Sex-based differences in the association between duration of type 2 diabetes and heart rate variability

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.

Associations between affective factors and high-frequency heart rate variability in primary care patients with depression

OBJECTIVE

Depression is a risk factor for cardiovascular disease (CVD), and subgroups of people with depression may be at particularly elevated CVD risk. Lower high-frequency heart rate variability (HF HRV), which reflects diminished parasympathetic activation, is a candidate mechanism underlying the depression-CVD relationship and predicts cardiovascular events. Few studies have examined whether certain depression subgroups - such as those with co-occurring affective factors - exhibit lower HF HRV. The present study sought to assess associations between co-occurring affective factors and HF HRV in people with depression.

METHODS

Utilizing baseline data from the 216 primary care patients with depression in the eIMPACT trial, we examined cross-sectional associations of depression's co-occurring affective factors (i.e., anxiety symptoms, hostility/anger, and trait positive affect) with HF HRV. HF HRV estimates were derived by spectral analysis from electrocardiographic data obtained during a supine rest period.

RESULTS

Individual regression models adjusted for demographics and depressive symptoms revealed that anxiety symptoms (standardized regression coefficient β = -0.24, p = .002) were negatively associated with HF HRV; however, hostility/anger (β = 0.02, p = .78) and trait positive affect (β = -0.05, p = .49) were not. In a model further adjusted for hypercholesterolemia, hypertension, diabetes, body mass index, current smoking, CVD prevention medication use, and antidepressant medication use, anxiety symptoms remained negatively associated with HF HRV (β = -0.19, p = .02).

CONCLUSION

Our findings suggest that, in adults with depression, those with comorbid anxiety symptoms have lower HF HRV than those without. Co-occurring anxiety may indicate a depression subgroup at elevated CVD risk on account of diminished parasympathetic activation.

Sleep quality and heart rate variability in adolescents with type 1 or type 2 diabetes

INTRODUCTION

Decreased sleep quality and lower heart rate variability (HRV) have both independently been associated with diabetes and may contribute to risks for cardiovascular disease. Although poor sleep quality has been associated with lower HRV in adults with type 2 diabetes (T2D), studies of sleep quality in adolescents with (T2D) or studies examining the possible association of poor sleep quality with lower HRV in adolescents with T2D or T1D are not available.

AIM

Thus, we conducted a secondary analysis of data from an existing study to determine if there were associations between sleep quality and HRV in adolescents with T1D or T2D.

METHODS

Adolescents with T1D (n = 101) or T2D (n = 37) completed 24-h HRV Holter monitoring and analysis and a self-reported global measure of sleep quality.

RESULTS

Poor sleep quality was significantly associated with lower HRV, a known predictor for CV risk. Those with T2D had lower measures of HRV.

CONCLUSIONS

The evaluation of sleep quality and early signs of cardiovascular autonomic changes should be considered in routine assessments of adolescents with diabetes. Future research is warranted to examine more robust measures of sleep and HRV in adolescents with diabetes.

AGING, HEART RATE VARIABILITY AND METABOLIC IMPACT OF OBESITY

The relationship between aging and changes in heart rate variability (HRV) could depend on the metabolic profile of obese people, i.e. metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). We aimed to determine the age at which obesity related autonomic dysfunction becomes significant and whether it decreases differently according to metabolic profile. We analyzed HRV in 99 adults using Wildman's criteria for metabolic profile and 5-minute HRV for autonomic nervous system. In MHO, high frequency (HF) decreased in the 4^th decade of life. In MUO, standard deviation of R-R intervals (SDNN), root mean square of successive differences of all R-R intervals (RMSSD), number of adjacent intervals differing by more than 50 ms expressed as percentage of all intervals in the collecting period (pNN50), HF, low frequency (LF), LF/HF (LF divided by HF) and total power (TP) decreased in the 4^th decade of life (partial shared variance 28%-36%). In conclusion, an age dependent decrease of HRV occurs in MUO between the third and fifth decade of life. In MHO, HF significantly decreases around the age of 40 years. Cardiometabolic profile influences metabolic aging, altering the autonomic nervous system.

