal baroreflex sensitivity (cBRS) and heart rate variability (HRV). However, whether consuming commercially available soft drinks containing high levels of fructose elicits similar responses is unknown. We hypothesized that high-fructose corn syrup (HFCS)-sweetened soft drink consumption increases BPV and decreases cBRS and HRV to a greater extent compared with artificially sweetened (diet) and sucrose-sweetened (sucrose) soft drinks and water. Twelve subjects completed four randomized, double-blinded trials in which they drank 500 mL of water or commercially available soft drinks matched for taste and caffeine content. We continuously measured beat-to-beat blood pressure (photoplethysmography) and R-R interval (ECG) before and 30 min after drink consumption during supine rest for 5 min during spontaneous and paced breathing. BPV was evaluated using standard deviation (SD), average real variability (ARV), and successive variation (SV) methods for systolic and diastolic blood pressure. cBRS was assessed using the sequence method. HRV was evaluated using the root mean square of successive differences (RMSSD) in R-R interval. There were no differences between conditions in the magnitude of change from baseline in SD, ARV, and SV (P ≥ 0.07). There were greater reductions in cBRS during spontaneous breathing in the HFCS (-3 ± 5 ms/mmHg) and sucrose (-3 ± 5 ms/mmHg) trials compared with the water trial (+1 ± 5 ms/mmHg, P < 0.03). During paced breathing, HFCS evoked greater reductions in RMSSD compared with water (-26 ± 34 vs. +2 ± 26 ms, P < 0.01). These findings suggest that sugar-sweetened soft drink consumption alters cBRS and HRV but not BPV.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual’s cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task.

Hyperinsulinaemic hypoglycaemia in non-anaesthetized Göttingen minipigs induces a counter-regulatory endocrine response and electrocardiographic changes

The potentially fatal cardiovascular effects of hypoglycaemia are not well understood and large animal models of the counter-regulatory responses and cardiovascular consequences of insulin-induced hypoglycaemia are needed to understand the mechanisms in humans. The aim of this study was to develop a human-like minipig model of hypoglycaemia including healthy and diabetic pigs to investigate endocrine, electrocardiographic and platelet effects. Hypoglycaemia was induced using a hyperinsulinaemic, hypoglycaemic clamp and an insulin bolus protocol. Plasma glucose, glucagon, C-peptide, insulin, epinephrine and platelet aggregation responses were measured before, during and after hypoglycaemia. Continuous electrocardiographic recordings were obtained. Hypoglycaemia at a plasma glucose concentration of 0.8–1.0 mM in the clamp induced 25-fold increase in epinephrine and sixfold and threefold increase in glucagon for healthy and diabetic pigs, respectively. The hypoglycaemic clamp induced QTc-interval prolongation and increase in cardiac arrhythmias. In the bolus approach, the non-diabetic group reached plasma glucose target of 1.5 mM and QTc-interval was prolonged after insulin injection, but before glucose nadir. The diabetic group did not reach hypoglycaemic target, but still demonstrated QTc-interval prolongation. These results demonstrate effects of hyperinsulinaemic hypoglycaemia closely resembling human physiology, indicating the minipig as a translational animal model of counter-regulatory endocrine and myocardial effects of hypoglycaemia.

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.

The autonomic nervous system in its natural environment: Immersion in nature is associated with changes in heart rate and heart rate variability

Stress Recovery Theory (SRT) suggests that time spent in nature reduces stress. While many studies have examined changes in stress physiology after exposure to nature imagery, nature virtual reality, or nature walks, this study is the first to examine changes in heart rate (HR) and vagally mediated HR variability, as assessed by Respiratory Sinus Arrythmia (RSA), after a longer duration of nature exposure. Consistent with SRT, we hypothesized that immersion in nature would promote stress recovery, as indexed by an increase in RSA and a decrease in HR. We also predicted that exposure to nature would improve self-reported mood. We used a within-subjects design (N = 67) to assess changes in peripheral physiology before, during, and after a 5-day nature trip. Results demonstrated a significant decrease in RSA and a significant increase in HR during the trip compared to before or after the trip, suggesting that immersion in nature is associated with a shift toward parasympathetic withdrawal and possible sympathetic activation. These results were contrary to our hypotheses and may suggest increased attentional intake or presence of emotions associated with an increase in sympathetic activation. We also found an improvement in self-reported measures of mood during the trip compared to before or after the trip, confirming our hypotheses and replicating previous research. Implications of this study are discussed in the context of SRT.

