Heart Rate Variability as Early Biomarker for the Evaluation of Diabetes Mellitus Progress

A maternal diet high in carbohydrates causes bradyarrhythmias and changes in heart rate variability in the offspring sex-dependent in mice

BACKGROUND

Maternal obesity prepregnancy, as well as gestational overweight produced by high-sucrose diet, could be evolved to the cardiometabolic diseases in offspring during adulthood. Until then, the cardiometabolic diseases were ignored that have been presented or inherited in the offspring for overnutrition were ignored, depend on gender. We proposed that maternal prepregnancy obesity in CD1 mice, as well as gestational overweight produced by a high sucrose diet, develop to cardiometabolic disease in offspring and even if gender. For detection of the cardiometabolic diseases in a Murine model with a high sucrose diet (HSD), the time series formed by the RR intervals taken from lead I of the ECG has used the corresponding Poincare plot. The heart rate variability was characterized by the standard deviation of width and length SD1, SD2 respectively of the Poincare plot and the SD1/SD2 correlation index in addition was calculated between to gender and body weight.

RESULTS

A maternal diet was based high sucrose diet and produced overweight on progeny in both sexes, but the cardiac arrhythmias depended on gender. Other results were due to the chronic effect of high sucrose diet in offspring with this intrauterine ambiance that contributes to changes in HRV, arrhythmias, and sinus pauses, also these phenomena were observed just in the male mice offspring with high sucrose diet during adulthood.

CONCLUSIONS

We propose, that the arrhythmias originated from fetal programming due to the maternal diet in mice model and produced alterations in the offspring female more than in the male, probably due to hormones.

Ventricular Dysfunction in Obese and Nonobese Rats with Metabolic Syndrome

Obesity and dyslipidemias are both signs of metabolic syndrome, usually associated with ventricular arrhythmias. Here, we tried to identify cardiac electrical alteration and biomarkers in nonobese rats with metabolic syndrome (MetS), and these findings might lead to more lethal arrhythmias than obese animals. The MetS model was developed in Wistar rats with high-sucrose diet (20%), and after twenty-eight weeks were obtained two subgroups: obese (OMetS) and nonobese (NOMetS). The electrocardiogram was used to measure the ventricular arrhythmias and changes in the heart rate variability. Also, we measured ventricular hypertrophy and its relationship with electrical activity alterations of both ventricles, using micro-electrode and voltage clamp techniques. Also, we observed alterations in the contraction force of ventricles where a transducer was used to record mechanical and electrical papillary muscle, simultaneously. Despite both subgroups presenting long QT syndrome (0.66 ± 0.05 and 0.66 ± 0.07 ms with respect to the control 0.55 ± 0.1 ms), the changes in the heart rate variability were present only in OMetS, while the NOMetS subgroup presented changes in QT interval variability (NOMetS SD = 1.8, SD2 = 2.8; SD1/SD2 = 0.75). Also, the NOMetS revealed tachycardia (10%; p < 0.05) with changes in action potential duration (63% in the right papillary and 50% in the left papillary) in the ventricular papillary which are correlated with certain alterations in the potassium currents and the force of contraction. The OMetS showed an increase in action potential duration and the force of contraction in both ventricles, which are explained as bradycardia. Our results revealed lethal arrhythmias in both MetS subgroups, irrespectively of the presence of obesity. Consequently, the NOMetS showed mechanical-electrical alterations regarding ventricle hypertrophy that should be at the NOMetS, leading to an increase of CV mortality.

Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram

Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST's performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.

Development and validation of an integrated portable heart rate variability (HRV) analysis system - STREME

In this paper, we introduce an integrated, portable, affordable and simple to use heart rate variability (HRV) analysis tool STREME. The system consists of an ECG acquisition device and software for HRV analysis. We assessed the reliability and validity of using STREME against the reference standards RMS VarioWin and Kubios HRV for the short term HRV analysis. The validation study was carried out with the participation of 46 healthy subjects that included 15 men and 31 women with an average age of 27.67 ± 7.75yrs. The results showed that there is a significantly strong correlation (r > 0.95, p < 0.001) between STREME and reference systems in HRV indices. The Bland-Altman analysis of all features computed from STREME and reference system represent a close agreement for all the parameters. Hence STREME HRV analysis tool can be recommended to researchers and other professionals for the evaluation of autonomic function using short term HRV.

Taking Systems Medicine to Heart

Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.