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.

THE EFFECT OF DIABETES ON CARDIOVASCULAR SYSTEM

Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.

Sex differences in associations between insulin resistance, heart rate variability, and arterial stiffness in healthy women and men: a physiology study

Diabetes confers greater cardiovascular risk to women than to men. Whether insulin-resistance-mediated risk extends to the healthy population is unknown. Measures of insulin resistance (fasting insulin, homeostatic model assessment, hemoglobin A1c, quantitative insulin sensitivity check index, glucose) were determined in 48 (56% female) healthy subjects. Heart rate variability (HRV) was calculated by spectral power analysis and arterial stiffness was determined using noninvasive applanation tonometry. Both were measured at baseline and in response to angiotensin II infusion. In women, there was a non-statistically significant trend towards increasing insulin resistance being associated with an overall unfavourable HRV response and increased arterial stiffness to the stressor, while men demonstrated the opposite response. Significant differences in the associations between insulin resistance and cardiovascular physiological profile exist between healthy women and men. Further studies investigating the sex differences in the pathophysiology of insulin resistance in cardiovascular disease are warranted.

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

BACKGROUND

Diabetes mellitus (DM) is a global metabolic epidemic associated with numerous adverse complications. Invasive finger prick tests or invasive monitors are currently the most common means of monitoring and controlling blood glucose levels (BGLs). Heart rate variability (HRV) is a noninvasive measure of the autonomic nervous system, and its dynamic physiological nature may provide an alternative means of blood glucose monitoring. However, the relationship between BGL and HRV parameters remains relatively unknown.

MATERIALS AND METHODS

Thirty-two participants with diabetes (39.97 ± 17.21 years of age) and 31 without diabetes (27.87 ± 10.55 years of age) participated in the current study. Fasting preceded a 10-min three-lead electrocardiogram (ECG), which was followed by a finger prick blood glucose assessment. Following this, a regular meal was consumed, and 30 min after ingestion, a second postprandial 10-min ECG was obtained, and blood glucose assessment was conducted.

RESULTS

Low-frequency (LF) power, high-frequency (HF) power, and total power (TP) of HRV were negatively associated with BGL in participants with DM. Additionally, the ratio of LF to HF was positively correlated with BGL. Duration of DM was also associated with multiple HRV parameters, with negative associations to both LF and HF parameters as well as TP.

CONCLUSIONS

This study demonstrates links between specific HRV variables and BGL. In the future the dynamic nature of HRV could provide a unique and real-time method for monitoring BGL, for continuous noninvasive prediction and/or management of DM.

Association of glucose homeostasis measures with heart rate variability among Hispanic/Latino adults without diabetes: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

BACKGROUND

Reduced heart rate variability (HRV), a measure of cardiac autonomic function, is associated with an increased risk of cardiovascular disease (CVD) and mortality. Glucose homeostasis measures are associated with reduced cardiac autonomic function among those with diabetes, but inconsistent associations have been reported among those without diabetes. This study aimed to examine the association of glucose homeostasis measures with cardiac autonomic function among diverse Hispanic/Latino adults without diabetes.

METHODS

The Hispanic community Health Study/Study of Latinos (HCHS/SOL; 2008-2011) used two-stage area probability sampling of households to enroll 16,415 self-identified Hispanics/Latinos aged 18-74 years from four USA communities. Resting, standard 12-lead electrocardiogram recordings were used to estimate the following ultrashort-term measures of HRV: RR interval (RR), standard deviation of all normal to normal RR (SDNN) and root mean square of successive differences in RR intervals (RMSSD). Multivariable regression analysis was used to estimate associations between glucose homeostasis measures with HRV using data from 11,994 adults without diabetes (mean age 39 years; 52 % women).