In Amateur Athletes With Type 1 Diabetes, a 9-Day Period of Cycling at Moderate-to-Vigorous Intensity Unexpectedly Increased the Time Spent in Hyperglycemia, Which Was Associated With Impairment in Heart Rate Variability

OBJECTIVE

In type 1 diabetes, autonomic dysfunction may occur early as a decrease in heart rate variability (HRV). In populations without diabetes, the positive effects of exercise training on HRV are well-documented. However, exercise in individuals with type 1 diabetes, particularly if strenuous and prolonged, can lead to sharp glycemic variations, which can negatively impact HRV. This study explores the impact of a 9-day cycling tour on HRV in this population, with a focus on exercise-induced glycemic excursions.

RESEARCH DESIGN AND METHODS

Twenty amateur athletes with uncomplicated type 1 diabetes cycled 1,500 km. HRV and glycemic variability were measured by heart rate and continuous glucose monitoring. Linear mixed models were used to test the effects of exercise on HRV, with concomitant glycemic excursions and subject characteristics considered as covariates.

RESULTS

Nighttime HRV tended to decrease with the daily distance traveled. The more time the subjects spent in hyperglycemia, the lower the parasympathetic tone was. This result is striking given that hyperglycemic excursions progressively increased throughout the 9 days of the tour, and to a greater degree on the days a longer distance was traveled, while time spent in hypoglycemia surprisingly decreased. This phenomenon occurred despite no changes in insulin administration and a decrease in carbohydrate intake from snacks.

CONCLUSIONS

In sports enthusiasts with type 1 diabetes, multiday prolonged exercise at moderate-to-vigorous intensity worsened hyperglycemia, with hyperglycemia negatively associated with parasympathetic cardiac tone. Considering the putative deleterious consequences on cardiac risks, future work should focus on understanding and managing exercise-induced hyperglycemia.

Impact of blood glucose control on sympathetic and vagus nerve functional status in patients with type 2 diabetes mellitus

AIMS

Present study observed the impact of blood glucose control on sympathetic and vagus functional status in type 2 diabetes mellitus (DM) patients through observing the association between glycosylated hemoglobin (HbA1c) level and sympathetic and vagus functional status detected by heart rate recovery (HRR) and heart rate variability (HRV) assessments.

METHODS

Consecutive hospitalized DM patients were divided into well glycemic control group (HbA1c < 7.0%, group WGC, n = 100) and poor glycemic control group (HbA1c ≥ 7.0%, group PGC, n = 100), 100 hospitalized patients without DM served as control group (group C). All subjects underwent blood biochemistry test, treadmill exercise testing and 24-h Holter monitoring.

RESULTS

HRR and HRV parameters were significantly lower in group WGC and PGC than in group C. Standard deviation of NN intervals (SDNN), standard deviation of all 5-min average NN intervals (SDANN), very low frequency (VLF) values were significantly lower in group PGC than in group WGC. HbA1c level was negatively correlated with HRR1, SDNN, SDANN, VLF, low frequency and high frequency. Logistic regression analysis showed that lower SDNN, SDANN and VLF values were risk factors for high HbA1c levels in DM patients after adjusting for gender, age and beta-blocker use in the model 1, and for gender, age, beta-blocker use, coronary artery disease and hypertension in the model 2.

CONCLUSIONS

Present results indicate that sympathetic and vagal functional status are impaired independent of HbA1c level, while poor glycemic control is related to more significant neurocardiac dysfunction in DM patients.

The social brain and heart rate variability: Implications for psychotherapy

PURPOSE

Humans evolved within the mammalian line as a highly social species. Indeed, sociality has been a major driver of human social intelligence. From birth, social relationships have emotional and self-regulating properties and operate through different body systems. This paper will explore how heart rate variability (HRV), an index of the vagal regulation of the heart and a central element of the physiological underpinnings of sociality, is related to mental health problems, with important implications for psychotherapy.

