Dependence of Heart Rate Variability Indices on the Mean Heart Rate in Women with Well-Controlled Type 2 Diabetes

Robles-Cabrera, Adriana; Torres-Arellano, José M.; Fossión, Rubén; Lerma, Claudia

doi:10.3390/jcm10194386

Cited by 3 publications

(3 citation statements)

References 62 publications

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“…In our study, we found that the vascular risk factors and comorbidity profiles of diabetic patients were higher than non-diabetic, which has been demonstrated in previous studies ( 21 ). Diabetic patients had a higher value of SDNN than non-diabetic patients, which is consistent with previous reports suggesting that ANS dysfunction is highly prevalent in patients with diabetes ( 22 , 23 ). Moreover, our results are consistent with previous findings in The Atherosclerosis Risk in Communities (ARIC).…”

Section: Discussionsupporting

confidence: 92%

Heart rate variability is associated with cerebral small vessel disease in patients with diabetes

Qiu

Song²,

Zhou³

et al. 2022

Front. Neurol.

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ObjectiveLow heart rate variability (HRV), an indicator of autonomic nervous system dysfunction, has been associated with increased all-cause and cardiovascular mortality and incident stroke. However, the relationship between HRV and cerebral small vessel disease (CSVD) showed contradictory results. We aimed to examine the relationship of HRV and total burden of CSVD and each of the magnetic resonance imaging (MRI) markers of CSVD.MethodsWe recruited 435 patients who attended our hospital for physical examination between June 2020 and August 2021. All underwent 24-h Holter monitoring and MRI scan. The standard deviation of normal-to-normal intervals (SDNN) was selected as the method for HRV assessment. The presence of severe white matter hyperintensity, lacunes, and >10 enlarged basal ganglia perivascular spaces, and cerebral microbleeds were added for estimating the CSVD score (0–4). Multivariate logistic analyses was performed to assess whether HRV was independently associated with the burden of CSVD and each of the MRI markers of CSVD, with and without stratification by prevalent diabetes.ResultsThis study included 435 subjects with a mean age of 64.0 (57.0–70.0) years; 49.4% of the patients were male, and 122 (28.0%) had a history of diabetes. In multivariate analyses, lower SDNN was independently associated with total burden of CSVD and the presence of enlarged perivascular spaces in all subjects. According to diabetes stratification, lower SDNN was independently associated with total burden of CSVD and each MRI markers of CSVD separately only in the diabetic group.ConclusionsLower HRV was associated with total burden of CSVD and each MRI markers of CSVD separately among diabetic patients, but not among non-diabetic patients.

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Section: Discussionsupporting

confidence: 92%

Heart rate variability is associated with cerebral small vessel disease in patients with diabetes

Qiu

Song²,

Zhou³

et al. 2022

Front. Neurol.

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“…According to changes indicated by RQA indices during an active standing test, the cardiovascular dynamics associated with both ESRD and HD are consistent with the loss of access to some dynamic physiological conditions ( Gonzalez et al, 2013 ; Calderon-Juarez et al, 2020 ). Furthermore, recent reports of the correlation between the mean duration of the cardiac cycle (meanNN) with RQA indices in HRV time series ( Calderon-Juarez et al, 2020 ; Robles-Cabrera et al, 2021 ), suggest that the meanNN parameter, which reflects changes in the cardiac activity required to address different hemodynamic challenges, may influence the nonlinear dynamics of HRV as well. Yet, it has not been fully demonstrated whether the RQA indices in short-term HRV time series exhibit nonlinear dynamics by using surrogate data approach, in particular, considering the nonstationary behavior of these time series.…”

Section: Introductionmentioning

confidence: 99%

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability

et al. 2022

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Exploring the presence of nonlinearity through surrogate data testing provides insights into the nature of physical and biological systems like those obtained from heart rate variability (HRV). Short-term HRV time series are of great clinical interest to study autonomic impairments manifested in chronic diseases such as the end stage renal disease (ESRD) and the response of patients to treatment with hemodialysis (HD). In contrast to Iterative Amplitude Adjusted Fourier Transform (IAAFT), the Pinned Wavelet Iterative Amplitude Adjusted Fourier Transform (PWIAAFT) surrogates preserve nonstationary behavior in time series, a common characteristic of HRV. We aimed to test synthetic data and HRV time series for the existence of nonlinearity. Recurrence Quantitative Analysis (RQA) indices were used as discriminative statistics in IAAFT and PWIAAFT surrogates of linear stationary and nonstationary processes. HRV time series of healthy subjects and 29 ESRD patients before and after HD were tested in this setting during an active standing test. Contrary to PWIAAFT, linear nonstationary time series may be erroneously regarded as nonlinear according to the IAAFT surrogates. Here, a lower proportion of HRV time series was classified as nonlinear with PWIAAFT, compared to IAAFT, confirming that the nonstationarity condition influences the testing of nonlinear behavior in HRV. A contribution of nonlinearity was found in the HRV data of healthy individuals. A lower proportion of nonlinear time series was also found in ESRD patients, but statistical significance was not found. Although this proportion tends to be lower in ESRD patients, as much as 60% of time series proved to be nonlinear in healthy subjects. Given the important contribution of nonlinearity in HRV data, a nonlinear point of view is required to achieve a broader understanding of cardiovascular physiology.

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“…HRV, which is calculated from the intervals between normal heart beats, is a non-invasive method for assessment of ANS function [3,10,11]. Abnormalities of autonomic input to the heart are generally reflected in decreased HRV [12][13][14][15][16]. However, the assumption that increased HRV is always a marker for better ANS functioning ignores the fact that the organization of the inter-beat intervals is also a component of health [17].…”

Section: Figurementioning

confidence: 99%

Neuroimaging Studies of the Neural Correlates of Heart Rate Variability: A Systematic Review

Matusik¹,

Zhong

Matusik

et al. 2023

JCM

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Direct and indirect links between brain regions and cardiac function have been reported. We performed a systematic literature review to summarize current knowledge regarding the associations of heart rate variability (HRV) and brain region morphology, activity and connectivity involved in autonomic control at rest in healthy subjects. Both positive and negative correlations of cortical thickness and gray matter volumes of brain structures with HRV were observed. The strongest were found for a cluster located within the cingulate cortex. A decline in HRV, as well as cortical thickness with increasing age, especially in the orbitofrontal cortex were noted. When associations of region-specific brain activity with HRV were examined, HRV correlated most strongly with activity in the insula, cingulate cortex, frontal and prefrontal cortices, hippocampus, thalamus, striatum and amygdala. Furthermore, significant correlations, largely positive, between HRV and brain region connectivity (in the amygdala, cingulate cortex and prefrontal cortex) were observed. Notably, right-sided neural structures may be preferentially involved in heart rate and HRV control. However, the evidence for left hemispheric control of cardiac vagal function has also been reported. Our findings provide support for the premise that the brain and the heart are interconnected by both structural and functional networks and indicate complex multi-level interactions. Further studies of brain–heart associations promise to yield insights into their relationship to health and disease.

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Dependence of Heart Rate Variability Indices on the Mean Heart Rate in Women with Well-Controlled Type 2 Diabetes

Cited by 3 publications

References 62 publications

Heart rate variability is associated with cerebral small vessel disease in patients with diabetes

Heart rate variability is associated with cerebral small vessel disease in patients with diabetes

Recurrence Quantitative Analysis of Wavelet-Based Surrogate Data for Nonlinearity Testing in Heart Rate Variability

Neuroimaging Studies of the Neural Correlates of Heart Rate Variability: A Systematic Review

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