RESULTS

Higher fasting glucose was associated with lower RR, SDNN, and RMSSD. Fasting insulin and the homeostasis model assessment of insulin resistance was negatively associated with RR, SDNN, and RMSSD, and the association was stronger among men compared with women. RMSSD was, on average, 26 % lower in men with higher fasting insulin and 29 % lower in men with lower insulin resistance; for women, the corresponding estimates were smaller at 4 and 9 %, respectively. Higher glycated hemoglobin was associated with lower RR, SDNN, and RMSSD in those with abdominal adiposity, defined by sex-specific cut-points for waist circumference, after adjusting for demographics and medication use. There were no associations between glycated hemoglobin and HRV measures among those without abdominal adiposity.

CONCLUSIONS

Impairment in glucose homeostasis was associated with lower HRV in Hispanic/Latino adults without diabetes, most prominently in men and individuals with abdominal adiposity. These results suggest that reduced cardiac autonomic function is associated with metabolic impairments before onset of overt diabetes in certain subgroups, offering clues for the pathophysiologic processes involved as well as opportunity for identification of those at high risk before autonomic control is manifestly impaired.

AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.

AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.

Dynamics of heart rate variability in patients with type 2 diabetes mellitus during spinal anaesthesia: prospective observational study

Background

Little is known about the changes in autonomic function during spinal anaesthesia in type 2 diabetic patients. The purpose of the study was to assess the influence of spinal anaesthesia on the heart rate variability in type 2 diabetic patients according to the glycated hemoglobin (HbA_1c) level.

Methods

Sixty-six patients who were scheduled for elective orthostatic lower limb surgery were assigned to three groups (n = 22, each) according to HbA_1c; controlled diabetes mellitus (HbA_1c < 7 %), uncontrolled diabetes mellitus (HbA_1c > 7 %) and the control group. The heart rate variability was measured 10 min before (T0), and at10 min (T1), 20 min (T2) and 30 min (T3) after spinal anaesthesia.

Results

Before spinal anaesthesia, total, low-and high-frequency power were significantly lower in the uncontrolled diabetec group than in other group (p < 0.05). During spinal anaesthesia, total, low- and high-frequency powers were did not change in the uncontrolled diabetec group while the low-frequency power in the controlled diabetec group was significantly depressed (p < 0.05). The ratio of low-to high-frequency was comparable among the groups, while it was reduced at T1-2 than at T0 in all the groups. The blood pressures were higher in the uncontrolled diabetec group than in the other groups.

Conclusions

Spinal anaesthesia had an influence on the cardiac autonomic modulation in controlled diabetec patients, but not in uncontrolled diabetec patients. There were no differences in all haemodynamic variables during an adequate level of spinal anaesthesia in controlled and uncontrolled type 2 DM.

Trial registration

ClinicalTrials.gov NCT02137057

Electronic supplementary material

The online version of this article (doi:10.1186/s12871-015-0125-6) contains supplementary material, which is available to authorized users.

Poor glycemic control impacts linear and non-linear dynamics of heart rate in DM type 2

INTRODUCTION: It is well known that type 2 diabetes mellitus (T2DM) produces cardiovascular autonomic neuropathy (CAN), which may affect the cardiac autonomic modulation. However, it is unclear whether the lack of glycemic control in T2DM without CAN could impact negatively on cardiac autonomic modulation. Objective: To evaluate the relationship between glycemic control and cardiac autonomic modulation in individuals with T2DM without CAN. Descriptive, prospective and cross sectional study.METHODS: Forty-nine patients with T2DM (51±7 years) were divided into two groups according to glycosylated hemoglobin (HbA1c): G1≤7% and G2>7.0%. Resting heart rate (HR) and RR interval (RRi) were obtained and calculated by linear (Mean iRR; Mean HR; rMSSD; STD RR; LF; HF; LF/HF, TINN and RR Tri,) and non-linear (SD1; SD2; DFα1; DFα2, Shannon entropy; ApEn; SampEn and CD) methods of heart rate variability (HRV). Insulin, HOMA-IR, fasting glucose and HbA1c were obtained by blood tests.RESULTS: G2 (HbA1c≤7%) showed lower values for the mean of iRR; STD RR; RR Tri, TINN, SD2, CD and higher mean HR when compared with G1 (HbA1c > 7%). Additionally, HbA1c correlated negatively with mean RRi (r=0.28, p=0.044); STD RR (r=0.33, p=0.017); RR Tri (r=-0.35, p=0.013), SD2 (r=-0.39, p=0.004) and positively with mean HR (r=0.28, p=0.045). Finally, fasting glucose correlated negatively with STD RR (r=-0.36, p=0.010); RR Tri (r=-0.36, p=0.010); TINN (r=-0.33, p=0.019) and SD2 (r=-0.42, p=0.002).CONCLUSION: We concluded that poor glycemic control is related to cardiac autonomic modulation indices in individuals with T2DM even if they do not present cardiovascular autonomic neuropathy.