METHODS

We conducted a narrative review of the literature on the bi-directional links between prosocial motivations, HRV, and psychophysiological functioning.

RESULTS

HRV is associated not only with the ability to downregulate physiological arousal, but also with a variety of psychological and behavioural variables which are usually the target of psychotherapeutic interventions. A modern neurovisceral integration model can be employed to explain the complex intercorrelation between HRV and psychophysiological functioning. In particular, the link between HRV, the experience of inter- and intrapersonal safeness, and the inhibitory function of the prefrontal cortex will be explored in the context of prosocial motives, such as compassion, that alleviate and help prevent mental health difficulties.

CONCLUSIONS

Our knowledge of the social brain and its physiological underpinnings might influence important elements of a therapeutic intervention, from the initial assessment of patient's difficulties to the evaluation of therapy outcomes.

PRACTITIONER POINTS

Social relationships have emotional and self-regulating properties. The experience of inter- and intrapersonal safeness is connected to prosocial motives, such as compassion, and the inhibitory function of the prefrontal cortex. Social relationships and compassion influence different body systems, such as the vagus nerve. Many forms of psychopathology represent the activation of evolved, defensive strategies especially in contexts where there are few stimuli indicating safeness and social support. Heart rate variability predicts psychotherapy outcome.

Effect of glycemic control and disease duration on cardiac autonomic function and oxidative stress in type 2 diabetes mellitus

Purpose

Cardiac autonomic neuropathy (CAN) is a commonly overlooked complication of type 2 diabetes mellitus (T2DM), with a complex pathogenesis involving hyperglycemia-induced oxidative stress which results in neuronal ischemia and cellular death. The level of hyperglycemia as well as disease duration might be significant determinants of the prognosis of T2DM, but limited studies have explored their relationship with these diabetic complications. Therefore, the purpose of this study was to examine the effect of glycemic control and disease duration on cardiac autonomic function and oxidative stress in patients with T2DM.

Methods

60 T2DM patients along with 63 healthy controls were recruited for the study. Diabetic patients were further classified based on glycemic control (HbA1c levels <8% vs. ≥8%) and disease duration (<5 vs. 5-10 vs. >10 years). All participants were assessed for cardiac autonomic function (HRR: heart rate recovery; HRV: heart rate variability), levels of antioxidant enzymes (CAT: catalase; SOD: superoxide dismutase), serum nitric oxide (NO) and other cardiometabolic risk factors (resting blood pressure, glycemic and lipid profile).

Results

T2DM patients showed a significant reduction in HRR, HRV, CAT, SOD and an increase in LFnu, LF: HF ratio and NO. These impairments were significantly greater for the group with poor glycemic control (p < 0.05). However, no difference for these parameters was observed with respect to different disease durations.

Conclusion

Cardiac autonomic regulation and endogenous antioxidant defense were compromised and levels of nitric oxide found to be raised in patients with Type 2 diabetes. These findings were more pronounced in subjects with poor glycemic control.

Autonomic imbalance in cardiac surgery: A potential determinant of the failure in remote ischemic preconditioning

Remote ischemic preconditioning (RIPC) is a cardioprotective strategy against myocardial damage by ischemia-reperfusion. Many in-vivo and ex-vivo animal researches have demonstrated that RIPC decreases significantly the ischemia-reperfusion myocardial damage, by up to 58% in isolated rat heart. Cardiac artery bypass graft surgery (CABG) is a clinical model of myocardial ischemia-reperfusion and a clinical potential application to RIPC. However, although RIPC has shown successful results in experimental studies, clinical trials on CABG have failed to demonstrate a benefit of RIPC in humans. Strikingly, the main proposed factors associated with this translational failure also impair the balance of the autonomic nervous system (ANS), which has shown to play a key role in RIPC cardioprotection in animal models. Comorbidities, chronic pharmacological treatment and anesthesic drugs - common conditions in CABG patients - cause an ANS imbalance through parasympathetic activity decrement. On the other hand, ANS and specially the parasympathetic branch are essentials to get cardioprotection by RIPC in animal models. Consequently, we propose that ANS imbalance in CABG patients would explain the failure of RIPC clinical trials. Whether our hypothesis is true, many patients could be benefited by RIPC: a cheap, simple and virtually broad-available cardioprotective maneuver. In this paper we discuss the evidence that support this hypothesis and its clinical implications.

Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality

Background:

Heart rate variability (HRV) is a non-invasive measure of the function of the autonomic nervous system, and its dynamic nature may provide a means through which stroke and its associated complications may be predicted, monitored, and managed.

Objective:

The objective of this review is to identify and provide a critique on the most recent uses of HRV in stroke diagnosis/management and highlight areas that warrant further research.

Methods:

The MEDLINE, CINAHL, and OVID MEDLINE databases were canvassed using a systematic search strategy, for articles investigating the use of HRV in stroke diagnosis and management. Initial paper selections were based on title alone, and final paper inclusion was informed by a full-text critical appraisal.

Results:

The systematic search returned 98 records, of which 51 were unique. Following screening, 22 records were included in the final systematic review. The included papers provided some information regarding predicting incident stroke, which largely seems to be best predicted by time- and frequency-domain HRV parameters. Furthermore, post-stroke complications and functionality are similarly predicted by time- and frequency-domain parameters, as well as non-linear parameters in some instances.

Conclusions:

Current research provides good evidence that HRV parameters may have utility as a biomarker for stroke and for post-stroke complications and/or functionality. Future research would benefit from the integration of non-linear, and novel parameters, the hybridisation of HRV parameters, and the expansion of the utilisation of predictive regression and hazard modelling.

Heart rate variability in type 2 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Cardiac autonomic neuropathy in type 2 dibetes mellitus (T2DM) patients is frequent and associated with high cardiovascular mortality. Heart rate variability (HRV) is the gold standard to measure cardiac autonomic neuropathy. We aimed to conduct a systematic review and meta-analysis to evaluate the impact of T2DM on HRV parameters.

METHODS

The PubMed, Cochrane Library, Embase and Science Direct databases were searched on 1st October 2017 using the keywords "diabetes" AND ("heart rate variability" OR "HRV"). Included articles had to report HRV parameters in T2DM patients and healthy controls measured during 24 hours with a Holter-electrocardiogram. Measurements of HRV retieved were: RR-intervals (or Normal to Normal intervals-NN), standard deviation of RR intervals (SDNN), percetange of adjacent NN intervals differing by more than 50 milliseconds (pNN50), square root of the mean squared difference of successive RR intervals (RMSSD), total power, Low Frequency (LF), High Frequency (HF) and LF/HF ratio, as per Task Force recommendations.

RESULTS

We included twenty-five case-control studies with 2,932 patients: 1,356 with T2DM and 1,576 healthy controls. T2DM patients had significantly (P<0.01) lower RR-intervals (effect size = -0.61; 95%CI -1.21 to -0.01), lower SDNN (-0.65; -0.83 to -0.47), lower RMSSD (-0.92; -1.37 to -0.47), lower pNN50 (-0.46; -0.84 to -0.09), lower total power (-1.52; -2.13 to -0.91), lower LF (-1.08; -1.46 to -0.69]), and lower HF (-0.79; -1.09 to -0.50). LF/HF did not differ between groups. Levels of blood glucose and HbA1c were associated with several HRV parameters, as well as Time from diagnosis of T2DM.

CONCLUSIONS

T2DM was associated with an overall decrease in the HRV of T2DM patients. Both sympathetic and parasympathetic activity were decreased, which can be explained by the deleterious effects of altered glucose metabolism on HRV, leading to cardiac autonomic neuropathy.

Continuous glucose monitoring adds information beyond HbA1c in well-controlled diabetes patients with early cardiovascular autonomic neuropathy

AIMS

Hyperglycemia as evaluated by HbA1c is a risk factor for the development of cardiovascular autonomic neuropathy (CAN). The aim of the present study was to investigate whether continuous glucose monitoring (CGM) may add information beyond HbA1c in patients with type 2 diabetes and CAN.

METHODS

81 patients with type 2 diabetes (43 men, mean age 58±11year, HbA1c 6.6±0.5%). Patients were tested for CAN using cardiovascular reflex tests (response to standing, deep breathing and Valsalva maneuver) and underwent CGM for three days. CAN was defined as early (one test abnormal), or manifest (two or three tests abnormal).