Accurate assessment of LV function using the first automated 2D-border detection algorithm for small animals - evaluation and application to models of LV dysfunction

Echocardiography is the most commonly applied technique for non-invasive assessment of cardiac function in small animals. Manual tracing of endocardial borders is time consuming and varies with operator experience. Therefore, we aimed to evaluate a novel automated two-dimensional software algorithm (Auto2DE) for small animals and compare it to the standard use of manual 2D-echocardiographic assessment (2DE). We hypothesized that novel Auto2DE will provide rapid and robust data sets, which are in agreement with manually assessed data of animals.2DE and Auto2DE were carried out using a high-resolution imaging-system for small animals. First, validation cohorts of mouse and rat cine loops were used to compare Auto2DE against 2DE. These data were stratified for image quality by a blinded expert in small animal imaging. Second, we evaluated 2DE and Auto2DE in four mouse models and four rat models with different cardiac pathologies.Automated assessment of LV function by 2DE was faster than conventional 2DE analysis and independent of operator experience levels. The accuracy of Auto2DE-assessed data in healthy mice was dependent on cine loop quality, with excellent agreement between Auto2DE and 2DE in cine loops with adequate quality. Auto2DE allowed for valid detection of impaired cardiac function in animal models with pronounced cardiac phenotypes, but yielded poor performance in diabetic animal models independent of image quality.Auto2DE represents a novel automated analysis tool for rapid assessment of LV function, which is suitable for data acquisition in studies with good and very good echocardiographic image quality, but presents systematic problems in specific pathologies.

Effects of smoking status, history and intensity on heart rate variability in the general population: The CHRIS study

Background

Heart rate variability (HRV) reflects the autonomous nervous system modulation on heart rate and is associated with several pathologies, including cardiac mortality. While mechanistic studies show that smoking is associated with lower HRV, population-based studies present conflicting results.

Methods

We assessed the mutual effects of active smoking status, cumulative smoking history, and current smoking intensity, on HRV among 4751 adults from the Cooperative Health Research In South Tyrol (CHRIS) study. The HRV metrics standard deviation of normal-to-normal (NN) inter-beat intervals (SDNN), square root of the mean squared differences of consecutive NN intervals (RMSSD), total power (TP), low (LF) and high frequency (HF) power, and their ratio (LF/HF), were derived from 20-minute electrocardiograms. Smoking status, pack-years (PY), and tobacco grams/day from standardized questionnaires were the main exposures. We fitted linear mixed models to account for relatedness, non-linearity, and moderating effects, and including fractional polynomials.

Results

Past smokers had higher HRV levels than never smokers, independently of PY. The association of HRV with current smoking became apparent when accounting for the interaction between smoking status and PY. In current smokers, but not in past smokers, we observed HRV reductions between 2.0% (SDNN) and 4.9% (TP) every 5 PY increase. Furthermore, current smokers were characterized by dose-response reductions of 9.8% (SDNN), 8.9% (RMSSD), 20.1% (TP), 17.7% (LF), and 19.1% (HF), respectively, every 10 grams/day of smoked tobacco, independently of common cardiometabolic conditions and HRV-modifying drugs. The LF/HF ratio was not associated with smoking status, history, or intensity.

Conclusions

Smoking cessation was associated with higher HRV levels. In current smokers, heavier smoking intensity appears gradually detrimental on HRV, corroborating previous evidence. By affecting both the sympathetic and parasympathetic nervous system indexes, but not the LF/HF balance, smoking intensity seems to exert a systemic dysautonomic effect.

The Role of the Autonomic Nervous System on Cardiac Rhythm during the Evolution of Diabetes Mellitus Using Heart Rate Variability as a Biomarker

Heart rate variability (HRV) is highly influenced by the Autonomic Nervous System (ANS). Several illnesses have been associated with changes in the ANS, thus altering the pattern of HRV. However, the variability of the heart rhythm is originated within the Sinus Atrial Node (SAN) which has its own variability. Still, although both oscillators produce HRV, the influence of the SAN on HRV has not yet been exhaustively studied. On the other hand, the complications of diabetes mellitus (DM), for instance, nephropathy, retinopathy, and neuropathy, increase cardiovascular morbidity and mortality. Traditionally, these complications are diagnosed only when the patient is already suffering from the negative symptoms these complications implicate. Consequently, it is of paramount importance to develop new techniques for early diagnosis prior to any deterioration on healthy patients. HRV has been proved to be a valuable, noninvasive clinical evidence for evaluating diseases and even for describing aging and behavior. In this study, several ECGs were recorded and their RR and PP intervals were analyzed to detect the interpotential interval (ii) of the SAN. Additionally, HRV reduction was quantified to identify alterations in the nervous system within the nodal tissue via measuring the SD1/SD2 ratio in a Poincaré plot. With 15 years of DM development, the data showed an age-dependent increase in HRV due to the axon retraction of ANS neurons from its effectors. In addition, these alterations modify the heart rhythm-producing fatal arrhythmias. Therefore, it is possible to avoid the consequences of DM identifying alterations in SAN previous to its symptomatic appearance. This could be used as an early diagnosis indicator.