Influence of type 2 diabetes on symbolic analysis and complexity of heart rate variability in men

BACKGROUND

Individuals with diabetes may develop cardiac autonomic dysfunction that may be evaluated by heart rate variability (HRV). The aim was evaluated heart rate variability (HRV) of individuals with type 2 diabetes, without cardiovascular autonomic neuropathy (CAN), in response to active postural maneuver by means of nonlinear analysis (symbolic analysis, Shannon and conditional entropy) and correlate HRV parameters between them, glycated hemoglobin and diabetes duration.

METHODS

Nineteen men with type 2 diabetes without CAN (T2D) and nineteen healthy men (CG), age-range from 40 to 60 years were studied. We assessed HRV in supine and orthostatic position using symbolic analysis (0V%, 1V%, 2LV% and 2UV%), Shannon and conditional entropy (SE and NCI).

RESULTS

In supine position T2D presented higher sympathetic modulation (0V%) than CG. However, there was not any difference between groups for indexes of complexity (SE and NCI). Furthermore, T2D presented a preserved response of cardiac autonomic modulation after active postural maneuver.

CONCLUSIONS

The present study showed that individuals with type 2 diabetes without CAN presented higher cardiac sympathetic modulation. However, the complexity of HRV was not influenced by imbalance of the autonomic modulation in individuals with type 2 diabetes. In addition, the response of autonomic nervous system in the heart remains preserved after active postural maneuver in individuals with type 2 diabetes, possibly due to the lack of CAN in this group.

Automated identification of normal and diabetes heart rate signals using nonlinear measures

Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.

COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

Exercise training improves hemodynamic recovery to isometric exercise in obese men with type 2 diabetes but not in obese women

OBJECTIVES

Women with type 2 diabetes (T2D) show greater rates of mortality due to ischemic heart disease than men with T2D. We aimed to examine cardiovascular and autonomic function responses to isometric handgrip (IHG) exercise between men and women with T2D, before and after an exercise training program.

MATERIALS/METHODS

Hemodynamic responses were measured in 22 men and women with T2D during and following a 3-min IHG test, and before and after 16 wks of aerobic exercise training.

RESULTS

Women had a smaller decrease in mean arterial pressure (MAP) and systolic blood pressure (BP) during recovery from IHG (ΔMAP(REC)) than men pre- and post-training (P<0.05). Men showed a greater reduction in diastolic BP during recovery from IHG (P<0.05), and exercise training improved this response in men but not in women (men, pre-training: -13.9±1.8, post-training: -20.5±5.3 mmHg vs. women, pre-training: -10.7±1.7, post-training: -4.1±4.9 mmHg; P<0.05). Men had a greater reduction in sympathetic modulation of vasomotor tone (P<0.05), as estimated by blood pressure variability, following IHG. This response was accentuated after training, while this training effect was not seen in women. Post-training ΔMAP(REC) was correlated with recovery of low frequency component of the BP spectrum (ΔLF(SBPrec), r=0.52, P<0.05).

CONCLUSIONS

Differences in BP recovery immediately following IHG may be attributed to gender differences in cardiovascular autonomic modulation. An improvement in these responses occurs following aerobic exercise training in obese men, but not in obese women with T2D which reflects a better adaptive autonomic response to exercise training.