RESULTS

Twenty patients had early CAN and two patients had manifest CAN. Blood pressure, HbA1c, cholesterol levels and smoking habits were comparable in patients with vs. without CAN. Post-breakfast glycemic peak was significantly higher in patients with CAN (peak 207 vs 176mg/dL, P=0.009). Furthermore, the nocturnal glucose drop and dawn glucose was significantly higher in patients with CAN compared with patients without CAN (mean 134 vs. 118mg/dL, P=0.017 and mean 143 vs. 130mg/dL, P=0.045, respectively). Removing the two patients with manifest CAN from the statistical analysis didn't change the results.

CONCLUSIONS

These findings emphasize the importance of monitoring glucose patterns over 24-h and not only rely on HbA1c as therapeutic target in patients with type 2 diabetes and CAN.

Investigation of glucose fluctuations by approaches of multi-scale analysis

Glucose variability provides detailed information on glucose control and fluctuation. The aim of this study is to investigate the glucose variability by multi-scale analysis approach on 72-h glucose series captured by continuous glucose monitoring system (CGMS), gaining insights into the variability and complexity of the glucose time series data. Ninety-eight type 2 DM patients participated in this study, and 72-h glucose series from each subject were recorded by CGMS. Subjects were divided into two subgroups according to the mean amplitude of glycemic excursions (MAGE) value threshold at 3.9 based on Chinese standard. In this study, we applied two types of multiple scales analysis methods on glucose time series: ensemble empirical mode decomposition (EEMD) and refined composite multi-scale entropy (RCMSE). With EEMD, glucose series was decomposed into several intrinsic mode function (IMF), and glucose variability was examined on multiple time scales with periods ranging from 0.5 to 12 h. With RCMSE, complexity of the structure of glucose series was quantified at each time scale ranging from 5 to 30 min. Subgroup with higher MAGE value (>3.9) presented higher glycemic baseline and variability. There were significant differences in glycemic variability on IMFs3-5 between subgroups with MAGE>3.9 and MAGE < = 3.9 (p<0.001), but no significant differences in variability on IMFs1-2. The complexity of glucose series quantified by RCMSE showed statistically difference on each time scale from 5 to 30 min between subgroups (p<0.05). Glucose series from subjects with higher MAGE value represented higher variability but lower complexity on multiple time scales. Compared with traditional matrices measuring the glucose variability, approaches of EEMD and RCMSE can quantify the dynamic glycemic fluctuation in multiple time scales and provide us more detailed information on glycemic variability and complexity.

The relationship between right ventricular deformation and heart rate variability in asymptomatic diabetic patients

OBJECTIVE

To investigate heart rate variability (HRV) and right ventricular (RV) remodeling in asymptomatic diabetic patients, as well as the relationship between HRV indices and RV structure, function and deformation.

METHOD

This cross-sectional study included 59 asymptomatic patients with type 2 diabetes and 45 healthy controls without cardiovascular risk factors. All study subjects underwent 24-h Holter monitoring, laboratory analyses and complete two-dimensional echocardiography examination (2DE).

RESULTS

RV diastolic function and longitudinal deformation were significantly impaired in diabetic individuals comparing with controls. RV global longitudinal strain and layer-specific longitudinal strains were significantly decreased in diabetic group. The same trend of changes in RV deformation was observed for global RV and lateral wall. All parameters of time and frequency domain of HRV were reduced in diabetic subjects. RV endocardial longitudinal strain together with LV mass index, mitral E/e' ratio and HbA1c correlated with HRV parameters. However, multivariate linear regression analysis showed that only RV endocardial longitudinal strain and LV mass index are associated with HRV parameters independently of age, BMI, HbA1c, RV free wall thickness and pulmonary artery pressure.

CONCLUSIONS

RV subendocardial strain is independently associated with HRV parameters in the whole study population. This reveals potentially important role of determination of layer-specific RV longitudinal function as important marker of preclinical cardiac damage, but also indirectly show the impairment of cardiac autonomic function in diabetic patients.