Effects of Coexistence Hypertension and Type II Diabetes on Heart Rate Variability and Cardiorespiratory Fitness

Background

Type 2 diabetes Mellitus (T2DM) is associated with cardiac autonomic dysfunction, which is an independent predictor of mortality in chronic diseases. However, whether the coexistence of systemic arterial hypertension (HTN) with DMT2 alters cardiac autonomic modulation remains unknown.

Objective

To evaluate the influence of HTN on cardiac autonomic modulation and cardiorespiratory fitness in subjects with DMT2.

Methods

60 patients of both genders were evaluated and allocated to two groups: DMT2 patients (n = 32; 51 ± 7.5 years old) and DMT2 + HTN patients (n = 28; 51 ± 6.9 years old). RR intervals were obtained during rest in supine position. Linear and nonlinear indices of heart rate variability (HRV) were computed using Kubios HRV software. Pulmonary gas exchange was measured breath-by-breath, using a portable telemetric system during maximal incremental exercise testing on a cycle ergometer. Statistical analysis included Shapiro-Wilk test followed by Student’s t Test, Pearson correlation and linear regression.

Results

We found that patients in the DMT2+HTN group showed lower values of mean RR intervals (801.1 vs 871.5 ms), Shannon entropy (3 vs 3.2) and fractal dimension SD 1 (9.5 vs 14.5), when contrasted with patients in the DMT2 group. Negative correlations were found between some HRV nonlinear indices and exercise capacity indices.

Conclusion

HTN negatively affects the cardiac autonomic function in diabetic patients, who are already prone to develop autonomic dysfunction. Strategies are need to improve cardiac autonomic functionality in this population.

Ultra-short heart rate variability recording reliability: The effect of controlled paced breathing

BACKGROUND

Recent studies have reported that Heart Rate Variability (HRV) indices remain reliable even during recordings shorter than 5 min, suggesting the ultra-short recording method as a valuable tool for autonomic assessment. However, the minimum time-epoch to obtain a reliable record for all HRV domains (time, frequency, and Poincare geometric measures), as well as the effect of respiratory rate on the reliability of these indices remains unknown.

METHODS

Twenty volunteers had their HRV recorded in a seated position during spontaneous and controlled respiratory rhythms. HRV intervals with 1, 2, and 3 min were correlated with the gold standard period (6-min duration) and the mean values of all indices were compared in the two respiratory rhythm conditions.

RESULTS

rMSSD and SD1 were more reliable for recordings with ultra-short duration at all time intervals (r values from 0.764 to 0.950, p < 0.05) for spontaneous breathing condition, whereas the other indices require longer recording time to obtain reliable values. The controlled breathing rhythm evokes stronger r values for time domain indices (r values from 0.83 to 0.99, p < 0.05 for rMSSD), but impairs the mean values replicability of domains across most time intervals. Although the use of standardized breathing increases the correlations coefficients, all HRV indices showed an increase in mean values (t values from 3.79 to 14.94, p < 0.001) except the RR and HF that presented a decrease (t = 4.14 and 5.96, p < 0.0001).

CONCLUSION

Our results indicate that proper ultra-short-term recording method can provide a quick and reliable source of cardiac autonomic nervous system assessment.

Comparison of frequency-based techniques for assessment of baroreceptor sensitivity and heart rate variability

Heart rate variability (HRV) and baroreceptor sensitivity (BRS) quantify autonomic variability in heart pacing and the autonomic response to blood pressure changes respectively. By necessity, the signals used to calculate HRV and BRS (systolic blood pressure (SBP) and RR interval) have one data point every cardiac cycle. Due to inherent variability in heart rate, these are non-uniformly sampled data. A number of calculation methods exist that adjust for non-uniform sampled signals. This study compared frequency domain methods of HRV and BRS calculation to ascertain whether more complex methods resulted in different results to simpler methods. Wistar rats (n=10), and rats with induced diabetes (n=8) were anesthetized and SBP and RR interval measured for a period of approximately 5 minutes. This data were analyzed using the sequence technique (for BRS), fast Fourier transform (FFT), non-uniform discrete Fourier transform (NDFT) and an extended Lomb-Scargle Periodogram (LSP). There were small but significant differences in NDFT from LSP technique for both BRS in the low frequency range (p=0.005) and HRV in the high frequency range (p=0.001). The NDFT technique was also significantly different to FFT technique for BRS in the low frequency range (p=0.023). All other methods were not statistically different. However, all techniques showed the same results comparing diabetic to control rats. This study shows more complex methods that correct for the non-uniformity of the sampling have significant differences but those differences are small to the point of not altering findings associated with HRV or BRS.